first steps in seismic interpretation

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Geophysical MonoGraph series

Number 16

First steps iN seismic iNterpretatioN

Donald A. Herron

Rebecca B. Latimer, managing editor

tulsa, oklahoma

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ISBN 978-0-931830-56-3 (Series)ISBN 978-1-56080-280-8 (Volume)

Society of Exploration GeophysicistsP.O. Box 702740Tulsa, OK 74170-2740

© 2011 by Society of Exploration GeophysicistsAll rights reserved. This book or parts hereof may not be reproduced in any form without written permission from the publisher.

Published 2011Printed in the United States of America

Cover background image courtesy of Thomas H. Wilson

Library of Congress Cataloging-in-Publication Data

Herron, Donald A., 1949-

First steps in seismic interpretation / Donald A. Herron ; Rebecca B. Latimer,

managing editor.

p. cm. -- (Geophysical monograph series ; no. 16)

Includes bibliographical references and index.

ISBN 978-1-56080-280-8 (volume : alk. paper) -- ISBN 978-0-931830-56-3 (series : alk. paper)

1. Seismology. 2. Geophysical surveys. I. Latimer, Rebecca B. II. Title.

QE534.3.H47 2011

551.22--dc23

2011047720


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iii

contents

about the author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vpreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiacknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

chapter 1: introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

chapter 2: seismic response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

chapter 3: seismic attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Amplitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22Coherence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Inversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

chapter 4: Velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Sonic logs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Well-velocity surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Seismically derived velocities . . . . . . . . . . . . . . . . . . . . . . . . 41Velocity anisotropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56Time-depth conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

chapter 5: migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

chapter 6: resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

chapter 7: correlation concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . 83First look . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Horizons versus faults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84Multiple reflections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94Manual versus automatic tracking . . . . . . . . . . . . . . . . . . . . 96Artifacts and interpretation pitfalls . . . . . . . . . . . . . . . . . . . . 105


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iv

chapter 8: correlation procedures . . . . . . . . . . . . . . . . . . . . . . . . . . 115Getting started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115Loop tying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120Jump correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133Correlations in depth-migration projects . . . . . . . . . . . . . . . 140Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145Interpretation processes and work flows . . . . . . . . . . . . . . . . 149

chapter 9: Data Quality and management . . . . . . . . . . . . . . . . . . . 153Data quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Data management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158Nomenclature systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

chapter 10: other considerations . . . . . . . . . . . . . . . . . . . . . . . . . . 163Gridding and contouring . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1634D seismic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165Seismic modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167Interpretive judgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167Curiosity and interpretive thinking . . . . . . . . . . . . . . . . . . . . 170The interpretation paradox . . . . . . . . . . . . . . . . . . . . . . . . . . 174Approximations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174Uncertainty and risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176The workstation environment . . . . . . . . . . . . . . . . . . . . . . . . 178Ergonomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179Presentations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180Career development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181Advanced interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184Time spent and value added . . . . . . . . . . . . . . . . . . . . . . . . . 185

references . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193


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v

about the author

Don Herron received a bachelor of sci-ence degree (with honors) in geological sci-ences from Brown University in 1971 and a master of science degree in geological sci-ences from the California Institute of Tech-nology in 1973. He enjoyed a career as a seismic interpreter at Texaco (1973–1977), Gulf (1977–1984), and most recently Sohio/BP (1984–2008). Since retirement in 2008, he has worked as an independent geophysi-cal consultant for Petroleum Geo-Services (PGS) as a geosciences advisor, and with several oil companies as a seismic interpre-

tation instructor. At Gulf and Sohio/BP he taught in-house courses in seis-mic interpretation and was co-instructor for the SEG Continuing Education course “Seismic Interpretation in the Exploration Domain” (1995–2007). He was a member of the Editorial Board of The Leading Edge (2002–2007, chairman in 2006–2007) and is author of the bi-monthly “Interpreter Sam” column in The Leading Edge. He is an active member of SEG, AAPG, and Sigma Xi.


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vii

preface

This book begins with an introduction that is more philosophical than technical, followed by five chapters on fundamentals of reflection seis-mic (titled Seismic Response, Seismic Attributes, Velocity, Migration, and Resolution). The gist of what I really have to say about the correlation of seismic records is in Chapters 7 (Correlation Concepts) and 8 (Correlation Procedures). Chapter 9 (Data Quality and Management) certainly should not be glossed over, and Chapter 10 (Other Considerations) contains my thoughts on several worthy topics that do not fit neatly into any of the pre-ceding chapters.

In large part, this book is a compilation of notes from seismic inter-pretation courses that I’ve had the good fortune to teach over the past three decades. Because I’ve assumed that readers are familiar with basic concepts and principles of geology and reflection seismology, the book is best viewed as a synthesis rather than a fundamental treatment of those concepts and principles. When I use the expression “geologically reasonable” to qualify interpretation results, which I do throughout the book, I mean “reasonable” in the sense of “analogous to known geology” or “consistent with known geology or sound geologic models” or “within the context of expectation or realization of some geologic concept or model.”

I certainly don’t intend this book to be the definitive primer on inter-preting reflection seismic data or a comprehensive treatise on the latest in correlation tools and techniques; rather, I’m seeking to give voice to a con-cern about “this particular art” that I’ve had ever since my first foray into interpretation in the early 1970s. My concern is founded on a statement by a man from whom I had the privilege to learn about exploration geophysics in the classroom and in the field. In his own book he wrote that “the cor-relation procedure itself is of such a nature that it can hardly be adequately described in a book.”

Well, with the utmost respect for that man, here goes.


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ix

acknowledgments

I thank Rebecca Latimer, Bill Barkhouse, Bruce Hart, and John O’Brien for their constructive reviews of my manuscript and also BP (Amal Ray and Tim Summers), PGS (Nathan Oliver), TGS (Tom Neugebauer), and West-ernGeco (Lee Hooper) for permission to include data and images from their companies in this book. I thank Mike Schoenberger for sharing his charac-terization of seismic data quality with me; it’s the most concise and practi-cal description of data quality I’ve ever known, so I’ve used it to set context throughout the book. I extend my thanks also to members of the SEG publi-cations and graphics groups in Tulsa, in particular Jennifer Cobb and Kathy Gamble, without whose skill and patience this book could not have come into being. I’m especially grateful to Kathy Pile and Gary Stewart, whose editing gave my text the clarity and consistency it needed. In creating this book, I’m indebted to countless geoscientists, old and young alike, from whom I’ve learned so much over the years. Among all those talented men and women, I owe the most to Tim Smith, perhaps the most insightful inter-preter I’ve ever known and an excellent teacher as well, with whom I’ve had the distinct privilege numerous times to share the front of a classroom.


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Interpretation is telling the geologic story contained in seismic data. It is correlating the features we see in seismic data with elements of geology as we know them. The story is read from a book having many chapters, some of which are either illegible or unintelligible, and others are lost or yet to be written. And although the story doesn’t always have a happy ending, only in its telling do we expand our knowledge.

—Interpreter Sam


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1

Chapter 1

Introduction

Accurate interpretation of geophysical data — in particular, reflection seismic data — is one of the most important elements of a successful oil and gas exploration program. Despite technological advances in data acquisition and processing and the regular use of powerful computers and sophisticated software applications, you still face a tremendous challenge each time you begin to reconstruct the geologic story contained in a grid or volume of seis-mic data — that is, to interpret the data. On occasion, this interpretive tale can be clearly told; but most of the time, each page of each chapter is slowly turned, and rarely is the full meaning of the story completely understood.

Where the correlation of one reflection record with another is very easy, little needs to be said. Almost anyone can understand such a correlation. On the other hand, this is a rare occurrence. The usual thing is for the correlation to be so difficult as to be impossible. It is for this reason that correlation procedure can hardly be described in words (Dix, 1952).

Although Dix is speaking about the correlation of individual reflec-tion records, which were used routinely before the advent of continuous common-depth-point (CDP) profiling, he clearly recognized the essence of interpretation as the considered extraction of geologic information from indirect geophysical measurements. His words are no less relevant and applicable now than they were 60 years ago, even in view of the high stan-dards of data quality made possible by advances in seismic acquisition and processing, to say nothing of accompanying developments in interpretation technology. In the modern interpretation environment, you still face correla-tions that are “so difficult as to be impossible” because these correlations

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2 First Steps in Seismic Interpretation

define the frontiers of opportunity, the ones posing the sternest challenges and ultimately leading to the greatest rewards.

The primary aim of this book is to describe Dix’s correlation procedure in terms of the science, data, tools, and techniques now used in seismic interpre-tation in the oil and gas industry. As an individual geoscientist, you develop and apply your own approach and style when interpreting seismic data. You continually revise and refine correlation procedures during the course of your career and expand them as you complete different interpretation projects. With experience, you learn to check and recheck the validity of your proce-dures to fully understand the rules of evidence that govern their use:

• What are the physical laws that control the phenomena you observe and consider as evidence?

• What are the uncertainties in your evidence?

You must have a good understanding of seismic acquisition and pro-cessing principles as well as fundamentals of geology before beginning to collect interpretive evidence and solve interpretation problems correctly. Continuing from Dix, then, you must also know when enough interpreting is enough:

The threshold of impossibility is reached by different interpreters at different levels. The important thing is for each interpreter to under-stand his limitations. Obviously it is foolish to go ahead and corre-late when no correlation is possible. This involves giving a definite interpretation that is almost sure to be misleading and therefore very expensive (Dix, 1952).

The primary goal of seismic interpretation is always to describe geology, and all aspects of interpretation facilitate and support this goal. The prod-ucts of seismic interpretation are an important subset of the indispensable elements used by geoscientists to define and develop oil and gas prospects. Although seismic interpretation is a very important part of the exploration-development-production stream, it is only one of the elements used when integrating all available data to build a geophysically consistent and geo-logically reasonable picture of subsurface structure and stratigraphy. Draw-ing this picture accurately is a critical factor in successful identification of drillable prospects and exploitation of known hydrocarbon accumulations.

Interpretation, the description of geology, depends critically on seis-mic data quality: The better the quality, the more accurate and reliable the interpretation. In the most general terms, quality is the degree to which


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Chapter 1: Introduction 3

something fulfills its intended purpose; because you use seismic data for different purposes, depending on where you are in the value stream (e.g., exploration versus production), you know that data quality appropriate and acceptable for one project may not be for another. For example, the quality of a high-resolution seismic survey used to detail the shallow subsurface and identify potential drilling hazards would be completely inadequate and essentially useless for deep exploration. In the same way, a 3D survey pur-posely acquired and processed to image deep subsalt targets would have little or no value for shallow hazards assessment. At the same time, qual-ity may be less than optimal owing to problems in data acquisition or pro-cessing, and you need to be able to recognize these shortcomings, seeking advice from acquisition and processing specialists as needed, accounting for the shortcomings during interpretation, and making appropriate recom-mendations for improvements.

There are three primary elements of seismic data quality: detection (sig-nal-to-noise), resolution (temporal and spatial), and image fidelity (focusing and positioning). All efforts in seismic data acquisition and processing are designed to optimize data quality and “interpretability.” You are responsible for assessing data quality for each of your interpretation projects and for communicating this assessment as part of any presentation of project results.

Seismic interpretation is, by the nature of seismic data and the earth itself, nonunique and highly subjective. You bring your perspective and powers of observation to bear on the interpretation problem at hand, the effects of which cannot be clearly identified in or separated from your maps and calculations — and yet are a controlling factor in your results. Stephen Jay Gould recognizes and appreciates the importance of talent for observa-tion in naturalists, which can easily apply to interpreters:

All field naturalists know and respect the phenomenon of “search image” — the best proof that observation is an interaction of mind and nature, not a fully objective and reproducible mapping of out-side upon inside, done in the same way by all careful and compe-tent people. In short, you see what you are trained to view — and observation of different sorts of objects often requires a conscious shift of focus, not a total and indiscriminate expansion in the hopes of seeing everything. The world is too crowded with wonders for simultaneous perception of all; we learn our fruitful selectivities (Gould, 1993).

Although acquiring, processing, and analyzing seismic data are math-ematically intensive and now almost exclusively digital, interpretation


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activity per se is still primarily a visual (human and therefore fallible) pro-cess. Correlation of seismic records involves pattern recognition, depending heavily on the display of data and your knowledge and understanding of patterns in geology. Interpretation of any element of geology from seismic data involves answering the questions “What is it?” and “Where is it?” — answers that are rarely independent of each other. In other words, you often interpret what something is by where it is in relation to other features, or where and how large a feature should be because of what it is. Hence, we confirm the importance of migration of seismic data and, ultimately, the ability to visualize and reconstruct in depth what is only indirectly measured in time. Of course, it goes without saying that you will not be too terribly successful in the oil and gas business if you can’t accurately specify what, where, and how big your exploration targets are.

Seismic acquisition, processing, and interpretation are related, as shown in Figure 1. Acquisition and processing can be thought of as forward pro-cesses in which acoustic-impedance contrasts in the subsurface produce measurable seismic responses (acoustic impedance [AI] and reflection coef-ficient [RC] are defined in Chapter 2). The interpretation of this response, which in Figure 1 is called “ideal” but often is very far from being so, is an inverse process that describes the original AI contrasts and ultimately the subsurface geology. Notice that the forward processes of acquisition and processing can give rise to different, nonunique responses, depending on the particular acquisition and processing techniques used. This is another way of saying that acquisition and processing determine data quality. The inverse process of interpretation can result in many different descriptions of geology, again because of varying data quality and also because the funda-mental relationships among subsurface geometry, acoustic impedance, and geology are nonunique. In your better humors, you thank your good fortune for this nonuniqueness because it is an important factor contributing to your job security.

Your domain of information in interpretation consists of facts (there may not be as many of these as you would like to believe), observations, inferences drawn from observations and their resultant models, and, of course, experience gained from having established facts, made observa-tions, drawn inferences, and revised models over time. Taken together, these still represent a relatively small volume of your domain, the largest por-tion of which is the unknown. Accurate, well-integrated interpretations can reduce the volume of the unknown, but only if you maintain awareness of the distinctions among facts, observations, and models, all of which can be considered interpretive “evidence.” This awareness is a critical element in your assessment of technical risk in exploration projects, which, contrary to


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the way you would like it to be, is at best as subjective as the interpretation on which it is based.

Perhaps the most common intellectual difficulty encountered in corre-lating seismic data is maintaining a clear distinction between observation and interpretation (see Figure 2). Observation is the essential foundation for meaningful interpretation; think of observation as “What do I see?” and interpretation as “What does it mean?” These questions can be easily and often unwittingly confused, allowing bias to enter an interpretation and resulting in premature or unwarranted interpretive conclusions. Experience does not guarantee that you will be able to keep observation and interpre-tation separate because there is a sense of urgency in the desire to explain observations and “get on with business” that can prevent you from devoting sufficient time to making an appropriate number of careful observations. Similarly, the lack of patience that often accompanies inexperience can lead to the same unfortunate result.

Figure 1. The interrelationship of seismic data acquisition and processing with seismic interpretation. The former are forward processes, and the latter is an inverse process. AI = acoustic impedance; RC = reflection coefficient.

Interpretation

Data acquisition and processing

– + Lithology

Acoustic impedance

Reflection coefficient

Ideal seismic

response

No depth scale

implied

No time scale

implied

+


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As said before, you can think of the objectives of seismic interpretation as seeking to answer two questions about subsurface geology:

• “What is it?” — What elements of geology can you recognize (observe and explain)?

• “Where is it?” — How accurately can you delineate elements of geol-ogy in three-dimensional space?

To be of any use in a successful exploration program, your answers to these questions require that you understand how to accurately transform measure-ments and observations made in the reflection time domain into the depth domain. Except in the simplest cases, the inherent nonuniqueness of inter-pretation often allows your answers to “What is it?” to promote erroneous inferences about “Where is it?” or “How big is it?” — and vice versa. Which of these questions can or should be answered first, and the confidence with which either can be answered at all, clearly depends on the quality of available data, the tools at hand for analyzing those data, and your skill and experience as an interpreter. Often, prior knowledge of and experience in an area enable you to answer one of these questions with greater certainty than the other, and you effectively conduct a model-based interpretation, in which the course of the interpretation is guided by more than just observations and correlation of the data. There is nothing implicitly wrong with such an interpretation because you should incorporate all available information and experience into your interpretations. The peril lies in the possibility that prior knowledge can subconsciously (or otherwise) drive your interpretation, and so contradictory observations or correlations are downplayed or ignored because they don’t fit the model. In such cases, the objectivity essential to all interpretations is seriously at risk, and you may see only what you want to see.

Figure 2. The observe– interpret–test cycle when working with seismic data. We make observations on uninterpreted data, explain those observations in an interpretation (telling the geologic story contained in seismic data), and test conclusions with wells or additional data, leading to more observations and revised interpretation.

Interpret Correlate/explain/synthesize

build model

Test

Observe


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Your fundamental concern in seismic interpretation is recognizing that reflection seismic data do not (yet) provide one-to-one images of true sub-surface geology. You must decide which features in the data are “real” and correlative and which are not, and you must always try to understand the differences between the two. At the same time, you need to determine how well resolved are the real features you see and how accurate are their spatial positions; hence, the importance of data quality and the ability to properly couch interpretation results within the context of that quality. In a philo-sophical sense, you should maintain healthy skepticism throughout your interpretations, using methodologies based on assumptions of doubt with the aim of gaining approximate or relative — but never absolute — certainty in your results.


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9

Chapter 2

Seismic Response

Seismic response is measured by the reflection generated at an acoustic impedance boundary according to the properties of the layers above and below the boundary and the nature of the seismic pulse impinging on that boundary.

Referring to Figure 1, the equation below defines acoustic impedance (AI) as the product of compressional-wave velocity V and bulk density r:

AI = Vρ

The following equation defines the reflection coefficient (RC) in terms of AI for normal incidence of a seismic pulse at an AI boundary:

RCAI AI

AI AI2 2 1 1

2 2 1 1

2 1

2 1

=−( )+( ) =

−( )+(

V V

V V

ρ ρρ ρ )) .

The Zoeppritz equations define the reflection coefficient for nonnormal angles of incidence of a seismic pulse at an AI boundary; these equations generally are applied in a simplified form (e.g., Shuey, 1985). For the pur-poses of this text and defining seismic as “having to do with elastic waves” (Sheriff, 2002), here we describe seismic response in terms of compres-sional-wave (P-wave) reflections but do not discuss shear waves (S-waves) or mode conversions in detail.

You can initially and most easily describe seismic response with refer-ence to an isolated impedance boundary and can further develop understand-ing of the composite response from multiple, closely spaced boundaries by way of the convolutional model (discussed later in this chapter). You need


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to be familiar with a mathematical description for a waveform in terms of its frequency, amplitude, and phase characteristics, being especially careful to define phase and polarity as used in describing the shape or “character” of a reflection. The confidence with which you identify and correlate a reflec-tion from an acoustic impedance boundary, which interpreters call a seismic event or horizon, based on its appearance or character depends on seismic data quality, on simple and well-known impedance relationships, and, per-haps most importantly, on correlation of seismic data to available well data via well ties.

The importance of horizon identification increases as you move along the value stream from wildcat exploration through appraisal and devel-opment to production because this movement is toward greater detail of description in telling your geologic story. When interpreting and mapping in a frontier area, it may not be important to know whether a particular reflec-tion corresponds to the top of a sand or a shale. But for a production project in the same area many years and millions of dollars later, it could be crucial to understand the seismic response for the top of a reservoir sand when choosing well locations and calculating reserves — hence, the importance of understanding seismic response in identifying horizons for interpretation.

Understanding the seismic response to an AI boundary requires knowl-edge of the seismic pulse incident to that boundary and the behavior of the

Figure 1. Definitions of acoustic impedance (AI) as a rock property, defined as the product of compressional-wave velocity V and bulk density r. The contrast in AI between two layers of rock gives rise to a seismic reflection when a seismic pulse impinges on the boundary between the layers.

V = compressional-wave velocity, r = bulk density

V1, r1

V2, r2

Upper layer

Lower layer

Incidentpulse

Reflectedpulse


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Chapter 2: Seismic Response 11

pulse as it propagates through the earth. The seismic pulse causes particle motion in the subsurface through a medium treated as elastic in response to stress applied in the form of an impulse (e.g., detonating a charge of dynamite or firing an air gun). Dix (1952, his Figures 11.4 and 11.5) presents schematic diagrams illustrating these particle motions for positive and negative reflec-tion processes. A seismic waveform is a description of this particle motion as a function of time, which can be treated as a composite of many individual functions of time for the different frequency components present in the wave-form; the analytical representation of a seismic waveform as the sum of indi-vidual sinusoidal functions is called Fourier analysis (Sheriff, 2002).

For the sake of clarity and proper use of terminology, you should always be careful to distinguish between a reflector and a reflection: the former is a surface or boundary across which there is an acoustic impedance contrast, and the latter is a measurement of the particle motion caused by impinge-ment of a seismic pulse upon the former. Keep in mind that you observe reflections and interpret reflectors (that is, elements of geology) from your observations — in that order. Maintaining a clear distinction between reflec-tions and reflectors will help you remember that no seismic line or volume, no matter how carefully acquired and processed, is a completely accurate representation of true subsurface geology.

A seismic pulse propagates through a subsurface that is not really elas-tic, so you can’t expect the pulse to retain its exact shape as it travels from the seismic source to a receiver. The change in shape of a wavelet, which is to say in the amplitude and phase characteristics of its different frequency components, because of propagation through a nonelastic earth is called attenuation. The physical properties of the subsurface of the earth cause the higher-frequency components of a wavelet to be preferentially reduced in strength, primarily because of converting the energy of particle motion to the heat of friction. In general, the farther or longer a signal travels, the more it is attenuated. Attenuation correction of seismic data, which can be done probabilistically (based on measurements of the data themselves) or deterministically (based on correlation with other physical measurements) is an important step in a seismic data-processing sequence.

The change in shape of a wavelet as a result of attenuation suggests that, all other things being equal, you should not expect to see the same seis-mic response to the same impedance boundary that occurs at two different depths. A modeled product such as a synthetic seismogram, which usually is generated with an invariant wavelet, will therefore be better for making an accurate well tie in that portion of the seismic section where the wavelet used for the synthetic seismogram is a good approximation for the actual wavelet in the data. This is why wavelets are extracted from seismic data


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over windows or intervals of specific interest and then are used to generate synthetic seismograms for correlation only in that interval. Where possible, these extractions are done at or near points of well control so that log data can be used in the extraction process.

In the time domain, a periodic function for a single frequency can be described as a sinusoidal wave, as with the cosine wave illustrated in Fig-ure 2. The general form of the equation for this cosine wave as a function of time is

y t A ft( ) cos ( ),= +2π φ

where A is the amplitude, f the frequency, t the traveltime, and φ the phase of the waveform. The value φ is the angle, measured in degrees (where 360° = 1 cycle), that represents the lead (the amount of time the waveform is advanced) or lag (the amount of time the waveform is delayed) with respect to a reference starting time. Phase φ is defined as the negative of phase lag (Yilmaz, 2001), which is to say that a negative time shift (time delay) cor-responds to a positive phase value and a positive time shift (time advance) corresponds to a negative phase value. For example, Figure 3 shows that a cosine wave lags a sine wave by /2 or 90°:

sin cos cos( ) , sin cosπ π π2 2 2

0 1 0

= −

= = ( ) = 002 2

0−

= −

=π π

cos ,. . .

or

cos sin sin( ) , cos sinπ π π π2 2 2

0 0

= +

= = ( ) = 002 2

1+

=

=π π

sin ,. . . .

Figure 2. A simple sinusoid defined as a cosine wave. The shape of this waveform is determined by its amplitude A, frequency f, and phase φ. T is the period of the waveform.

t

T

A


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The waveforms shown in Figures 2 and 3 are infinite, single-frequency sinusoids; however, all of the wavelets with which you work in practical seismic interpretation are finite and have limited bandwidth. They are the summation of discrete sinusoids, each with its own amplitude, frequency, and phase characteristics. This is the basis of Fourier analysis. An exam-ple of a finite, band-limited wavelet and its component sinusoids is shown in Figure 4; in this example, the amplitude and phase of the components are constant (phase = 0) and only the frequency of the individual sinusoids varies.

Knowledge of the phase of a waveform is important in Fourier analysis because this angle sets a reference for the starting time (zero time, effec-tively) for each component waveform defined by its own frequency and amplitude. An illustration of phase rotation of a simple band-limited wave-let symmetric about t = 0 through one full cycle from 0° to 360° for 90° increments is shown in Figure 5. As expected, phase rotations of 180° and –180° are identical.

The wavelet in the center trace in Figure 5 is symmetric about t = 0, meaning that it literally describes particle motion that occurs before t = 0, which is physically nonrealizable. For this reason, the wavelet is called a non-causal wavelet (see Figure 6). Because of its symmetry, it is also referred to as a zero-phase wavelet; each of its component sinusoids is zero phase, and each is uniquely defined by its own amplitude and frequency according to Figure 2. In terms of signal processing, a zero-phase wavelet has the shortest time duration (pulse width) for a given bandwidth (frequency range). The

Figure 3. Phase relationship between a sine wave (red) and a cosine wave (blue). The sine wave leads the cosine wave by 90°, and the cosine wave lags the sine wave by 90°.

cos(0) = sin(0 + p /2) = sin(p /2) = 1sin(0) = cos(0 – p /2) = cos(–p /2) = 0

t

–p /2 p /2 3p /2 2pp0


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Figure 4. Illustration of a finite, band-limited wavelet as the summation of five component sinusoids. All of the components have the same amplitude and phase (phase = 0).

Finiteband-limited

wavelet

40 Hz 30 Hz 20 Hz 10 Hz 5 Hz

t

Figure 5. Phase rotation of a zero-phase wavelet (center trace) through a full 360° in increments of 90°. The display convention used in this figure is described in Figure 7.

0°–180° –90° +90° +180°

Tim

e

+

_


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seismic response for a zero-phase wavelet also is easier and more intuitive to visualize because its maximum amplitude corresponds exactly to the posi-tion of the reflecting interface (see Figures 5 and 6). Displays that show the amplitude and phase characteristics of the sinusoids for every frequency component of a wavelet are called the amplitude (amplitude as a function of frequency) and phase (phase as a function of frequency) spectra. Given these amplitude and phase spectra, a resultant wavelet can be uniquely con-structed by summing individual frequency components having the charac-teristics defined by these spectra.

Figures 5 and 6 use the same display convention, i.e., they represent seismic response in the same way with reference to a standard impedance configuration. The display convention most commonly used by SEG is the positive standard polarity convention (Figure 7), in which polarity means positive or negative trace deflection. When discussing or presenting your work, you should state the phase of your data, to the degree it is known, and the display convention you are observing. Similarly, you should ask about wavelet phase and the display convention being used in any discussion or presentation involving seismic data if that information is not communicated or clearly annotated on seismic displays.

Figure 8 illustrates the four different display formats for reflection seis-mic data. Of these, the most common used on workstation displays is vari-able density, often with user-defined or customized color schemes. Wiggle traces superimposed on a variable density background is also a popular dis-play format.

Figure 6. Noncausal and causal wavelets. The causal wavelet involves particle motion only after time = 0, whereas the noncausal wavelet involves particle motion before time = 0, which is not physically realizable. The display convention used in this figure is described in Figure 7.

0

Causalwavelet

Noncausalwavelet

Tim

e

+

_


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In virtually all cases, reflection seismic data represent a composite response to many closely spaced impedance boundaries, some of which are sharp and distinct and others of which are gradational. This composite response actually is the result of constructive and destructive interference of the discrete responses to individual impedance boundaries, described by the so-called convolutional model. Convolution is a mathematical operation that, in simplest terms, involves multiplication, shifting, and summation of two functions of the same variable (for seismic data the variable is traveltime t). You can think of convolution as simulating the propagation of a seismic pulse through a layered earth. The output of a 1D convolution, such as the convolution of an RC series calculated from an AI log (which has been converted to the time domain) with a seismic wavelet to produce a synthetic seismogram is probably much easier to visualize than to describe in words or to understand from exacting math-ematical language.

In Figure 9, the RC series consists of four coefficients, each correspond-ing to an AI boundary; the coefficients are not evenly spaced, and they do not all have the same magnitude and sign. This RC series will be convolved with the zero-phase wavelet shown to the left of the series, and both must have the same sample rate. Note that this wavelet is a wiggle trace that uses the SEG positive standard polarity convention. In the convolutional model, the seismic response to a given RC is created by reproducing the seismic wavelet scaled to the magnitude and sign of that RC. As shown in Figure 9, the scaled wavelet is reproduced as the seismic response for each of the four RCs, and the final convolution output or composite response is

Figure 7. The SEG positive standard display convention for reflection seismic data. “For a zero-phase wavelet, a positive reflection coefficient is represented by a central peak, normally plotted black on a variable area or variable density display” (Sheriff, 2002).

Acoustic impedance

Reflection coefficient

+ – Low

High

Wavelet

0


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Figure 8. Four display formats for reflection seismic data. Display formats are independent of the polarity convention used for a given data set.

Variable density Variable area Variable-area wiggleWiggle

the sum of the individual scaled responses. There is both constructive and destructive interference between individual seismic responses in the com-posite response. This interference is substantial when the effective width of the seismic pulse is greater than the interval between adjacent RCs. For purposes of this discussion, consider the pulse width to be the breadth of the central peak or peak/trough. Notice also that there is no individual seismic response for any points in the RC series where RC = 0, that is, where there is no impedance contrast. The differences between the composite responses in Figure 9a and 9b indicate that your interpretation of geology from seismic data depends critically on the wavelet in your data.

Knowledge of wavelet phase is important because it relates seismic response to geology in terms of the characteristics of the source wavelet (pulse) as defined in Figure 2, that is, the reflection seismic response to a given geologic boundary or feature changes for different source wavelets. The phase of the wavelet contained in any seismic data set can vary laterally and vertically (temporally) and is estimated most accurately by determin-istic methods using well control. In the absence of well control, you can


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Figure 9. (a) The convolutional model. The individual responses of each reflection coefficient to the input seismic wavelet, scaled to the magnitude and sign of the reflection coefficient, are summed to generate the composite seismic response. There are destructive and constructive interference of the individual responses in producing the composite response. (b) Convolution of the reflection coefficient series shown in (a) with a different source wavelet. The differences between the composite responses for the two wavelets show that accurate interpretation of these responses depends on knowledge of the source wavelets.

Input wavelet

Reflection a)coefficients

Individual responses

Overlay responses

Composite response

– +

Input wavelet

Reflection b)coefficients

Individual responses

Overlay responses

Composite response

– +

visually estimate wavelet phase by observing certain reflections that may be present in your data (see Table 1).

Using reflections from any of the boundaries listed in Table 1 assumes that the boundary can be identified conclusively, that there is a well-known and consistent acoustic impedance contrast across it (the algebraic sign of the reflection coefficient across the boundary is known), and that it is isolated


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from other nearby boundaries so that its character is not a composite reflec-tion response. In marine settings, the seafloor reflection is commonly used to check wavelet phase because the impedance contrast between seawater and sediment is almost always positive. Similarly, a hydrocarbon/water contact, which appears as a seismic flat spot in a reservoir that is thick enough to be resolved seismically, can be used confidently to estimate wavelet phase (see the discussion of seismic resolution and tuning in Chapter 6). A seismic flat spot occurs because the presence of hydrocarbons as the pore-filling fluid lowers the AI of the hydrocarbon-bearing portion of a reservoir below that of the nonhydrocarbon-bearing or brine-filled portion of that reservoir. Not all flat spots are perfectly flat because velocity effects in time imaging can tilt or distort them and because some hydrocarbon/water contacts are not truly horizontal. A flat spot can occur only for reservoirs in which the hydrocarbon-bearing portion of the reservoir is seismically resolved because the seismic response from a hydrocarbon-bearing interval whose thickness is below a cer-tain value called the tuning thickness will be a composite of responses from the top and base of the interval that will not directly represent wavelet phase.

The flat spot indicated by the arrow in Figure 10 shows a well-defined, symmetric peak (black). According to the accepted polarity standard and display convention for this image, within the visual acuity of the observer to see asymmetry in the waveform, the phase of the data is zero. Note that near the right-hand edge of this flat spot is a high-amplitude trough-over-peak amplitude response; this point marks the tuning thickness of the low-imped-ance, hydrocarbon-bearing portion of the reservoir. Continuing to the right, the decrease in the amplitude of the trough-over-peak signature reflects the decrease in thickness of the hydrocarbon-bearing portion of the reservoir. Note also that the top of the reservoir is not marked by a single, sharply defined reflection (a trough or a peak) along its full extent, suggesting that the top of the reservoir interval might be gradational in some places.

Table 1. Subsurface boundaries that can be used for visual estimation of wavelet phase. No single boundary is absolute or foolproof.

Best:

Seafloor

Hydrocarbon/water contact (seismic flat spot)

Use with care:

Top of salt/volcanics

Base of salt

Basement


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The problem with using boundaries such as top and/or base of salt, top of volcanics, and basement (which can take on a variety of geologic and eco-nomic meanings) for estimating wavelet phase is that these boundaries often are gradational and poorly defined, so their seismic responses are effec-tively composite responses to multiple, closely spaced impedance contrasts rather than to a single, well-known impedance contrast. At the same time, the impedance properties of the materials above and below these boundar-ies, especially for basement, are not necessarily well known or regionally consistent; so neither the magnitude nor the sign of the impedance contrast across such boundaries can be inferred confidently without well control.

Most interpreters prefer to work with zero-phase data, for which a seis-mic event or horizon is symmetrically disposed about its correlative imped-ance boundary and thus is most easily and intuitively visualized. Knowledge of wavelet phase and the display convention of your data should enable you to draw geologically reasonable conclusions when correlating a given seis-mic response to a particular AI boundary. At the same time, you should rec-ognize that a given impedance boundary can give rise to different seismic responses, depending on the phase of your data. This knowledge is critical for accurate interpretation of seismic attributes, as discussed in the next chapter.

Figure 10. Example of a well-imaged seismic flat spot, denoted by the yellow arrow, on time-migrated data. This image suggests that the seismic data are zero phase (courtesy PGS).

t


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21

Chapter 3

Seismic Attributes

By definition, a seismic attribute is a measurement based on seismic data (Sheriff, 2002). In the strictest sense, then, two-way traveltime, also known as horizon time, is perhaps the most important and frequently used seismic attribute, although it isn’t usually considered an attribute.

Brown (1996) includes horizon time in his list of 66 different attributes and indicates that an attribute is “necessarily a derivative of a basic seismic measurement.” He presents a generalized classification scheme that breaks attributes into four categories: time, amplitude, frequency, and attenuation. Brown also poses two questions that all interpreters must address when ana-lyzing seismic attributes:

1) What do they all mean?2) When do we use one and when another?

In the same vein, a paper with the delightful title “Redundant and Useless Seismic Attributes” by Barnes (2007) offers several common-sense sugges-tions for distinguishing “useful attributes from those of doubtful utility,” including, among other characteristics, their clear and useful meanings in a geologic and/or geophysical context as opposed to mathematical terms.

With Barnes’ distinctions in mind, you can visualize a “utility spectrum” for seismic attributes (Figure 1), in which using an attribute or combination of attributes (e.g., by way of principal component analysis) to identify and correlate features of interest proceeds with varying degrees of attention to the true physical meaning(s) of the attribute(s). At one end of this spectrum is the mentality that “I’ll use this attribute for correlation when it helps me find what I’m looking for, regardless of what it means physically.” At the other end is “I’ll only use attributes whose physical meaning I fully under-stand.” In practice, there is no right or wrong mindset or approach — only


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personal preference. Your position on this spectrum for any project in which attributes are important will depend on preference (that is, experience), seis-mic data quality, and availability of calibration information, such as well control.

Seismic attributes are used to assist interpretation at all scales, rang-ing from analyzing regional depositional systems to mapping fine details of structure, stratigraphy, and rock properties.* They are also used to illustrate data quality (see Chapter 9). Two of the most commonly used seismic attri-butes are amplitude and coherence.

Amplitude

The reference or baseline value for the amplitude of reflection seismic data is zero; so amplitudes are positive or negative in accordance with agreed polarity and display conventions, as discussed in Chapter 2. In terms of the simple two-layer model shown in Figure 1 of Chapter 2, the magnitude and algebraic sign (positive or negative) of the amplitude of a reflection from a single, isolated acoustic-impedance (AI) boundary is directly proportional to the magnitude and algebraic sign of the reflection coefficient (RC) at that boundary; the convolutional model (Figure 9 of Chapter 2) extends this rela-tionship to the general case of the composite seismic response to a reflection coefficient series. In Figure 2, amplitude A is the departure of the waveform from the baseline value, as shown by the red arrows. The time separation between the apex of a peak (or trough) and the apex of its adjacent trough (or peak) is often called delta time or delta T (DT), and the absolute value of the difference in amplitudes measured at the same two points is called delta amplitude or delta A (DA). Notice that Figure 2 refers to data that are

*Excellent references for seismic attribute analysis are Seismic Attributes for Pros-pect Identification and Reservoir Characterization by Chopra and Marfurt (2007) and Interpretation of 3D Seismic Data by Brown (2011).

Figure 1. Utility spectrum for a seismic attribute. Depending on experience and the project at hand, you analyze attributes with varying degrees of attention to their physical meaning.

“What does itphysically mean?”

“Does it help mecorrelate?”


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Chapter 3: Seismic Attributes 23

processed in the time domain, and DT is equal to one-half of the period of the displayed waveform.

Before you proceed with any quantitative analysis of seismic amplitudes, you must ensure that amplitudes were handled carefully and consistently through all stages of data acquisition and processing. In many cases, this involves some detective work on your part, but your effort is well worth the trouble if you are to gain any meaningful and reliable information or value from amplitude analysis. You need to review all processing steps involving gain recovery and preservation of relative amplitudes as well as the qual-ity of stacking (to be discussed in Chapter 4), giving particular attention to processes such as automatic gain control (AGC) that affect prestack or post-stack amplitude balancing. Many interpreters prefer to apply AGC to their data when the primary objective is structural interpretation, but they would not use the same data for amplitude or attribute studies. At the same time, the data used for those types of studies must have sufficient dynamic range to include the maximum processed amplitude values and not have restricted this range by excluding or clipping values (a workstation-related issue).

Figure 2. Seismic amplitude (red arrows) and the quantities DT and DA (blue arrows) that are based on picking adjacent peak and trough reflections. This display is for time-processed data.

0

– +

∆A

∆T


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Any quantitative use of seismic amplitudes must be based on careful picking of reflections so that amplitudes are consistently and accurately measured or extracted from the seismic data. You should automatically track these reflections whenever possible (data quality permitting) so that they are consistently picked as peaks (maxima) or troughs (minima) if they are to be used for amplitude analysis. You can autotrack — track or pick reflections using computer-based processes (see Chapter 7) — or you can use an event that has not been autotracked as a reference horizon to construct a gate or window of sufficient size to contain the individual reflection or reflection package whose amplitude you want to measure. These gates do not need to be symmetrically arranged around a reference horizon and can be defined by two separately tracked horizons.

Seismic amplitudes are manifestations of geology because they are the response to AI contrasts that are themselves measures of rock and fluid properties. Changes in amplitudes therefore reflect changes in geology, and every seismic line or volume exhibits a range of seismic amplitudes that can be correlated to trends in rock and fluid properties and ultimately to lithol-ogy and pore-fluid type. Amplitudes at the extremes of this range are anom-alous in the sense that they are out of the ordinary or are departures from an established trend and, as such, can be of particular exploration interest. For example, in Cenozoic basins with predominantly clastic fill, anomalously high amplitudes known as bright spots have proven to be attractive explora-tion targets, although not guaranteed or completely risk-free. Bright spots reflect the reduction of the acoustic impedance of a reservoir sand caused by the presence of hydrocarbons in the pore-filling fluid in comparison to the acoustic impedance of the same reservoir filled with brine. Validation of seismic amplitude anomalies as direct hydrocarbon indicators (DHIs) is a very important element of successful exploration and development pro-grams in many areas of the world.

In general, you observe reflections as having anomalous amplitude when you visually inspect data (see Figure 3). This is a very qualitative measure, and amplitudes identified in this way are said to be above back-ground, meaning that there is an ambient or background level of reflec-tivity associated with the overall impedance trends (i.e., the geology) of the area under investigation. Figure 4 shows the results of one quick and easy technique for highlighting amplitude anomalies by simply clipping or blanking all amplitudes below a certain threshold or background level. You should reference amplitude values to a statistically based background level and calibrate them to well control if you intend to use them for quan-titative purposes such as calculating reservoir thickness and estimating reserves.


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Figure 3. A 2D time-migrated line with an anomalous high-amplitude reflection, indicated by the black arrow. Red events are negative amplitudes (troughs) and blue events are positive amplitudes (peaks). The trough-over-peak signature of the anomaly suggests that it can be interpreted as a thin (below tuning) hydrocarbon-bearing reservoir. Note similar trough-over-peak anomalies to the right of the highlighted event (courtesy PGS).

t

Most if not all modern workstation systems and interpretation pack-ages have standard routines for extracting amplitudes of individual reflec-tions as well as of user-specified intervals of interest. In the latter case, the attribute is usually a statistical measure of the data within an interval (such as average absolute amplitude, maximum positive amplitude, or root mean square [rms] amplitude). As indicated by Barnes (2007), amplitude-related attributes such as these often provide duplicate measurements. You should test different attributes before choosing the one that works best — gives the most stable and clearly defined results, obviously involving interpretive judgment and experience. As Barnes goes on to say, “If you can’t tell which one works best, then it doesn’t matter which one you choose.” Not only does Barnes imply that you should take the time to test different attributes on your data, but he also requires that you have some idea of what the different attributes mean physically.

The extraction of amplitude-related attributes over user-specified inter-vals can be particularly helpful when integrated with the results of sequence


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stratigraphic or seismic facies analysis to aid in identifying and interpreting depositional systems, especially valuable in regional studies. You should carefully select and then pick the horizons that bound intervals of interest so that these intervals are neither too broad (effectively mixing or masking several different amplitude signatures) nor too narrow (excluding data that are needed for accurate characterization of an interval). Again, appropri-ate definition of intervals involves interpretive judgment, and any integrated analysis using seismic facies and attributes should include well control when calibrating results.

The quantities DT and DA shown in Figure 2 are used in tuning or time-amplitude (time-amp) analysis (see Chapter 6, especially the wedge model shown in Figure 2 of that chapter) to study thin beds. In general terms, you should recognize that meaningful results from tuning analysis depend on critical factors in data processing and interpretation. For processing, these factors include true relative amplitude recovery and preservation, knowl-edge of the seismic wavelet and the signal-to-noise ratio (S/N) of the data, and the availability of carefully edited well data for calibrating the seismic

Figure 4. Same line as in Figure 3 but displayed with amplitudes less than approximately half of the maximum absolute amplitude value for the entire line clipped to highlight anomalously high and low amplitudes. Clipping was done visually by dynamic modification of the display color table. The clipping value can be thought of as a threshold between background and anomalous amplitude values (courtesy PGS).

t


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response. For interpretation, the seismic data being analyzed must have the objective peak-and-trough reflections consistently and accurately picked, which requires autotracking and thorough quality control before proceeding with tuning calculations.

Measurement of DT also provides a quick means for estimating the dominant frequency within a window of seismic data. By definition, domi-nant frequency is the predominant frequency determined by measuring the time between successive peaks or troughs (the period T of a waveform, as shown in Figure 2 of Chapter 2) and taking the reciprocal (Sheriff, 2002). In practical terms, the dominant frequency within a window of data can be thought of as the frequency of the waveform that dominates your view of the data within that window. Remember that the dominant frequency tends to decrease with increasing reflection time owing to attenuation, so any esti-mate of dominant frequency is applicable only to a window of data and not to the full reflection record.

Using a visual estimate of DT in milliseconds for well-defined, coherent reflections within a window of interest and recalling that DT as defined in Figure 2 is equal to one-half the period of the waveform, you can calculate the dominant frequency in hertz (Hz, cycles per second) as 1000/(2 × DT). In the example shown in Figure 5 for an estimated DT of 30 ms for the

Figure 5. Example of estimation of dominant frequency based on observed peak-to-trough time separation DT (courtesy PGS).

Dominant frequency = 1000/(2 × 30 ms) = 17 Hz

~30 ms

t


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high-amplitude peak/trough reflections within the window outlined in red, the dominant frequency is 1000/(2 × 30 ms) = 17 Hz. Note that this tech-nique is implicitly meaningful only for time-domain data because depth-processed data cannot be characterized by a measurement (DT) that can be made only in the time domain unless the depth data have been converted back or stretched to the time domain using the velocity model with which they were processed.

For additional information on physical principles and techniques for analysis and interpretation of seismic amplitudes, refer to Seismic Ampli-tude Interpretation by Hilterman (2001).

Coherence

Coherence is a seismic volume attribute; it is run only on 3D seismic data and measures the trace-to-trace similarity of the seismic waveform within a small analysis window. Coherence technology was originally developed by Amoco (Bahorich and Farmer, 1995) to enable more complete use of the abundance of information contained in a 3D seismic volume to complement standard interpretation techniques. Because important elements of geology such as faults and stratigraphic features (e.g., channel margins) are evident as discontinuities in seismic data, an attribute such as coherence can be very useful in identifying and visualizing these features (see Figures 6 and 7). Generating a coherence volume is an automated process that requires select-ing values for several input parameters, most important of which is the size of the data-analysis window (in three dimensions). A very large window will include too much data and produce output with a pronounced structural overprint, whereas a window that is too small will include too little data and produce output that is more a manifestation of noise in the data rather than geologic content. Input-parameter values usually are chosen following a series of tests to determine which combination of values produces the most interpretable output. As with the results of many other analytical processes, the quality or interpretability of the output depends heavily on the noise content of the input; a very noisy data set will probably contain very little useful or reliable coherence information. Your experience comes into play in selecting input parameters for coherence processing and evaluating/inter-preting coherence output; that experience also helps you properly gauge the value added to your interpretation through the use of coherence data.

Many interpreters generate a coherence volume for their 3D data as one of the first steps in a new interpretation project. A cursory review of these coherence data can be very helpful in gaining initial impressions of the geol-ogy of the project area and building work flows and schedules for ensuing


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interpretation tasks. Coherence displays such as that shown in Figure 7b also can be very useful for quality control of fault picks to ensure that faults are not miscorrelated in structurally complicated areas. Some interpreters prefer to pick faults primarily on coherence slices rather than on vertical sections when data quality permits.

Coherence data usually are viewed on a horizontal slice through the coherence volume or along a suitably tracked horizon (the latter producing a coherence horizon slice). Alternatively, the original 3D volume can be flattened along a suitably picked horizon, and coherence can be generated from that volume to produce a coherence horizon slice. These slices can be particularly helpful for interpreting details of stratigraphy, but you must pick the input horizon very carefully — entirely by automatic tracking if possible — so that tracking artifacts are not passed through the coherence process and subsequently interpreted as real geology.

Figure 6. (a) Traditional 3D time slice; faults parallel to strike are difficult to see. (b) Coherence time slice; faults are clearly visible. From Bahorich and Farmer (1995).

a) b)


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Inversion

Although you can consider seismic inversion to be more a process than an attribute in the sense that amplitude and coherence are attri-butes, some discussion of inversion is warranted because interpretation of inversions most often involves analysis of attributes extracted from the inverted data.

The place of seismic inversion within the sequence of acquisition- processing-interpretation of reflection seismic data is best visualized using Figure 8, a slightly modified version of Figure 1 from Chapter 1. In simplest terms, the inversion process involves calculating AI data from reflectivity data; as such, seismic inversion can be considered the first step in inter-preting ideally processed seismic data. As shown in Figure 8, conventional reflectivity data, the ideal seismic response, provide information about the boundaries between subsurface layers, whereas seismic inversions (AI) measure the properties of the layers themselves.

If you approximate an RC series as the derivative of an AI function, then the inversion of “optimally processed” reflection seismic data effectively is integrating those data to produce AI data. The following equations show that this approximation is based on the assumption that the difference between

Figure 7. Comparison of a conventional 3D horizontal slice through (a) a reflectivity volume and (b) a coherence volume generated from the parent reflectivity volume. In this example from a depth-processed 3D volume, faults that appear as dark lineations on the coherence slice are more clearly seen and accurately interpreted on the coherence data than on the reflectivity data (courtesy BP).

a) b)


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adjacent samples in an AI series is incrementally small, that is, AI varies slowly and smoothly with depth. From the equation in Chapter 2,

RCAI AI

AI AI( ) .

( ) ( )

( ) ( )

jj j

j j

=−( )+( )

+

+

1

1

The first equation below contains the key assumption that allows the RC series to be approximated as the derivative of an AI function:

RC

RC

RC

( )( )

( ),

( ) ln( )

,

(

tt

t

tt

C

et

~AI

2AI

~AI

∆

∫ +2 1

))( ),∫ ~ AIC t2

where C is a constant.

Figure 8. Seismic inversion, creating AI data from an “ideal seismic response,” is an inverse process that can be considered an interpretive process (refer to Figure 1 of Chapter 1).

– + Lithology AI RC

Ideal seismic

response

No depth scale

implied

No time scale

implied

+

Inversion


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Inversion is done in the time domain, so depth-processed data must be converted to time before inversion processing and then converted back to depth after inversion if desired. Note that scaling of the inverted data is implicit in the integration; this scaling is determined by calibration to well control or to regional impedance trend curves. Of course, any reflection seismic data can be inverted, but the quality of the inversion depends on calibration and the quality of the input reflectivity data.

Lindseth (1979), who refers to seismic inversion as generating a syn-thetic sonic log from a processed seismic trace, captures the uncertainty inherent in seismic inversion:

The inversion of seismic reflection data is much more demand-ing (and revealing) of data acquisition and processing quality than are conventional displays. While inferior data, as in many seismic operations, does not totally impede the execution of any process, the quality of output will be degraded, eventually reaching a point where any benefits from the procedure are doubtful.

In the context of Lindseth’s comment on the dependence of seismic inver-sion on the quality of data acquisition and processing, following is a list of conditions you should check about the processing of input reflectivity data before proceeding with inversion:

• Amplitudes are true relative amplitudes.• Amplitude variation with offset (AVO) effects are accounted for.• Data are zero phase.• Seismic wavelet is invariant (at least over the window of interest).• Bandwidth is maximized.• All multiple reflections have been removed (see Chapter 7).

All of these conditions are rarely if ever met, so the quality of a seismic inversion will always need to be assessed carefully in terms of its correlation to well data and the accuracy of its representation of real geology.

Seismic inversions are correlated in much the same way as are con-ventional reflectivity data (see Chapters 7 and 8). Although a primary objective of correlating both types of data is to define the boundaries of the intervals of interest, you pick zero crossings on inverted data to iden-tify these boundaries and then examine attributes of the defined layers to study their internal properties. Contrast this with picking troughs, peaks, and, occasionally, zero crossings on zero-phase reflectivity data, where the picked horizons also define layers of interest but extracted attributes


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Figure 9. Comparison of inverted and conventional reflectivity data. (a) Inverted data on which the top and base of a low-impedance interval are picked as zero crossings (black = relatively high impedance, yellow/brown = relatively low impedance). (b) Corresponding reflectivity data onto which the blue horizons from (a) have been directly transferred (black = peak, yellow/brown = trough). On this display, the blue horizons coincide approximately with a trough and a peak that correspond to the top and base, respectively, of the same low-impedance interval; these correlations are confirmed by well control not shown on these images (courtesy BP).

a)

b)


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provide information only about layer boundaries and not internal layer properties.

Figure 9 compares inverted data and the reflectivity data from which the inversions were generated; the blue horizons were autotracked as zero crossings on the inverted data and transferred directly to the correspond-ing reflectivity data. The good correlation of the zero-crossing picks on the inverted data to the troughs and peaks on the reflectivity data confirms that, in terms of wavelet phase, the inversion process can be thought of as apply-ing a phase rotation of –90° to the reflectivity data (see Figure 5 of Chapter 2), which is consistent with the concept that acoustic impedance can be calculated by integrating a reflection coefficient series. Note also in the left-center of Figure 9a that the upper blue horizon is not defined (cannot be automatically tracked) where there is no zero crossing between two closely spaced low-impedance layers. In Figure 9b, however, the upper blue horizon can be tracked automatically as a trough through the same area to a point of termination with the lower blue horizon (peak).

Seismic attributes play an increasingly important role in interpretation as you move along the value stream from exploration through appraisal and development to production. Often, attributes are a deciding factor in select-ing well locations and designing well trajectories, whether in an exploration or a production setting. Although you can interpret attributes solely on the basis of uncalibrated observations, with well control for calibration you can more accurately and confidently explain the geologic meaning and signifi-cance of the attributes of your data.


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35

Chapter 4

Velocity

Reflection seismic data measure the traveltime of a seismic pulse from its source to subsurface reflectors and back to a receiver or array of receiv-ers. A complete description of geology requires that any interpretation derived from these data, recorded in the time domain, be placed accurately in the 3D depth domain, and you use velocity information to accomplish this transformation from time to depth. The importance of time-to-depth conver-sion would be evident in the look of consternation on a driller’s face if you told him that the exploration target in a proposed well was at 5.3-s two-way time, with no mention of the depth to which this reflection time corresponds.

Velocity is defined as distance traveled per unit time. It is a vector quan-tity, that is, it has magnitude and direction. The scalar quantity associated with velocity is speed. Although we customarily talk about the velocity of propagation of compressional (P-) and shear (S-) waves through different materials, in a strict sense we are describing only the speed of wave propaga-tion through these materials because usually we do not specify the direction of measurement. In other words, we simplify our statements by assuming the materials are isotropic. Many materials exhibit velocity anisotropy, which means that the velocity of P- and S-waves in those materials depends on the direction in which it is measured (in the direction of wave propagation). The concept of velocity anisotropy is very important for accurate seismic imag-ing and time-depth conversion and is addressed later in this chapter.

Three different types of velocity are commonly used in the oil and gas industry. The first, interval velocity Vint, is the distance traveled per unit time, where the distance traveled is the thickness of a single well-defined layer or stratum:

Vz

ti

i

iint .= 2

∆∆


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Here, Vinti is the interval velocity of the ith layer, Dzi is the actual thickness

of the ith layer, and Dti is the two-way-time thickness of the ith layer. The reciprocal of interval velocity is called slowness.

Average velocity Vavg is the distance traveled per unit time, where the distance traveled is the total thickness of many layers or strata measured from the top of the uppermost layer to the base of the lowermost layer:

Vz

t

V t

t

i

i

i

i

i

avg =

=

Σ ∆Σ∆

Σ ∆Σ∆

2.

int

Root-mean-square (rms) velocity Vrms is a statistical quantity calculated from Vint and Dt:

VV t

ti i

irms2

2

=Σ ∆

Σ∆int .

These definitions are generalized for an arbitrary number of subsurface layers; Figure 1 illustrates them for simple two-and three-layer models.

Three sources of velocity data are used in the oil and gas industry: sonic logs, well-velocity surveys, and seismically derived velocities.

Sonic logs

A borehole sonic tool continuously emits a high-frequency signal from a transmitter and records that signal at a receiver; the transmitter and receiver are located within the same wireline tool. The travel path of the signal from transmitter to receiver is typically 3–10 ft (1–3 m) long, and the tool is engineered to compensate for its tilt within the borehole and for variations in borehole size. The traveltime from the transmitter to the receiver is the interval transit time (ITT), also known as slowness, measured in microsec-onds per foot. With correction by a factor of 10–6 to convert microseconds to seconds, interval velocity (measured over the very small interval between the transmitter and receiver in the sonic tool) is equal to the reciprocal of interval transit time:

Interval velocityInterval transit time

= 1


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Chapter 4: Velocity 37

For example, a measured interval transit time of 80 ms/ft is equal to an inter-val velocity of 12,500 ft/s (3800 m/s):

1

80 10

112 50

6( ),

s/ft s/ s

ft

0.00008 sµ µ×= =− 00 ft/s

t1

t2

Z

Datum

Z1

Z2

Vint1 = 2 Z1 / t1

t is thickness in two-way time

Vrms2 = Vint

2 t / t = (Vint1

2 t1 + Vint2

2 t2) / ( t1 + t2)

Vint2 = 2 Z2 / t2

Vavg = 2 Z / t = (2 Z1 + 2 Z2) / ( t1 + t2)

a)

Figure 1. Schematic of the definitions of average, interval, and root-mean-square (rms) velocities for (a) two-layer and (b) three-layer models.

t1

t2

t3

Z

Datum

Z1

Z2

Z3

t is thickness in two-way time

Vint1 = 2 Z1 / t1

Vint2 = 2 Z2 / t2

Vint3 = 2 Z3 / t3

Vavg = 2 Z / t = (2 Z1 + 2 Z2 + 2 Z3) / ( t1 + t2 + t3)

Vrms2 = Vint

2t / t = (Vint1

2 t1 + Vint2

2 t2 + Vint3

2t3) / ( t1 + t2 + t3)

b)


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Errors in measuring true rock velocities with a sonic log can occur for several reasons. First, when formation velocity is less than the mud (drill-ing-fluid) velocity no refraction occurs at the borehole wall, and the signal does not take a standard path to the receiver. Second, areas of washed-out borehole allow the signal to travel through the mud rather than the forma-tion, so correlation between slow velocity and large borehole size indicates that the measured velocity probably is too slow. This commonly occurs below a casing shoe or in salt. Finally, fracturing and invasion of porous formations can cause the measured velocity to be different from that of the undisturbed rock. This occurs particularly in gas-bearing formations where the mud filtrate tends to invade the rock and remove the low-velocity effect of the gas.

Well-velocity surveys

The basic field setup for a well-velocity survey is illustrated in Figure 2. In such a survey, the seismic source — an air gun for a marine survey and most commonly vibroseis for a land survey — is placed as near to the wellbore as possible. In a marine environment, an air gun is hung over the side of the rig or drillship into the sea. On land, if an air gun is used instead of vibroseis, it is put in a water- or mud-filled pit adjacent to the rig. The survey illustrated in

Figure 2. Schematic of the field setup for a conventional well-velocity or check-shot survey. In the figure, Tobs is recorded traveltime from source to receiver, Tvert is recorded traveltime converted to vertical traveltime, Tstat is vertical traveltime from datum to source depth, and D is depth from datum to receiver.

Datum

Source

Receiver(geophone)

Tobs

Tvert

Tstat

DVavg =

D

Well

Tvert + Tstat


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Figure 2 uses a single downhole geophone. However, for increased operating efficiency and cost savings, most velocity surveys now use an array of evenly spaced geophones rather than a single geophone, thereby enabling recording of arrival time at many depth points for a single shot.

The survey begins by lowering the geophone to the total depth (TD) of the well and clamping it to the borehole wall. The source is fired, and the arrival time of energy that travels directly from the source through the earth to the geophone (the first arrival) is recorded; the arrival time of the source impulse at a receiver placed very close (but not too close) to the source also is recorded to establish a time datum for the survey. The source is fired several times at each geophone station, and the records for each station are summed with bad records edited out, to produce a final output record for each level. The survey proceeds by raising the geophone up the borehole, stopping at predetermined levels to clamp the geophone and fire the source. The levels at which the geophone is positioned can be spaced regularly to achieve a desired vertical sampling of velocities or set at specific geologic boundaries or forma-tion tops. The survey is finished when the geophone reaches the level at the top of the survey program or when the recorded data fail to meet acceptable signal-to-noise (S/N) criteria. This type of well-velocity survey, known as a check-shot survey, provides direct measurements of average velocity, from which you can calculate interval velocity, as shown in Figure 3.

Figure 3. How to calculate interval velocities from data collected in a well-velocity survey.

Z1

Datum

Z

Z1 = depth of station 1

Vavg to station 1 = Z1 / T1

Z2

T2

T1

Z2 = depth of station 2

T1 = one-way arrival time at station 1

T2 = one-way arrival time at station 2

Vavg to station 2 = Z2 / T2

Vint = (Z1 – Z2) / (T1 – T2) = Z / (T1 – T2)

Z = station spacing = (Z1 – Z2)


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There are two main differences between a check-shot survey and a verti-cal seismic profile (VSP). First, the listening time for a VSP is longer than that for a check-shot survey because a VSP records not only the direct arrival of energy from source to receiver but also energy reflected from impedance boundaries below the geophone position in the well (see Figure 4). Second, the station spacing for geophones in a VSP is always regular and fairly short (50–75 ft; 15–20 m) to achieve the sampling required to avoid aliasing the desired signal (see Chapter 6). The VSP records downgoing (direct) and upcoming (reflected) energy, so it contains the same information as a check-shot survey with the added advantage of denser sampling of the subsurface velocity field.

The source and receiver geometries in a well-velocity survey vary according to wellbore geometry and the objectives of the survey (see Fig-ures 5 and 6). The source can be placed very near (essentially at) the well or some distance away and can be fixed throughout the survey or moving with respect to the well or the wellbore trajectory. The receivers also can be fixed with a moving source, as in the case of a walkaway VSP, or moving, as in the case of a walkover VSP when the receiver is located vertically beneath the source. In itself, VSP processing is a complicated subject that is not discussed in this text; however, Figure 7 provides a good idea of what raw VSP data look like before processing is done. Needless to say, well-velocity

Figure 4. Same field setup as shown in Figure 2 but including upcoming arrivals as recorded in a VSP. The green arrows indicate downgoing and upcoming raypaths.

Datum

Source

Receiver(geophone)

Tobs

Tvert

Tstat

DVavg =

D

Well

Tvert + Tstat

Reflector


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surveys should be designed carefully in conjunction with the results of for-ward seismic modeling.

Seismically derived velocities

Although sonic logs as well as check-shot surveys and VSPs directly measure P-wave velocity, they operate at very different geologic scales and use considerably different source and receiver instrumentation. They

Figure 5. Field geometries for well-velocity surveys with a fixed source.

OffsetWellhead

Source

Receiver

Figure 6. Field geometries for well-velocity surveys with a moving source.

Walkaway Walkover

Source

Receiver


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are direct in the sense that they measure the elapsed time of propagation of a compressional pulse from an energy source to its arrival at a receiver a known distance from the source. When using a near-wellbore source, a check-shot survey or VSP typically measures only the vertical component of propagation velocity and does not account for the possibility (probabil-ity) of velocity anisotropy.

In contrast, the velocity derived from analyzing reflection seismic data provides an indirect measure of P-wave velocity that does include the effects of anisotropy. Seismically derived velocities actually are calculated using known acquisition geometry and observed reflection arrival times. The validity and utility of these velocities depend heavily on the accuracy of velocity-analysis techniques and the degree to which certain critical assumptions underlying the velocity calculations are met.

The concept of multiplicity of subsurface (or multifold) coverage, in which reflections from a single subsurface point are recorded using mul-tiple source-receiver geometries, establishes the basis for conventional seis-mic velocity analysis. As conceived, multifold acquisition was developed to more effectively enhance coherent signal and eliminate unwanted ran-dom and coherent noise, which could be achieved by adding, or stacking, the multifold data. Central to the stacking process is estimating the optimal

Figure 7. A raw VSP record (depth of receiver on the x-axis, traveltime to receiver on the y-axis). Downgoing arrivals dip from right to left; upcoming arrivals dip from left to right (courtesy BP).

Depth

Tim

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stacking velocity, determined in the velocity-analysis step of a normal seis-mic processing sequence. This velocity analysis is based on the assump-tions that the propagating media are horizontal layers that are homogeneous (uniform in physical properties throughout) and isotropic (having the same physical properties regardless of the direction of measurement). Given these conditions, the recorded traveltimes, source-receiver distances, and stack-ing velocity are related by the so-called normal-moveout (NMO) equation. Unfortunately, earth scientists know that these assumed conditions are rarely, if ever, met in the real earth.

Figure 8 illustrates a source and multiple-receiver array positioned over a single horizontal reflector at depth. In this diagram, the distance X between the source and any receiver in the array is referred to as the source-receiver offset, which is zero when the source and receiver are coincident. The reflector in Figure 8 is the boundary between two horizontal layers that, respectively, have constant P-wave velocities V1 and V2 and densities r1 and r2 (review Figure 1 in Chapter 2). For the geometry shown in Figure 8, a reflection from the boundary between the layers arrives at successively later times for receivers with greater offset because a signal travels successively greater distances through the upper layer for larger offsets.

Figure 9 shows that as the entire source-receiver array is moved, a given point on the reflector is effectively sampled multiple times (called fold of coverage), each time by energy traveling along a different path. Figure 9 also illustrates how the recorded energy from these multiple paths for the same reflecting point, referred to as a common depth point (CDP) or, more accurately, as a common midpoint (CMP), can be collected or gathered into

Figure 8. Seismic acquisition for (a) a simple two-layered earth model (depth domain) and (b) the corresponding shot record (time domain). This model forms the basis for the common-depth-point (CDP) method of acquisition.

Offset X

Receivers

Reflectingpoints

Source

DepthZ

a) b)

Offset X

ReflectiontimeT

V1, ρ1

V2, ρ2


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a single group known as a CDP or CMP gather. For a given CDP gather, the increase in reflection traveltime with increasing offset, referred to as normal moveout (NMO), is described by the well-known NMO equation:

T TX

V2

02

2

2= +

where T is the two-way traveltime to a receiver at offset X and T0 is the two-

way traveltime for X = 0.All of the offsets X for the geometry in Figure 9 are known, and T for

each offset is recorded. It is possible to correct each T in a CDP gather to T

0 using the NMO equation and the appropriate value for V. Figure 10

illustrates how accurate moveout corrects the recorded arrival time T of a

Figure 9. Building on the model shown in Figure 8, the sequence of three different source-receiver configurations shown on the left illustrates how moving a source-receiver array allows you to sample a given reflecting point in the subsurface (the CDP, marked by the red dot) multiple times, each with a different incident angle and raypath. As shown on the right side, collecting or gathering the traces corresponding to these different raypaths for the same subsurface reflecting point enables formation of a CDP gather, on which seismic velocity is analyzed. NMO is the increase in reflection time for a given reflecting point that occurs as the source-to-receiver distance increases.

CDP gather

A B C

A B C

A B C

A B C

A B C

Shot #1

Shot #2

Shot #3

Offset X

1 2 3

Common depth point (CDP)

T0

V1, ρ1

V1, ρ1

V2, ρ2

V1, ρ1

V2, ρ2

V2, ρ2

ReflectiontimeT


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reflection at each receiver to T0, flattening the corrected reflection across the

gather. Figure 10 also illustrates the power of stacking moveout-corrected traces in a gather to produce a composite trace in which signal is enhanced and unwanted noise is greatly reduced.

Conventional seismic velocity analysis solves for the unknown quan-tity V, commonly referred to as the stacking velocity Vstk or NMO velocity VNMO, by applying moveout corrections to all traces in a gather with a suite of velocities and then measuring the accuracy of the corrections accord-ing to how well reflections are flattened across the moved-out gather. This measure is valid only when assuming normal hyperbolic moveout, which is in itself an approximation. Notice that in the NMO equation, reflection time T is a hyperbolic function of offset X. Accurate, good-quality velocity data are those which, in making the move-out correction, result in exact flattening of primary reflections in a corrected gather, assuming hyperbolic moveout.

Stacking velocities can be identified, or picked, in several ways, one of which uses a contoured semblance plot or velocity spectrum on which the stacking velocity that results in the best flattening of a reflection corre-sponds to the maximum value for the semblance or trace-to-trace similarity of that reflection across the NMO-corrected gather. When picking veloci-ties on a velocity spectrum, a data processing geophysicist also looks at a real-time display of the NMO-corrected gather at the analysis location, on which he considers how well the selected velocity flattens the reflection at the two-way time of the velocity pick. He adjusts each pick until satisfied

Figure 10. NMO correction, by which each of the traces in a CDP gather is corrected so it represents the trace that would have been recorded if the source and receiver were coincident (offset X = 0). The second-order NMO equation, which is an approximation, describes this behavior in terms of reflection time, offset, and velocity (NMO or stacking velocity). The velocity term in the NMO equation, although having units of velocity, can be most accurately described as the variable which, when solved for, best flattens the event on the CDP gather.

Offset X

Offset X

ReflectiontimeT

ReflectiontimeT

T0T0


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that it produces optimal event flattening, and for each analysis location he creates a series of two-way time/stacking-velocity pairs that will be used in stacking. His assessment of flattening is very interpretive and depends heavily on his visual acuity, the noise content of the data, and the smooth-ness of the velocity field, such that the assumption of hyperbolic move-out is valid. In accordance with the NMO equation and in view of the interpre-tive nature of NMO velocity analysis, the accuracy of velocity picks gener-ally decreases with increasing reflection time (depth) because the amount of NMO decreases with increasing depth. You can see this effect on velocity spectra as smearing or stretching of semblance clusters along the velocity axis with increasing reflection time.

Figure 11 illustrates how a velocity spectrum is produced. The NMO equation is applied sample by sample to the entire time range of an uncor-rected gather (from T = 0 to 5.9 s two-way time, or TWT) over a velocity domain of V = 4800 to 12,000 ft/s (1460–3660 m/s). For a reflection at a given time, there is a moveout velocity for which the measure of sem-blance at that reflection time is maximum, that is, for which the trace-to-trace alignment of the reflection character at that time is most consistent across the entire offset range of the gather. The (V,T) pair corresponding to that point is marked, or picked, on the velocity spectrum, and similar maxima are picked over the full time range of the gather to produce the best time-velocity function for moving out the entire gather. These veloc-ity picks are the white dots connected by white line segments shown in Figure 11b.

Figure 12 shows how moveout with velocities that are too slow or too fast results in a moveout correction that is too large (overcorrected) or too small (undercorrected). In Figure 12a, the moveout correction is too large because the velocity is too slow; the applied velocity function is shifted to the left of the best function toward the slower velocities, and the overcor-rected events can be viewed as smiling — think of s for “slow and smiling.” In Figure 12b, the moveout correction is too small because the velocity is too fast; here, the applied velocity function is shifted to the right of the best function toward the faster velocities, and the undercorrected events can be viewed as “frowning” — think of f for “fast and frowning.”

The uncorrected gather, moveout-corrected gather, and stacked trace in Figure 13 summarize the velocity analysis/NMO correction/stacking sequence. NMO correction is very important because you very often see only stacked seismic traces and are unable to directly verify the accuracy of preceding velocity analysis and moveout correction. Figures 14 and 15 show that even relatively small errors in moveout velocities can affect the appearance of final stacked traces, which ultimately affects how you


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Velocity spectrum CDP gather

V 2

T 2 = T02 + X 2

a)

Figure 11. Velocity spectrum on the left for (a) the raw (no moveout correction) and (b) moveout-corrected CDP gather on the right. Highest semblance values on the spectrum correspond to the dark blue, purple, and red areas. Stacking velocity picks in (b) are marked as white dots connected by white line segments.

b)


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OvercorrectedV 2

T 2 = T02 + X 2

Velocity spectrum CDP gathera)

Figure 12. Example of a CDP gather (right) for which the applied moveout correction is (a) too large or (b) too small. The gather is overcorrected in (a) and undercorrected in (b) because the moveout velocities are too slow in (a) and too fast in (b), respectively, as shown in the spectrum (left).

Undercorrected

b)


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interpret them. Note that the volume of data processed is reduced in stack-ing by a factor equal to the fold of the data; with reference to Figure 13, the CDP gather composed of 45 traces stacks to a single output trace, which is approximately a 98% reduction in the number of traces handled from prestack to poststack.

All of the traces in the NMO-corrected gather shown in Figure 13 were summed to create the single stacked trace on the right side of the figure; how-ever, any user-specified number or combination of traces from the gather can be summed in the stacking process. Partial stacking, which does not use all of the traces in a corrected gather, is usually done to enhance imaging by stacking only those traces for which signal strength is higher over a given range of offsets or incidence angles than for other ranges or the entire gather. The change in signal strength can accurately reflect real subsurface condi-tions such as illumination, amplitude variation with offset (AVO), or ampli-tude variation with angle (AVA); but it can also represent inaccurate velocity estimation — events are not flattened across the full range of offsets or angles.

Figure 16a is an example of partial stacking of the near and far halves of the corrected gather shown in Figure 13, and Figure 16b is an enlargement,

Figure 13. (left) A raw CDP gather, (center) the same gather with best moveout correction applied, and (right) the single output trace generated by stacking the moveout-corrected and muted gather.

Raw CDP gatherNMO-corrected

CDP gather 45-fold CDP stack


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Figure 14. Groups of stacked traces (each group represents a single stacked trace that has been duplicated 10 times for visual effect) corresponding to the best corrected gather from Figure 13 and the same gather corrected with a range of different velocity functions (1%–3% greater or less than the best function). Note that the stacked responses change as the applied moveout-velocity functions change.

– 2% + 2% + 1% – 1% Best– 3% + 3%

showing in detail the differences in reflection character between the two par-tial stacks. You can quickly visualize from Figure 16a how correlations of partial stacks depend critically on the reflectivity of the different ranges of offsets or angles included in the stacks. As an interpreter, it is your respon-sibility to always inspect corrected gathers before partial stacking to ensure that data have been NMO corrected as accurately as possible and that no undesired illumination or AVO/AVA effects carry through into the stacks.

For the simple case shown in Figures 8 and 9, the NMO velocity VNMO is equal to the interval velocity Vint of the single subsurface layer through which the seismic energy passes. For the case of a single dipping subsurface layer, VNMO is equal to Vint divided by the cosine of the angle of dip of the layer. For the case of multiple horizontal subsurface layers, VNMO is approxi-mately equal to rms velocity Vrms. For the case of multiple parallel, dipping


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subsurface layers, VNMO is approximately equal to Vrms divided by the cosine of the angle of dip of the layers. If there are many dipping nonparallel layers in the subsurface, then accurate estimation of VNMO is a ray-tracing problem and not a simple or straightforward calculation. Note that Vrms is a good approximation for VNMO (or stacking velocity Vstk) only under particular conditions. Generally, these terms are not synonymous.

Interval velocity can be calculated from VNMO using the well-known Dix equation (Dix, 1955): for i horizontal layers and small offsets (Vrms ~ VNMO),

( )( ) ( )

(int

NMO NMOV

V T V T

T Ti

i ii i

i

2

2 211=

− −

− −

ii−1)

It is very important to note that the Dix equation is valid only for horizontal layers and small offsets (a small offset is equal to or less than the depth of

Figure 15. Displays of 21 adjacent CDPs (the center CDP corresponds to the gather shown in Figure 13) with the same range of moveout-velocity functions as in Figure 14. The stacked responses change as the applied moveout-velocity functions change.

– 2% + 2% + 1% – 1% Best– 3% + 3%


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Raw CDP gatherNMO-corrected

CDP gather

Near half(inside)

CDP gather

Far half(outside)

CDP gatherNearstack

Farstack

a)

Figure 16. (a) Raw and NMO-corrected CDP gather from Figure 13 with near- and far-half CDP gathers and their associated near-trace and far-trace partial stacks. The near-stack and far-stack displays represent the single near-half and far-half stacked traces, respectively, which have been duplicated 10 times for visual effect. The red box outlines the area enlarged in (b). (b) Enlargement from (a) showing the difference in reflection character between the near-trace and far-trace partial stacks for the NMO-corrected gather.

Near half(inside)

Far half(outside)

Nearstack

Farstack

b)


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the layers to which the Dix equation is being applied; see Dix [1955]). This equation will probably continue to be misused to calculate interval veloci-ties in situations that do not satisfy these conditions. Having calculated Vint from VNMO, quantities such as average velocity and interval transit time can be calculated easily. Figure 17 shows Dix interval velocities superimposed on a velocity spectrum from which stacking velocity picks for the Dix cal-culations were taken.

The sensitivity of interval velocity calculated with the Dix equation to the precision of stacking velocity picks is best illustrated by a numeri-cal example. Stacking velocity picks from NMO velocity analysis are for the two reflections (peaks) marked by red arrows on Figure 18. For pick 1 (upper peak), VNMO1 is 5557 ft/s (1694 m/s) and T1 is 2.994 s. For pick 2, VNMO2 is 5737 ft/s (1749 m/s) and T2 is 3.210 s. The conditions required for valid application of the Dix equation — horizontal layers and small offsets at the level of the target reflections — are reasonably well met in this exam-ple. The Dix interval velocity calculated for these picks is

Vint

(( ) ( . ) ( ) ( . ))

( .= −

−5737 3 210 5557 2 994

3 210

2 2

22 9947816

12

. )

= ft/s (2382 m/s) .

Figure 17. Velocity spectrum from Figure 11b, showing Dix interval velocities calculated from stacking velocity picks.

Interval velocity

Velocity spectrum CDP gather


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Now increase the stacking velocity value for pick 2 by 3% to 5909 ft/s (1801 m/s) and recalculate the interval velocity

Vint

(( ) ( . ) ( ) ( . ))

( .= −

−5909 3 210 5557 2 994

3 210

2 2

22 9949532

12

. )

= ft/s (2905 m/s) .

In this example, an increase of 3% in a stacking velocity pick results in a corresponding increase of 22% (7816 to 9532 ft/s; 2382 to 2905 m/s) in the calculated interval velocity. Figure 22 shows that a change in one stacking velocity pick causes two corresponding changes in interval velocity because the altered pick is used in two interval velocity calculations, one for the inter-val above the level of the revised pick and one for the interval below.

The sensitivity of Dix interval velocities to stacking velocity picks strongly suggests that you should review the results of stacking velocity analyses, usually in the form of NMO-corrected gathers, before using cal-culated interval velocities for interpretive purposes such as lithology predic-tion, pore-pressure modeling, or time-depth conversion. Using Dix interval

Figure 18. Time-migrated seismic line for which stacking velocity picks for the two reflections marked by red arrows are used to illustrate the sensitivity of the Dix interval velocity calculation.

2.0 s

3.0 s

4.0 s

5.0 s

Pick 1

Pick 2


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velocities without this quality assurance can lead to serious technical errors and consequent expense.

Keep in mind these limitations when calculating interval velocities from stacking velocities:

• Layer geometry — dip, nonparallel layers, and layer curvature can invalidate the results of Dix calculations.

• Layer properties — the Dix calculation assumes layers are homoge-neous and isotropic.

• Precision of data analysis — picking reflection times and stacking velocities is interpretive.

Figure 19. Plot of the stacking velocity function (blue) and calculated Dix interval velocities (red) for the CDP location in Figure 18. The annotations in black on these functions represent the change in one stacking velocity pick and the corresponding changes in two calculated interval velocities.

Velocity (ft/s)

00.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

2000 4000 6000 8000 10000 12000 14000

Two-

way

tim

e (s

)

Stackingvelocity

Intervalvelocity


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Remember that stacking velocity is the velocity that optimizes the effective-ness of stack; it results in the best enhancement of signal and reduction of noise by the stacking process. We interpret stacking velocities using a vari-ety of analytical tools and displays and assess the quality of the stacks pro-duced. Because of the inherent subjectivity of these interpretations, correct or objectively determined stacking velocities as such probably do not exist. Only under certain conditions and having made several important assump-tions does stacking velocity approximate true propagation velocity. These conditions and assumptions (horizontal, homogeneous, and isotropic layers and small offsets) must be recognized and understood before any interpreta-tion or application of seismically derived velocities can proceed correctly and meaningfully.

Velocity anisotropy

Anisotropy is the variation in a physical property depending on the direction in which it is measured. Velocity anisotropy is important when working with seismic data because the strata through which a seismic pulse travels very often exhibit velocity anisotropy. In other words, the pulse does not move at the same speed in all directions of propaga-tion. The velocity anisotropy commonly encountered in working with reflection seismic data is called polar anisotropy, also known as vertical transverse isotropy (VTI), a condition associated with layered strata in which the axis of symmetry is vertical (perpendicular to layer boundar-ies; the layers themselves are transversely isotropic). If the axis of sym-metry is tilted, then the velocity anisotropy is called tilted transverse isotropy (TTI).

Five independent Thomsen parameters are used to characterize polar anisotropy; the two most often mentioned in discussing this type of anisot-ropy (Sheriff, 2002) are d (delta) and h (eta). The parameter d is associ-ated with weak anisotropy, most critical for describing polar anisotropy and frequently associated with the short-offset moveout correction applied to vertical velocity:

VNMO = aII × (1 + d),

where aII is the P-wave velocity parallel to the axis of symmetry for anisot-ropy. This velocity is equivalent to that measured in a well-velocity survey. The parameter h captures the deviation of long-offset P-wave moveout from what it would have been in an isotropic medium. It is calculated from d and


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e (epsilon), the latter of which is another of the five independent Thomsen parameters for polar anisotropy:

η ε δδ

= −+1 2

Correction for velocity anisotropy must be done whenever NMO veloci-ties are used in depthing processes such as time-to-depth conversion or depth migration. For time-to-depth conversion, recall that the vertical propagation velocities measured in well-velocity surveys are slower than NMO velocities, as related by d, so any time-to-depth conversion performed using uncorrected NMO velocities will result in calculated depths that are too large. Similarly, isotropic prestack depth migration (PSDM) using a sediment-velocity model based on NMO velocities will result in lateral and vertical mispositioning of migrated reflections; the vertical mispositioning often is of greater magni-tude than the lateral mispositioning, so a so-called Z-to-D vertical correction (where Z is migrated depth and D is true vertical depth) based on calibration to well control must be applied to achieve accurate depthing. If anisotropy information from well data is available, then depth migration can be done anisotropically (APSDM) so that the anisotropy condition is accounted for during migration. Figure 20 illustrates the value of APSDM for producing a depth volume that exactly ties well control. Of course, the problem of how to extend anisotropy parameterization away from well control remains.

Time-depth conversion

Because seismic data are acquired in the time domain T, they must be imaged in or converted to depth as measured in real earth space — in the domain of depth measurements made in wells — to describe subsurface geology accurately. You must never forget that a time-processed image can be, and very often is, distorted because of the complexity of the velocity field through which the seismic energy passes and that accurate seismic imaging and depth conversion are necessary to interpret and map seismic data correctly. Velocity data provide the essential link between the time and depth domains that enables you to (1) provide spatially accurate maps of the subsurface, (2) predict well depths, and (3) calculate bulk rock volumes and hydrocarbon reserves.

As mentioned in the preceding section, depths measured on isotro-pic depth-migrated data (Z-depths) need to be corrected to D. Depths on anisotropic depth-imaged data do not require this correction, at least at the


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calibration points for an anisotropic velocity model as shown in the example in Figure 20. A fair amount of depth imaging is done with isotropic models, owing largely to lack of anisotropy data for parameterizing velocity models, so Z-to-D conversion is necessary to accurately correlate and map these data.

a)

Figure 20. (a) A 3D isotropic PSDM showing a mis-tie between the yellow seismic horizon at 8687 m (28,500 ft) and the correlative formation top at 8077 m (26,500 ft) true vertical well depth (dashed blue line). (b) A 3D anisotropic PSDM of data in (a), showing exact well tie to the interpreted yellow seismic horizon at 8077 m (26,500 ft) true vertical well depth (courtesy TGS).

b)


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Techniques for vertical conversion from T to D and from Z to D fall into five general categories, depending on the type and quantity of available velocity data. The following techniques apply to vertical conversion only with no lateral repositioning of points in the subsurface:

• Single velocity function (sonic log, check-shot survey, VSP, trend curve)• Multiple velocity functions (any combination of single functions)• Layered velocity model• Continuous velocity model (e.g., stacking or migration velocity

volume)• Calibrated velocity model (with single or multiple T-to-D or Z-to-D

functions)

The two critical factors in all of these techniques are (1) the quality and dis-tribution of available velocity control and (2) the accuracy of interpolation, vertically and laterally, between these control points. Interpolation must be mathematically correct and, perhaps more importantly, must be geologi-cally reasonable in the sense that trends introduced into the interpolated data are consistent with known geology or sound geologic models. Obviously in cases for which more than one model fits the available velocity control, the final depth conversion must be accompanied by an estimate of uncertainty that reflects multiple possible outcomes.

Vertical conversion with velocity functions uses one or more time-depth functions (Figure 21) to calculate depths to individual seismic horizons or to any points within the range of the function(s). This is commonly done in areas where velocity varies smoothly vertically and laterally and is not defined discretely by formation boundaries. For Z-to-D conversion, the pro-cedure can involve two steps, the first converting from Z to T followed by converting from T to D. Alternatively, these two steps can be combined into a one-step process converting directly from Z to D. In the latter case, there is no actual velocity involved in the conversion; rather, the conversion factor is dimensionless, based on the ratio between the Z-to-T velocity from the two-step conversion and the T-to-D velocity from that same process (this conversion accounts for d anisotropy).

Vertical conversion with a layered velocity model, such as the one shown in Figure 22, uses interval velocities defined for each interval in the model to calculate thicknesses of those intervals, which are then summed appropriately to determine the depth to each layer boundary or interpolated to estimate depths to points within intervals. The layers in these models are most often defined by interpreted seismic horizons or surfaces calculated from those horizons. The interval velocity for each layer in the model can


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be assigned as a constant velocity or as a velocity function applied only within that layer. This is commonly done in areas where velocity corre-sponds closely to geologic formations or specific structural elements, e.g., layered depth conversion involving salt most often assigns a constant inter-val velocity to a mapped salt body.

Figure 21. (a) A time-depth function from a conventional check-shot survey and (b) an average velocity-depth function calculated from the original time-depth data.

0

2000

4000

6000

8000

10000

12000

14000

16000

2000

4000

6000

8000

10000

12000

14000

16000

00.0 0.5 1.0 1.5 2.0 2.5 40

0050

0060

0070

0080

0090

0010

000

D (

ft)Average velocity (ft/s)One-way time (s) b)a)

Figure 22. A vertical slice through a layered interval-velocity model. The full model would be a 3D interval-velocity volume. Interval velocities are assigned to layers whose boundaries are defined by or calculated from interpreted seismic horizons.

Time

Vint1

Vint2

Vint3

Vint4

Vint5

Vint6

Vint7

Horizon 1

Horizon 3

Horizon 4

Horizon 5

Horizon 6

Horizon 7

Horizon 2


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Vertical conversion with a continuous velocity model, such as a stack-ing velocity volume or as an interval velocity–depth model used for depth migration, differs from using discrete velocity functions only in the sense that interpolation between control points is an implicit part of creating a continuous model. Figure 23 is an example of a depth-migration velocity model superimposed on a PSDM seismic line. Such a continuous model is built primarily as an integral step in the depth-migration process, and it is an extension of its original purpose to use the model for Z-to-D conversion. In fact, because continuous velocity models are often based on seismically derived velocities, they should not be used for vertical conversion if they have not been calibrated to well control or, in the absence of well control, to trend curves or some other source of information that accounts for velocity anisotropy.

In summary, you will most frequently use four basic types of velocity:

• Interval velocity — distance traveled per unit time, where the distance traveled is the thickness of a single well-defined layer or stratum.

• Average velocity — distance traveled per unit time, where the distance traveled is the total thickness of many layers or strata measured from the top of the uppermost layer to the base of the lowermost layer; is calculated directly from the known depth of a receiver and the one-way traveltime to that receiver as recorded in a well-velocity survey.

Figure 23. A 3D PSDM seismic line with its associated depth-migration velocity model shown as a colored overlay (interval velocity increases from blue to red). The irregularly shaped high-velocity feature in this image is a salt body (courtesy PGS).


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• Stacking velocity — also known as normal moveout (NMO) velocity, a quantity having the units of velocity that is estimated during stacking velocity analysis by flattening reflections on an NMO-corrected gather via application of the NMO equation.

• Root-mean-square (rms) velocity — a statistically defined velocity which, under the constraints of horizontal layers and small offsets, is approximately equal to stacking velocity; when these constraints are satisfied, this approximation allows you to calculate interval velocities from stacking velocities using the Dix equation.


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63

Chapter 5

Migration

Because of the physics of wave propagation, the 3D nature of subsur-face geology, and the techniques with which reflection seismic data are acquired and processed, seismic data must be migrated, or repositioned, to place reflections in their true subsurface positions. This migration, which addresses the image-fidelity element of data quality, is done routinely as part of a data-processing sequence for all 2D or 3D data before or after stacking in the time or the depth domain, depending on the complexity of the geo-logic structure and the subsurface velocity field.* As an interpreter, you are concerned with migration because in every interpretation you describe the size and position of the elements of geology that you see in your data, and you can’t accurately do this without taking migration into account, either in data processing or as part of your interpretation work flow.

The need for migration is illustrated in Figure 1, which is a 2D model of a single dipping reflector with constant P-wave velocity above the reflector and seismic source and receiver coincident at point SR. By convention, the recorded two-way traveltime t to the dipping reflector is plotted on a vertical trace at point SR, even though the true normal-incidence reflecting point on the reflector is not located vertically below point SR. The dashed red curve in Figure 1, which is an arc of a circle with radius equal to t, represents all possible positions for the reflector for a given value of t. This curve is known as a wavefront, a locus of equal traveltimes through a propagating medium for an impulse occurring at t0 = 0 (see Figure 2). The migration

*A paper by Gray et al. (2001) contains an excellent historical perspective of migra-tion as well as practical treatment (with a minimum of high-level mathematics) of migration problems and solutions, and a paper by Etgen et al. (2009) provides a comprehensive overview of the current state and future direction of depth imaging in exploration geophysics.


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operation moves the reflecting point from its position vertically below point SR along this curve to its true subsurface position, that is, to its migrated position at the point of normal incidence reflection. Notice in the very sim-ple example of Figure 1 that the relationship between the dip angles of the unmigrated and migrated reflectors is derived directly from trigonometry of the triangles formed by the origin of the diagram, point SR, and the reflect-ing points on the unmigrated and migrated reflectors.

Figure 1. Schematic of migration of a dipping interface in a 2D constant-velocity model, where u is unmigrated dip angle, m is migrated dip angle, t is two-way traveltime, and X is horizontal distance.

X

u m

sin m = tan u

SR

MigratedUnmigrated

t

t

Figure 2. Schematic (2D view) of the definitions of wavefronts and rays. A wavefront is a locus of equal traveltimes for a pulse propagating through an elastic medium. The shape of a wavefront depends on the velocity distribution in the transmitting medium; in this example, the wavefronts are circular because the propagation velocity is constant and isotropic. A ray (red arrow) is a line (or curve) everywhere perpendicular to wavefronts that represents the travel path of a pulse from t0 to traveltime t.

t2

t1

t3

Impulse at t0

t3 > t2 > t1


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Chapter 5: Migration 65

A horizon interpreted on an unmigrated 2D seismic section can be migrated by constructing arcs for many source points along the section and then drawing a smooth curve connecting points of tangency to these arcs, as shown in Figure 3. Think of this process as generating a wavefront for the observed traveltime to the unmigrated reflection (horizon) at each individual source point and then defining the migrated horizon as the surface tangent to all of these wavefronts. The shapes of the arcs (wavefronts) depend on the velocity distribution in the section above the dipping reflector. In the simplest case of constant velocity, the arcs are circular; but they become more complicated when the velocity distribution varies vertically or later-ally (or both). Hence, repositioning reflections — migration — is a velocity-dependent process.

Before the advent of 3D data or computerized migration as an essential step in a standard data-processing sequence, migration of 2D seismic data was addressed in several ways. One primary way was to interpret horizons and faults on unmigrated data. The horizon maps constructed from the inter-preted lines were then migrated using an appropriate velocity function or distribution. This technique, called map migration, was done separately for each interpreted horizon, requiring consistency of velocity trends, vertically and laterally, from one horizon to the next to produce geometrically correct and geologically reasonable maps.

Another way to migrate data was to interpret horizons and faults on unmigrated data; then the interpreted lines were migrated individually using an appropriate velocity function or distribution (as illustrated in Figure 3). Because this migration could be done only in the plane of an individual 2D

Figure 3. Migration of a horizon (dashed black curve) interpreted on a 2D unmigrated seismic section. The final migrated horizon (solid red curve) connects points of tangency to the arcs (wavefronts) constructed from the source positions.

Unmigrated

Migrated

Datum

t


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line, migration could not properly account for the effects of a 3D structure — for reflections from points located out of the vertical plane of the line. These out-of-plane reflections (see Figures 4 and 5) are called sideswipe. Only in the relatively uncommon case of a 2D line being true dip to actual subsur-face structure can 2D migration be trusted to be accurate; even at that, its results depend on the accuracy of the velocity used for migration. The failure of 2D migration, whether manual or computerized, to handle 3D subsurface structure accurately is the source of the mis-tie problem present in virtually all 2D interpretation projects; 3D imaging is required to address this issue properly.

An example of the power of migration to more accurately define true subsurface geology is shown in Figure 6. Figure 6a is a 2D unmigrated seismic line on which you see what is commonly referred to as a bow tie,

Figure 4. The antiformal feature within the red circle on this 2D time-migrated display is an example of the out-of-plane effect known as sideswipe on 2D seismic data. Apparent structural discordance such as this is an obvious positive indication of sideswipe; the antiform and the dipping reflections that dominate the bottom half of the display cannot coexist as reasonable subsurface geometries. Even the dipping reflections on this display will be mispositioned (mismigrated) if the 2D line is not a true dip line. Several fault-plane reflections also can be seen in this image (courtesy BP).

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Figure 5. (a) Image of a 2D time-migrated seismic line, showing sideswipe (crosscutting antiformal reflections within the yellow circle). The red arrow marks the intersection of this line with an orthogonal 2D time-migrated line. (b) Image of a 2D time-migrated seismic line orthogonal to the line shown in (a). The red arrow marks the intersection of the two lines. There is no sideswipe on this line, and the dipping salt body to the left of the line is the source of the sideswipe reflections observed on the orthogonal line shown in (a). The distance between the intersection of the two lines and the edge of the salt body on this line is approximately 8000 ft (2450 m) (courtesy WesternGeco).

t

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so named for the pattern of crossing reflections in the center of the image. This reflection configuration as it appears cannot in all likelihood represent real geology, so migration is needed to resolve the actual structure. Figure 6b is the migrated version of this 2D line; the bow-tie reflections have been

Figure 6. (a) Image of a 2D unmigrated line exhibiting a classic bow-tie reflection configuration. (b) Prestack time migration (PSTM) of the line shown in (a). The crossing reflections in the center of the unmigrated image are resolved to reveal a relatively simple syncline. Note also that small faults, especially on the left side of the image, are more sharply defined. Focusing of reflections in general is improved (courtesy PGS).

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repositioned to their true locations to reveal a relatively simple syncline. This type of structure is called a buried focus syncline because the center of curvature of the syncline is below the recording surface of the seismic data (see Figure B-11 in Sheriff [2002] for illustrations of the raypath geometries and a synthetic record section for this structure). Comparison of Figure 6a and 6b shows that in addition to resolving the true structure of the syncline correctly, migration more clearly defines small faults, especially to the left of the syncline, and generally focuses reflections more sharply. The smooth-ness and clarity of the migrated image in Figure 6b suggests that the orienta-tion of this line is very nearly perpendicular to the axis of the syncline, that is, the line is a dip line. Keep in mind, though, that this is still 2D migra-tion, no matter how striking the results, and that 3D migrated data would be needed for optimum imaging accuracy.

Reflection seismic data are migrated in the time or the depth domain, depending on the complexity of the subsurface structure and the subsurface velocity field (see Figure 7). As a result of progress in computer power and sophistication of migration algorithms as well as in response to the advance of exploration into more challenging subsurface settings, migration is now done routinely on prestack data, although there are still many areas in which poststack imaging in time or depth can provide acceptable results.

Figure 7. Different migration types for prestack and poststack time and depth domains. Most seismic imaging is now done on prestack data, so the acronyms for poststack time (PoSTM) and poststack depth (PoSDM) migration are no longer commonly used. The shortened acronyms PSTM for prestack time migration and PSDM for prestack depth migration are now widely accepted.

Simple velocitiesSimple structure

PoSTMPoststack time

migration

Simple velocitiesComplex structure

PrSTMPrestack time

migration

Complex velocitiesSimple structure

PoSDMPoststack depth

migration

Complex velocitiesComplex structure

PrSDMPrestack depth

migration


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Depth imaging is needed primarily for areas of large lateral velocity con-trasts in the subsurface where ordinary time-domain imaging fails because it does not account for refraction of seismic energy (defined by Snell’s law; see Figure 8) at the boundaries across which these contrasts occur. Because depth imaging includes the effects of refraction in calculating travel paths by way of traveltimes through an interval-velocity model, its results more accurately describe the true positions of subsurface reflectors. However, you must be aware that migration output in depth does not guarantee one-to-one correspondence with true geology. Depth imaging can fail when the depth-migration velocity model is inaccurate, either in defining the geometries of anomalously high- or low-velocity bodies or in assigning specific velocity values, gradients, or anisotropy parameters in the velocity model. Figure 9 clearly illustrates the differences between time and depth migration, dem-onstrating that accurate description of geology requires depth imaging in areas where there are large lateral velocity contrasts, in this example caused by salt bodies.

To produce an accurate image of subsurface features of interest, seis-mic data must first be acquired in such a way that energy reflected from those features is recorded at the surface. The term illumination is defined as

Figure 8. Snell’s law for reflection and refraction of P-wave energy at and across an acoustic impedance (AI) boundary. The critical angle of incidence θc is the angle at which θ2 = 90° (sin θ2 = 1), that is, sin θ1 = V1/V2, and no energy is transmitted across the AI boundary into the deeper layer. Raypaths for reflected and transmitted shear (S-wave) energy are shown by the dashed arrows.

sin 1

sin 2

=V1

V2

Refraction (P)

Reflection (P)

2

VP1, 1

VP2, 2

Incident P

Transmitted P

Reflected P

Reflected S

Transmitted S

1

q1 = q1´

q1´


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the placement of seismic sources and receivers so that seismic energy will fall on desired portions of reflectors and be recorded for processing. Obvi-ously, you cannot migrate reflections to their true subsurface positions if the energy reflected from those positions was never recorded. Often you will find yourself correlating horizons through poorly imaged zones that were only partially illuminated or not illuminated at all, so that you are effec-tively conducting a model-guided interpretation, connecting illuminated and properly migrated patches of the subsurface together in a geologically reasonable way. This is to be expected, especially in frontier exploration or in areas with severe imaging problems such as subsalt plays, and you must be sure to risk your interpretation of these areas accordingly. If illumination

Figure 9. (a) A 2D PSTM seismic line, approximately 75 km (47 mi) long, from offshore Brazil. (b) A 2D PSDM image of the line in (a).The differences between the two images are striking. The PSDM image is a more accurate representation of subsurface geology, certainly leading to a very reasonable explanation for the location of the exploration well (annotated in green) on the right side of the line (courtesy PGS).

a)Tw

o-w

ay ti

me

belo

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ea le

vel (

ms)

D

epth

(m

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modeling was not done as part of acquisition design for your data, then it is good practice to do this modeling using first-pass interpretation results to identify the areas in which your correlations are probably less reliable and to provide input for additional data acquisition.

Seismic migration has become more important as exploration targets are being sought in increasingly challenging and complex settings. There are many different migration approaches and algorithms, some better suited to specific imaging problems than others, all having their own strengths/limitations and corresponding cost implications (for example, see Figure 10). As an interpreter, you will often contribute to decisions involving which migration algorithms to use for a given problem, so you will need to develop at least a basic understanding of how the different algorithms work. This is part of building experience, and it requires you to work closely and com-municate effectively with processing geophysicists.

You will frequently be called on to assess the quality of migration out-put. For all of the mathematical and computational complexity of migration,

Figure 10. Matrix of migration algorithms in modern depth-migration methods, illustrating the range of migration algorithms that can be used to address different subsurface imaging problems. In general, greater structural and/or velocity complexity in the subsurface requires algorithms from the upper-right quadrant of the matrix, which involve increased time and cost in their applications (Figure 1 by Biondi in Herron [2009]).


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your assessment will often consist exclusively of visual and very non-quantitative determination of improved S/N and reflection continuity — ultimately, whether the output appears to look more reasonable geologically within the context of expectation or realization of some geologic concept or model. At best, these will be subjective assessments, and you will make them with greater confidence as you gain experience.


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75

Chapter 6

Resolution

By definition, resolution is the ability to separate two features that are close together (Sheriff, 2002). Resolution applies to seismic data and to products derived from interpreting seismic data (e.g., maps) in the temporal and spatial domains. We often speak about the resolving power of seismic data and what can be done to increase it because, in doing so, we’ll be able to interpret finer details of subsurface geology. The concept of resolving power of seismic data follows from the sampling theorem, also known as the Nyquist theorem, which formally states that band-limited functions can be reconstructed from equispaced data if there are two or more samples per cycle for the highest frequency present (Sheriff, 2002). On the basis of this theorem, you can describe commonly used measures of temporal and spatial resolving power of seismic data.

The sampling of seismic data is specified by a sample rate, such as 2 or 4 ms, and sampling frequency is defined as the inverse of the sample rate, which is 500 Hz for a sample rate of 2 ms and 250 Hz for a sample rate of 4 ms. The Nyquist frequency is defined as half the sampling fre-quency: For a sample rate of 2 ms, the Nyquist frequency is 250 Hz; and for a sample rate of 4 ms, the Nyquist frequency is 125 Hz. According to the sampling theorem, when there are fewer than two samples per cycle of a given signal, a signal at one frequency yields the same values as those for another frequency, and the one signal can be mistaken for the other. This frequency ambiguity, or aliasing, is illustrated in Figure 1, in which a 200-Hz sine wave is aliased, or misread, as a 50-Hz sine wave when sampled at 4 ms. When acquiring seismic data, you can prevent frequency aliasing by using an antialias filter during recording to attenuate frequencies above the Nyquist frequency.


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The temporal resolving power of seismic data is usually described by the tuning thickness of the data, which is based on the fundamental equation that relates velocity V, dominant frequency f, and wavelength l:

V = fl .

The dominant frequency in this equation can be estimated easily from the time separation between adjacent peak and trough reflections on a seismic section (see Figure 5 in Chapter 3); in general, the dominant frequency changes vertically and laterally on a seismic section.

Given estimates of the dominant frequency of the data and the vertical propagation velocity in the vicinity of the features to be resolved, you can calculate the wavelength of the seismic signal from the preceding equa-tion, from which the Rayleigh limit of vertical resolution is derived as l/4. Because propagation velocity and the dominant frequency of the seismic signal change vertically and laterally throughout the subsurface, it follows that temporal resolving power will vary across a given area of investigation.

Tuning thickness usually is visualized with the aid of a diagram known as a wedge or tuning model (Figure 2). The purpose of such a model is to illustrate the seismic response to the wedge and determine the thickness

Figure 1. A 200-Hz sine wave that aliases as a 50-Hz sine wave when sampled at 4 ms. The Nyquist frequency in this case is 125 Hz, so a 125-Hz antialias filter used during recording would attenuate the 200-Hz signal.

50 Hz

200 Hz 4 ms

Nyquist fN = 1 / 2 ∆t = 1 / 2 (4 ms) = 125 Hz Sample rate = ∆t


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Chapter 6: Resolution 77

for which the amplitude response is maximum, that is, for which construc-tive interference of the individual responses from the top and base of the wedge is maximum. The point at which this composite amplitude response is maximum is the tuning thickness for the model, with given input wavelet, wedge geometry, and layer impedances. Notice that above the tuning thick-ness, the seismic responses from the top and base of the wedge are separate and distinct (the bed thickness is “resolved” by the time separation between these individual responses). Below the tuning thickness, the waveform of the composite response does not change, but its amplitude decreases as the bed thickness decreases. These observations suggest that with good data quality (based on seismic processing from which wavelet phase and true rel-ative amplitudes can be reliably determined), careful horizon interpretation, and available well data for calibration, you can use seismic data to estimate layer thicknesses, a study commonly referred to as tuning or time-amplitude (time-amp) analysis (see Chapter 3).

Figure 2. Wedge model using a 30-Hz Ricker wavelet and P-wave velocities VP of 6000 and 7000 ft/s (1800 and 2100 m/s) for the wedge (in blue) and encasing medium, respectively. Using the formula V = fλ, the tuning thickness for this model is calculated to be 50 ft (15 m), which corresponds to the point on the model (red line) at which the trough-peak amplitude response is greatest.

Distance (ft) D

epth

(ft)

TW

T (m

s)

No density contrast

VP = 6000 ft/s

VP = 7000 ft/s

30-Hz Ricker wavelet

Tuning thickness = 50 ft

V = f λ


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The spatial resolving power of seismic data is usually described in terms of the Fresnel zone, defined as the portion of a reflector from which reflected energy can reach a detector within one-half wavelength of the first reflected energy (Sheriff, 2002). Figure 3 shows the geometry of the first Fresnel zone, which is the smallest and innermost of a succession of higher-order annular Fresnel zones. The equation

FV

fr =

2

12TWT

defines the radius of the first Fresnel zone Fr in terms of the two-way travel-time (TWT) to a reflector, the average propagation velocity V to that reflec-tor, and the dominant frequency f of the seismic signal impinging on the reflector. This formula implies that the size of the first Fresnel zone almost always increases with depth (corresponding to increasing propagation veloc-ity and two-way time) and decreasing dominant frequency of signal (owing to attenuation). Fresnel zones are measured with respect to unmigrated seis-mic data. Seismic migration collapses these zones; however, 2D migration collapses the zones only in the direction of shooting of the 2D line. For 3D data, a full 3D migration collapses the first Fresnel zone to a circle with a diameter of l/2 (radius = l/4), where l is the dominant wavelength of the seismic signal.

Spatial sampling is an important consideration when designing 3D seis-mic surveys. The size of the unit of area into which a 3D survey is subdi-vided, called a 3D bin, ideally should be sufficient in terms of the Nyquist theorem, to properly sample the dip of the steepest reflector and/or the area of the smallest feature of interest within the survey. Figure 4, which represents a 2.5D model of the subsurface (the third dimension in the strike direction

Figure 3. Schematic of the geometry of the first Fresnel zone.

Z Z + /4

Reflector

First Fresnel zone

Z = depth


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is perpendicular to the plane of the page), illustrates how the required bin size is related to the average velocity Vavg to the target reflectors, the maxi-mum dip q of those reflectors, and the dominant frequency f of the seismic signal. Notice that the Rayleigh resolution limit (the tuning thickness) also appears in this relationship. It is important to realize that 3D survey design, in addition to addressing technical requirements such as maximum dip and minimum area to be imaged, must also take into account economic consid-erations that can balance or even outweigh technical factors.

Seismic trace displays can exhibit aliasing related to spatial sampling, as shown in Figures 5 and 6. Figure 5 shows four arrays, each consisting of four identical variable-area wiggle traces. Traces in Figure 5a are aligned such that the zero crossing marked in red (the red horizon) is correlated horizontally from trace to trace. On the succeeding arrays (Figure 5b –5d), each trace within the array is shifted downward by a constant amount from the trace on its left, with the amount of shift increasing from array to array. The dip of the red horizon increases in direct proportion to the amount of trace-to-trace shift in each array and in the direction of the shift (from left to right). In Figure 5c and 5d, the dashed blue horizon that dips from right to

Figure 4. Schematic of the maximum 3D bin spacing required to image the maximum dip of target reflectors in terms of average velocity Vavg to the targets, maximum dip q of the targets, and dominant or peak frequency f of the seismic signal.

Maximum bin spacing = Vavg/(4f sinq)

q = True dip4

Bin at surface

q


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left is marked as a possible correlation of the same zero crossing; note that as the dip of the red horizon increases, the dip of the dashed blue horizon decreases, and vice versa. This correlation ambiguity is a manifestation of aliasing, which in this example is related to the trace spacing and the mag-nitude of the dip (the trace-to-trace shift) of the red horizon.

Figure 6 illustrates aliasing behavior by changing the interval between traces (effectively, the trace-to-trace sample rate) rather than by trace-to-trace vertical shift, as done in Figure 5. The array of traces in Figure 6a is identical to Figure 5b. In Figure 6b, every other trace has been dropped, effectively doubling the trace interval and halving the trace sample rate (the

Figure 5. Aliasing in an array of four identical traces. The red horizon is the correct trace-to-trace correlation; with increasing vertical shift of adjacent traces, another possible correlation, marked by the dashed blue horizon, appears.

a) b) c) d)

Figure 6. Aliasing caused by deleting every other trace from the original four-trace array in Figure 5. The red horizon is the correct trace-to-trace correlation; the dashed blue horizon is an aliased correlation.

a) b)


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left-to-right-dipping red horizon is in the same position on both arrays). In Figure 6b, the under sampled array, the dashed blue horizon dipping from right to left, is shown as a possible correlation of the zero crossing from trace to trace; this aliasing is related to the change in spatial sampling rate.

As stated, the Nyquist theorem applies to reconstruction of band-limited functions from equispaced data, and these can be functions of time or dis-tance. Referring to band-limited functions of distance, the wavenumber k of a waveform is defined as the number of wave cycles per unit distance, which is the inverse of wavelength l. These parameters are the spatial equivalents of the frequency f and period T of a time-domain waveform (as shown in Figure 2 of Chapter 2). The spatial sample rate Dx for a 2D seismic line is the common-depth-point (CDP) interval, and for a 3D survey this rate is the bin size. For a given Dx, the sampling wavenumber is 1/(Dx) and the cor-responding Nyquist wavelength and Nyquist wavenumber are 2Dx and 1/(2Dx), respectively.

When working with a grid of 2D data, you need to think about the size of a feature that can be resolved in terms of the line spacing of your grid. To illustrate this concern, consider a simple 2D anticline that is X units wide and thus has a wavenumber of 1/X. Again for simplicity, assume that you are working with a grid of 2D lines that is Y units by Y units square, and that the axis of the anticline is parallel to one of the directions of the lines in the grid. The Nyquist wavenumber for this grid of lines is 1/(2Y). The Nyquist theorem states that a feature with a wavenumber larger than the Nyquist wavenumber will be aliased, that is, the anticline will be aliased if 1/X > 1/(2Y). Using a numerical example, if the anticline is 2 km wide, it will be aliased by a grid with a line spacing greater than 1 km; said another way, you need a grid spacing of 1 × 1 km or less to map this anticline accurately.

Geologic features come in all shapes and sizes, and you need to under-stand resolution as one of the fundamental elements of seismic data quality to represent geology properly in an integrated interpretation. Each seismic data set has a characteristic resolving power. Good practice dictates that you should investigate each of your data sets carefully to be fully aware of temporal and spatial limits of resolution, especially in the context of your interpretation objectives.


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83

Chapter 7

Correlation Concepts

The most important concept to keep in mind when correlating reflection seismic data is that the reflection seismic method provides indirect time-domain measurements of the 3D space of subsurface geology. The character and position of the reflections you correlate, which are the seismic responses to impedance contrasts across real geologic boundaries, depend on the geometry and properties of the subsurface velocity field — critical factors in stacking quality and in the accuracy of positioning your data (migration), controlling what you see and where you see it on a record section.

The fundamentals of acquisition and processing of reflection seismic data and the nature of the earth imply that there will always be some dis-tortion in every seismic image, so you should never consider any seismic section — no matter how carefully processed — to be a true geologic cross section. In other words, all seismic sections require interpretation. As you interpret, you must remember that there is no such thing as a noise-free seismic record, that all seismic data have limits to their temporal and spatial resolving powers, that virtually every 2D seismic line suffers from its inabil-ity to image a 3D earth accurately, and that even the most sophisticated 3D PSDM volume will not be an exact replica of the subsurface. Again, these elements of data quality affect the interpretability of reflection seismic data.

First look

When taking your first look at a seismic section, you should see the whole section and not focus or concentrate on any particular portion of the image. Scan the section from top to bottom and from side to side, taking in as much of the image as you can to form initial concepts about the geologic setting and overall quality of your data. In the workstation


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environment, use the computer’s capability to modify display size and scale to change the magnification (zoom and unzoom) and/or the aspect ratio (stretch or compress) to facilitate viewing the data. You might also try different color tables to see if any features in the data are more clearly visible (to your eyes, at least) using a particular color scheme. It’s a good idea to inspect all of your data using movie or animation functionality on your workstation before beginning your actual interpretation. This allows you to more fully assess data quality and provides an additional check that you have all of your data. Beyond knowing the acquisition and processing history of your data, at first you should assume as little as necessary about your project area; you’ll have plenty of time and oppor-tunity throughout your interpretation to focus effort and carefully inte-grate observations and correlations into a consistent story. You are not well served by running off with premature speculation or unwarranted assumptions early in your project.

Your initial assessment of data quality — noise content, resolving power, and fidelity of imaging — will affect three important interpretive decisions:

1) Are the data of sufficient quality to deal with the interpretive issues at hand, or is there need for reprocessing or additional acquisition?

2) What fraction of the data can be correlated using automatic versus manual picking (in the workstation environment)?

3) What is the best way to record variations in data quality across the project area?

Answers to these questions determine how and with what confidence hori-zons and faults (if any) can be correlated; the geologic setting and com-plexity of the project area, together with any prescribed objectives for the interpretation, determine how many horizons (and faults) need to be cor-related to describe the geology accurately and to meet business objectives.

Horizons versus faults

The essence of correlation is recognizing patterns in seismic data, fol-lowed by associating these patterns with known analogs or modeled rep-resentations of real geology. At its most basic level, seismic interpretation involves correlating two primary types of geologic surfaces: horizons and faults.

A horizon is the surface separating two different rock layers (Sheriff, 2002), which gives rise to a seismic reflection according to the acoustic


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Chapter 7: Correlation Concepts 85

impedance contrast between the two layers (recall Figure 1 in Chapter 1). According to Vail et al. (1977), two types of physical surfaces are present in sediments at the time of deposition: stratal surfaces and unconformities. Each of these can cause seismic reflections if there is a sufficient impedance con-trast across it. Stratal surfaces have chronostratigraphic implications. Many are geologic-time surfaces because they are former depositional bedding surfaces that were synchronous over their areas of occurrence; unconformi-ties have chronostratigraphic significance because, by definition, all of the rocks below the unconformity are older than those above the unconformity.

A fault is a fracture or fracture zone along which there has been displace-ment of the two sides relative to one another parallel to the fracture. Sheriff (2002) defines a fault as a displacement of rocks along a shear surface.

The fundamental difference between correlating seismic horizons ver-sus faults is that the former is based on recognizing and tracking continu-ous or predictably changing patterns of reflections, whereas the latter is based on recognizing discontinuities or offsets of patterns that are other-wise continuous or predictably changing (see Figure 1). Of course, faults themselves often can be tracked as predictable patterns of discontinuities,

Figure 1. Image of a seismic line, illustrating the difference between correlating seismic horizons as continuous or predictably changing patterns of seismic reflections versus faults as discontinuities or offsets of patterns that are otherwise continuous or predictably changing (courtesy WesternGeco).

t


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depending on the structural setting and quality of imaging. Occasionally, reflections from fault planes are imaged clearly (see Figure 4 in Chap-ter 5) and can be used to assist fault picking. In a strictly geometric sense, you can think of correlating horizons and faults as marking the boundaries that define common dip families (packages of reflections with internally consistent character and orientation), after which you explain the geologic nature of the boundaries and then reconstruct the geologic history of the interpreted data.

You have two primary concerns in correlating faults; both depend on the quality of your data — specifically, noise content and imaging fidelity:

1) Tracking fault surfaces on individual lines (or on 3D horizontal slices) and from line to line (2D) or across a volume (3D).

2) Accurately correlating horizons across faults.

Addressing the first concern, you should naturally pick faults on images that most clearly show the discontinuities, offsets, or reflection terminations that are your evidence for faulting. Whether 2D or 3D, a seismic line that is true dip to a fault, assuming that migration and other processes are done satisfactorily, should afford the clearest and sharpest view of the reflection terminations that you would pick as the fault (the block diagram in Figure 2 illustrates the definitions of strike and dip). In working with 2D data, you are limited to the orientations of lines in your grid of data, which often are not true dip to the faults of interest; so when correlating faults, you will have deal with the problems inherent in 2D imaging (see Chapter 8). In the 3D world, however, if neither the inline nor the crossline direction is in true dip orientation to faults, then you can create arbitrary lines that are in true dip orientation to faults, on which you can very accurately pick the faults, data quality permitting.

Figures 3 and 4 are an inline and crossline, respectively, from a depth-migrated 3D survey on which a normal fault has been picked. In this exam-ple, the fault in question is more clearly imaged and easily picked on the line that is more nearly true dip to the fault (in this case, the inline) rather than on the orthogonal line (the crossline), on which there is no obvious termination evidence for it. Here, you would pick the fault first on the inline, and then you would use that pick as a tie or reference point to find evidence for the fault on the crossline. Figures 5 and 6 illustrate the difference between dip and strike views of normal and reverse (thrust) faults, respectively. As you can envision, termination evidence for a fault will be hard to see on lines that are parallel or subparallel to the fault trend, no matter whether the fault is a normal or a reverse fault. Note also that regardless of the apparent dip of


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a fault, interpretation of that fault as normal or reverse depends on how you correlate reflections across it.

If you are working with 3D data and decide to correlate faults on horizontal slices, you have the option of picking faults on reflectivity or coherence data. As mentioned in Chapter 3, coherence data are intended to highlight discontinuities such as faults, and these data can provide excellent images on which you can pick faults with good confidence. The examples illustrated in Figures 6 and 7 of Chapter 3 show that fault picking can be done with considerably more confidence and accuracy on coherence data than on the reflectivity data from which the coherence data were generated. However, this result depends heavily on the quality of the parent reflectiv-ity data.

Not all discontinuities that you observe on seismic data are neces-sarily faults; there are real geologic features such as unconformities that mark discontinuities between reflection packages, and there are imaging

Figure 2. Diagram defining strike and dip. Strike is the direction of the line formed by the intersection of an inclined plane with an imaginary horizontal plane (line ab). Dip is the angle of the inclined surface below the imaginary plane measured perpendicular to strike, or the true dip (angle c). Apparent dip is the angle of any line on the inclined surface that is not perpendicular to strike. Apparent dip is always less than true dip and is equal to zero along the line of strike.

a

b

Line ab = strikeAngle c = dip

c

Horizontalsurface

Beddingsurface


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Figure 3. (a) Uninterpreted version of an inline from a depth-migrated 3D survey (displayed with no vertical exaggeration). The vertical black line marks the intersection of this image with the 3D crossline shown in Figure 4. (b) Interpreted version of the same 3D inline with a normal fault shown by the solid yellow line. The red dot marks the intersection of the fault on the crossline image in Figure 4 (courtesy PGS).

z

z

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b)


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Figure 4. (a) Uninterpreted version of a crossline from a depth-migrated 3D survey (displayed with no vertical exaggeration). The vertical black line marks the intersection of this image with the 3D inline shown in Figure 3, and the red dot marks the intersection of this image with the normal fault picked on Figure 3. (b) Interpreted version of the same 3D crossline with a normal fault shown by the dashed yellow line. The control point provided by the fault as picked on the inline guides the picking of the same fault on the crossline, on which the termination evidence is much less distinct (courtesy PGS).

z

?

z

a)

b)


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Figure 5. True dip and strike views of a listric normal fault.

Map view

tluaf ot enil ekirtSDip line to fault

Dip line to fault Strike line to fault

down

up

Vertical views

Seismic horizon

Figure 6. True dip and strike views of a reverse (thrust) fault.

Map view



Vertical views

Seismichorizon


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artifacts that appear as discontinuities and can easily be mistaken for faults. You might say that all faults are discontinuities in reflection patterns, but not all discontinuities in reflection patterns are faults. Herron (2000b) discusses an example of a discontinuity observed on depth-migrated data that might have been interpreted as a fault but actually was an imaging artifact. This discontinuity was observed on a first pass of prestack depth migration (Figure 7) that used salt-body geometry interpreted on poststack depth-migrated data. The discontinuity was “healed” on a second pass of prestack depth migration (PSDM) that used revised salt-body geometry based on interpretation of the first pass of PSDM (Figure 8). In addition to serving as a warning that depth-migration artifacts can masquerade as faults, this example emphasizes that depth migration is an iterative process in which multiple passes of imaging (time and funding permitting) are often required to achieve accurate results.

Figure 7. First iteration of PSDM using the base of salt, shown as the light blue horizon, from interpretation of a poststack depth-migrated data set. The discontinuity in question is the trend of disrupted reflections, marked by the yellow arrows. The base of salt picked on this image is shown as the green horizon (courtesy WesternGeco).

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An unconformity is an important geologic boundary that has correlation aspects of both horizons and faults. Although you typically correlate the seismic response to such a boundary as a horizon — as a coherent or pre-dictably changing reflection — it actually involves tracking reflection termi-nations or discontinuities between groups of reflections that are genetically related and distinctly different in character from other groups of reflections. These are the so-called seismic sequences above and below the unconfor-mity. At times, you will have to pick an unconformity manually because of its discontinuous and variable character caused by lateral variation in impedance contrast between the post- and preunconformity sections, but in other settings you will be able to automatically track an unconformity quite easily because of its laterally consistent signature (as in Figure 9). Recognizing and accurately tracking unconformities and “their correlative conformities” (Mitchum, 1977) is of central importance in seismic strati-graphic interpretation.

Figure 8. Second iteration of PSDM. The discontinuity seen in Figure 7 is no longer apparent. This second iteration uses the updated base of salt, shown as the green horizon (courtesy WesternGeco).

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In addition to correlating/picking horizons, faults, and unconformities as the fundamental elements of an interpretation project, at times you will be called on to identify and map the distribution of distinctive patterns of reflections. Always observe and note reflection patterns and configurations, even when you do not intend to map them. This activity is the part of seis-mic stratigraphic interpretation known as seismic facies analysis, where the term analysis includes not only identifying and mapping reflection patterns and their bounding surfaces but also calibrating the patterns to well control. Mitchum et al. (1977) define seismic facies as a group of seismic reflections

Figure 9. (a) Uninterpreted 2D time-migrated line, showing a well-imaged angular unconformity. (b) Enlargement of a portion of (a), showing detail of reflection terminations that mark the position of the unconformity (courtesy PGS).

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whose parameters (configuration, amplitude, continuity, frequency, and interval velocity) differ from adjacent groups. Sheriff (2002) defines them as the character of a group of reflections involving the general amplitude, abundance, continuity, and configuration of reflections. From these defini-tions, it is clear that accurate and reliable identification of seismic facies depends heavily on seismic data quality and requires pattern-recognition skills. Although you can analyze seismic facies on any good-quality seismic data, 3D data greatly facilitate this activity because the power of 3D imag-ing allows you to view facies from different angles and perspectives, thus enabling more confident interpretation.

The standard classical reference for principles and practices of seismic stratigraphic interpretation is Seismic Stratigraphy — Applications to Hydro-carbon Exploration (Payton, 1977). Other sources of information on this topic are Bertram and Milton (1996), Hardage et al. (2011), and Hart (2011).

Multiple reflections

Your fundamental skills in pattern recognition, knowledge of geology, and ability to assess data quality affect your ability to identify and cor-relate valid and geologically meaningful patterns of reflections. These are the primary reflection responses that represent true subsurface geology, whether they are horizons, faults, or unconformities. At the same time, this knowledge and experience gained over time enables you to correctly identify and avoid correlating spurious features such as multiples, imag-ing artifacts, and noise. These nonprimary reflections should be clearly marked on interpreted lines so as not to be included in the interpretation of primary events.

Figure 10 shows that in contrast to a primary reflection, one that repre-sents seismic energy which has been reflected only once, a multiple reflec-tion is energy that been reflected more than once. Figure 11 illustrates three types of multiples commonly encountered when interpreting reflection seis-mic data (see Figure M-13 in Sheriff [2002] for a more complete summary of multiple types). A multiple reflection is often described by its period, which is the difference between the traveltime of the multiple and the trav-eltime of the primary reflection from the deepest reflecting point on the multiple’s travel path (see Sheriff [2002] for corresponding definitions of long- and short-path multiples). When describing multiples, the terms long-period and short-period are relative, in the sense that they depend on the depths to and distances between the primary and the multiple-generating reflecting boundaries and also on propagation velocities. For example, in Figure 11, the traveltime of the double multiple is longer than that of the


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peg-leg multiple, but you can’t absolutely characterize either of them as long period or short period unless you know something about depth and velocity.

You must always be aware that residual multiple energy may remain in data, even though demultiple processing has been applied. There are no perfect demultiple techniques. When trying to decide whether a particular reflection might be a multiple, you should be able to identify the primary

Figure 10. Schematic of primary and multiple reflections. The primary reflection is seismic energy that has been reflected only once; a multiple reflection is seismic energy that has been reflected more than once.

Primary Multiple

Red = UpcomingBlack = Downgoing

Depth

Figure 11. Three common multiples.

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reflection from the interface that served as a multiple-generating bound-ary before concluding that the reflection in question is in fact a multiple. You can often recognize multiples because they cut across primary reflec-tions. Multiples usually are generated at the surface or at interfaces in the shallow subsurface where the geology is relatively simple and the arrival times of the multiples are close to or coincide with those of primary reflec-tions from deeper and usually more highly structured reflectors — hence, the crosscutting reflections. These multiples also have relatively high ampli-tudes because they are generated at interfaces across which there are rela-tively high impedance contrasts (such as the seafloor in marine data). For these reasons, a long-period multiple such as the seafloor double multiple is particularly obvious as a crosscutting event on marine seismic data, espe-cially in deepwater and ultra-deepwater settings. By the same reasoning, short-period multiples such as peg-leg or intrabed multiples are not easy to identify and separate from primary reflections; they interfere with the primaries and hinder accurate interpretation of the primary reflection signa-tures. These multiples are especially difficult to remove in processing.

The positions of multiples can be predicted simply on time-processed data using the reflection times to primary horizons that serve as the multi-ple-generating boundaries. These positions are calculated by summing the traveltimes for intervals between primary horizons, as shown in Figure 12; the real data example for these calculations is shown in Figure 13. Notice in these figures that the dips (in time) of the multiples are exaggerated in comparison to the dips of the primary horizons, making the multiples easier to identify. This technique for predicting multiple positions cannot be used on depth-processed data because the multiples are placed in depth according to their traveltimes through the velocity model used for the depth imaging (see Figure 14).

Manual versus automatic tracking

In the modern workstation environment, you correlate or pick hori-zons manually or automatically, choosing between these two modes of picking on the basis of reflection continuity and ultimately on data quality (Herron, 2000a). Manual picking or tracking on a workstation is exactly what it sounds like — the computerized equivalent of what used to be done by hand with a colored pencil on a paper section, literally connect-ing the dots to create a curve that follows the trend of the reflection you are correlating. In contrast, automatic picking, or autotracking, is done on data for which a computer algorithm can accurately and, obviously, more rapidly reproduce what was formerly done by hand, reflection continuity


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Figure 12. Method for calculating positions of multiples on time-processed data. (a) Blue horizon DT1 is the two-way reflection time to the seafloor, (b) red horizon DT2 is the two-way reflection time to the top of salt, (c) dashed purple horizon is the seafloor/top-of-salt peg-leg multiple (DT2 + DT1), and (d) dashed red horizon is the top-of-salt double multiple (DT2 + DT2).

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Figure 13. A 2D time-migrated seismic line, showing seafloor/top-of-salt peg-leg multiple and top-of-salt double multiple. These data are the basis for the calculations illustrated in Figure 12. The seafloor double multiple appears faintly below the top-of-salt reflection on the left side of the display (courtesy BP).

Top of salt

Seafloor/top-of-saltpeg-leg multiple

Top-of-saltdouble multiple

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and data quality permitting. As the interpreter, you set the parameters that control the operation of an autotracking algorithm, again based on your assessment of data quality. As few as one or two or as many as seven or eight parameters, depending on the sophistication of the algorithm, are usually set to default values that you can modify. You choose final tracking

Figure 14. Multiple reflections on 3D depth-migrated data. These data are the product of a 3D PSDM sediment flood on which no demultiple processing was applied. Primary reflections identified as the top and base of salt are marked with dashed blue lines. Note that the positions of multiples cannot be calculated without using information from the PSDM velocity model. For example, you cannot calculate the position of the seafloor double multiple M (green arrow) by doubling the distance to the primary seafloor event P (red arrow); rather, you must calculate the distance traveled through the PSDM velocity model for the two-way reflection time of the double multiple. The depth of the double multiple is more than twice the depth of the seafloor because the sediment velocity is greater than seawater velocity and increases with depth (courtesy BP).

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parameters based on testing ranges of values on representative samples of your data. In addition to selecting parameters for autotracking, you must also decide on the number and locations of seed points for autotracking, which are the starting points for correlations to be made using the chosen tracking parameters.

No one set of tracking parameters is necessarily optimal for a given horizon in a given project area. It is most common for these parameters to vary across any area, especially large areas — a consequence of naturally occurring spatial variations in geology as well as nongeologic variations in data quality. In every case, the final tracked horizon must represent your view accurately, regardless of how it was picked. To ensure that this is so, when using an autotracking algorithm, you must devote time to quality-checking your results to be sure the tracker has placed picks in the same places that you would have. An equally critical aspect of autotracking is to remember that you are responsible for understanding how an autotracker works before using it.

Most if not all fault picking is done manually, although some worksta-tion systems have automatic fault tracking algorithms whose effectiveness is directly proportional to the clarity of evidence for faulting and, again, ultimately depends on data quality.

Figure 15 illustrates the observations that you make in deciding whether to pick a horizon manually or automatically. Figure 15a shows an unin-terpreted 3D depth-migrated line on which the most prominent reflection is a peak rising from the lower-left corner of the image and extending all the way across the section. Regional knowledge indicates that this event marks the top of a salt sheet. Figure 15b shows the results of autotracking two horizons that you decided could be autotracked on the basis of event continuity and overall data quality. Both horizons were seeded with single control points positioned on the left side of the image (exact locations of the seed points are not shown).

A comparison of Figure 15a and 15b shows that the upper horizon ter-minates at the point where it no longer has the continuity or character that it does at its seed point or over the extent where it was successfully auto-tracked. This termination suggests that the autotracking parameters for the horizon on this line are acceptable, i.e., the autotracker stopped where you would have stopped. Also, there is a short segment of picks where the auto-tracker made its pick above the overall smooth trend of the horizon; this relatively small error, or mis-pick, is a result of the autotracker trying to honor a relatively high-amplitude reflection slightly above the smooth trend of the horizon. It could be corrected by adding more seed points or manually tracking in the immediate vicinity of the mis-pick.


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Figure 15. (a) Uninterpreted 3D depth-migrated line. (b) Partially interpreted version of (a), showing two autotracked horizons, the deeper of which marks the top of a salt sheet. (c) Partially interpreted version of (a), showing a manually tracked horizon (light blue dashed line) that marks the base of a channel. The light blue horizon cannot be autotracked because in this example the seismic response to the base-of-channel surface does not have a distinctive or laterally continuous character (courtesy WesternGeco).

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Further inspection of the tracking results on Figure 15b indicates that the shallow horizon terminates at a left-to-right-dipping boundary above the top-of-salt horizon that separates two intervals with distinctly differ-ent internal reflection configurations. This boundary, which becomes less well defined from left to right across the image, can be drawn with a con-cave-upward shape (the dashed light blue horizon in Figure 15c) and is interpreted as the base of a channel, the axis of which would be roughly perpendicular to the plane of the image. Even though this boundary can be observed clearly on the image, the horizon picked to represent it must be tracked manually because there is no consistent reflection response suitable for autotracking. In other words, the fundamental nature of geology and the seismic response to geology can and do conspire to prevent you from autotracking every surface of interest, no matter how good your data qual-ity might be.

You should always inspect your data at the level of the horizon to be autotracked to assess data quality, geologic complexity, and suitability for autotracking because these factors determine not only your choices for tracking parameters but also where and how many seed points you will need to pick. Figure 15 illustrates that for relatively simple geology and good data quality, you need very few seed points for good tracking (in this case, only one per horizon). But in general, more highly variable data qual-ity and greater complexity mean that you need proportionally more seed points for accurate tracking. In this context, you should be careful not to waste time picking more seed points than the autotracker needs to operate efficiently and accurately. Your goal is to strike the right balance between time spent on picking seed points and adjusting tracking parameters and the number of tracking iterations required to achieve acceptable results. As with many other aspects of seismic interpretation, you develop this balance with experience.

Autotracking errors are most evident on vertical seismic sections. An example of an autotracking error (not all are this obvious) is shown in Fig-ure 16, on which the red horizon (picked as a trough) was tracked from right to left. The automatic pick is accurate as far to the left as the yellow arrow. Beyond this point, it is seriously wrong, having jumped irregularly from cycle to cycle and eventually following a migration artifact to the far left against the base-of-salt reflection. The pick is not where you would have put it. The red horizon must be tracked manually to the left of the yellow arrow on this image in order to produce a geologically reasonable representation of the structure below salt.

Figure 17 is an example of an autotracking error caused by failure to constrain tracking properly with seed points and fault picks. The red horizon


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in Figure 17a, seeded as a trough, has been tracked incorrectly across a fault (not shown on this figure) that cuts the horizon at the point marked by the yellow arrow. The picked horizon is not offset by the fault; you would say that the horizon as picked does not honor the fault. This horizon was tracked using a single seed point, and the results were the same whether the seed was placed to the right or the left of the point of intersection with the fault. Note that although the correlation across the fault is incorrect, you cannot tell the exact nature of the error from the display alone without knowing the location of the seed point; it is either to the left of the fault, in which case the correlation across the fault is one cycle too high, or to the right of the fault, in which case the correlation across the fault is one cycle too low. This type of miscorrelation commonly occurs where reflections align across a fault in such a way that the autotracking algorithm fails to recognize the discontinuity. The fault is effectively invisible at the level of the individual event being tracked but is clearly evident upon inspecting a broader window of data.

You can solve this problem by picking the fault before autotrack-ing and by specifying seed points in the upthrown and downthrown fault blocks. The correct correlation across the fault for a seed location to the left of the fault is shown as the dashed red horizon in the downthrown fault block in Figure 17b; note that several other faults on this line probably

Figure 16. An autotracking error. The automatic pick for the red horizon is obviously wrong to the left of the yellow arrow. This horizon should be tracked manually below the salt on this image (courtesy BP).

Direction of tracking

Base of salt

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would need to be picked before autotracking to prevent similar miscor-relations. Most if not all interpretation applications can automatically stop or block autotracking at specified boundaries such as previously picked

Figure 17. (a) An autotracking error caused by failing to pick a fault before tracking and not specifying seed points on both sides of the fault. The yellow arrow shows the point at which a down-to-the-right extensional fault intersects the red horizon. (b) The actual fault pick. Note the down-to-the-right extensional fault marked in yellow, which actually offsets the red horizon. The dashed red horizon to the right of the fault is the correct correlation of the red horizon, given an initial seed point to the left of the fault (courtesy WesternGeco).

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faults, meaning that you should pick faults before attempting to autotrack horizons in faulted areas.

You do most but not all quality control of autotracking results using a map view of the autotracked horizon, although you make corrections to the input grid or seed horizon on vertical sections. Following are some of the observations you can make on the map view of an autotracked horizon that indicate probable errors in autotracking:

1) Abrupt changes in display colors which, when highlighted by dynami-cally adjusting or flexing the color table, suggest that the autotracked horizon has jumped cycles.

2) Holes or voids in the horizon where no automatic pick was made. These should be investigated on vertical sections through the areas in question to help you decide whether they represent real geology, such as fault heaves, or poor tracking, in which case you may need to adjust tracking parameters.

3) Sharp or angular features in the horizon that do not look geologic.4) Features that follow the 3D inline or crossline directions.

With reference to Figure 17, consider that you would not see the miscorrela-tion of the red horizon on this line as a discontinuity in map view because the horizon was smoothly but erroneously tracked through the fault. This tracking error, so clearly evident on a vertical section, would probably appear as an abrupt change or discontinuity in areas of the map where the fault displacement is larger or smaller and where reflections are no longer fortuitously aligned across the fault.

Autotracking errors are corrected by editing existing picks, adding seed points, and/or updating tracking parameters. You run the sequence of seed picking–autotracking–quality control, updating iteratively until you are satisfied that the autotracked horizon is a sufficiently accurate rep-resentation of the surface as you would pick it, in the extreme, on every line in your data set. A final interpretation most often is a composite of different regions in which the horizon was manually or automatically tracked as dictated by data quality and geologic complexity, and you are responsible for keeping track of (no pun intended) which picking mode was used for which portions of a horizon. The importance of quality con-trol in autotracking results cannot be overstated: There may be nothing more damaging to your project results, and to your reputation, than to have to apologize for autotracking errors that surface long after you completed your interpretation.


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Artifacts and interpretation pitfalls

Despite efforts to produce noise-free reflection seismic data — the ideal seismic response of Figure 1 of Chapter 1 with maximum interpretability — there always will be some unwanted information in your data that will com-plicate or obstruct your interpretation. So you will need to learn how to recognize seismic artifacts, the undesirable effects of seismic acquisition and processing. Artifacts can be quite obvious or extremely subtle. In any case or degree of severity, they are hidden or deceiving pitfalls or difficulties that, if not correctly identified and properly handled, can completely invali-date an interpretation. These pitfalls will test your correlation skills sharply, and there is no doubt that, as an interpreter, you are ultimately responsible for recognizing seismic artifacts and pitfalls and dealing with them accord-ingly. Your ability to do so will depend on your knowledge of data acquisi-tion and processing and your level of experience.

Interpreters have been aware of seismic artifacts and pitfalls since the first day that seismic data were correlated from one station to another. Many of those artifacts might have been expected or anticipated through understanding the seismic method and its attendant limitations and ambi-guities, while others probably were quite surprising, becoming evident only after drilled wells revealed what neither the seismic data nor the most thorough and insightful interpreters could ever have seen. We most often learn by our own mistakes, and we don’t really learn anything by guessing correctly. But because we usually are not eager or able to pub-licize errors, we find that opportunities to learn from the mistakes of others — from their close encounters with interpretation pitfalls — are not that numerous.

One of the earliest publications that directly addresses seismic artifacts and pitfalls and the lessons to be learned from them is Tucker and Yorston’s (1973) classic Pitfalls in Seismic Interpretation, followed by the companion volume, Pitfalls Revisited (Tucker, 1982). The former is one of the earliest (if not the first) publications to draw attention to the different types of pit-falls into which the well-intentioned but unsuspecting interpreter can stum-ble. Tucker and Yorston describe and tabulate examples of three categories of pitfalls: velocity, geometry, and recording (acquisition) and processing. In the follow-up publication, Tucker adds two categories to the original list: stratigraphic mapping and general, the latter comprised of pitfalls that are more behavioral than technical in nature. Although both books include seis-mic images that are now dated, their treatment of pitfalls is still valuable for its candor and historical perspective. After reading the texts, you can easily imagine that even with the technological advances of the decades that have


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passed since they were written, you are not yet and may never be completely free of interpretation pitfalls.

Perhaps the greatest danger or pitfall that you may face is correlating overly optimistically in low signal-to-noise (S/N) regions and then failing to properly communicate your lack of confidence and uncertainty (remember Dix’s threshold of impossibility in Chapter 1). You should persistently query your data and be aggressive in asking questions such as the following:

• How likely is it that this reflection is not primary signal?• What else might this be?• Is there a geologically reasonable explanation for this?• Am I being too optimistic, or should I be more skeptical?

You should avoid special pleading to support an interpretation, however opti-mistic, that is difficult to explain by sound geologic reasoning. By no means is optimism bad; vast quantities of oil and gas have been discovered by explorers who were not afraid to be optimistic. However, optimism should be tempered by the realities of physics and geology. This is the unmistak-ably human factor inherent in every seismic interpretation.

Following are some of the more common interpretation pitfalls and related seismic artifacts.

Interpreting coherent noise such as multiples or migration swings as primary signal. — Picking the seafloor/top-of-salt peg-leg multiple as the base of salt (Figure 13) is an excellent illustration of erroneously interpret-ing a nonprimary reflection as a primary event. Your first step in investigat-ing whether a reflection might be a multiple is observing the similarity of its geometry to another event that you know or strongly suspect is a pri-mary event. You should investigate which primary event on your data is the multiple-generating interface for the event that you think might be a multi-ple, what the travel path is for the multiple, and what reflectors are involved. Although there are not always definitive answers to these questions, they can show you the way to proceed with your interpretation.

Often, it is helpful to sketch a simple diagram illustrating a possible travel path for a suspected multiple. In your analysis of the multiple, be sure to account for any polarity reversals at reflection points along its travel path. For example, the seafloor/top-of-salt peg-leg multiple in Figure 13 should be opposite the polarity of the top-of-salt and seafloor events because the travel path of the multiple includes a reflection of upcoming energy at the sea surface. This polarity reversal of the multiple can be seen clearly on the left side of the figure but less so in the center and right side, where the


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reflection response appears to be more complex. Notice that the polarity for this image appears to be opposite that of the SEG positive standard display convention (Figure 7 of Chapter 2), in that the seafloor reflection is a sym-metric trough with well-developed side-lobe peaks.

In many instances, coherent noise such as residual multiples is obvious because it cuts across primary (or what are interpreted to be primary) reflec-tions, as shown in Figure 18. At the same time, the observation of two cross-cutting reflections or sets of reflections in itself does not mean that one must be an artifact. Figure 19 shows an example of the reflection from the base of a gas hydrate stability zone (GHSZ) that cuts across more steeply dipping primary reflections; such an event, which is often mistakenly interpreted as a multiple reflection, is commonly called a bottom-simulating reflection (BSR) (see the May 2006 issue of The Leading Edge for an excellent collec-tion of papers on the geology and geophysics of gas hydrates). Similarly, the flat spot shown in Figure 20 clearly cuts across primary dipping reflections, and there is no doubt that it is a primary reflection from a real hydrocarbon/water contact. But you can also see in this figure a set of steeply dipping migration “tails” that equally clearly are imaging artifacts cutting across more gently dipping primary reflections. Especially in areas that require

Figure 18. A 2D time-migrated seismic line on which multiple reflections cut across primary dipping reflections. Multiple reflections such as the seafloor double multiple and peg-leg multiples from reflectors in the shallow subsurface are most clearly evident in the left and center portions of the line. The positions of these multiples can be calculated easily using the method illustrated in Figure 12 (courtesy BP).

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Figure 19. A 3D depth-migrated line showing the cross-cutting primary reflection (red arrow) from the base of the GHSZ in the deepwater Gulf of Mexico. No simple or geologically reasonable travel path could give rise to this event as a multiple reflection. Its depth below seafloor and polarity (a trough on an SEG positive standard polarity display) are consistent with it being interpreted as the base of the GHSZ (courtesy PGS).

z

Figure 20. The 2D time-migrated image shown in Figure 10 of Chapter 2 on which the reflection from a flat-lying hydrocarbon-water contact cuts across primary dipping events. Crosscutting coherent noise, probably a migration artifact, is highlighted by the dashed red circle (courtesy PGS).

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complex imaging (PSDM) you will find that correlating dipping reflections as primary events versus migration artifacts is ambiguous and interpretive.

Interpreting noise as discontinuous signal. — Interpreting data with very poor S/N can involve correlating random or discontinuous noise segments that accidentally or coincidentally align or are aliased along an interesting or favorable trend. This is tantamount to “seeing what you want to see” because in doing so you allow yourself to fit a tracked horizon to a prospec-tive geometry. These correlations reflect the natural tendency toward opti-mism in most interpreters, which is not bad as long as it is balanced with well-founded geologic thinking (for example, comparing an interpretation to an established analog) and supported by other geophysical analyses such as seismic modeling. An excess of optimism can lead to correlations that are “almost sure to be misleading and therefore very expensive” (Dix, 1952), while a dearth of optimism can cause you to miss good exploration oppor-tunities. Hence the need for balance.

Interpreters at all levels of experience are susceptible to the tendency to interpret noise as discontinuous signal when they feel compelled or are instructed to “pick something, no matter how poor the data are.” This ten-dency has aspects of a condition or experience known as apophenia, a term coined by German psychiatrist Klaus Conrad that refers to seeing patterns or connections in random or meaningless data. You are well advised to heed the advice offered by Dix (1952):

There is, of course, nothing to be gained by straining the imagina-tion to such an extent that reflections are seen where none exist. Such a ridiculous procedure should be avoided. Though it is ridicu-lous it is quite natural, especially in those areas where a certain zone of weak reflections is known to be of major importance.

Interpreting false time structures as real depth structures. — This pit-fall is a result of lateral changes in velocity, which in time-domain imaging is commonly manifested as velocity pull-up, whereby a high-velocity body such as shallow salt in a normally compacting sedimentary basin creates false structure below it. Alternatively, an anomalously low-velocity body or interval causes a time sag that depresses the structure below it, as frequently observed in areas with shallow gas accumulations. Often the key to identi-fying these spurious, velocity-related features is the observation of coinci-dence, a particular feature that just happens to line up with another feature, the false image underlying the causative high- or low-velocity body. Figure 21, in which deep synclines underlie shallower anticlines one for one, very


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strongly suggests that the image is probably distorted, in this case owing to anomalously low-velocity material, perhaps gas-charged or overpressured sediments, in the interval above the synclines; there is no simple or straight-forward explanation for these synclines as primary structural features. Simi-larly, observing the coincidence that the “fault” shown in Figure 7 originates at the tip of the salt body on the left side of the image should cause you to take a second look at your salt-body geometry and depth-migration velocity model. This fault geometry is not geologically impossible, but it isn’t all that common either. The coincidence gives reason to investigate the depth imaging further, and the results of the second pass of depth imaging shown in Figure 8 essentially confirm that the “fault” on the initial pass of PSDM is an artifact.

Figure 21. A 2D time-migrated line on which synclines directly underlie anticlines. The vertical (and coincidental?) alignment of these structures suggests that the synclines may be distorted images caused by anomalously low velocities in the overlying section, perhaps associated with gas-charged or overpressured sediments. Careful layer-based depth conversion or depth imaging would be needed to accurately resolve the true structure (courtesy PGS and the Ministry of Commerce, Industry and Tourism of the Republic of Cyprus).

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A corollary to interpreting false time structures as real depth structures is interpreting false structures in seismic depth as real structures in true depth. This pitfall occurs in the depth-imaging domain and is caused by errors in a PSDM velocity model that are not large enough to significantly degrade the focusing of reflections but are large enough to position those reflections inaccurately. As mentioned in Chapter 4, isotropic depth-imaged data require a Z-to-D correction to account for anisotropy, but anisotropic depth-imaged data may need similar (usually smaller) correction, espe-cially in areas between control points for calibrating the velocity model if the anisotropy parameterization of the model is not spatially accurate. You should carefully investigate the effects of perturbing velocity and anisotropy values in a PSDM velocity model on the resulting depth images, especially when the exploration targets have low relief.

An example of this type of pitfall is shown in Figure 22, a line from a 3D isotropic depth-imaged volume on which very gently dipping reflections

Figure 22. A 3D isotropic depth-migrated line on which an apparent dip reversal in the subsalt section coincides with the edge of a salt sheet and an overlying seafloor scarp. The question is whether the dip reversal is real: Could it be a depth-imaging artifact caused by a slightly inaccurate migration-velocity model? The vertical exaggeration of the image is approximately 2.5 to emphasize the relatively low dips in the subsalt section (courtesy PGS).

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on the left side of the image appear to be folded as they extend to the right below a shallow salt sheet. The location of the “anticline” in the center of the image coincides with significant changes in water depth and thickness of overlying salt, so you should question whether the apparent folding is an artifact of the migration-velocity model, and, ultimately, whether the subsalt section is deformed or tilted at all.

In areas with significant lateral velocity variations, depth imaging is now routinely done to improve image quality and reduce distortion; how-ever, even in areas where velocities are supposed to be well behaved, that is, have little or no lateral variation and depth imaging is not thought to be necessary, there can still be significant image distortion, especially when dealing with subtle or gently tilted or warped (long-wavelength) structures. In these areas, you should be very careful with time-to-depth conversion and be sure to characterize the uncertainty in conversion. In a similar way, when working with land data, you should always check that statics corrections, especially long-wavelength corrections, have been properly calculated and applied. These corrections are notorious for erroneously creating or destroy-ing structures.

When interpreting on partial stacks, failing to recognize that incorrect moveout or migration-velocity error has created spurious high-coher-ence events or destroyed real signal. — In working with partial stacks, you should always check prestack data to be sure the data have been moved out correctly, particularly when amplitude variation with offset (AVO) or amplitude variation with angle of incidence (AVA) effects may be present. In cases of complex imaging, examine depth gathers to see that primary events are flattened properly and, in terms of target illumination, that the appropriate angle ranges are being stacked. Variable amplitude response (or lack of response) can easily arise if moveout is not applied correctly. As Figure 16 in Chapter 4 shows, even with moveout based on the best picked velocities, the appearance of stacked data can vary markedly, depending on the range of offsets included in the stack.

Assuming that wavelet phase is known and then misinterpreting observed amplitudes. — Although determining or estimating wavelet phase does not involve seismic artifacts in the strictest sense, it is nonethe-less an interpretation pitfall that warrants mention, particularly as it applies to working with seismic amplitudes and attributes in general. As discussed in Chapter 2, knowledge of wavelet phase is critical for correct geologic interpretation of individual waveforms at the fine scale of thin beds and closely spaced reflectors. Assumption or plain ignorance of wavelet phase


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can result in serious misinterpretation of lithology and/or pore-fluid type from seismic attributes.

As an example of this type of pitfall, consider the following story. A well-meaning geophysicist interpreted a trough-over-peak reflection on what were thought to be zero-phase data displayed with the SEG positive standard convention. He interpreted this reflection as a prospective gas sand, mapped it in a trapping configuration, and proposed an exploration well. The target reflection turned out to be the seismic response of an acoustically soft (low-impedance) shale overlying an acoustically hard (high-impedance) shale; there was no gas reservoir. This particular shale-over-shale configura-tion has a trough-over-peak signature on data that are phase-shifted by –90° and displayed with the SEG positive standard convention, so it can easily be misinterpreted as a gas sand. Postdrill analysis revealed that the original seismic data were in fact not zero phase, and many people were justifiably upset, thinking the expense of the well could have been saved if only the phase of the seismic data had been known.

Recognizing artifacts requires that you maintain a healthy skepticism about the information content of your data and a keen sense of knowing what to throw away and what to keep. Remember that the acquisition and processing histories of your data often contain information that can help you decide whether certain reflections in your data might be artifacts. When in doubt, conduct additional studies such as seismic modeling to investi-gate suspected artifacts, and do not hesitate to seek the advice and coun-sel of more experienced interpreters. You should always keep an eye out for seismic artifacts, especially in the more challenging exploration areas where complex imaging is becoming commonplace and artifacts are likely to occur. Again, your skill in recognizing artifacts develops over the course of your career and is fostered by making and taking the time to look at as much data as you can.


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115

Chapter 8

Correlation Procedures

This chapter describes the basic correlation procedures used in a typi-cal interpretation project, beginning with how to start an interpretation and then discussing fundamentals of the two main correlation techniques (loop tying and jump correlation). The section on correlations in depth-migration projects provides helpful information and guidance for handling aspects of interpretation that are specific to working with depth-migrated data, especially for building velocity models. The discussion of visualization emphasizes the importance of this procedure for validating correlations and communicating interpretation results. The chapter closes with a summary of individual processes and an example of a generic interpretation workflow.

Getting started

After checking to be sure that all data (seismic, well, cultural, potential field, etc.) for your project are in hand, take time to review the data before beginning correlations. You often make many important observations with a minimum of bias at this early stage of a project. Here are the steps you should follow:

1) Look at the horizontal and vertical extents of the data to gain a sense of their dimensions and the scale of your project.

2) Become familiar with the orientation of the data (strike and dip direc-tions and their areal consistency) with respect to the project base map and in terms of absolute compass directions.

3) View the data on a display scale that shows the entire extent of a line, and inspect the shallow section for (a) near-surface features such as channels (with associated velocity anomalies that can distort underly-ing time structure) or amplitude anomalies (possible drilling hazards),


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(b) on land data, time structures that correspond to applied statics, and (c) velocity anomalies that create false time structures (e.g., pull-up under shallow salt).

4) Experiment displaying your data in workstation-based projects with different color tables or schemes to see if any particular one favors or complements your pattern-recognition skills or more effectively enhances features of interest. Color tables are not “one size fits all” for all interpreters and all data sets. The use of color in seismic inter-pretation is very personal; some interpreters prefer to use a grayscale color scheme for structural interpretation (see Figure 22 in Chapter 7), whereas others use multicolored schemes (see Figures 3 and 4 in Chap-ter 3) when working with seismic attributes. Still others use a rainbow scheme (Figure 23 in Chapter 4) when corendering a migration-veloc-ity model with trace data. You should not hesitate to create a custom color table if doing so helps you interpret your data and communicate your results. Brown (2011) contains many useful illustrations, com-ments, and suggestions regarding color in seismic interpretation.

5) Look at the processing sequence applied to the data (often this is not readily available on a workstation project) and review in particular the type of migration used to see if that migration was appropriate for the structure and velocity complexity of the project area.

You select horizons for interpretation and mapping early in a project; some horizons may be more geologically significant or more obvious than others, and in some projects there is neither time nor need for picking the entire set of common dip families that make up an individual seismic line or a whole data set. These choices, which often involve correlation with other control such as well information by way of well ties, are determined by the exploration objectives for the project, the geologic complexity of the study area, and the quality of the available seismic data. On occasion, you modify the number of horizons and faults that you interpret during a project as you learn more about the study area. Within the limits of project objectives and available time, there is no point picking horizons (boundaries) and faults that do not contribute meaningfully to building the geologic history of an area. Although there might be many interesting features and surfaces visible in your data, attempting to identify and track all of them can compromise the objectives of an interpretation, destroy interpretive focus, and waste time and resources.

Correlating reflection seismic data to well information (logs, cuttings, cores, biostratigraphic and geochemical data) is a well tie (correlate = tie). It establishes the fundamental link between indirect geophysical measurements


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Chapter 8: Correlation Procedures 117

and real geology. Well ties very often involve using a model-based seismic response known as a synthetic seismogram that is created by convolving a seismic wavelet, preferably one extracted from the seismic data to which the synthetic will be correlated, with a reflection-coefficient (RC) series gener-ated from acoustic impedance (AI) data calculated from sonic and density logs collected during a borehole evaluation program. Well ties also are made using reflectivity data from vertical seismic profiles (VSPs), as discussed in Chapter 4. In either case, you correlate the well-based seismic response to the surface seismic data based on the character of individual reflections and distinctive reflection packages or intervals, as shown in Figure 1.

There is always some uncertainty in making the best correlation for a well tie, and there are two primary sources of this uncertainty: (1) quality of the reflection seismic data to which the well is being tied and (2) assump-tions made in generating a synthetic seismogram (or in processing a VSP). Although details for creating a synthetic seismogram or processing a VSP are not included in this text, the following list includes some of the factors to consider when you find that a well-to-seismic tie does not work:

• Well or seismic data (or both) are not positioned accurately.• Seismic data do not image the actual well location.• Seismic data processing is deficient (e.g., data are excessively noisy or

contain residual multiples).• Time-depth relationship is incorrect.• Logs used to generate synthetic seismogram were not edited properly.• Synthetic seismogram used an incorrect or inappropriate wavelet.

Each horizon that you decide to correlate, whether established by a well tie or recognized on your seismic data as a geologically significant boundary such as a major sequence boundary or an unconformity, should illustrate a particular element of the geology of your project area. You should carefully record your starting point for correlating a horizon in the same way that a field geologist identifies a type locality for a geologic formation.

Often you can divide your project area into subareas separated by one of two types of barriers: geology (faults, intrusions, etc.) or control (gap in data, difference in data quality). Within these subareas may be several seis-mic lines that illustrate a certain geologic element, so choose one of these lines per subarea as a type line that you can use later as a display line when presenting your completed interpretation. (Such a line often is one that you can correlate across barriers between subareas with greatest confidence.)

It is good practice to correlate several horizons at once during the course of an interpretation, although you should carry no more horizons than you


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Well location

a)

b)

Syntheticseismogram

Well locationSynthetic

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Figure 1. (a) A 3D time-migrated line with well location identified and the synthetic seismogram generated from sonic and density logs for the well. This seismogram uses a wavelet extracted from the 3D data and is calibrated with a velocity survey acquired in the well. (b) The well tie is established by translating the synthetic seismogram to the well location on the line and then shifting it up or down to achieve the best visual match to the seismic data (courtesy BP).


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can comfortably and accurately handle (a matter of personal preference and interpretive style). Correlating several horizons at once facilitates interpre-tation of faults and generally gives you a broader view of the geology con-tained in your data. At the same time, you should not overly restrict your view by focusing on discrete horizons but should correlate intervals (with characteristic seismic signatures) as well as horizons. Phantom horizons that mimic the form of an adjacent (overlying or underlying) horizon are commonly used to carry interpretation through poor data areas where pri-mary interpretation by character correlation is not possible (see Pennington et al., 2004). Such a horizon often is constructed by connecting discontinu-ous dip segments within poor data zones. Phantom horizons can be very useful for producing relatively coarse form maps in poor data areas when a rough idea of structural trends is needed; in fact, maps of phantom hori-zons usually are the only ones that can be produced in such areas. In most instances, phantom horizons should be assigned greater risk of accuracy and not be assumed to have primary stratigraphic significance. Be mindful of the advice offered by Nettleton (1940):

Under favorable conditions where there are clear reflections from certain continuous beds, such as a continuous limestone under or within a thick shale section, the seismograph map may show accu-rately the depth to the stratum (limestone) giving a certain reflected event. Then the map may be almost as accurate as could be made by an actual contact by drilling each detector spread.

More usually, the construction of the map is not so simple, and the results are more uncertain. More frequently than not, any partic-ular reflection is not continuous over a wide area, and closer control with dip determinations or phantom horizons must be used to tie a picture together.

In very difficult areas, such as portions of the Gulf Coast, there are not enough reliable reflections to make even a reasonably con-tinuous phantom horizon. However, it is usually possible to get at least an approximate idea of the dip within one or two zones of depth at many or most of the detector spreads. The dips so indi-cated, of course, are the components along the line of the spread … . With fair dip control it may be possible to construct an approximate contour map to give a fairly good general picture, but it cannot be taken as representative of structure at any particular depth.

Having divided a project area into subareas, your interpretation usually proceeds from the simplest (least complicated) to the most difficult subarea.


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In effect, you first quickly interpret the easiest areas, effectively isolating problem areas. In these problem areas, you will spend the most time and make critical interpretive decisions, following the so-called 80–20 rule: 80% of the work (the easiest) is done during the first 20% of project time, and the remaining 20% of the work (the hardest) consumes the remaining 80% of project time. Often, important control points such as wells delimit these areas, and you expend much interpretive effort to ensure that horizons can be correlated with confidence away from these points and into other areas.

As shown in Figure 2, you use two basic procedures to correlate reflec-tion seismic data: loop tying and jump correlation, as discussed in the fol-lowing sections.

Loop tying

Loop tying correlates horizons and faults in a geologically reasonable fashion from line to line across a grid of lines; remember that a fault is a geologic surface which you must tie consistently from line to line in the same way that you tie a horizon. Loop tying begins at a starting point (X on Figure 2) on a given line and proceeds by trace-to-trace correlation along that line to its intersection with another line, effectively spawning another starting point for correlation along the intersecting line. This procedure is repeated along lines in the grid until correlation returns to the original

x

Area of interest

Loop tying

Jump correlation

Seismic grid

Figure 2. Schematic of loop tying and jump correlation.


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starting point on the first line. The closed track of seismic lines along which correlations are made is called a loop.

You should tie loops in a regular, disciplined manner, moving the inter-pretation successively from completed loops to adjacent untied loops and leaving no intervening lines uncorrelated. If possible, you should expand correlation away from the starting point(s) that serves as a fixed reference; when you discover correlation errors, you should trace them back to the starting point(s) to identify the correlations that need to be changed. With 3D data, it is good practice to begin tying loops on a relatively coarse grid and fill in as needed; the grid does not need to be uniform across an entire project area, but it should not vary greatly because large variations can affect the accuracy of subsequent operations such as autotracking, interpolation, or gridding. Whether correlating horizons or faults, you should pick on a grid that is sufficiently closely spaced for accurate definition (resolution, according to the Nyquist theorem) of all features of interest.

Figure 3 is an example of a loop tie consisting of five line segments from a 3D survey. Although a loop normally is defined by four line segments as shown in Figure 2, line segment A is repeated on the far right side of this display to establish visual continuity of the data and picked horizons around

A B C D A

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Figure 3. A loop tie from a 3D survey. Correlating the shallow (yellow) horizon is straightforward, but correlating the deep (red) horizon requires a geologic explanation for correlating across a discontinuity in segment C (courtesy BP).


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the entire loop. On this display, the tracking of the yellow horizon is simple (as Dix says, “almost anyone can understand such a correlation” [1952]), but there is a correlation problem to be solved for the red horizon. Consider the starting point for this horizon to be the peak (black) shown in segment D. Correlating to the right into segment A is straightforward (it could easily be autotracked), but to the left the horizon does not track smoothly across segment C. There is a discontinuity about one-fourth of the way from right to left across segment C, at which point you must decide whether to cor-relate to a shallower or deeper peak before continuing the red horizon on to segment B. This discontinuity must have a reasonable explanation in terms of data processing or geology, and, given the overall appearance of the data, a geologic explanation such as a small fault or a depositional/stratigraphic difference is more likely. Continuing across segment C toward segment B, if the red horizon is correlated to the lower peak, it can be connected to the pick made from segment D to segment A (right side of the display). Alter-natively, if the peak is correlated to the shallower peak in segment C and then carried on toward segment B, there will be a correlation error or mis-tie between segments B and A that is not as simply explained by geology as is making the correlation to the deeper peak in segment C.

The loop tie illustrated in Figure 3 is procedurally straightforward, in the sense that you only need to know the exact positions of the intersections of the lines that form the loop; referring to Figure 2, these are the corner points of the loop. This example is for 3D data, so reflections will tie at the line intersections, that is, the reflections will directly line up with (correlate to) each other because the data have been 3D migrated. But what happens if the loop is formed from a grid of 2D migrated lines? Recall from Chapter 5 that migration can be done in two or three dimensions; more importantly, migra-tion of any given 2D line cannot fully account for 3D effects unless that line is a true dip line. But all of the lines that form a loop of 2D lines cannot be true dip lines unless the geology is perfectly flat (in which case dip = 0 and there is no true dip direction); so in tying loops of 2D migrated lines, an interpreter will almost always encounter a mis-tie. In this case, a mis-tie is the misalignment of reflections at a line intersection caused by incomplete migration of one or both of the lines.

Figures 4 and 5 are images of two of the four 2D time-migrated lines that form a loop as shown in Figure 2. The intersection of these images is marked by the vertical yellow line on each image. Note that the reflection geometry and patterns in Figure 4 (line A) show a major down-to-the-right extensional (listric) fault, with steep and variable dips in both fault blocks and well-defined left-to-right convergence of reflections in the right-hand fault block. In contrast, the reflections in Figure 5 (line B) are horizontal to


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Line A

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Figure 4. A 2D time-migrated dip line. The vertical yellow line marks the intersection with the line shown in Figure 5. Although the true dip direction cannot be determined from this line alone, the relatively steep dip on this line and the gentler dip on the orthogonal line in Figure 5 indicate that this line is more dip than strike oriented (courtesy WesternGeco).

relatively gently dipping at depth, with no obvious faults visible. These lines are perpendicular to each other, so line A must be nearer to true dip than line B. The lines will not tie at their intersection because at least one of them (probably line B and possibly both) is not fully and correctly migrated. Fig-ure 6 illustrates this mis-tie, which becomes greater with increasing reflec-tion time because of the increasing dip with depth on line A.

Figure 7a illustrates that lines A and B can be tied by reflection charac-ter, but not necessarily in terms of absolute two-way reflection time, by sim-ulating migration of line B. This is done by rotating line B updip into line A until there is good character match. Recalling Chapter 5, consider that line A was acquired and processed in the plane of Figure 1 of Chapter 5 and so is more accurately migrated, whereas line B was acquired and processed in the plane perpendicular to the figure and is less accurately migrated. Rotating line B moves reflections along the path of the wavefront drawn, as shown in Figure 1 of Chapter 5, to the point at which reflections on line A have been accurately repositioned by migrating that line. Lines A and B are tied when reflections are smoothly aligned and good character match is established.


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Figure 7b shows that variable amounts of rotation of line B are needed to tie different reflections or reflection packages, in this case greater rotation being required to tie more steeply dipping reflections at depth. The different amounts of rotation reflect the reality of lateral and vertical velocity varia-tions; consider that the “rigid” rotations in Figure 7 effectively are constant-velocity migrations along circular wavefronts. As a practical note, keep in mind that in addition to rotation of line B, some translation of the line may also be necessary for establishing a good character tie, based on data quality at or near the tie point or on lateral variations in seismic response caused by real changes in geology.

The procedure described above achieves line ties by reflection character but does not necessarily determine the true position of the actual line tie in X-Y (horizontally) or Z (vertically in time or depth). In the simplest but not the most common case, the established tie point is correct if one of the two intersecting lines can conclusively be shown to be a true dip line; even this

Figure 5. A 2D time-migrated strike line. The vertical yellow line marks the intersection with the line shown in Figure 4. Although the true strike direction cannot be determined from this line alone, the relatively gentle dip on this line and the steeper dip on the orthogonal line in Figure 4 indicate that this line is more strike than dip oriented (courtesy WesternGeco).

Line B

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assumes that the migration of the true dip line has been done correctly with an accurate migration-velocity field.

Figures 8–10 illustrate two special cases and one general case of tying two orthogonal 2D time-migrated lines. For simplicity, these cases exclude 2D depth-migrated lines to avoid consideration of migration errors arising from differences in depth-migration velocity models; however, even for per-fectly accurate velocity models, 2D depth-migrated lines can mis-tie for the same reason that 2D time-migrated lines do. The critical factor is not the domain in which the migration is done but whether the migration is two- or three-dimensional. The reflecting surface in Figures 8–10 is an inclined plane dipping from the top of the figures to the bottom; effectively, the true dip direction of this plane is parallel to the vertical axis of the figure (the top-to-bottom direction of the drawing), and the strike direction of this plane is parallel to horizontal axis of the figure (the left-to-right direction of the drawing). Structure contours are shown as horizontal dashed lines on each figure. The position of the schematic cross section shown in Figure 1 of Chapter 5 is marked as the true dip line, and the migrated line segment in

Line A Line B

t

Figure 6. The left portion of line A (Figure 4) juxtaposed against the right portion of line B (Figure 5). The mis-tie at the intersection of the two lines becomes more obvious with increasing reflection time and increasing dip on line A. Red arrows point to reflections that are obviously misaligned (courtesy WesternGeco).


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a)

b)

Line A

Line A

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Figure 7. (a) Rotation of line B (strike line) updip into line A (dip line) simulates migration of line B in the direction of line A and enables matching of reflection character to tie the two lines. In this display, the character match is good in the shallow section but not in the deeper section (as marked by the red arrow), so line B needs more rotation into line A to tie deeper reflections. (b) Further rotation of line B (strike line) updip into line A (dip line) establishes good character match for deeper reflections (courtesy WesternGeco).


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that figure is the intersection of the inclined plane with the vertical plane. The recording datum of the 2D lines is a horizontal plane (the plane of the page), and the positions of the lines are plotted on this plane as heavy black lines, projected vertically downward to the inclined plane on which reflect-ing points are located.

Figure 8 is the simple case in which one of the two 2D time-migrated lines is a true dip line. Migration of the dip line moves reflecting points on this line to their true subsurface positions (in the direction of the blue arrow on the figure), but there is no repositioning of reflecting points on the orthogonal line (the strike line). Even though the strike line is imaging updip of its surface location, reflections from the horizontal line of reflect-ing points on the inclined plane form a horizontal event for which there is no repositioning by migration in the vertical plane of the line. In this case, the mis-tie is at the mapped position of the intersection point of the two lines. The reflection from the inclined plane on the true dip line is correctly placed, having been migrated to the intersection point from a position down-dip of the intersection; whereas the reflection from the inclined plane on the strike line, the reflecting point for which is updip of the line intersection, is incorrectly placed at the line intersection. You can see that at the point of intersection of the two lines as plotted on the horizontal recording plane, the

Surface location of2D seismic line(true dip line)

Surface location of2D seismic line

(strike line)

Reflecting pointson dipping plane

Migration in verticalplane of 2D line

Map view ofcontours on

dipping plane

Updip

Downdip

Figure 8. Schematic of a dipping plane and the mis-tie at the intersection of two orthogonal 2D time-migrated seismic lines, one of which is a true dip line.


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two-way reflection time to the inclined plane on the strike line is less than the two-way reflection time to the inclined plane on the true dip line, and the magnitude of this difference is the amount of the mis-tie. Thus, in a region where 2D time migration is acceptable — where there is simple or complex structure and simple velocity as described in Chapter 5 — the deeper of the two reflection times to a given horizon measured at the intersection of two 2D time-migrated lines is the more accurate (less incorrect) measure of the true two-way traveltime to that horizon.

Figure 9 is more complicated but is not the general case for the inclined reflector and two orthogonal 2D time-migrated lines. In this case, each line is oriented 45° to the true dip direction, and both lines are apparent dip lines — neither can be recognized conclusively as more strike or dip than the other. Migrating each line (in the direction of the blue arrow) in its own respective plane is incomplete, yet reflecting points and the reflections from them are mispositioned on both lines by the same amount (both blue arrows terminate at the same map contour), giving the erroneous view that the lines do tie. This observation supports the previous statement that a character tie






True dip line


dipping plane

Updip

Downdip

Figure 9. Schematic of a dipping plane and the mis-tie at the intersection of two orthogonal 2D time-migrated seismic lines, both of which have the same apparent dip.


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alone does not guarantee that the true subsurface location of a tie point has been determined accurately.

Figure 10 is the general case for the inclined reflector and two orthog-onal 2D time-migrated lines; strictly speaking, the most general case for tying intersecting 2D time-migrated lines would have an arbitrary angle of intersection between the two lines. Here, both lines are oblique to the true dip direction, and the one whose azimuth is nearer to the true dip direction is considered more the dip line and the other more the strike line. Both are incompletely migrated, the dip line less so than the strike line. A mis-tie is associated with these migrations, equal to the difference between the values of the contours at which the blue arrows terminate. Figure 10 also illustrates that the deeper of the two reflection times to a given horizon measured at the intersection of two 2D time-migrated lines is the more accurate (although still incorrect) value for the true two-way traveltime to that horizon.

Although Figures 8–10 represent an almost ridiculously uncomplicated model of the subsurface, they illustrate several important concepts and useful





True dip line Surface location of2D seismic line


dipping plane

Updip

Downdip

Figure 10. Schematic of a dipping plane and the mis-tie at the intersection of two orthogonal 2D time-migrated seismic lines, one of which has greater apparent dip than the other.


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techniques when correlating 2D reflection seismic data. In practice, a grid of 2D seismic lines usually is laid out in an orthogonal pattern aligned with the predominant strike and dip directions. These directions are not always known at the outset of exploration in a given area; on land, they may be determined from outcrops or based on trends of surface features, and in a marine set-ting they may be as simple as perpendicular and parallel to the coastline. As exploration matures, the 2D program design is refined as these directions and variations become better known, although many programs are infill to existing surveys with no special attention given to dip and strike. When you correlate lines in a 2D grid, the most common procedure is to track horizons on the lines that are more nearly in the dip direction and have greater imag-ing accuracy and to use the strike lines to correlate horizons from dip line to dip line by character. This approach may help you visualize elements of geology that are defined implicitly in dip section, but it does not remedy the mis-tie problem that you must address in mapping. Of course, this procedure is not applicable in 3D work, although the trend and magnitude of dip in the subsurface can figure into 3D survey designs, as mentioned in Chapter 6.

Because migration is a velocity-dependent process, the mis-ties you observe on 2D time- or depth-migrated data may be caused not only by geometric effects — the fundamental limitation of 2D imaging of a 3D sub-surface — but also by differences between the migration velocities used to migrate individual 2D lines. These velocities almost always are determined on a line-by-line basis and do not necessarily tie at line intersections. You can easily envision a situation similar to Figure 9, in which you can tie two lines because a geometric mis-tie is effectively cancelled by differences in the migration velocities used for the lines. You can just as easily imagine an instance where two 2D depth-migrated lines imaging horizontal strata (e.g., undeformed abyssal plain sediments) do not tie because the velocity model for one of the lines is faster (its rate of increase of velocity with depth is greater) than the model for the other line.

It is important to realize that not all mis-ties are caused by 2D imaging; they can also be the result of differences in acquisition and/or processing between adjacent or overlapping surveys and, as such, can be problems in both 2D and 3D projects. Mis-ties of this kind often are corrected by simple static or bulk shifting of one survey to another, which requires that one sur-vey be established as a reference to which the other is corrected. This pro-cedure can account for relatively small mis-ties in terms of vertical position of reflections but cannot do so for changes in reflection character caused by processing differences, which usually appear as differences in phase and/or amplitude, or for mispositioning caused by having imaged the surveys with different migration velocities.


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In addition to migration of seismic lines, a process known as map migration can help you accurately position subsurface reflecting points. This process follows correlation of 2D unmigrated seismic lines, and it uses the same geometric principle relating unmigrated and migrated sur-faces as illustrated in Figure 1 of Chapter 5. When using map migration, you correlate horizons on 2D lines by loop tying and/or jump correlation and then create a contoured surface of the two-way reflection time to a given horizon. Usually such a surface is purposefully smooth, but accurate positioning of faults is problematic, owing to the lack of precision in pick-ing faults on unmigrated data. You migrate your mapped surface by reposi-tioning contours using an average velocity distribution for that surface and the true dip direction and dip rate measured directly from the contoured map. This process can be done relatively easily by hand using velocity information for the horizon to be migrated (from seismically derived velocities or well velocity surveys; see Chapter 4), but difficulties arise in areas with complex structure, such as steep dips, closely spaced faults, intersecting faults, or several different fault trends. In the modern interpre-tation environment, map migration is done with computerized algorithms and rarely by hand. Chun and Jacewitz (1984) provide equations that are easily adapted to map migration computations. The omnipresence of 3D data has nearly eliminated map migration from interpretation but not from the modeling tool kit.

Loop tying applies to correlation of horizons and faults, but there is an additional concern when correlating faults on a grid of 2D data. These correlations can be problematic if the grid is widely spaced with respect to the distance between individual faults, a manifestation of spatial sampling (see Chapter 6). You can accidentally miscorrelate, or alias, fault picks if you make your correlations in map view only and have not carefully tied the faults from line to line. Figure 11a shows a map view of several fault picks marked on three parallel seismic lines, but the picks have not been correlated from line to line. In this example, the faults are normal faults and have trapezoids or tents annotated on the downthrown blocks; for simplicity, the faults are shown schematically as single fault cuts.

When you first look at this display, you should ask at least three questions:

1) What is the interpreter’s confidence in these fault picks?2) How accurate are the positions of these fault cuts?3) Has the interpreter been able to tie the faults from line to line on any

intersecting lines (not shown in Figure 11)?


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Obviously, the third question is the most important for making the correct fault correlations. But if the answer to that question is “no,” then correla-tions based only on the marked positions of the fault cuts are ambiguous. Figure 11b and 11c shows two possible correlations. The correct correla-tions should be made by loop tying the faults using intersecting lines; if no intersecting or crosslines are available, then the correlations should be made, albeit with less confidence, by correlation based on similarity of fault shape, position, and displacement of interpreted seismic horizons.

In addition to tying the faults, you should ensure that your correlations are consistent with the senses and amounts of displacement of your corre-lated horizons. Important sources of information that you might use to guide or support your correlations are well data that establish fault positions and displacements based on missing or repeat section and geologic studies that

Normal fault (tent on downthrown block)

a)

c) b)

Figure 11. Correlation ambiguity for fault cuts in map view. (a) Fault cuts marked on three parallel seismic (dashed) lines. The faults are normal faults, with tents annotated on the downthrown blocks; they have not been correlated from line to line. (b,c) Two different correlations for the fault cuts shown in (a). The correct fault correlations can be determined by loop tying the fault surfaces using intersecting lines (not shown) or by correlating from line to line based on similarity of fault shape, position, and displacement of interpreted seismic horizons.


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describe the regional trends and/or structural style of your project area. In a 3D interpretation, you can check your fault correlations by displaying fault picks on horizontal reflectivity or coherence slices, such as shown in Figures 6 and 7 in Chapter 3. Note in these examples that fault trends are particularly well defined on the coherence data, again as enabled by good data quality. You can also validate your correlations of faults and horizons by viewing arbitrary lines parallel to faults, effectively within individual fault blocks, to be sure that you haven’t aliased any fault picks or missed any cross faults.

Even though you will spend a lot of time at the helm of powerful work-stations and will very often be totally immersed in 3D data, you should not lose sight of the real world of geology, in which reflecting boundaries are 3D curved surfaces frequently interrupted by sharp discontinuities (faults). The impedance contrasts across these surfaces vary laterally and widely in scale, and the seismic responses to these surfaces overlap and interfere. None of these complications is addressed in the discussions of Figures 8–10. Cou-pling your awareness of this reality with the ever-present noise in seismic data and the assumptions, approximations, and uncertainty implicit in data acquisition and processing/imaging, you should be very careful when work-ing with 2D data and very appreciative of the immense value of 3D data.

Jump correlation

In its most general sense, jump correlation involves correlating an area or segment of data having a particular or distinctive reflection character to a noncontiguous or nonoverlapping area or segment of data. This type of cor-relation, which is based on similarity of reflection character, position, and geologic setting, puts the greatest demand on your pattern-recognition skills because your correlation is not visually continuous. As shown in Figure 2, you use jump correlation to tie outlying lines that do not intersect the main control grid of available data; this is historically associated with interpreting grids of 2D seismic data, but on a large scale it can be thought of as cor-relating from any data set to another nonintersecting 2D or 3D data set (for instance, from one 3D volume to another nonoverlapping 3D volume).

In the modern interpretation environment of workstations and 3D data, the most common use for jump correlation is to pick horizons across faults, in which the noncontiguous or nonoverlapping areas are separated by faults. You most often make jump correlations on a workstation using a tool called a correlation polygon, with which you outline a limited area of data in one region (e.g., in a fault block) that you will translate or jump across a dis-continuity or fault to make a visually continuous correlation to reflections in another region (such as in an adjacent fault block; see Figure 12). In


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drawing a correlation polygon, it is important to include enough but not too much data in the polygon for you to correlate an interval of reflections and not just an individual event; in this way, you take full advantage of pattern- recognition skills and avoid tunnel vision in focusing too narrowly on a single reflection. In some workstation applications, an entire fault block, defined by a previously picked fault, can be translated along that fault to investigate possible correlations into the adjacent fault block. This can miti-gate, but not necessarily eliminate, the tendency toward tunnel vision.

One helpful check on the validity of jump correlations across faults is flattening the interpreted seismic section on a correlated horizon (in Figure 12c, you would flatten on the red horizon). You can do this by hand on a paper copy of the interpreted line by cutting it along the traces of picked faults and then sliding the block adjacent to a given fault up and down along

ta)

tb)

tc)

Figure 12. (a) A 2D time-processed seismic line. The objective is to correlate the red horizon to the right into the fault block between the two picked faults (in yellow). The red box, often referred to as a correlation polygon, outlines the segment of data that will be translated and/or rotated to establish the best character correlation with data in the adjacent fault block. (b) The correlation polygon from (a) has been translated into the adjacent fault block and rotated counterclockwise to achieve good character correlation (visual alignment of reflections) at the level of the red horizon. (c) Final correlation of the red horizon based on jump correlation into the fault block between the two picked faults. Correlation along this line into other fault blocks would proceed in similar fashion by selecting and then translating/rotating other correlation polygons (courtesy BP).


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ta)

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Figure 12. (Continued)

the fault to find the best visual correlation. Most workstation systems now provide flattening on a user-specified horizon; some let you select a fault block defined by existing fault picks and then translate it to make your cor-relation — a simulation in the electronic world of the manual procedure described above. In this jump correlation or any related flattening process, you are not doing a rigorous structural restoration of the line you’re cor-relating; you are only using a technique to facilitate character correlation.


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Proper structural restorations require accurate seismic correlations as input and involve considerations such as decompaction and balancing to preserve length or volume. They are usually done by specialists using software appli-cations designed for this type of work.

Figure 13 illustrates the importance of viewing the full seismic section when making jump correlations because the character of a relatively large interval, vertically or laterally, often provides the best evidence for making the most likely jump correlation. Again, flattening this line on the red or the blue horizon would help validate the accuracy of the correlations.

Figure 14 shows how using geologic insight can help you make the cor-rect jump correlation when alternative correlations are possible. The red and green faults on Figure 14a are picked with good confidence (other faults that might also be picked are not shown). At first glance, the pronounced high-amplitude reflection highlighted by the yellow arrow appears to be down-thrown to the right across the green fault, making it a reverse fault. A more inclusive top-to-bottom view of this line reveals individual reflections and reflection packages that correlate across the green fault as shown in Figure 14b, making it a normal fault. The bulk of correlation evidence, in combina-tion with knowledge of the regional structural setting of the area, indicates that the green fault is in fact a normal fault.

The false correlation suggested by the bright reflection highlighted on Figure 14a can be readily explained and confirmed by additional correlation and mapping. The anomalously high amplitude of the bright reflection to the left of the green fault is the reflection response to a thin hydrocarbon-charged sand. To the right of the green fault this sand is either brine-filled or absent, thus having no correlative high-amplitude response. The anomalously high amplitude of the bright reflection to the right of the green fault, which appears to be downthrown from the fault block to the left, also is the reflec-tion response to a thin, hydrocarbon-charged sand but not from the same sand as that corresponding to the bright event in the adjacent fault block. This stratigraphically older sand is hydrocarbon charged to the right of the green fault but is either brine-filled or absent to the left of the green fault.

The preceding example shows that often you can resolve conflicting correlation evidence by incorporating sound geologic reasoning and knowl-edge of regional geologic trends in addition to taking full views of the sec-tions being correlated, that is, not being distracted by or focusing too closely on individual reflections.

Many times the question is asked whether faults or horizons should be picked first in a given interpretation project. There is no right answer to this question. In some projects, faults are very clearly imaged and can be picked first, providing a framework for subsequent picking of horizons (recall the


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za)

c)

b)z

z

Figure 13. (a) Restricted portion of a depth-migrated seismic line on which there appears to be no clear-cut evidence for major faulting. (b) Same line as in (a) but displayed with greater vertical extent of data. Now there is very good evidence for a major fault downthrown to the right in the left-center portion of the image; other smaller faults are also visible. The lack of good evidence for this fault in the shallow part of the image is the result of fortuitous alignment of reflections masking the fault; when more data are displayed, the discontinuities that define the fault are readily apparent. (c) Partial interpretation of (b) with a major down-to-the-right fault picked as shown. A correlation polygon would help establish correlations for the red and blue horizons, and either of these could subsequently be used for flattening to validate the correlations (courtesy PGS).


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a)

b)

t

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Figure 14. (a) Time-migrated line with two picked faults marked in red and green. The yellow arrow points to a high-amplitude reflection that appears to be downthrown to the right across the green fault, a reverse fault. (b) Three different reflections marked in yellow above and below the bright event highlighted in (a) provide good evidence that the green fault is a normal fault and not a reverse fault (courtesy BP).


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discussion of horizon autotracking in Chapter 7). In other projects, horizons need to be picked first to guide fault picking. In all cases, the only definitive statement to be made is that all final horizon and fault correlations must be geophysically consistent and geologically reasonable, and the order of and manner in which these correlations are done according to the work flows used are matters of interpretive judgment based largely on experience.

No matter how you proceed with an interpretation, you should track the progress of correlation across your study area on an index map, on which you mark tied line intersections and fault cuts (this is done automatically on a workstation). You select one of the following end-member procedures to use in correlating and mapping:

1) Interpret all lines in the area; then grid and contour the entire area at once.

2) Interpret only a few lines in a relatively small area, grid and contour that area to establish trends, and then proceed by steps in this fashion across the entire area.

Whether using one of these approaches or some combination of the two (often the case), you might consider tying loops on a relatively coarse or loose grid and then adding interpretation to or infilling that grid — essen-tially the process used in seeding autotracking in a 3D seismic interpreta-tion. This approach affords you a view of the geology of the area fairly early in your project, which can guide additional detailed interpretation or, if needed, provide a quick view of the project. Remember that interpretation on a coarse grid is, by itself, no excuse for not interpreting all available lines in a project.

As stated before, it is good practice to mark fault cuts for each inter-preted horizon to assist fault correlations, especially in structurally complex and/or poor-data-quality areas; on a workstation, you can do this automati-cally, usually with an option that you turn on or off. Comparing fault cuts on different horizons helps to identify miscorrelations and to ensure that the fault patterns on all interpreted horizons are reasonable and consistent with one another.

During the course of an interpretation, it is very important for you to make notes directly on seismic lines and maps to mark features of interest and questionable areas; unfortunately, you cannot do this very easily on a workstation. Notes focus attention on areas that are critical elements of the interpretation or that need further work. In many instances, these notes should become part of the permanent record for your interpretation because they can have important implications for project and data management.


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Correlations in depth-migration projects

Although detailed treatments of depth-migration principles and prac-tices are not included in this text, it is important to discuss the interpretive input required in a typical depth-migration processing sequence. The fol-lowing generic depth-migration processing sequence for marine 3D data used for subsalt exploration outlines the four main steps of this sequence.

1) Water-flood migration to image the seafloor. In this step, the entire data volume is migrated with the velocity of seawater.

2) Sediment-flood migration to image the top of the salt. Having picked the seafloor on the water-flood migration from step 1, the migration-velocity model is updated by replacing the seawater velocities below the seafloor horizon with a sediment-velocity field. The data volume is migrated a second time.

3) Salt-flood migration to image the base of the salt. Having picked the top of salt on the sediment-flood migration from step 2, the sediment-velocity model is updated by replacing the sediment velocities below the top of salt horizon with salt velocity. The data volume is migrated a third time.

4) Final migration to image the subsalt section. Having picked the base of salt on the salt-flood migration from step 3, the migration-velocity model is updated by replacing the salt velocity below the base-of-salt horizon with sediment velocities. The data volume is migrated a final time.

Interpretive input is required after each of these steps. Tracking the sea-floor on the water-flood migration is usually straightforward and is espe-cially important in areas with significant seafloor relief. Tracking the top of salt on the sediment-flood migration is less straightforward and can be very difficult where this surface is rugose or steeply dipping. Tracking of the base of salt on the salt-flood migration is often very difficult and is some-times done only in model-guided fashion. The full integrated interpretation is performed on the final migration, where the preceding efforts bear fruit. In settings with multiple overlying or irregularly shaped salt bodies such that along any vertical line through the 3D volume there would be more than one top-of-salt penetration (and, necessarily, more than one base-of-salt penetra-tion), steps 2 and 3 are repeated to image successively deeper salt bodies properly. As you can easily envision, the accuracy of correlations at each step in this migration sequence is critical to the accuracy of following steps. Said another way, inaccurate correlation of the top of salt will necessarily preclude accurate imaging of the base of salt.


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Correlating the top- and base-of-salt reflections, or the horizons that bound any bodies with anomalously high or low velocity, in a depth-migra-tion project must be done in such a way that the bodies are completely closed spatially before proceeding with the final migration. In a workstation system that has multivalued horizon capability [for any X-Y (bin) position there can be multiple Z-values], a salt body can be completely defined with a single horizon, even though the top- and base-of-salt events are picked separately at succeeding stages in the normal processing/model-building sequence. For example, the top-of-salt horizon picked on the sediment flood could be updated to include picks for the base-of-salt horizon on the salt-flood migra-tion. Good practice would include copying the original top-of-salt horizon for archival purposes before updating it to include base-of-salt picks. Unfor-tunately, for the time being, most workstation systems are single valued, meaning that for any X-Y position, there can be only one Z-value. This func-tionality constrains the interpretation technique that you can use to pick the top- and base-of-salt horizons to ensure that the final picks completely close a salt body.

Figure 15 illustrates a technique for picking top- and base-of-salt horizons that helps you construct closed salt bodies. Figure 15a shows the outline of a salt body on which the top-of-salt horizon (Top salt 1) actually extends beyond the right-hand edge of the body. This horizon is intentionally projected as shown and must be done so manually to ensure it will intersect and overlap a similarly projected base-of-salt horizon (Base salt 1). In this example, the base-of-salt horizon and a deeper top of salt (Top salt 2) are picked in similar fashion to ensure they overlap and hence close the body. This second deeper top of salt would be named and picked as a separate horizon. When all of the top- and base-of-salt hori-zons for all salt bodies in a project have been picked, the overlap areas are trimmed away (using workstation functionality); then the salt bodies are completely defined as shown in Figure 15b. You should exercise care in picking the edges of salt bodies to ensure they are smooth and fully closed; even small errors in picking at these edges can have detrimen-tal effects on the final migration. Note that accurate salt-thickness maps can be calculated easily from these carefully picked top- and base-of-salt horizons.

As mentioned, if there are complex or multiple overlying salt bodies in the project area, you may have to repeat the sediment- and salt-flood migrations in your depth-migration processing sequence to accurately image these features and, ultimately, your exploration or development targets. In conjunction with processing geophysicists and project man-agers, you decide whether to do this based on the extent and degree of


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Trim overlap

Trim overlap

Top salt 1

Top salt 2

Base salt 1

za)

b) z

Figure 15. (a) Outline of an arbitrarily shaped salt body; two top-of-salt horizons (light blue) and one base-of-salt horizon (red) are shown. The top- and base-of-salt horizons are intentionally picked to overlap as shown to ensure the salt body is completely closed, and the overlap areas (dashed rectangles) are trimmed away prior to final depth migration. (b) Final salt-body outline; if using a workstation system with multivalued horizon functionality, this shape would be picked as a single horizon.


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salt complexity, the imaging accuracy required for acceptable definition of your targets, and the time and funding available for additional processing and interpretation. Figure 16 is an example of a final depth migration on which multiple top- and base-of-salt horizons for a relatively shallow salt body were picked using the technique illustrated in Figure 15. In actual practice, you would pick each horizon after the migration step specifically intended to image it. Ideally, you would need two separate applications of the sediment-flood–salt-flood sequence to most accurately image the salt overhang on the right-hand side of the body. In this example, the extent of the overhang is relatively limited, and a single application of sedi-ment flood followed by salt flood produces acceptable imaging as shown. However, this is not always the case; your decision whether to proceed with extra imaging steps will be project specific and not always simple or straightforward.

z

Figure 16. A 3D PSDM seismic line showing multiple top-of-salt (dashed blue lines) and base-of-salt (dashed red lines) horizons defining a salt overhang. These horizons are drawn with overlaps according to the technique shown in Figure 15 (courtesy PGS).


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On a depth-migration project in which you pick top and base of salt, you may occasionally observe a salt suture, a reflection that marks the bound-ary between two salt bodies which have merged or coalesced. The acoustic impedance contrast that gives rise to such a reflection can be a measure of the difference in rock properties between the adjoined salt bodies or between salt and sediments entrained in the suture zone. Accordingly, there can be portions of the suture zone across which there is no impedance contrast, e.g., clean salt against clean salt, so no reflection will be visible; the suture is not detected, but geologically it is still there. In these areas, you pick the suture, if you choose to do so at all, in model-guided fashion based on the shapes of the individual (unsutured) salt bodies or the trend of the suture where it is visible.

Figure 17a is a schematic of the correlations of three events you would pick to define two merged salt bodies: top of salt, base of salt, and salt suture. Figure 17b shows the top- and base-of-salt events that you pick for building a migration-velocity model for depth imaging; note that the salt suture shown in Figure 17a is not needed for imaging unless you intend to assign different velocity values to the two salt bodies. Figure 17c and 17d shows how you can define the two salt bodies separately using the salt suture and appropriate portions of the top- and base-of-salt horizons that you originally picked; in particular, note that the salt suture actually is part of the top-of-salt surface for the body on the left and at the same time is part of the base-of-salt surface for the body on the right. You construct these “geologic” top- and base-of-salt horizons by merging the suture with edited copies of the top- and base-of-salt horizons picked for imaging. As you can imagine, editing and merging horizons in this way requires care-ful data management, to say nothing of the demands on data management caused by picking salt (especially base of salt) on multiple seismic volumes processed with different migration algorithms, each having its own distinct imaging advantage.

The essential work of picking top and base of salt is unfortunately regarded by some as a bottleneck in the depth-migration processing sequence. For reasons such as poor image quality and the need for model-guided pick-ing, these correlations can often be “so difficult as to be impossible” (Dix, 1952) and thus cannot be done quickly and easily in every instance. It may be that the places in which the correlations are the most important are where they take the most time. Follow your own standards of quality in this work within the greater context of project schedules and availability of computer resources, and always strive to maintain a stable balance between these fre-quently opposing concerns. Never forget item 3 in the list of characteristics of an interpreter (Herron, 2003): You can tolerate the criticism that you are never working fast enough.


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Visualization

Visualization is seeing with the mind’s eye, and exploration for oil and gas has always required that geoscientists visualize subsurface geology. To do this in practice in the world of 2D seismic interpretation has always been difficult, and to communicate results and make recommendations to nongeoscientists has been even more difficult. You might say that the more experienced and talented interpreters have a native ability to visualize the subsurface clearly and to render their interpretations with well-drawn maps, cross sections, and block diagrams as well to fully use the power

Top of salt Base of salt Salt suture

Correlations a) b)

c) d)

z

Figure 17. (a) Schematic of top-of-salt (blue lines), base-of-salt (red lines), and salt-suture (green line) horizons picked for two merged salt bodies. (b) Top- and base-of-salt horizons picked for two merged salt bodies, the horizons used for velocity model building and depth imaging. The salt suture is not needed for imaging unless the two salt bodies are assigned different interval velocities. (c) Top- and base-of-salt horizons used to geologically define the individual salt body on the left in (a) and (b). The horizon originally picked as the salt suture is now part of the top of this salt body. (d) Top- and base-of-salt horizons used to geologically define the individual salt body on the right in (a) and (b). The horizon originally picked as the salt suture is now part of the base of this salt body.


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of technical language to communicate within and outside their discipline. The emergence of 3D seismic techniques and accompanying advances in computer capabilities has created space for you to develop your skills for interpreting seismic data and visualizing your results. There may never have been, nor should there have been, a sharp line between interpretation and visualization, and this is especially true now with the preponderance of 3D data. Whatever distinction exists between the two may continue to fade as technology moves ahead and 3D data are truly interpreted in three dimensions — time will tell. In the meantime, the need to visualize geology in seismic data and especially to use visualization to effectively communi-cate interpretation results will continue to be critical for exploration success.

Visualization of seismic data and interpretations has two primary objectives:

1) To manipulate seismic data in order to gain perspective and increase the amount of reliable geologic information to be derived from them.

2) To construct accurate 3D representations of geology derived from seis-mic images.

To address the first of these objectives, you can increase the vertical exag-geration of a display by reducing its trace spacing to enhance dipping reflec-tions, angular relationships, and faults. In the world of paper sections, a display with compressed trace spacing is affectionately and appropriately called a squash plot (Figure 18), a deliverable specifically requested from a geophysical contractor. If you do not have the time or funds to gener-ate these displays, which involves some effort in data processing, you can quickly and easily achieve the same visual effect by viewing your data from a foreshortened perspective. This involves looking at a seismic section along rather than across your line of sight, which causes the size of a feature’s dimensions on the section to appear relatively shorter, thereby enhancing angular relationships (see Figure 19). Many jokes about sleeping on the job originated in situations when an interpreter using this technique was observed laying his head down on a desk or work table to get a foreshort-ened view of his data.

You can now take the same perspective view of your 2D or 3D data in workstation applications that allow you to rotate an image or volume on a screen. You just change the angle between your line of sight and the image by moving the image; whereas with a paper section, you fix the image and move your head to change that angle. It is similarly easy to change the trace spacing or to vertically stretch traces on a workstation display to create a


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Squash plotOriginal display

Trace spacing = x/4Trace spacing = x

Figure 18. A squash plot of a seismic line is generated by plotting the line on the left with a trace spacing which is one-fourth that used for the original display.

a) b)

Figure 19. (a) Original display of the line shown in Figure 18. (b) A foreshortened display of the line shown in (a) that is created by rotating the original display in the direction indicated by the red arrow.


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real-time squash plot, although this is not foreshortening in a true sense (Figure 20).

A helpful technique for visualizing geology across a grid of 2D seis-mic lines involves laying out dip lines in sequence, on a worktable or fastened to a wall, and aligning them along a common reference such as the tie point to a particular strike line. This allows you to scan the sequence of lines back and forth to check for consistency of correlations and to see how the geology changes along strike. You can also take a foreshortened view of the sequence of lines. Do not wait until the end of a project to use this technique as a final visualization or quality-control step, but apply it whenever it can help you make correlations. The move-ment of your view from line to line in sequence is much the same as the animation of images on a workstation, the difference being stationary images and a moving observer versus moving images and a stationary observer. The use of motion in interpretation, whether of the interpreter or the images, is fundamental to our attempts to interpret and visualize in three dimensions.

In trying to represent the results of any seismic interpretation, you are faced with the singular problem of rendering 3D features on 2D displays.

a) b)

Figure 20. (a) A squash plot and (b) a foreshortened display of the line shown as the original display in Figures 18 and 19a.


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No doubt the best way to describe geology is to construct a true 3D scale model, but this is a very complicated and impractical solution, especially in view of the power and flexibility of available visualization applications. In a workstation setting, an important concern is to ensure that your interpreta-tion and visualization software are compatible. If they are incompatible, you have to reformat your data and interpretation products so they can be loaded into a visualization application. Ideally, both are part of the same system, but this is not always the case, depending on your company’s information technology (IT) architecture.

Maps, the most common products of an interpretation, are 2D render-ings of 3D surfaces that you create routinely by hand for projects involv-ing paper sections and by computer gridding and contouring applications for workstation-based projects. Other 2D products are fence and block dia-grams, which require good graphics skills to depict 3D geology accurately, especially to represent perspective (e.g., foreshortening) properly. Manually drawing these illustrations is a challenging task; many workstation systems have functionality specifically designed to create illustrations as part of your interpretation, and in many instances you can capture electronic images from your workstation screen. But without exception, you must ensure that all 2D representations of your interpretation, no matter how you make them, are accurate and comprehensible to those viewing them.

Interpretation processes and work flows

In a general sense, a work flow is an outline of the steps in a procedure for doing something. Because there is a certain amount of granularity in any outline, that is, what one person considers an individual step may to another person be the consolidation or merging of several steps, it follows that the details of work flows for a given interpretation project probably vary from one interpreter to another.

Table 1 summarizes individual interpretation steps or processes, and Table 2 is an example of a generic interpretation work flow. Interpretation work flows such as Table 2 should address all of the interpretation processes to be undertaken in a given project and thus must be designed to meet the specific objectives and requirements of that project. Given the wide variety of interpretation projects in our business, ranging from exploration through appraisal and development to production, it follows that there is no single


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work flow that can or should be applied routinely to every project. Rather, you should build your own library of work flows based on experience gained from different projects over the course of your career and from sharing expe-rience and exchanging effective practices with other interpreters.

As a seismic interpreter, you will devote much of your time to perform-ing the nuts-and-bolts correlation procedures described in this chapter. The summary of interpretation processes and the generic interpretation work flow presented in Tables 1 and 2 should be particularly helpful as you gain experience in the art of correlating seismic data, and you are encouraged to develop your own work flows and interpretive style. In seismic interpreta-tion as in the pursuit of any craft, you bring individual talents and insights to your work and should naturally strive to hone your skills and synthesize best practices throughout your career.

Table 1. Summary of interpretation processes.

Ensure you have all of your data (includes data review)

Establish well ties/select starting points for correlation

Correlate horizons and faults

Measure times/depths on sections*

Post values on (grid) map*

Contour map**

Perform quality control (QC)

Verify transfer of information from sections to map*

Ensure map is geologically reasonable

*Done automatically on a workstation**May be done automatically on a workstation


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Table 2. Generic interpretation work flow.

Establish objectives of interpretation

Make sure you have all data (and verify positioning of data)

Review acquisition and processing histories of data if available

Review data (use animation/visualization applications)

Make initial assessment of data quality

Determine structural style

Estimate strike/dip directions

Estimate phase and polarity of data if possible

If multiple data sets, establish reference data set

Establish starting points for correlation

Make well ties

Identify framework horizons and sequence boundaries

If multiple data sets, confirm or reselect reference data set

Correlate horizons and faults

Set up nomenclature system for data management

Determine size of correlation grid(s)

Determine need for manual versus autotracking

Determine need for seed (automatic) tracking versus interpolation of grid(s)

Review quality (QC) of all results


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153

Chapter 9

Data Quality and Management

The importance of data quality and management continues to increase as we address ever more demanding interpretation challenges and move irreversibly farther into the workstation environment. In the glare of techno-logical developments enabled by massive growth in computing power, we must not lose sight of the critical needs to assess data quality as an essen-tial element of every interpretation project and to manage the burgeoning volumes of data and interpretation products that threaten to overwhelm us. More than you may realize, these two concerns can affect the quality, timeli-ness, and ultimately the business value of your interpretation.

Data quality

A discussion of seismic data quality necessarily begins by defining exactly what is meant by “quality.” In its most general sense, quality is the degree to which something fulfills its intended purpose. All measures of seismic data quality are inherently subjective, so it is important to know why a particular data set was acquired and processed the way it was so as to set the proper context for assessing its quality. For example, you wouldn’t grade data from a conventional 3D survey purposely acquired and processed for deep exploration as poor quality because they aren’t suitable for evaluation-ing shallow drilling hazards. Similarly, you wouldn’t consider data from a 2D high-resolution shallow hazards survey as poor quality because they are useless for deep exploration (compare Figures 1 and 2). Given the purpose for a data set, you evaluate quality based on specific characteristics accord-ing to the degree to which the data set suits its purpose.

Assessing seismic data quality is one of the most important aspects of your job as a seismic interpreter. It is an expectation that you satisfy and a requirement that you meet in every interpretation project. Your ability to


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describe and effectively communicate your evaluation of data quality devel-ops over time as you gain experience, expand your knowledge of seismic data acquisition and processing, and broaden your exposure to different ele-ments of geology in a wide range of settings and environments.

At times, assessing data quality can seem to be no more than a beauty contest. In a comparison of several lines from the same area or of the same line with different processing sequences applied, the one that is most pleas-ing to the eye or looks most geologic or has the highest signal-to-noise ratio (S/N — perhaps the most common quantitative measure of data quality) or is visually dominated by smooth and coherent reflections and sharply defined faults is judged to have the highest quality. Although this view may often be correct, it should be taken only after you have reviewed the acquisi-tion and processing history of the data and placed the seismic image into the context of the geologic setting in which the data reside. After all, beauty can be only “skin deep,” and data processing has been known to turn the occa-sional sow’s ear into a silk purse. Your view of data quality takes shape and

Figure 1. Line from a conventional 3D time-migrated data set that was purposely acquired and processed for use in deep exploration (below 4.0 s). Compare this line to the 2D high-resolution line in Figure 2 (courtesy WesternGeco).

2.5 s

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Chapter 9: Data Quality and Management 155

Figure 2. Line from a 2D high-resolution (time-migrated) shallow hazards survey that was purposely acquired and processed to optimize resolution of the shallow section. Compare this line to the conventional 3D time-migrated line shown in Figure 1; both lines are located along the same surface track (courtesy BP).

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evolves throughout a project as you look at all of your data. So take care not to assess and communicate data quality until you have had the opportunity to view all of the available data. Similar to the perils of the one-line inter-pretation, assessing data quality based on the view of only one or a small number of lines from a larger grid or survey can be seriously misleading.

The three primary elements of seismic data quality are detection, reso-lution, and image fidelity (Schoenberger, 2004, personal communication). All of the time and effort expended in acquiring and processing seismic data in one way or another addresses one or more of these elements, with the intent of providing you, the interpreter, with as near to an ideal representa-tion of the true reflection seismic response to subsurface geology as possible (refer to the “ideal” seismic response in Figure 1 of Chapter 1). Detection is determination by the seismic method that a feature is present, or gives rise to a seismic reflection that is recognizable as signal above the level of ambient or background noise. The power of a data set to detect subsurface features of interest is often described in terms of its S/N. Resolution is the ability to resolve by the seismic method two features that are close together


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(refer to Chapter 6). The so-called resolvable limit, then, for discrete seis-mic reflectors is the minimum separation for which you can tell that more than one interface is involved in the observed seismic response (Sheriff, 2002). Finally, image fidelity is the degree to which the processed seismic response accurately depicts the true subsurface positions and magnitudes of seismic reflections. It consists of correct focusing and accurate positioning of reflections.

Usually you assess overall data quality using a range of subjective terms such as “excellent,” “good,” “fair,” “poor,” and “unusable” (avoid colloqui-alisms). You should be mindful of and prepared to discuss and illustrate which of the three elements of data quality dominates in your assessment. It is good practice in any interpretation project to map areal variations in data quality, remembering that quality very often will vary with time/depth as well. In other words, a single data-quality map for a particular project will not necessarily represent variations in quality and, ultimately, in confidence of picking from horizon to horizon.

A common product of mapping seismic data quality is shown in Fig-ure 3, in which a green-yellow-red or “traffic light” color sequence is used to represent quality grades of good-fair-poor. As with any geologic or geo-physical map, it is essential to include a legend on a data quality map to ensure that color and grade conventions are communicated clearly. You can construct maps such as Figure 3 by hand or in a workstation by pick-ing a horizon that reflects different grades of data quality. The workstation

Good

Fair

Poor

Figure 3. A “traffic light” data-quality map for an interpretation project area. The legend shows how the colors correspond to the different grades of data quality.


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Quality ofblue pick

Map view

Color table

Time(depth)

Figure 4. Constructing a data-quality map from quality picks associated with an interpreted horizon. The quality picks are made as a single horizon whose times (depths) or time (depth) ranges uniquely correspond to specific quality grades. The color table for the resulting map is designed to portray the different grades in a traffic light scheme according to the time (depth) of the quality picks.

approach involves manually tracking a horizon composed of quality picks whose values or ranges of values correspond to different grades of data quality (see Figure 4); the horizon could represent overall data quality or the quality or confidence associated with picking specific horizons of interest. This approach requires additional picking effort and concentration on con-tinuous assessment of data quality — well worth the time in terms of value added to overall interpretation results.

Data-quality maps can be used as stand-alone maps or as overlays to other maps created during an interpretation project. Do not consider any interpretation to be complete until it includes an assessment of data quality in the form of a finished data-quality map(s) and accompanying descrip-tive text in a final project report. Your conclusions about data quality are a critical element in characterizing uncertainty and risk for your interpreta-tion as well as providing a basis for recommendations to acquire or repro-cess data.


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As mentioned, grading the quality of a seismic data set is undoubtedly subjective and depends heavily on your experience and your knowledge of seismic acquisition and processing. At the same time and in the same way that you improve correlation skills by looking at as much data as possible, you also develop the ability to evaluate data quality and effectively com-municate the results of a quality evaluation. Simply stated, it is impossible for anyone who actively seeks to enhance interpretive skills and judgment to look at too much data.

Data management

Data management involves procedures and practices to help you keep track of the large quantities of data and interpretation products common in the oil and gas business. In the modern interpretation setting, data man-agement is primarily an electronic concern. In the preworkstation days, data management involved activities such as printing and storing maps and paper sections; losing, misplacing, or physically damaging maps and paper sections; sharing maps and paper sections with other interpreters; and nuisances such as running out of a particular shade of colored pencil or other critical resource. In retrospect, there were recognizable boundaries in those days — sets of tangible constraints that limited the amount of data and information a company or individual could amass. In contrast is the modern workstation environment, in which there appear to be few bound-aries, especially in view of the increasing power and efficiency of comput-ers and the seemingly unlimited amount of available electronic storage space. The quantity of data volumes and derived interpretation products continues to expand unchecked. It is more important than ever for you to manage data, that is, to bear responsibility and accountability for what to keep, where to keep it, how to find it later, and, perhaps most importantly, what to throw away.

The increasing number of depth-migration projects has placed special demands on the need for effective data management. These projects involve multiple stages of interpretation on several different migration volumes (sediment floods, salt floods, etc.), and in the most complex settings there may be migrations with different algorithms in a particular stage of the proj-ect. For example, as illustrated in Figure 10 of Chapter 5, there are a num-ber of different algorithms, each with its own technical and cost benefits, that you might use to address a difficult base-of-salt imaging problem in a subsalt interpretation project. In such a case, your final base-of-salt surface could be an amalgamation of horizons picked on several different data vol-umes into a single surface you would then use for depth migration. If you


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would ever need to refine or update this surface, you would have to know which part of it had been picked on which version of migration, which requires that you properly manage your horizons and migration volumes. In such a project, it will not always be clear which migrated volume affords the most accurate base-of-salt image, so your experience often will dictate which image is most nearly correct (or least incorrect) or most geologically reasonable in the sense that it is analogous to known geology.

Recommended practices for good data management are independent of specific workstation systems. Most commercial workstation systems include basic data-management functionality that varies only in specifics from system to system. The most important practice might well be self-restraint in creating and maintaining only those data volumes and products that contribute measurably to a project and that serve as parent entities from which you can recreate other volumes and products as necessary; this, of course, depends on the frequency with which the child entities are used and the amount of time it takes to recreate them. The practice also involves communication with other interpreters on the same project to avoid needless duplication of data and products.*

Nomenclature systems

Effective management of an interpretation project requires that, from the outset, you establish and maintain nomenclature systems for data sets and interpretation products. These systems must be intuitive, rigorous in the sense that they must contain core information without fail, and flexible or expandable to accommodate information that is unique to the type of data or product being managed. A well-designed system standardizes nomencla-ture for any type of data or product that can be categorized: trace data files, arbitrary lines extracted from 3D volumes, horizons, faults, mapping files, wells, and culture files (such as lease outlines and political boundaries). Within a system, you should subdivide any particular category in whatever way facilitates information storage or retrieval. It goes without saying that you should thoroughly document all nomenclature conventions and link this documentation to its project, even if there are corporate standards for nomenclature, in the same way and for the same reason that every map has a scale bar, north arrow (or some azimuth reference), and legend.

* See Herron (2001) for a more detailed discussion of seismic data management, with particular reference to the pitfalls involved in failure to manage multiple data sets effectively and the inability to conduct an integrated interpretation based on multiple data sets.


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A hypothetical nomenclature system for interpreted horizons in a work-station project might read as follows:

Each horizon name must have a minimum number of core elements, each delineated by an underscore (_). These elements must appear in the following order in each unique horizon name:

• Project identifier• Numerical designation for the approximate geologic age of the hori-

zon (this number increases with the age of the interpreted horizon and enables the horizon list to be displayed in order of horizon age)

• Color assigned to the horizon• Biostratigraphic age of the horizon• Name of the trace data file on which the horizon was interpreted (for

individual horizons only, does not apply to merged horizons)• Initials of the interpreter who picked the horizon

Following this convention, the components for a horizon named X2005_0600_green_A_pl20_ewfa04_DH would be defined as in Table 1. In our hypothetical system, a process such as interpolation, filtering, or attri-bute extraction applied to an original (parent) horizon would be specified in the horizon name after the designation of the biostratigraphic age or the trace data file but before the interpreter’s initials. In our example, the par-ent horizon smoothed with a 5 × 5 equally weighted spatial filter would be X2005_0600_green_A_pl20_ewfa04_sm5x5eq_DH.

Although it is common practice to name trace data files carefully in 2D and 3D workstation projects, many interpreters do not recognize the

Table 1. Components in a hypothetical nomenclature system for an inter-preted horizon named X2005_0600_green_A_pl20_ewfa04_DH.

Term Definition or meaning

X2005 Exploration project that began in the year 2005

0600 Numerical designation for the horizon

green Color assigned to the interpreted horizon

A Named lithologic unit (in this case, the designated “A” sand)

p120 Biostratigraphic age of the horizon (in this case, lower Pliocene)

ewfa04 Name of trace data file on which the horizon was interpreted (in this case, the final gained version of a PSTM data volume with a sample rate of 4 ms)

DH Interpreter’s initials


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importance of using consistent and intuitive nomenclature for arbitrary lines extracted from 3D volumes. You should assign names to these lines in such a way that another interpreter can easily grasp the purpose for which the line was created. For example, if an arbitrary line is a well-tie line, then somewhere in the name for that line should be the name(s) of the well(s) to which the line is tied. At the same time, you should specify the orientation of an arbitrary line in X-Y-space whenever possible. You can easily do this by naming the line according to its compass direction. For instance, if an arbitrary line is extracted in the northeast to southwest direction, then its name should include the characters N45E or S45W to indicate its azimuth. Compass directions are preferred over the standard abbreviations NE or SW because often the line direction cannot be described by these abbreviations, e.g., N60E or S30W. The operating philosophy in nomenclature schemes is to establish and maintain order and consistency in your data management — to eliminate obfuscation, if you will.

The importance of systems such as this cannot be overstated. The story is told of an interpreter who arrived at a new posting and asked which of the many interpretations in his new project was the best or latest or least incor-rect. No one could answer this question, and the previous interpreter had left no decipherable records (a problem in itself). So the interpreter did what almost everyone else would do: Judging that it would take longer to evalu-ate all of the existing interpretations than to do his own, he proceeded with his own interpretation. The unfortunate result was to further entangle the project with more picked horizons and faults, many of which were probably duplications of existing work.

This was not lack of data management; rather, it was data misman-agement. Eventually, data mismanagement has a negative business impact that may not become evident until many years and interpreters later. The excuses that “It doesn’t matter because some day this project will have no value” or “Don’t worry, we can get by” are unacceptable because poor data management is habit forming, and its effects can be latent, harmful, and unpredictable.


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163

Chapter 10

Other Considerations

In preceding chapters, we considered interpretation from the comple-mentary standpoints of seismic fundamentals and data quality, leading natu-rally to discussions of correlation concepts and procedures. This chapter addresses additional topics and issues you probably will encounter in the normal course of work as a seismic interpreter. Ranging from technical fields such as 4D seismic and seismic modeling to philosophical concepts such as interpretive judgment and the interpretation paradox, the following sections are intended to raise your awareness of these topics as building blocks in the foundation of your career. Also included in this chapter are several sections on professional development, the substance of which is based solely on the experience of the author.

Gridding and contouring

In the modern workstation environment, gridding and contouring inter-preted 2D or 3D seismic data are accomplished routinely and relatively quickly as automated processes. This is in sharp contrast to the previous generation of interpretation of exclusively 2D seismic data on paper sec-tions, in which gridding, the measurement of two-way reflection times to picked horizons at specific user-defined points, and contouring consumed a considerably greater fraction of an interpreter’s time and were dramati-cally more subjective and dependent on the interpreter’s individual skills and experience. Gridding of interpreted seismic data, even 3D data which are inherently “gridded” by the nature of their acquisition and processing, is now done primarily for two reasons: to facilitate automated contouring and to manipulate picked horizons (e.g., calculate the thickness of the interval between two horizons).


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Gridding programs vary in their degrees of simplicity and user friend-liness, but all have the common functionality of allowing you to specify the spatial frequency or grid spacing with which to sample your data in preparation for contouring or horizon-based computations (bearing in mind, of course, the fundamentals of spatial sampling according to the Nyquist theorem as discussed in Chapter 6). Most often you use these programs in iterative fashion, testing different combinations of gridding and contour-ing parameters and then visually assessing output in the form of contoured maps until achieving acceptable results. It is also important to ensure that the input grids for any calculation involving gridded horizons were gener-ated using common grid parameters. In addition to processing speed, grid-ding programs have the overwhelming advantage of precision; the same input data and gridding parameters always yield the same results. In the historical interpretation setting of 2D paper sections, so-called gridding parameters corresponded to selection of the interval between points along a line at which you measured reflection times. You defined this interval based on the dimensions of your 2D seismic grid and the dominant wavelength of features of interest as well as on data quality. As a natural consequence of machine-based picking of horizons and faults, the speed and precision with which you now measure or simply record reflection times or depths are vastly improved in the workstation environment—in comparison to the historical interpretation setting, where you had to measure times manually using an analog device such as a ruler or timing strip, for which your visual acuity and/or patience and attention span often factored into your precision.

In the historical setting, contouring a seismic horizon was very time con-suming and involved considerable artistic input and skill on the part of the inter-preter. It was the part of the interpretation work flow into which you blended style, based on experience, as well as sound knowledge and understanding of geologic principles. To this day, the inability to easily (if at all) introduce subjective elements of geology, i.e., trends or styles of contouring, into an interpretation is one of the most serious limitations of machine-based gridding and contouring. The freedom to incorporate style through contouring can be critical to an interpretation in the sense that contouring should be appropriate for the geologic setting of interpretation, effectively using the “empty space” between data points while honoring those points within their associated uncer-tainties. Tucker’s (1988) classic paper, which is very good reading even in our workstation-dominated time, refers to seismic contouring as a “unique skill,” the practice of which is becoming less common in the interpretation com-munity and is often nostalgically referred to as a “lost art.” His Figures 4–6 illustrate how the same input data can be contoured three different ways, each conveying a distinct message about the geology they represent.


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Chapter 10: Other Considerations 165

In spite of the power and flexibility to grid and contour data on a work-station, it is still good advice for you to know how to manually time and record reflection times and to contour a seismic horizon map because this experience affords perspective on the strengths and weaknesses of auto-mated processes and enables you to conduct an effective interpretation out-side of the workstation environment, if needed. Remember that gridding and contouring, whether manual or machine-based, are essential steps in every interpretation work flow. But also remember that the greatest value in any interpretation lies in the accuracy of your correlations and the geologic thinking incorporated into your work. Be wary of slick maps and presenta-tions, which are easy to create using automated gridding and contouring but can mask or smooth through errors and are not necessarily based on accu-rate, thorough, and consistent correlations.

4D seismic

In 4D seismic techniques, the fourth dimension, time, is added to the three spatial dimensions of conventional 3D seismic data. A more accurate description of the fundamental concept of 4D seismic is time-lapse seismic, referring to seismic data that are reacquired or repeated at a later time at the same location as an initial line or survey. In a rigorous sense, you can say that reshooting a 2D seismic line at a later time produces a 3D line, but you must be careful to specify what the three dimensions are.

The purpose of time-lapse seismic is to accurately measure true changes in subsurface conditions, such as reservoir pressure and pore-fluid content, which can be detected by the reflection seismic method. Successful time-lapse surveying requires that the repeat data be acquired and processed in a manner as similar as possible to the initial data to ensure that the time-lapse signal is not confused with or obscured by differences in acquisition and/or processing. This process requires exacting control over acquisition (source and receiver equipment, recording fidelity, and positioning accuracy) and processing (sequence and algorithms, very close QC) of the original and the time-lapse data.

The initial survey in a time-lapse project is called the baseline survey because it is the data set to which all subsequent work is referenced. The repeat survey, of which there can be several, is called the monitor sur-vey because it is used to measure or monitor changes in the subsurface that have occurred since the baseline survey was acquired. These changes can be seen by careful comparison of the position and character of reflec-tions and associated seismic attributes between the baseline and monitor surveys.


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Interpretation of 4D surveys and analysis of differences between base-line and monitor data are customarily done in one of two ways. The first way is to interpret the baseline and monitor surveys separately, followed by differencing interpreted horizons. This approach allows you to study time shifts and attribute differences between baseline and monitor data. The time shifts are manifestations of geomechanical changes, such as stretching overburden and compaction of reservoirs, and can be up to 8–10 ms (e.g., from producing Pliocene-Pleistocene turbidite reservoirs at subsea depths of 10,000–15,000 ft [3048–4572 m] in the Gulf of Mexico; see Ebaid et al. [2008]). The amplitude/attribute differences are the result of impedance changes related to variations in pore pressure as well as fluid type and satu-ration within producing reservoirs. The amplitude/attribute changes can be quantified and analyzed with stacked or prestack seismic data in the reflec-tivity or acoustic/elastic-impedance domains.

The second way is to interpret a difference survey, created by subtract-ing the baseline survey from the monitor survey, done after any static differ-ences or time shifts between the surveys have been purposefully removed in data processing. This difference survey necessarily focuses on amplitude/attribute changes and significantly reduces the amount of time devoted to interpretation because only one survey, rather than two, is interpreted. How-ever, the method places an additional requirement on the fidelity of data processing for accurate statics corrections. A difference survey clearly is not useful for studying 4D time-shift effects.

The choice of approach is up to the interpreter, obviously in coordination with processing geophysicists. In creating a difference survey, differenc-ing by subtracting baseline data from monitor data, rather than subtracting monitor data from baseline data, is by convention only.

In the simplest terms of normal incidence or stacked-trace response, the measurable change in acoustic impedance (AI) caused by reservoir production is positive; the AI increases as the reservoir “hardens” when reservoir pressure decreases or water saturation increases. The change in AI is negative, that is, it decreases as the reservoir “softens” when the res-ervoir pressure increases, as in response to water injection or when free gas enters the reservoir pore fluid by exsolution (referred to as gas breakout). Note that an increase in reservoir pressure, which by itself softens a res-ervoir, can harden that same reservoir by dissolving gas in the pore fluid. So there can be opposite impedance effects to resolve when analyzing the impedance changes in a reservoir undergoing artificial pressure support. Analysis of impedance changes can be done in the prestack domain, a topic beyond the scope of this text. In any case, quantitative interpreta-tion of time-lapse seismic should proceed only with careful calibration to


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well control from the producing field over which the time-lapse data were acquired.*

Seismic modeling

Although details of seismic modeling are beyond the scope of this text, several comments of a philosophical nature are in order.

Modeling is a topic for which approximations play a critical role. Mod-els should be built with a complexity neither greater than nor less than that required by the detail of investigation at hand. (As noted by Albert Einstein, “The model should be as simple as possible, but no simpler” [Hawking, 2010].) For example, there is no point expending the time and energy to construct a 3D model to study a structural problem that could be adequately addressed with a 2D model. In other words, a 2D approximation can be quite satisfactory for some problems. Similarly, one should not expect a 2D modeling study to shed meaningful light on a complex 3D problem. You are responsible for determin-ing when and how approximations are to be applied in modeling studies.

Forward seismic modeling is done to investigate the expected seismic response from a given subsurface model. A forward model can help guide or evaluate the acquisition and/or processing of actual seismic data from a set-ting that the model ideally represents, or it can be used to help decide among alternative interpretations by comparing the results of modeling to those possible interpretations. Forward modeling is the opposite of inverse seis-mic modeling, in which actual seismic data are used to determine a model that could have given rise to the observed seismic response. As opposed to forward modeling, inverse modeling is nonunique.

The validity and utility of any modeling results depend critically on the model input; remember, garbage in, garbage out. As mentioned in Chapter 1, geoscientists should not allow their work to be driven by models; to do so is to put the objectivity essential to all interpretations seriously at risk. In the words of Stephen Jay Gould (1995), “Models in science are judged as useful or detrimental, not as true or false.”

Interpretive judgment

In any field of conscious endeavor, increasing opportunities to exer-cise judgment come with experience, and correlating seismic data is no exception. Experience is having been put to the test, which involves having

* A very good reference for 4D seismic principles and applications is Calvert’s Insights and Methods for 4D Reservoir Monitoring and Characterization (2005).


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learned by doing, from both success and failure (but probably more last-ing and meaningfully from failure; see Petroski [1992, 2008]), and having developed confidence in one’s abilities and acceptance of the fallibility of individuals and groups. You will regularly be called upon to use experience-based judgment in choosing approaches to solve interpretation problems and decide about seismic correlations — not the correlations that “almost anyone can understand,” but those that are “so difficult as to be impossible” yet must be attempted. On any given day, you might make hundreds of cor-relation decisions, some much more challenging than others, giving few or none of them a second thought. But as you go about your work, you contrib-ute, often subconsciously, small bits of knowledge from each of your proj-ects to your ever-broadening and deepening bank of experience, building an interconnected network of decisions and results that is stimulated and trig-gered into action by new problems. This network enables and fosters cre-ative thinking and serves as a source for synthesizing innovative approaches to problem-solving, without which neither the science nor the art of seismic interpretation can advance and flourish.

Figure 1 is an example of using judgment to solve a correlation problem. Figure 1a is an image taken from a 3D isotropic PSDM volume on which a shallow salt sheet is clearly seen. In spite of the good to very good quality of this line, there is at least one major correlation problem: how to correlate events in the extrasalt section on the right through a disrupted zone below the edge of the salt sheet into the subsalt section on the left. Immediately, you should wonder if the disruptions or discontinuities are faults or possibly depth-migration artifacts. Is there more to the appearance and location of these features than mere coincidence, and what in your store of geologic and geophysical knowledge and experience will help you decide? Observing the steepness of the edge of the salt sheet and the associated overlying seafloor scarp, both of which necessitate depth migration for accurate imaging, you reason that any errors in picking the seafloor, top of salt or base of salt, and/or assignment of seawater, sediment, and salt velocities might have caused imaging errors that are manifested as the disruptions. But there is neither time nor money available to revise the depth-migration velocity model and remigrate the data; you must proceed with an interpretation with the data in hand.

So you make a jump correlation into the subsalt section and try to corre-late the disruptions as faults through the 3D volume. In doing this, you find that the “faults” form a pattern that can be interpreted as geologically rea-sonable or inferred to be spurious because it coincides closely with the trend of the salt edge and does not fit with known fault trends elsewhere in the study area. If, for the sake of argument, you conclude that the disruptions


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Figure 1. (a) A 3D PSDM line on which reflection continuity is disrupted below the steeply dipping edge of a salt sheet. (b) The dashed blue line illustrates simplified correlation of an arbitrarily chosen reflection from extrasalt to subsalt through the disrupted zone below the edge of the salt sheet. This correlation is based on your judgment that the disrupted zone is a manifestation of inaccurate depth imaging and that the true reflection configuration is best represented by the smooth correlation as shown (courtesy PGS).

z

a)

z

b)


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are indeed migration artifacts, then you must think about how to carry your correlations through the disrupted zone and how to map them. You know that there is greater correlation uncertainty in the disrupted zone than else-where on the line, even if you are confident in your jump correlation into the subsalt section. You decide to make a smooth correlation through the zone as shown in Figure 1b, realizing that you might be missing some details of real structure that are obscured by what you have interpreted as imaging artifacts. At the same time, you consider the possibility that there are other, more subtle problems in the image, such as the sharp flexure in the extrasalt section immediately to the right of the edge of the salt sheet. You will have to address this when you create a final true depth map.

You are aware that you have not conclusively solved this correlation problem; rather, you have developed a solution that is geophysically con-sistent and geologically reasonable in view of the available data and in light of your experience. You are fully cognizant that more data, perhaps a remi-grated depth volume, some well control, or more mapping of regional fault trends, might cause you to revise your correlations and maps.

Curiosity and interpretive thinking

The very first item on Herron’s (2003) neither exhaustive nor exhausting list of characteristics of an interpreter is that an interpreter is naturally curious about the earth. This is much the same as Dix’s (1952) exhortation for geo-physicists to look at seismic records “with the assumption that every event has a significance that we can find and that is worth finding.” This persistent inquisi-tiveness translates into a manner of thinking for the practicing interpreter — not a bias or rigidity in approaching problems, but a continual querying of data in seeking to understand the full meaning of observed seismic responses. Follow-ing is an example of one line of inquiry, not the “right” one or the only one, that you might follow in trying to explain what you see on the image in Figure 2.

You recognize immediately that you must find out how these data were processed in order to establish context for what you see. In other words, are you looking at time- or depth-processed data, and is this a final migration or the output from an intermediate stage in the processing sequence? The most obvious feature on the line is a relatively flat-lying, lozenge-shaped body in the center of the image that has three candidate reflections (labeled 1, 2, and 3) defining its base. There are essentially no continuous reflections below the deepest of the three possible base-of-body events, and the overall strength of reflections below the body is very low. You ask a few questions about regional geology and learn that there is active salt tectonism where the data were acquired. From this, you infer that the lozenge-shaped feature is


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a slice through a salt body of unknown shape. You know that time-domain imaging is likely to fail in this area owing to the large lateral velocity con-trast between the relatively shallow salt and surrounding sediments, so you suspect that you might be looking at a depth-processed image, perhaps the output of a sediment-flood migration, because the top of salt is well imaged but three possible base-of-salt reflections are visible. Only one of these three reflections, all of which originate at the left-hand terminus of the body, can be the primary base-of-salt reflection.

Processing geophysicists confirm that you are indeed looking at a line from a 3D sediment-flood (depth) migration. Although the upcoming salt-flood migration will reveal the position of the true base of salt, you are nev-ertheless curious about the origins of the three events you see: Which is the primary base of salt reflection, and what are the other two? You reason that the shallowest of the three possible base-of-salt reflections, event 1, is prob-ably the true base of salt because any nonprimary reflection would have a greater traveltime than the primary reflection and so would occur deeper on the depth-migrated image. You realize that if you have a well through this salt body very near to or on this line, you could use the sediment-velocity model

Figure 2. An example line extracted from a 3D survey for which you might be asked to make and then explain your observations. For the sake of argument, you begin with no knowledge of the processing that has been done to the data (courtesy WesternGeco).

1

2

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and a reasonable value for salt velocity to estimate the depth to the base of the salt on this image. But you check your database and find no such well.

You proceed, assuming that the deeper candidate reflections, events 2 and 3, are not primary reflections unless one or both of them is an intrasalt reflection. You propose that either or both them are nonprimary reflections, perhaps multiples. But multiples of what? If they are multiples, then they were not removed by demultiple processing, but you don’t know what, if any, demultiple processing has been done. You can’t predict positions of multiples as you might if you were working with time-processed data (see Chapter 7), so you must estimate these positions in depths for traveltimes through the sediment-velocity model along possible nonprimary travel paths. You suspect that the polarity of these events might help determine which is which because the polarity of a seafloor/top-of-salt or a base-of-salt/top-of-salt/base-of-salt peg-leg multiple would be opposite that of the primary top-of-salt event. The character of the seafloor and top-of-salt reflections suggests that the data are not exactly zero phase, but their char-acter is distinctive enough that you can distinguish events of opposite polar-ity. You eliminate peg-leg multiples involving the top or base of salt and an extra reflection from the seafloor or any suprasalt reflecting surface. Such a multiple would roughly mimic the top- or base-of-salt event rather than fan downward from the edge of the salt body as you have observed. A peg-leg multiple involving the water column would occur much deeper on the image than the observed base-of-body reflections do.

Your initial hypothesis is that event 3 might be an intrasalt multiple reflection: a base-of-salt/top-of-salt/base-of-salt peg-leg multiple. Figure 2 shows that the polarity of this reflection is opposite that of the top of salt reflection, as it should be. Proceeding cautiously from this tentative identifi-cation of event 3 as a peg-leg multiple, you now try to explain the nature of event 2, which has the poorest continuity and weakest amplitude of the three events. You reason that this reflection must involve a travel path within the salt body because its origin coincides with the edge of the salt body like the proposed peg-leg multiple below it. You formulate three possible explana-tions for event 2:

1) It is a primary reflection from the base of a layer of unknown lithol-ogy, the top of which is apparently conformable with the true base of the salt marked by event 1. This layer thickens away from the edge of the salt body, at least in the plane of this section, and you don’t have a simple geologic explanation for this configuration.

2) It is the primary base-of-salt reflection, and event 1 is a primary reflec-tion from an intrasalt interface (you could make a similar argument for


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event 3 being the primary base-of-salt reflection and events 1 and 2 being primary reflections from intrasalt interfaces).

3) It is a base-of-salt reflection, but one leg of travel through the salt involves propagation of energy as a shear wave (S-wave) converted from incident compressional (P-wave) energy. By observation alone, you can’t tell at which interface (top or base of salt) the mode conver-sion takes place.

The third explanation for event 2 is quite plausible. Although you don’t usually think about S-waves when working with marine seismic data, you know they are a real physical phenomenon. Using an estimated S-wave velocity for salt, you find that the calculated position of a reflection involv-ing converted wave energy is fairly close to the observed position of event 2. Unable to think of a simple geologic explanation for this event as a P-wave reflection, you conclude that it involves mode conversion and propose that a modeling study be undertaken (time and funding permitting, of course) to understand the physics of its origin. At the same time, you revisit your previ-ous thinking and wonder if event 3, which you originally thought might be a peg-leg (P-wave) multiple, might also be a converted wave reflection, in this case having converted from a downgoing P-wave to an S-wave at the top of salt, reflected from the base of salt as an S-wave, and then converted back to an upcoming P-wave at the top of salt. This explanation also is consistent with the observed polarity of event 3. Because you don’t have access to prestack data (depth gathers) that might help you identify these events based on move-out correction, you prudently decide to reserve final judgment until your modeling study is completed. Even if the modeling is never done, for the time being you leave your questions unanswered but not forgotten.

In speaking with other interpreters, you learn that this triad of base-of-salt reflections has been observed in other depth-imaging projects and is common (you might recall having seen a similar set of three reflections in Figure 14 of Chapter 7). On the salt-flood migration for your data (not shown in this text), you observe a single, sharply focused base-of-salt event, and the other candidate base-of-salt reflections have disappeared as you expect. On the final depth migration (also not shown), you see that reflec-tions extend smoothly from the extrasalt area on the left in Figure 2 into the subsalt portion of the image. One of your fellow geoscientists also refers you to a published case study (Ogilvie and Purnell, 1996) in which mod-eling results and real 3D data confirm the occurrence and significance of mode-converted waves in subsalt exploration.

The preceding discussion is a single example of the chain of intercon-nected observations, inferences, hypotheses, and tentative conclusions that


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often forms during the course of an interpretation. This chain is pinned by hard data (e.g., a control point for the base of salt in a well) and is as strong as the validity of your observations and sound application of physical and geologic principles. In this example, the salt-flood and final 3D migrations validate identification of event 1 on the sediment-flood migration as the true base-of-salt reflection, but you need the results of further studies such as forward seismic modeling before you can conclude which of your tentative interpretations of events 2 and 3 is correct.

The interpretation paradox

An interpreter is naturally curious about the earth — a statement not made in jest. When coupled with your problem-solving skills, this very per-sonal characteristic eventually leads you to deal with the interpretation para-dox: When interpreting seismic data, you think that more control will enable you to solve your interpretation problems; however, more control, although helping to solve old problems, tends to uncover new ones.

This paradox often manifests itself in statements such as “If only this line were a little longer” or “If only we had extended this 3D survey a little farther” or “If only I had a little more time to work on this” or “If only we had drilled the well a little deeper.” The common phrase in these statements is “a little,” representing that quantum of information or time you wish you had. Unfortunately, because you are curious and because the earth has so many secrets awaiting discovery at scales large and small (and of course because seismic response is never ideal), you will always be able to find several interpretation problems worthy of your attention in virtually every project. But temporal and financial resources are finite; and since you can’t afford to lose sight of your project objectives, whether in business or aca-demia, you can’t pursue every interesting and potentially rewarding line of investigation that presents itself. Your experience will guide you in choos-ing which problems to address and which to defer. You will learn how to avoid unwarranted “science projects” (disparagingly called by some) and how to argue effectively for and commit to worthwhile studies — and how to decide between the two.

Approximations

Approximations are essential to good geologic thinking. Consider the staggering number of approximations involved in attempting to under-stand geologic time in terms of a human lifespan and how to consistently


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visualize geologic processes (such as seafloor spreading and plate con-vergence, deposition of deep marine sediments, diagenesis, hydrocarbon migration) that operate at vastly different rates and scales. As discussed and implied throughout this text, indirect measurements of the subsurface of the earth by the reflection seismic method necessarily involve approxi-mations in physics, mathematics, and geology. For example, Chapter 3 describes several important approximations in the basic theory for invert-ing reflectivity data. To be successful as a geoscientist, you must be cre-ative within a framework of approximations built by science and your own knowledge of your craft. This is expected of you, especially in exploration, but is more easily said than done.

In his lecture titled “The Uncreative Scientist” given in 1967 at the Uni-versity of Chicago, physicist Richard Feynman attempted to link the con-cepts of creativity and approximations:

In particular, there are one or two skills that I see in the creative physics students — but I think it applies more generally — that are missing in the noncreative ones. And that skill is how to deal with approximations. This is very difficult to teach because it’s an art. Noncreative students can’t learn it — it goes back to the business of them wanting something exact when what they really have to deal with is approximations.

You must approximate if you’re doing physics because what you’re doing is to take one view of a nearly hopelessly complicated situation. Nothing is simple. The world is enormously complicated … . But the fact to appreciate is that you do approximations all the time … . I’m just trying to show you that in the simplest situations — in every situation — approximation is necessary in order to think.

Feynman’s words echo the admonition offered by C. H. Dix (1984) in an interview for The Leading Edge: “After all, the earth is a nonhomogeneous mess and no matter how hard you try not to smooth it out or idealize it in your processing, you have to do some of that. So eventually you’re going to have to use your instinct.” Feynman’s “approximate” and Dix’s “smooth it out” say much the same thing, and you are well advised to heed the words of these scientists as you proceed with your work.

Although not an approximation in the strictest sense, the concept of significant figures is frequently ignored or abused in computations based on the results of seismic interpretation, such as time-to-depth conversion, esti-mation of hydrocarbons in place, and calculation of recoverable reserves. Hence, it is important to remember that accuracy is freedom from error


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(closeness to true value) and precision is repeatability (see Figure 3). A measurement system or calculation is valid if it is accurate and precise.

By definition, the significant figures of a number are those digits that carry meaning contributing to its precision, including all digits except for (1) zeros serving as placeholders to indicate the scale of the number and (2) spurious digits introduced, for example, by calculations carried out to greater accuracy than that of the original data or measurements reported to a greater precision than the equipment supports. Always be careful to honor item 2 to avoid communicating false accuracy in your calculations. At the same time, you should always specify the ranges of uncertainty associated with your measurements and calculations.

Uncertainty and risk

You should always take time to characterize the uncertainty associated with the results of a seismic interpretation because the risk of any venture based in part or in whole on those results will depend on your assessment of data quality and measure of uncertainty. For these reasons, the difference between uncertainty and risk must be considered and clarified as much as possible.

With reference to Figure 3 and according to Sheriff (1992), uncer-tainty has to do with the precision with which a measurement is known and does not necessarily imply anything about accuracy. This description of uncertainty as a quantitative measure is complete; but because seismic interpretation is very much a subjective and qualitative activity, in char-acterizing uncertainty you must allow for nonquantitative aspects that do in fact carry implications about accuracy. For instance, because seismic response is most often if not always nonunique, it is possible that a very

Figure 3. Representation of the concepts of precision and accuracy (with permission from Wikipedia).

Reference value

Probabilitydensity Accuracy

Precision Value


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reasonable interpretation can represent several significantly different geo-logic models, any one of which by itself can be confidently and precisely determined in quantitative terms. So the uncertainty of any seismic inter-pretation includes the precision of measurements made and calculations based on its results as well as the degree to which it fits a range of geologi-cally reasonable models.

Implicit in this view is the notion that you should place interpretive results within an established quantitative range of uncertainty and, in doing so, be sure you clearly understand and communicate the meaning and limits of that range. Perhaps the most common measure of uncertainty is specify-ing an error bar or plus-or-minus description, usually expressed as the varia-tion about a mean value (“The predicted depth to the formation is 20,000 ft [6096 m] plus or minus 2%” or “The predicted depth to the formation is 20,000 ft [6096 m] plus or minus 400 ft [122 m]”). In any real measure of an interpretive result, you are expected to know all of the factors that control the magnitude of the uncertainty and how you assigned their values. Your estimation of uncertainty doesn’t need to be symmetric about a mean or most likely value because the individual factors contributing to uncertainty, especially those of a geologic nature, are not necessarily symmetrically dis-tributed in the earth.

Intimately associated with uncertainty is the concept of risk, which involves assessing the state of uncertainty, or lack of certainty, regarding the occurrence of a given outcome within a range of possible outcomes for a condition or activity. Risk is most easily expressed qualitatively by degrees within a range using subjective terms such as “low,” “medium,” or “high.” The danger is obviously that these words don’t have the same meaning to all who will base further computations or decisions on them (you might say that their meanings are not precise). In probabilistic terms, you say that cer-tain outcomes have a greater or lesser chance of occurring. In the petroleum industry, this translates into the ever-optimistic determination of probabil-ity of success, which communicates the likelihood that the results of your integrated interpretation, if acted upon, have a specified chance of finding oil and/or gas within the ranges of the location and the quantity you have determined. Remember always to speak of ranges rather than single-point values because you have uncertainties associated with the proposed location and quantity. Difficulties arise in attempting to assign quantitative measures to qualitative states of risk, and risking systems most often are established using agreed-upon numerical values or ranges of values associated with what are inescapably qualitative assessments. These qualitative judgments can be tempered based on a study of a large number of known outcomes and the factors contributing to them.


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Every measurement or calculation from a seismic interpretation has its associated uncertainty that must be communicated as part of the interpreta-tion. Keep in mind that risk assessment is a qualitative process and must include consideration of seismic data quality and interpretation uncertainty; it is heavily influenced by experience and is meaningfully communicated only within a system of conventions that objectively assigns numerical val-ues for risk to ranges of possible outcomes.

This book does not address how risk systems are developed and applied, largely because they are most often built as proprietary systems by individ-ual companies. It is sufficient to say that all risking systems include at least three primary risk factors: source, reservoir, and trap. Each of these can be subdivided into a number of interdependent subfactors; for example, “res-ervoir” could be subdivided into “reservoir presence” and “reservoir effec-tiveness.” Any one or more of what one company might consider to be only one component of a primary factor could in itself be thought of as a primary factor by another company. The interdependencies among factors and sub-factors, however defined, would be determined within each risking system.

The workstation environment

Undoubtedly, most seismic interpretation will be done in a workstation environment for the foreseeable future, and the power and sophistication of data processing and interpretation applications will continue to evolve. The history of oil and gas exploration and concomitant advances in sci-ence and technology are such that you always will be engaged primarily in the business of describing geology. What will continue to change are the tools you use and the nature of the environment in which you use them. Among all of the changes that have taken place, the move from working with paper sections to electronic images on workstations has had the most far-reaching consequences, especially by replacing many tasks formerly done by hand with vastly more efficient and reliable automated processes that have enabled interpreters to reap the considerable rewards of increased productivity.

But can it also be said that some situations remain in which interpretation in the old paper section or analog domain is preferable to working in the digital domain of the 21st century? In seeking to answer this question, it is fitting to review the strengths and weaknesses of the modern workstation environment (see Table 1). The last item under workstation weaknesses is perhaps the most important of all. You are a craftsman, and the quality of your work is measured by your judgment and skill in using your tools. The workstation is the most powerful and versatile tool you have, but it is not the only tool in your kit.


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Your job requires that you continually improve your skills in using your employer’s workstation system of choice. To the degree possible, you should become “fluent” in working with as many different systems as you can — somewhat akin to learning several languages. All viable workstation systems include the same sets of tools needed to perform basic interpretation tasks. The differences among systems are for the most part manifestations of the uniqueness of each system’s fundamental architecture, which in turn reflects the approaches to interpretation of the geoscientists and software engineers who designed the system. You will hear the term functionality used to describe what you can do on a particular workstation. You must remember that as an interpreter you decide what you need to do in your interpretation project (that is, you develop your own work flows), so you should never become a slave to workstation functionality. Always be on the lookout for ways to increase and improve the functionality of the workstation system on which you work.

“Will the computer replace the interpreter? In our opinion, this is unlikely.”

—Etgen et al. (2009)

Ergonomics

Ergonomics, the science of designing the workplace environment to fit the user, is especially important in the modern workstation interpretation

Table 1. Strengths and weaknesses of the modern workstation environment.

Strengths Weaknesses

• Organization of large volumes of data (especially 3D data)

• Integration of different types of data

• Mechanization of processes formerly done by hand (e.g., event picking, timing, posting, contouring)

• Speed, power, and precision of individual operations

• Flexibility of display, real-time display modification

• Prompt documentation of interpretation work (e.g., electronic screen captures)

• Misapplication or abuse of poorly understood technologies

• Reliability of hardware systems and software

• Seismic title blocks (acquisition and processing parameters) usually not displayed

• Limitations on interpretive perspective and capacity to incorporate individual style into interpretations

• Inability to represent subjective elements (uncertainty) in interpretations

• Lack of appreciation of workstation limitations, and perception that the workstation is a panacea, not a tool


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setting. In the historical setting of interpreting paper sections, your ergo-nomic concerns might have included preventing back strain caused by lean-ing over a drafting table for too long, bending over to pick up a pencil that you had dropped, or keeping from cutting your finger on the sharp edge of a seismic section. Now these concerns involve conditions such as maintain-ing proper sitting height, posture, and positions of your hands and forearms; having your workstation monitor(s) set at the appropriate height; and ensur-ing good lighting. Lighting has always been important because you can eas-ily stress your eyes whether working on paper sections or at a workstation; in fact, this may be an even more serious matter in the workstation environ-ment because the effect of looking at a computer screen for hours at a time not only can be tiring but also hypnotic. Many new interpreters become so engrossed in their work that they are genuinely surprised when told that they have been sitting at their workstations, intently correlating horizons and faults, for two or three hours without a break. So in addition to having a well-designed ergonomic setup for your workspace, you should also dis-cipline yourself to take periodic breaks for your mental as well as physical health, both of which contribute to your effectiveness as an interpreter.

Most companies provide ergonomics consultation regularly, usually in conjunction with an office move, or by request; you should take advantage of this service. Ergonomics is not just corporate-speak. It is a personal mat-ter that you should take seriously.

Presentations

Many professionals consider presentations to be the bane of their exis-tence. Like taxes, they are necessary and unavoidable, and you can’t just hire someone to do them for you. The following suggestions for effective presentations are born of the experience of inadequate preparation, untimely scheduling, failed projector bulbs, nervousness, poorly composed slides (especially the dreaded word slides), restless or inattentive audiences, hid-den agendas, and many other alternately frustrating and comical episodes too numerous to recount.

The objective of every presentation, independent of its content, should be unambiguous communication of information. In any presentation, you should do the following:

• Introduce yourself; some in the audience may not know who you are.• Clearly state the title and objective(s) of your presentation.• Tell your audience what map scale(s) you are using, and give a refer-

ence direction (the proverbial “north arrow”) before being asked.


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• Describe the phase and polarity (with reference to an accepted polarity convention) of all seismic data shown in the presentation before being asked.

• Be able to discuss the acquisition and processing history of your data.• Be prepared to discuss the control used to make every map you show.• Describe every map symbol you are using, and include map legends

as needed.• Avoid garish or kaleidoscopic color tables for displayed lines and

maps.• Compose graphics/slides to be as simple as possible.• Maximize the “data-ink ratio” of your slides: Use every “drop of ink”

on the slides to convey measured quantities and information, and mini-mize words, lines, arrows, and other redundant or extraneous annota-tion (see Tufte, 2001).

• Never use a long word when a short one will do (very good advice from George Orwell [1946]).

• Don’t be afraid to say “I don’t know” when you don’t know something.• Always acknowledge the work of others when you use it.• Look at your audience, not at the floor, ceiling, walls, or projection

screen.• As trite as it sounds, “Tell them what you’re going to tell them, tell

them, and then tell them what you told them,” and don’t do so in a condescending manner.

• Build flexibility into a presentation, even a short one, so you can adjust your delivery as you continually assess the attention span and interest level of the audience.

In summary, give a presentation in exactly the same way that you would want to see and hear the same presentation from someone else. An effective presentation should be long enough to cover all of your essential points yet short enough to maintain the attention of your audience. No doubt there are aspects of marketing in the presentation of interpretation results, but thorough, accurate, and consistent technical work, communicated as such, effectively sells itself, whether in getting you into good business deals or keeping you out of bad ones.

Career development

The progression of formal and on-the-job training or learning that is best suited for developing a career as a seismic interpreter has been discussed many times by interpreters young and old and by supervisors, functional


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managers, and training managers. This issue can be broken down into three questions:

1) Are there ideal academic qualifications or degree programs for a career as an interpreter?

2) Should you begin your career in an interpretation assignment, that is, on a project for which you actually begin correlating seismic data, or should you begin in a data acquisition or data processing assignment?

3) Where in the exploration-through-production value stream is the best place for you to begin your career?

This text does not propose a “best” learning and development sequence for new interpreters or comment on how companies achieve their training and career development objectives — why the structure and content of in-house training programs are what they are. Instead, we discuss the questions above in terms of the strengths and shortcomings of alternative answers to each. This is especially appropriate because each interpreter brings unique talents and personality to the job, so there is no one-size-fits-all course of learning and development. The best training sequence should be tailored to individuals within a broader and more generalized framework.

The first question might be rephrased as, “Must you have a degree or degrees in geology to be a good interpreter, whatever ‘good interpreter’ means?” The embedded question here is about the level of formal education required, as is the case for some companies, or the level shown by historical performance to be best for the job. The short answer to this question is “no.” It might be said that if you have talent in visualization and problem solving, regardless of your specialization in formal education, then you have a good foundation for interpretation work and can learn what you need to learn about physics and/or geology on the job. This of course does not consider personality traits that might favorably incline you to the demands of inter-pretation work (see Herron, 2003). At the same time, it would be foolish not to recognize that maximum exposure to geology in the classroom and in the field, as early and often as possible, is of great benefit to a career in interpre-tation — highly recommended, but not absolutely required. Labeling a per-son as an interpreter rather than as a geologist or a geoscientist should never prevent a person from going on field trips that provide first-hand experience with the geology expected to be seen in seismic data. Also, the titular for-mality of geologist versus geophysicist should be abandoned; a person who works with seismic data other than for highly specialized purposes should be a geoscientist. The title “interpreter,” unless applied in a very informal sense, should probably be discarded.


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The second question involves two schools of thought, one of which is that if you’re hired to be a seismic interpreter, then you should begin cor-relating seismic data as soon as possible, given a certain amount of basic training beyond your formal education, so you can begin to build hands-on interpretation experience early in your career. In this scenario, you are expected to acquire working knowledge of data acquisition and processing on the job, with augmentation by way of formal industry-sponsored or in-house instruction. The opposing school is that, as a new hire, you should intentionally learn about data acquisition and processing at the outset, pref-erably in an assignment to an acquisition or processing group, because you should not begin to correlate seismic data until you have sufficient experi-ence with the processes and techniques that control data quality.

There certainly are merits to both views, and the decision on which approach to take may be based not so much on what’s best for you but, unfortunately, what an individual company has determined is best and possible in terms of budgets, human resources, and business demands. In a perfect world, the concerns of both schools might be satisfied if interpre-tation projects were organized and run so that data acquisition, processing, and interpretation were truly integrated by a team of geoscientists with the requisite experience in each discipline. On this team, you as a novice interpreter (geoscientist) could learn about each discipline through master/apprentice relationships with more experienced team members, and this learning could be supplemented by timely formal classes. In any case, you have to stay active in your first few assignments, building and maintaining good working relationships with senior technical staff to ensure that you are gaining breadth and depth of experience as well as receiving the for-mal training you need. Your development objectives and a plan to accom-plish them should be established and agreed upon according to company procedures and guidelines. Most companies have training or experience-broadening programs, but the ultimate responsibility for career develop-ment is yours.

The third question is related to the function of the business unit or group to which you are initially assigned, ranging from exploration (including regional studies) through appraisal and development to produc-tion. In a very general sense, we think of geoscience work as becoming more detailed and fully integrated with technical disciplines such as reser-voir engineering as we move from exploration to production, an obvious result of the increasing quantity of and dependence on well data farther along in the value stream. Without a doubt, one of the most important aspects of working with seismic data is the correlation of well data to seis-mic data, the well-to-seismic tie that links geology and seismic response.


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This link always includes some uncertainty; but in dealing with this uncer-tainty, you gain valuable experience and develop greater understanding of the strengths and limitations of seismic and well data. Some interpreters tie wells to seismic data, assuming that the greater amount of uncertainty resides in the seismic data and that the well data are always (or almost always) correct. Others tend to be willing to recognize the limitations of well data and spread uncertainty more evenly between the well and seismic data. Well-to-seismic ties are always data dependent and very often are quite subjective, hence the importance of experience and appreciation of uncertainty in working with seismic and well data at the same time, which is more likely to happen with greater frequency in a production rather than an exploration setting.

A classic well-to-seismic conundrum that severely tests interpreters is the case of tying two wells to a single seismic line. Imagine that you make each tie independently and with acceptable confidence, but you cannot cor-relate reflections between the tie points with the same degree of confidence in any geophysically consistent or geologically reasonable way. You must make some adjustments, within the limits of uncertainty of the well data (e.g., the well-log data have not been edited properly) or the seismic data (e.g., the imaging is inaccurate). But how to decide? You must produce “most likely” correlations no matter what and will have to choose the appropriate weights to assign to the well and the seismic data, based on experience and knowledge of acquisition and processing of both types of data.

If it is true that some geoscientists are too quick to assume that well data are ground truth and that all problems with well-to-seismic ties originate with the seismic data, then it is important to develop an in-depth understand-ing of the consistency of seismic data acquisition and processing, especially 3D data, as well as to recognize the limitations of these data. It may be (again, this is not always the case) that this understanding is more likely to develop in a setting where there is less well control, as toward the explora-tion end of the value stream. Here you can concentrate primarily on building correlation skills, confidence, and a greater understanding of data limita-tions before you are required to extend or modify your correlations to make well ties. Perhaps this is analogous to learning rules, so that with advancing skills you know when and how to stretch those rules.

Advanced interpretation

As with most disciplines or courses of study, seismic interpretation makes increasing demands on its students and practitioners as their profi-ciency grows. It is safe to say there is general agreement on the fundamentals


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of seismic interpretation, but there probably is no consensus on what con-stitutes advanced interpretation. You might characterize this lack of consen-sus by asking the question, “Does the ‘advanced’ in advanced interpretation mean an increase in the complexity of the interpretation problems to be solved or an increase in the sophistication of tools and techniques brought to bear on interpretation problems?”

The two parts of this question are not mutually exclusive. As is the case for constructive discussion of any well-posed question, there is merit in an answer that incorporates the strengths of arguments that support either end-member position. Notice that both parts of the question include “increase,” which implies that advanced interpretation, however defined, involves an increase in something — what you might know as experience. So you can rephrase the initial question as, “Do you advance in experience by working on interpretation problems with an ever-increasing degree of complexity and difficulty or by developing proficiency with an ever-increasing num-ber of sophisticated high-tech tools?” This might seem to be a word game now, having substituted “ever-increasing” for “increase” but not really hav-ing changed the core question. Rephrasing again, but not in an “either-or” format, the question becomes, “How do you gain meaningful interpretation experience?”

There is a relatively simple answer to this question: You gain experience by looking at and working with as much data as you can. The more varied the geologic settings of your project areas, the more complex your problems; the more sophisticated your tools, the better — as long as you look at your data with Dix’s (1952) advice in mind, that “every event has a significance that we can find and that is worth finding.” As your experience grows, you may find that “advanced” interpretation skills are not necessarily introduced in a classroom but rather are learned in team settings or at professional soci-ety meetings where you share your experience with other geoscientists and benefit from honest discussions of not only successes but also failures.

Time spent and value added

As with any of the technical tasks performed in exploring for and pro-ducing hydrocarbons, seismic interpretation has value that is a function of the time invested in it. Of particular interest and concern is the form this function can take. Figure 4 is a representation of the 80–20 rule men-tioned in Chapter 8. It illustrates that the value of an interpretation, in terms of observations, inferences, correlations made, and information gained, increases sharply early in an interpretation project, i.e., very much is learned with very little time invested. This proportionality changes as time passes,


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with increasingly smaller increments of value added for increasingly greater amounts of time consumed (diminishing returns). In effect, most of an inter-pretation, usually the easiest or most straightforward parts, is done quickly, with the bulk of the time spent on problem areas.

The actual value of an interpretation hypothetically reaches a maximum after a certain time (point A in Figure 4). Continuing with the interpretation beyond this point can result in a decrease in the overall value of the project, if for no other reason than the wasting of time. This is similar to “gambler’s ruin” — using the commutative law of time and money in throwing good money (or time) after bad. Before this critical point of diminishing returns is reached, you should undertake additional tasks or pursue different lines of investigation that can add value more quickly (upper curve in Figure 4). For example, in a 2D interpretation project, such tasks might include repro-cessing, acquisition of additional 2D data, or recommendation to acquire 3D data. In any case, always strive to maximize the value gained from your efforts over time, realizing that “obviously it is foolish to go ahead and cor-relate where no correlation is possible” (Dix, 1952).

A confounding element in the time-value relationship for seismic inter-pretation, whether 2D or 3D, is that the value of a particular interpretive task

Figure 4. In a typical interpretation project, the tendency is to add incrementally less value as time passes; beyond a certain point, marked A on the figure, you can actually begin to erode value. You can prevent this from happening by adding to your interpretation project (e.g., acquire new data or reprocess existing data and carefully avoid “busy work”) or by moving to a new project.

Initial project

Addition to initial projector new project

Time

Val

ue

A


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may not always be apparent at the time the task is done. The general form of the curves in Figure 4 assumes that the value of a task can be measured accurately at any time in its duration. If there is a lag between completion of a task and realization of its full value, then the time-value relationship represented in Figure 4 can look very different. This means you may have to defend work whose value is not readily apparent at the time you are doing it. In most cases, you successfully advance arguments for doing such work only by illustrating its value from experience.

And what is meant by experience? As said before, experience is hav-ing been put to the test. A more lighthearted definition is that experience is the acquired ability to recognize a mistake when you make it again. No interpreter has ever passed every test faced or answered every question posed without having erred at one time or another. So, contrary to what many might think, an experienced interpreter does not know all the answers; rather, he or she knows when and how to ask questions that can be answered with confidence.


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189

References

Bahorich, M., and S. Farmer, 1995, 3D seismic discontinuity for faults and stratigraphic features: The coherence cube: The Leading Edge, 14, 1053–1058.

Barnes, A. E., 2007, Redundant and useless seismic attributes: Geophysics, 72, no. 3, P33–P38.

Bertram, G. T., and N. J. Milton, 1996, Seismic stratigraphy, in D. Emery and K. J. Meyers, eds., Sequence stratigraphy: Blackwell Scientific, 45–60.

Brown, A. R., 1996, Seismic attributes and their classification: The Leading Edge, 15, 1090.

Brown, A. R., 2011, Interpretation of three-dimensional seismic data, 7th ed.: AAPG Memoir 42 and SEG Investigations in Geophysics No. 9.

Calvert, R., 2005, Insights and methods for 4D reservoir monitoring and characterization: SEG/EAGE Distinguished Instructor Series No. 8.

Chopra, S., and K. J. Marfurt, 2007, Seismic attributes for prospect identification and reservoir characterization: SEG.

Chun, J. H., and C. A. Jacewitz, 1981, Fundamentals of frequency domain migration: Geophysics, 46, 717–733.

Dix, C. H., 1952, Seismic prospecting for oil: Harper & Brothers.———, 1955, Seismic velocities from surface measurements: Geophysics, 20,

68–86.———, 1984, Interview with TLE editor Robert Dean Clark: The Leading Edge,

3, 14–17.Ebaid, H., A. Tura, M. Nasser, P. Hatchell, F. Smit, N. Payne, D. Herron, D. Stanley,

J. Kaldy, and C. Barousse, 2008, First dual-vessel high-repeat GoM 4D survey shows development options at Holstein field: The Leading Edge, 27, 1622–1625.

Etgen, J., S. H. Gray, and Y. Zhang, 2009, An overview of depth imaging in exploration geophysics: Geophysics, 74, no. 6, WCA5–WCA17.

Feynman, R. P., 1985, Surely you’re joking, Mr. Feynman!: Bantam Books.Gould, S. J., 1993, Eight little piggies: W. W. Norton & Co.———, 1995, Dinosaur in a haystack: Harmony Books.Gray, S. H., J. Etgen, J. Dellinger, and D. Whitmore, 2001, Seismic migration

problems and solutions: Geophysics, 66, 1622–1640.

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Hardage, B. A., M. V. DeAngelo, P. E. Murray, and D. Sava, 2011, Multicomponent seismic technology: SEG Geophysical Reference Series No. 18.

Hart, B. S., 2011, An introduction to seismic interpretation: AAPG Discovery Series No. 16, CD-ROM.

Hawking, S., and L. Mlodinow, 2010, The grand design: Bantam Books.Herron, D. A., 2000a, Horizon autopicking: The Leading Edge, 19, 491–492.———, 2000b, Pitfalls in seismic interpretation: Depth migration artifacts: The

Leading Edge, 19, 1016–1017. ———, 2001, Problems with too much data: The Leading Edge, 20, 1124–1126.———, 2003, Characteristics of an interpreter: The Leading Edge, 22, 49.———, 2009, Interpreting depth-imaged data: Case studies, examples and pitfalls

from the interpreters’ perspective: The Leading Edge, 28, 364–367.Hilterman, F. J., 2001, Seismic amplitude interpretation: SEG/EAGE Distinguished

Instructor Series No. 4.Lindseth, R. O., 1979, Synthetic sonic logs — A process for stratigraphic

interpretation: Geophysics, 44, 3–26.Mitchum, R. M., Jr., 1977, Glossary of terms used in seismic stratigraphy, in C. E.

Payton, ed., Seismic stratigraphy — Applications to hydrocarbon exploration: AAPG, 205–212.

Mitchum, R. M., Jr., P. R. Vail, and J. B. Sangree, 1977, Stratigraphic interpretation of seismic reflection patterns in depositional sequences, in C. E. Payton, ed., Seismic stratigraphy — Applications to hydrocarbon exploration: AAPG, 117–133.

Nettleton, L. L., 1940, Geophysical prospecting for oil: McGraw-Hill Book Company.

Ogilvie, J. S., and G. W. Purnell, 1996, Effects of salt-related mode conversions on subsalt prospecting: Geophysics, 61, 331–348.

Orwell, G., 1946, Politics and the English language: Harcourt.Payton, C. E., ed., 1977, Seismic stratigraphy — Applications to hydrocarbon

exploration: AAPG. Pennington, W. D., A. Minaeva, and S. Len, 2004, Uses and abuse of “phantom

horizons”: The Leading Edge, 23, 454–456.Petroski, H., 1992, To engineer is human: The role of failure in successful design:

Vintage Books.———, 2008, Success through failure: The paradox of design: Princeton University

Press.Sheriff, R. E., 2002, Encyclopedic dictionary of exploration geophysics, 4th ed.:

SEG Geophysical Reference Series No. 1.Shuey, R. T., 1985, A simplification of the Zoeppritz equations: Geophysics, 50,

609–614.Tucker, P. M., 1982, Pitfalls revisited: SEG Geophysical Monograph Series No. 3.———, 1988, Seismic contouring: a unique skill: Geophysics, 53, 741–749.


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References 191

Tucker, P. M. and H. J. Yorston, 1973, Pitfalls in seismic interpretation: SEG Geophysical Monograph Series No. 2.

Tufte, E. R., 2001, The visual display of quantitative information: Graphics Press LLC.Vail, P. R., R. G. Todd, and J. B. Sangree, 1977, Seismic stratigraphy and global

changes of sea level, part 5 — Chronostratigraphic significance of seismic reflections, in C. E. Payton, ed., Seismic stratigraphy: Applications to hydrocarbon exploration: AAPG, 99–116.

Wikipedia, Accuracy and precision, accessed 15 June 2011, http://en.wikipedia.org/wiki/accuracy_and_precision.

Yilmaz, O., 2001, Seismic data analysis: Processing, inversion, and interpretation of seismic data: SEG Investigations in Geophysics No. 10.


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http://en.wikipedia.org/wiki/accuracy_and_precision

http://en.wikipedia.org/wiki/accuracy_and_precision


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193

Index

A

accuracy, as distinguished from precision, 175–176 (see also precision, as distin-guished from accuracy)

acoustic impedance (AI), 4, 5, 9, 22, 30, 31, 34, 117, 166

and density logs, 117and properties of layers, 30and reflection-coefficient

(RC) series, 30, 31, 117and sonic logs, 117calculation from reflection-

coefficient series, 34magnitude and algebraic

sign, 22measurable change, posi-

tive, 166reflection coefficient, 22

acoustic-impedance (AI) bound ary, 9, 10

seismic pulse incident to, 10and structural interpreta-

tion, 23acoustic-impedance (AI)

function, and reflection-coefficient (RC) series, 30, 31

AI (see acoustic impedance)air gun, 38aliasing, 75, 76, 79, 80amplitude, 22–28

above-background ampli-tude, 24

acoustic-impedance bound-ary, magnitude and alge-braic sign, 22

acoustic-impedance boundary, reflection coefficient, 22

and convolutional model, 22and dominant frequency, 27and gain recovery, 23and manifestation of geol-

ogy, 24and quantitative analysis, 23and reflection-coefficient

series, 22and seismic facies analysis,

26and sequence stratigraphy, 26and stacking, 23and time-amplitude (tuning)

analysis, 26autotracking, 24average absolute amplitude,

25balancing, 23baseline value, 22convolutional model, 22ΔA, delta amplitude, 22ΔT, delta time, 22maximum positive ampli-

tude, 25quantitative use of, and

picking of reflections, 24root-mean-square (rms)

amplitude, 25amplitude anomalies, 24, 115

highlighting, 24amplitude balancing, 23amplitude spectra, 15amplitude variation with angle

of incidence (AVA), 49, 112

amplitude variation with offset (AVO), 32, 49, 112

amplitudes, misinterpreted, and misestimated wavelet phase, 112–113

angular unconformity, 93

anisotropic depth-imaged data, 57, 111

anisotropic prestack depth migration (APSDM), 57, 58

anisotropy, 56 (see also veloc-ity anisotropy)

polar, 56 (see also polar anisotropy)

vertical transverse, 56tilted transverse, 56

antialias filter, 75anticlines, 109, 110apophenia, 109apparent-dip 2D migrated

seismic lines, and time migration, 128

approximation and thinking, 175

approximations, 174–176artifacts, and interpretation

pitfalls, 105–113categories, 105coherent noise, misinter-

preted as primary signal, 106–109

correlation, 106detection of, 113false time structures inter-

preted as real depth structures, 109–112

noise interpreted as discon-tinuous signal, 109

partial stacks and nonrecog-nition of high-coherence events, 112

seismic artifacts, recognition of, 105

wavelet phase misestimated and amplitudes misinter-preted, 112–113

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attenuation, 11, 27and distance, 11and dominant frequency, 27correction of, determinis-

tic, 11correction of, probabilistic, 11

attribute, seismic, 21automatic gain control (AGC), 23

and structural interpreta-tion, 23

automatic tracking, versus manual tracking (see tracking, manual versus automatic)

autotracking, 24, 27, 29, 96– 104, 121, 139, 151

average absolute amplitude, 25average velocity, 61AVO/AVA effects, 50

B

balancing and decompaction, 136

bandwidth, 13baseline survey, 165basement, 19, 20beam-migration depth-migra-

tion method, 72bias, 6bin size, and spatial sample

rate, Δx, 81biostratigraphic data, 116block diagrams, 149bottom-simulating reflection

(BSR), 107boundaries, subsurface, reflec-

tions, use of, conditions, 19–20

and visual estimation of wavelet phase, 19–20

bow-tie reflection, 66, 68Brazil, 71bright spots, 24buried focus syncline, 69

C

calibrated velocity model, for vertical conversion of points, 59

causal wavelets, 15CDP (see common depth point)

CDP gathers, 47, 48, 49Cenozoic basins, 24channel, 100, 101, 115channel margins, 28check-shot survey, 38, 39,

40, 60compared with vertical seis-

mic profile, 40circular wavefronts, and con-

stant-velocity migrations, 124

clipping values, 23CMP (see common midpoint)coherence, 28–30

and channel margins, 28and coherence slices, 29and coherence volume, 28and faults, 28, 29and noise, 28and 3D seismic data, 28

coherence data, and faults, 87coherence horizon slice, 29coherence slices, 133coherence time slice, 29coherence volume, 28–29, 30

and data-analysis window, 28reflectivity volume, and

faults, 30coherent noise, 106, 107

crosscutting primary reflec-tions, 107

interpreted as primary sig-nal, 106

color table, 116common-azimuth depth-migra-

tion method, 72common depth point (CDP),

43, 44, 47, 48, 49gather, 44, 47, 48, 49method of acquisition,

model of, 43common-depth-point interval,

and spatial sample rate, Δx, 81

common-depth profiling, 1common midpoint (CMP),

43, 44compaction of reservoirs, 166composite seismic response and

reflection coefficients, 18composite seismic responses,

accurate interpretation and source wavelets, 18

compressional-wave reflec-tions, 9

constant-velocity migrations and circular wavefronts, 124

continuous velocity model, for vertical conversion of points, 59, 61

contouring (see gridding and contouring)

convolution, 16and simulation of propaga-

tion of seismic pulse, 16of reflection-coefficient

(RC) series, 16convolutional model, 16–18, 22

composite response, and interference, 16–17

scaled wavelet, 16seismic response to reflec-

tion coefficient, 16cores, 116corrected gathers, inspection,

before partial stacking, 50correlation, and depth-migra-

tion projects (see depth-migration projects)

correlation, jump (see jump correlation)

correlation, processes and tech-niques that control data quality, 183

correlation ambiguity, 132correlation concepts, 83–113

artifacts and interpretation pitfalls, 105–113

coherent noise interpreted as primary signal, 106–109

false time structures inter-preted as real depth structures, 109–112

first look, 83–84horizons versus faults,

84–93manual tracking versus

automatic tracking, 96–104

multiple reflections, 94–96noise interpreted as discon-

tinuous signal, 109partial stacks and nonrecog-

nition of high-coherence events, 112


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Index 195

wavelet phase misdeter-mined and amplitudes misinterpreted, 112–113

correlation of seismic records, 4, 5

and most common intellec-tual difficulty, 5

and pattern recognition, 4correlation polygon, 133, 134,

137correlation procedures,

115–151depth-migration projects,

correlations, 140–145interpretation processes and

work flows, 149–150jump correlation, 120,

133–139loop tying, 120, 121–133reflection seismic data, pro-

cedures, 120visualization, 145–149

cosine wave, 12, 13and sine wave, phase rela-

tionship, 13as function of time, 12shape of waveform, vari-

ables in, 12crosscutting coherent noise,

108cuttings, 116

D

data, phase of, description, 15data acquisition and process-

ing, as related to seismic interpretation, 5

data management, 158–159 (see also data quality, and data management)

data quality, and data manage-ment, 153–161 (see also data management; data quality, responsibility for assessment)

and depth-migration proj-ects, 158

and detection, 155and image fidelity, 155–156and resolution, 155–156assessment of, 153data quality, 153–158

data-quality maps, 156–157nomenclature systems, 159–

161power of data set, 155primary elements of, 155quantitative measurement

of, 154relative to purpose of data,

153–155resolution, 155“traffic-light” map, 156–157variation, mapping of, 156

data quality, responsibility for assessment, 3 (see also data management)

data-quality maps, 156–157decompaction and balancing,

136δ, Thomsen parameter, and

short-offset moveout correction, vertical veloc-ity, 56

ΔA (delta amplitude), 22, 23, 26

and time-amplitude analy-sis, 26

and tuning analysis, 26ΔT (delta time), 22, 23, 26

and time-amplitude analy-sis, 26

and tuning analysis, 26Δti (two-way-time thickness,

ith layer), 36Δx (spatial sample rate), 2D

and 3D seismic lines, 81relative to bin size, 81relative to common-depth-

point interval, 81relative to sampling wave-

number, 81Δzi (thickness, ith layer), 36demultiple processing, 95, 172density logs, 117, 118

and reflection-coefficient (RC) series, 117

and synthetic seismogram, 118

depositional systems, 26depth domain, 43depth gathers, 112depth imaging, 58, 70, 169

and isotropic models, 58failure of, conditions, 70

inaccurate, manifestation of, 169

depth-imaging domain, pitfalls, 111

depth migration, 57, 70–71, 73, 91, 173

and correction for velocity anisotropy, 57

and time migration, differ-ences, 70–71

artifacts, and faults, 91methods, migration algo-

rithms, 72depth-migration artifacts, 91, 168depth-migration processing

sequence, marine 3D data, subsalt exploration, 140

final migration, and imaging subsalt section, 140

salt-flood migration, and imaging base of salt, 140

sediment-flood migration, and imaging top of salt, 140

waterflood migration, and imaging of seafloor, 140

depth-migration projects, 140–145, 158

and data management, 158and subsalt exploration, 140correlations in, 140–145depth-migration processing

sequences, steps in, 140final migration, and imaging

subsalt section, 140salt-flood migration, and

imaging base of salt, 140sediment-flood migration,

and imaging top of salt, 140


detection, 3, 155diagenesis, 175difference survey, 166dip, 87dip line, time-migrated, 123direct hydrocarbon indicator

(DHI), 24display formats, reflection seis-

mic data, 15, 16, 17display lines, 117


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distortion, inevitability of, 83Dix equation, 51, 53

conditions for valid applica-tion of, 53

Dix interval velocities, 54, 55example, 55sensitivity, to stacking-

velocity picks, 54Dix interval-velocity calcula-

tion, sensitivity, illus-trated, 54

Dix’s correlation procedure, in modern seismic interpre-tation, 2

Dix’s threshold of impossibil-ity, 106

domain of information, 4dominant frequency, 27, 28, 76,

78, 79and attenuation, 27and depth-domain data, 28and time-domain data, 28and tuning thickness, 76calculation of, 27, 28

double multiple, 94, 95, 96, 97, 98

E

80-20 rule, 120, 185elastic medium and seismic

pulse, 100elastic waves, 9ε (Thomsen parameter), 57ergonomics, 179–180η (Thomsen parameter), and

deviation of long-offset P-wave moveout, 56

evidence, rules of, 2extensional fault, 122, 123

F

f (dominant frequency, seismic signal), 79, 81

false time structures, 109–112, 116

fault block, 133fault correlations, 131–133

and coherence slices, 133and horizontal reflectivity

slices, 133

fault cuts, 139comparing, to identify mis-

correlations, 139fault heaves, 104fault picks, aliasing of, 131faults, 28–30, 68, 69, 84–93,

94, 116, 117, 119, 120, 122, 123, 131, 133, 136, 138, 168

accurate positioning of, 131and coherence data, 87and depth-migration arti-

facts, 91, 168and tents, 131correlating, concerns in, 86faults versus horizons, 84–93interpretation of, 119listric normal, 90normal, 86, 131, 136, 138reverse, 86, 90, 136, 138tracking of, 85–862D and 3D data, compared,

86fence diagrams, 149first arrival, 39flat spot, seismic, 19, 20fold of coverage, 43fold of data, volume of data,

and reduction in stack-ing, 49

foreshortened perspective, for viewing data, 146, 147, 148, 149

formalities, titular, recom-mended abandonment of, 182

forward seismic modeling, 167, 174

4D seismic (time, and three spatial dimensions), 165–167

amplitude-attribute analysis of, differences, 166

baseline and monitor sur-veys, 165

compaction, 166difference survey, 166fluid saturation, variation

of, 166geomechanical changes, 166“hardening” of reservoirs, 166impedance changes, analysis

of, 166

interpretation and analysis, 166

overburden, stretching of, 166

pore pressure, variation of, 166

purpose, 165requirements, 165“softening” of reservoirs,

1664D time-shift effects, 166Fourier analysis, 11, 13

and waveform phase, 13Fresnel zone, 78

and spatial resolving power, seismic data, 78

geometry, 78measurement, 78

frowning events, 46

G

gain recovery, 23gambler’s ruin, 186gas breakout, 166gas-charged sediments, 110gas hydrate stability zone

(GHSZ), 107, 108Gulf of Mexico, 108

gates, and reflections, 24geochemical data, 116geology, value of, in geophys-

ics, 182ghost multiples, 95gridding, 121gridding and contouring,

163–165gridding programs, and

subjective elements of geology, 164

manual operations, merits of, 164–165

purposes, 163ground truth, 184Gulf Coast, 119Gulf of Mexico, 108, 166

H

“hardening” of reservoirs, 166high-amplitude response and

hydrocarbon-charged sand, 136


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Index 197

horizon, 10, 84–85horizon autotracking, 139horizon identification, wildcat

exploration compared with production proj-ects, 10

horizon names, 160–161horizon time, 21horizons, 84–93, 94, 136, 160

components, 160picking, 93, 136versus faults, 84–93

horizontal reflectivity slices, 133

hydrocarbon migration, 175hydrocarbon/water contact,

19, 108for estimation of wavelet

phase, 19hypotheses, working, 170–174

I

illumination, 49, 70–73image fidelity, 3, 156imaging accuracy, 2D data

compared with 3D data, 66, 69

imaging artifacts, 94, 170impedance changes, analysis

of, prestack domain, 166implicit uncertainty, data acqui-

sition and imaging, 133impossibility, threshold of, 2inclined reflector and two

orthogonal 2D time-migrated lines, mis-tie, 129–130

interpretation, 1, 2, 184–185advanced, 184–185and seismic data quality, 2

interpretation paradox, 174interpretation pitfalls (see arti-

facts, and interpretation pitfalls)

interpretation processes and work flows, 149–150

generic interpretation, 149, 151

granularity in work flow, 149processes, summary of, 150

interval transit time (ITT), 36, 37, 53

and interval velocity, 36, 37and the term “slowness,” 36

interval velocity, 36, 37, 39, 51, 53, 55, 60, 61, 62

and interval transit time, 36, 37

calculated from stacking velocities, 55

calculated with Dix equa-tion, 53, 62

calculation, 39, 51model, layered, 60

intrusions, 117inverse seismic modeling, 167inversion, 30–34

and quality of data acquisi-tion and processing, 32

and synthetic sonic log, 32and time domain, 32assumptions about process-

ing input reflectivity data, 32

correlation of, compared with conventional reflec-tivity data, 32–33

relative to acquisition-pro-cessing-interpretation, reflection seismic data, 30

inverted data, scaling of, 32isotropic depth-imaged data

and Z-to-D correction, 111

J

jump correlation, 115, 131, 133–139, 168, 170

and correlation polygon, 133and fault blocks, 133–134and pattern recognition, 133and tunnel vision, 134common use of, 133flattening, on correlated

horizon, 134–135

K

k (wavenumber), relative to wavelength, 81

Kirchhoff multiarrival depth-migration method, 72

Kirchhoff single-arrival depth-migration method, 72

L

lag, 12λ (wavelength), 76, 81

relative to wavenumber, k, 81layered interval-velocity model, 60layered velocity model, for

vertical conversion of points, 59

lead, 12limestone, 119limitations, necessity of under-

standing, 2line ties by reflection character,

122–126lines, mis-tied, critical factor, 125listric normal fault, 90, 122, 123

dip and strike views, 90long-wavelength structures, 112loop, 121loop tying, 115, 120–133

and 3D data, 121–122and faults, 120

M

map migration, 65, 131and subsurface reflecting

points, positioning of, 131maximum positive amplitude, 25migration, 4, 63–73, 116, 170

algorithms, matrix of, for variations in complexity of subsurface geology, 72

and dip-field complexity, 72and multipathing waves, 72and velocity complexity, 72artifacts, 170for structure, simple or com-

plex, 69for velocities, simple or com-

plex, 69map migration, 65purpose, 63steep/overturned reflec-

tors, 72time domain compared with

depth domain, 69types of, for prestack and

poststack depth domains, 69

migration, of hydrocarbons, 175


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migration swings, interpreted as primary signal, 106

migration velocities, differ-ences between, 130

migration velocity model, 116mis-tie, 122, 123

2D data, 122mis-tied lines, critical fac-

tor, 125 (see also time migration; time migra-tion and apparent-dip 2D migrated seismic lines)

mis-ties caused by differences in acquisition and pro-cessing, 130

mode-converted waves, subsalt exploration, 173

model-based interpretation, 6monitor survey, 165moveout-corrected CDP gath-

ers, 47, 48, 49moveout-corrected traces,

stacking, and noise reduction, 45

moveout correction, as related to velocity, 46

moveout velocities, and errors in stacked traces, 46–47

multifold acquisition, 42multifold coverage, and con-

ventional seismic veloc-ity analysis, 42

multiple reflections, 94–96, 97, 98

common multiples, 95demultiple processing, 95double multiple, 94, 95, 96,

97, 98ghost, 95long-period, 94peg-leg, 95, 96, 97period, 94positions of, prediction,

96, 97recognition of, 96short-period, 94

multiple velocity functions, for vertical conversion of points, 59

multiples, 94, 106, 172interpreted as primary sig-

nal, 106

multivalued horizon capability, of workstation system, 141

N

NMO (see normal moveout)NMO-corrected gathers, 54noise, 106, 109

coherent, interpreted as primary signal, 106

interpreted as discontinuous signal, 109

nomenclature systems, 159–161attributes, 159horizon names, core elements

and purposes, 160–161horizons, components, 160importance, 159requirements of, 159

noncausal wavelet, 13, 15and causal wavelet, 15

normal hyperbolic moveout, 45normal-incidence reflection,

point of, 64normal moveout (NMO), 43–45

correction, 45equation, 43, 44traces, stacking, and noise

reduction, 45normal-moveout (NMO) veloc-

ity, 45, 57and correction for velocity

anisotropy, 57and stacking velocity, Vstk, 45

Nyquist frequency, 75, 76Nyquist theorem, 75, 78, 81,

121, 164Nyquist wavelength, and sam-

pling wavenumber, 81Nyquist wavenumber, and sam-

pling wavenumber, 81

O

observe-interpret-test cycle, 6oil-water contacts, tilted, 19onland data, 116optimal stacking velocity,

42– 43orthogonal 2D time-migrated

lines, tying, 125, 127– 129

overburden, stretching, 166overpressured sediments, 110

P

partial stacking, purpose, 49partial stacks, and nonrecogni-

tion of high-coherence events, 112

particle motion as function of time, and seismic wave-form, 11

particle motions, positive and negative reflection pro-cesses, 11

pattern recognition, and cor-relation of seismic records, 4

peg-leg multiples, 95, 96, 97, 106, 107, 172, 173

phantom horizons, 119phase, 10 (see also wavelet

phase)phase lag, 12phase rotation, 13, 34

and inversion of reflectivity data, 34

phase spectra, 15pitfalls, interpretation (see arti-

facts, and interpretation pitfalls)

plate convergence, 175polar anisotropy, 56 (see also

anisotropy, defined, polar)

and reflection seismic data, 56and vertical transverse isot-

ropy (VTI), 56characterization, and Thom-

sen parameters, 56polarity, 10polarity reversals, 106pore pressure, variation, and

impedance, 166poststack depth migration

(PoSDM), 69poststack time migration

(PoSTM), 69power, of data set, 155precision, as distinguished from

accuracy, 175–176 (see also accuracy, as distin-guished from precision)


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Index 199

presentations, 180–181requirements, 180–181

prestack depth migration (PSDM), 57, 58, 61, 69, 91, 92, 98, 110, 111, 168, 169

and Z-to-D vertical correc-tion, 57

isotropic, and migrated velocities, 57

line, 169velocity model, 98, 111volume, 168

prestack time migration (PSTM), 69

primary reflection, 94, 95principal component analysis, 21probability of success, 177problem solving, 182PSDM (see prestack depth

migration)pull-up, and shallow salt, 116pulse width, 17P-wave conversion to S-wave,

173P-wave energy, 173P-wave reflections, 9

Q

qualitative assessments, and quantitative expressions, 177

quality, 2–3 (see also seismic data quality)

of data, determinants in, 4

R

Rayleigh limit, vertical resolu-tion, 76, 79

rays, 64reflection and reflector, distin-

guished, 11reflection coefficient (RC), 4, 5,

9, 16, 18and composite seismic

response, 18defined in terms of AI, 9responses, 18

reflection-coefficient (RC) series, 16–18, 22, 30–31, 34, 117

and acoustic-impedance data, 117

and four coefficients, 16and properties of layers, 30as derivative of AI function,

assumptions, 30–31as distinguished from reflec-

tivity data, 30convolved with zero-phase

wavelet, 16, 18derivative of AI function,

assumptions, 31reflection seismic data, 15, 16,

120composite response to

boundaries, 16correlation, procedures, loop

tying and jump correla-tion, 120

display formats, 15variable density, 15

reflection times, manual record-ing, 165

reflections, multiple, 94–96reflections, ties, 3D data, mi-

grated, 122reflectivity data, correlation,

compared with correla-tion, seismic inversions, 32, 33

reflectivity data, zero-phase, 32–33

reflectivity volume, coherence volume, and faults, 30

reflector and reflection, distin-guished, 11

refraction, of seismic energy, 70reservoir engineering, 183resolution, 3, 75–81, 155–156

aliasing, 75, 76and Nyquist theorem, 75, 78antialias filter, 75resolvable limit, 156sampling frequency, 75sampling, specified, 75

resolvable limit, for discrete seismic reflectors, 156

reverse fault, dip and strike views, 90

reverse time depth-migration method, 72

risk, and uncertainty, 176–178and geologic models, 177

and risk, quantitative expres-sion of, 177, 178

and symmetry of estimate, 177

qualitative assessments and quantitative expressions, 177

risking systems and risk factors, 178

uncertainty, and aspects free of implication about accuracy, 176

uncertainty, and implication about accuracy, 176

uncertainty, common mea-sure of, 177

uncertainty, relative to preci-sion of measurement, 176

risk assessment and seismic data quality, 178

rock velocities, error in mea-surement, sources, 38

root-mean-square (rms) ampli-tude, 25

root-mean-square (rms) veloc-ity, 39, 62

rotation of image, 146

S

salt, 19, 20, 60, 67, 70, 91, 97, 98, 101, 109, 110, 116, 140, 141–144, 145

and pull-up, 116and salt-flood migration,

140base-of-salt reflection, 101closed bodies, top and base,

picking, technique, 141–143, 144

correlation, top and base, accuracy, 140

sutures, 144, 145top and base of, picking, and

tracking, 140, 144salt bodies, 141, 144, 145

merged, 144, 145spatial closing and correla-

tion, 141salt-body geometry, 91salt-flood migration, and imag-

ing base of salt, 140, 141, 171, 173, 174


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salt floods, 158salt overhang, 143salt sheet, 99, 100, 111, 112,

168, 169salt sutures, 144, 145

AI contrast, 144picking of, 144

salt tectonism, 170sampling frequency, seismic

data, defined, 75sampling wavenumber, 81

relative to Δx, 81relative to Nyquist wave-

length, 81relative to Nyquist wave-

number, 81seafloor, 96, 140

tracking, 140seafloor double multiple, 107seafloor scarp, 168seafloor spreading, 175search image, 3sediment flood, 98, 141, 158sediment-flood migration, 140,

171, 174and imaging base of salt,

171, 174and imaging top of salt, 140

sediment-flood–salt-flood se-quence, 143

seed points, 99, 101, 102, 103seed tracking, 151SEG positive standard display

convention, reflection seismic data, 16

seismically derived velocity, 41–56

seismic amplitudes, quantita-tive use of, and picking of reflections, 24

seismic attribute analysis, refer-ences, 22

seismic attributes, 21–34, 116amplitude, 22–28coherence, 28–30horizon time, 21inversion, 30–34two-way traveltime, 21use of, 22utility spectrum, 21

seismic data, correlation with well data, 10

seismic data, sampling, 75

seismic data quality, 3, 155, 156 (see also data qual-ity, and data manage-ment; quality, defined)

detection, 3, 155elements of, 3image fidelity, 3, 156resolution, 3, 155

seismic facies, defined, 93–94seismic facies analysis, 26, 93seismic flat spot, 19seismic event, 10seismic interpretation, subjec-

tivity of, 3seismic inversion, 30–34

and quality of data acquisi-tion and processing, 32

and synthetic sonic log, 32and time domain, 32assumptions about processing

input reflectivity data, 32correlation of, compared

with conventional reflec-tivity data, 32–33

relative to acquisition-processing-interpretation, reflection seismic data, 30

seismic migration, importance, 72–73

seismic modeling, 167appropriate complexity, 167forward modeling, 167inverse modeling, 167utility of, 167

seismic pulse, normal incidence at acoustic-impedance boundary, 9

seismic records, limitations of, 83

seismic response, 9–20and standard impedance

configuration, 15described by way of convo-

lutional model, 9measurement of, 9

seismic sequences, and uncon-formities, 92

seismic stratigraphy, 94seismic waveform and particle

motion as function of time, 11

selectivity, 3semblance plot, 45

sequence boundary, 117sequence-stratigraphic analy-

sis, 26shale, 119shear waves, 9, 173short-offset moveout correction

applied to vertical veloc-ity, 56

shot-profile one-way depth-migration method, 72

sideswipe, 66, 67signal processing, 13signal-to-noise ratio (S/N), 26,

39, 154, 155significant figures, 175, 176sine wave, and cosine wave,

phase relationship, 13single-valued workstation sys-

tems, 141single velocity function, for

vertical conversion of points, 59

sinusoidal wave, a periodic function, single fre-quency, time domain, 12

slowness, 36small offset, 51, 53smiling events, 46Snell’s law, 70“softening” of reservoirs, 166sonic logs, 36–38, 117, 118

and reflection-coefficient (RC) series, 117

and synthetic seismogram, 118interval transit time, 36rock velocities, error in,

sources, 38source-receiver offset, 43source wavelet, and wavelet

phase, 17source wavelets, and differ-

ences between composite responses, 18

spatial sample rate (Δx), 2D and 3D seismic lines, 81

speed, a scalar quantity, 35squash plot, 146, 147, 148stacked traces, 46–47, 50

and errors in moveout veloc-ities, 46–47

stacking, of data, 42, 43, 49 (see also stacking velocity)


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Index 201

and optimal stacking veloc-ity, 42, 43

and volume of data, 49partial, purpose, 49

stacking velocity (Vstk), 45, 51, 54, 55, 56, 62 (see also stacking, of data; Vstk)

and Dix equation, 62and NMO velocity, 45and optimization of signal, 56and true propagation veloc-

ity, 56and root-mean-square veloc-

ity (Vrms), 45identification of, 45

stacking-velocity volume, 61standard impedance con-

figuration and seismic response, 15

statics corrections, 112strike, 88strike and dip, 115strike line, rotated into dip line,

124strike line, time-migrated, 124structural restorations, 136structures, false, in seismic

depth, interpreted as real, in true depth, 111

subsalt exploration, and mode-converted waves, 173

subsalt plays, 71subsalt section, imaging, 140subsalt targets, 3syncline, 69, 109, 110

buried focus, 69synthetic seismogram, 11, 46,

117, 118and well tie, 11, 118generated with invariant

wavelet, 11two-way time/stacking-

velocity pairs, 46synthetic sonic log, 32S-wave conversion to P-wave,

173S-waves, 9, 173

T

T (period, time-domain wave-form), 81

t (traveltime), 16

tents, 131θ (maximum dip), 79Thomsen parameters, 56

δ, and short-offset moveout correction, 56

η, and deviation of long-off-set P-wave moveout, 56

3D bin, 78, 79relation to average dip, 79relation to average veloc-

ity, 793D data compared with 2D

data, general, 1333D features on 2D displays,

rendering of, 1493D scale model, 1493D time-migrated line, 118tie point, 124–125, 148

correct, conditions for, 124–125

to a strike line, 148tilted plane-wave depth-migra-

tion method, 72tilted transverse anisotropy

(TTI), 56time-amplitude analysis, 26, 77time-depth conversion, 57–62

(see also vertical conver-sion, T to D and Z to D)

time-depth function, from check-shot survey, 59, 60

time domain, 43time-domain imaging, 171time-domain waveform, 81time-migrated dip line, 123time-migrated strike line, 124time migration, 70–71, 125,

127, 128 (see also mis-tied lines, critical factor)

and depth migration, differ-ences, 70–71

and true-dip lines, 125, 127, 128

2D, and mis-tied lines, 125, 127, 128

time migration and apparent-dip 2D migrated seismic lines, 128 (see also mis-tied lines, critical factor)

time sag, and shallow gas accu-mulations, 109

time spent and value added, correlation of, 185–187

diminishing returns, 18680-20 rule, 185gambler’s ruin, 186hidden value, 186

time structures, false, inter-preted as real depth structures, 109–112

time-to-depth conversion, and correction for velocity anisotropy, 57

titular formalities, recom-mended abandonment of, 182

Tobs (recorded traveltime, source to receiver), 38

trace spacing, 146trace stretching, 146tracking, manual versus auto-

matic, 96–104, 151 (see also automatic tracking, versus manual tracking)

errors, examples, 101–104faults, picking of, 99, 101–

104parameters for, 98–104precautionary measures, 101quality control, importance,

104seed points, 99, 101, 102, 103seed tracking, 151vertical seismic sections,

101tracking artifacts, 29“traffic-light” data-quality map,

156–157transformation of observations,

reflection-time domain into depth domain, 6

trough-over-peak amplitude response, 19, 25

and hydrocarbon-bearing reservoirs, 25

trough-over-peak reflection, misinterpreted, 113

true-dip lines, 66, 122, 125, 127, 128

and time migration, 125, 127, 128

true propagation velocity, 56true relative amplitude recovery

and preservation, 26Tstat (vertical traveltime, datum

to source depth), 38


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tuning analysis, 26, 77tuning model, 76–77tuning thickness, 19, 76–77, 79

and resolution, 76turbidite reservoirs, 166Tvert (recorded traveltime, con-

verted to vertical travel-time), 38

2D data compared with 3D data, general, 133

2D imaging of subsurface, fun-damental limitation, 130

migration velocities, differ-ences between, 130

2D migration, failure of, 662D seismic lines, orthogonal

pattern aligned with predominant strike and dip, 130

tracking horizons on dip lines and strike lines, 130

2D time-migrated lines, 122, 123, 124

two-way time/stacking-velocity pairs, 46

two-way traveltime (TWT), 21, 46, 78

type line, 117type locality, of geologic for-

mation, 117

U

uncertainty, 176–178and aspects free of implica-

tion about accuracy, 176

and geologic models, 177and implication about accu-

racy, 176and risk, quantitative expres-

sion of, 177, 178and symmetry of estimate, 177common measure of, 177qualitative assessments and

quantitative expressions, 177

range of, 176relative to precision of mea-

surement, 176, 177unconformities, 85, 87, 92, 93,

94, 117and seismic sequences, 92

angular, 93utility spectrum, 21

V

V (average propagation veloc-ity), 78

validity, of measurement or calculation, 176

Vavg (average velocity), 36, 37variable amplitude response,

112variable-density display for-

mat, reflection seismic data, 15

velocity, 35–62and Dix equation, 62and sonic logs, 36and transformation from

time to depth, 35and well-velocity surveys,

36anisotropy, 35average velocity, schematic,

37, 61basic types, 61, 62error in, sources, 38interval velocity, 35, 37,

39, 61moveout, errors in, and

stacked traces, 46–47P-wave, and anisotropy, 42root-mean-square (rms)

velocity, 37, 39, 62seismically derived, 41–56sonic logs, 36–38sources of data, 36stacking, 52, 62time-depth conversion,

57–62vector quantity, and associ-

ated scalar quantity, 35velocity anisotropy, 56–57well-velocity survey, 38–41

velocity anisotropy, 42, 56–57 (see also anisotropy)

and check-shot survey, 42and time-to-depth conver-

sion, 57and vertical seismic profile,

42correction for, conditions, 57polar anisotropy, 56

velocity anomalies, and false time structures, 116

velocity picks, accuracy, as related to depth, 46

velocity pull-up, 109velocity spectrum, 46vertical conversion, T to D and

Z to D, 59–60 (see also time-depth conversion)

calibrated velocity model, 59

continuous velocity model, 59

layered velocity model, 59multiple velocity functions,

59single velocity function, 59uncertainty, estimation of,

59with layered velocity model

and interval velocities, 59, 60

with velocity functions, and time-depth functions, 59–60

vertical exaggeration, seismic display, purpose of, 146

vertical resolution, Rayleigh limit, 76

vertical seismic profile (VSP), 40, 42, 59, 117

advantages over check-shot survey, 40

and velocity anisotropy, 42and well ties, 117and well-velocity surveys,

40critical factors, 59geophones, station spac-

ing, 40walkaway VSP, 40walkover VSP, 40

vertical transverse isotropy (VTI), 56

and tilted transverse isotropy (TTI), 56

vibroseis, 38Vint (interval velocity), 35, 36,

37, 50, 53visualization, of subsurface

geology, 145–149, 182and motion of observer,

148–149


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Index 203

foreshortened perspective, 146, 147, 148, 149

objectives, 146rotation of image, 146trace spacing, 146trace stretching, 146

VNMO (normal-moveout veloc-ity), 45, 50, 51, 53

accurate estimation, and ray tracing, 51

and calculation of interval velocity by Dix equa-tion, 51

relative to Vint, 50relative to Vrms, 50, 51

Vrms (root-mean-square veloc-ity), 36, 37, 51, 52

as approximation for VNMO, or stacking velocity Vstk, 51

root-mean-square velocity, 36, 37

volcanics, 19, 20VSP (see vertical seismic

profile)Vstk (stacking velocity), 45,

51 (see also stacking velocity)

W

walkaway vertical seismic profile (VSP), 40

walkover vertical seismic pro-file (VSP), 40


water injection, effect on acoustic impedance, 166

waveform, 10and frequency, amplitude,

and phase characteris-tics, 10

mathematical description for, 10

wavefront, 63, 64, 65

wavelet phase, 17, 18, 19, 20, 112–113 (see also phase)

and hydrocarbon/water con-tact, 19

and seafloor reflection, 19and source wavelet, 17critical value of, 112estimation of, 17–18, 19, 20impedance boundaries and

seismic responses, 20limitations in estimating, 20misestimated, and ampli-

tudes misinterpreted, 112–113

wavelets, 11–12, 13, 14, 15, 16, 17

and extraction from seis-mic data over windows, 11–12

and interpretation of geol-ogy, 17

causal, 16finite, band-limited, 13, 14higher-frequency compo-

nents and preferential reduction in strength, 11

noncausal, 15scaled, 16zero-phase, seismic re-

sponse, 15wavenumber, k, 81wedge model, 26, 76well data, correlation to seismic

data, importance, 183well locations, selection, 34well tie, 11, 116, 117, 118, 184

and density log, 118and synthetic seismogram,

11, 117, 118and vertical seismic profiles,

117correlation for, uncertainty,

117unsatisfactory, causes, 117

well trajectories, selection, 34

well-velocity surveys, 36, 38–41, 131

and check-shot surveys, 38, 39

and vertical seismic pro-files, 40

field setup, 38procedure, 39source and receiver geom-

etries, 40with fixed source, field

geometry, 41with moving source, field

geometry, 41wiggle traces, 15work flow and interpretation

processes, 149–151generic interpretation, 149,

151granularity in work flow,

149processes, summary of, 150

workstation systems, single-valued and multivalued, 141, 142

Z

Z-depths, and isotropic depth-migrated data, 57

compared with anisotropic depth-imaged data, 57

zero-phase wavelet, 13, 14, 15phase rotation of, 14seismic response, 15time duration for given

bandwidth, 13Zoeppritz equations, 9

and nonnormal angles of inci-dence at AI boundary, 9

Z-to-D (migrated depth-to-true vertical depth) correc-tion, 57, 111

isotropic depth-imaged data, 111


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first steps in seismic interpretation

Documents