Download - Interpretation 23.12.13
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Oil & Natural Gas Corporation Ltd.
Seismic Data Interpretation
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What is seismic interpretation?
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. Depositional environments and depositional
history Structure
Anticline, syncline Faults Stratigraphy
unconformity Pinch-out Channels, reefs, salt domes
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Objective
Prospect generation and identification of suitable locations for drilling by interpreting subsurface geo-data Petroleum system
Reservoir rock Porous and permeable sandstone, limestone Any other rock type forming trap, e.g., fractured shales
Source rock Shale/carbonates
Cap rock Shale/carbonates
Reservoir characterization Estimation of reservoir parameters
Area, thickness, porosity, saturation etc. The primary goal of seismic interpretation is
to make maps that provide geologic information (reservoir depth structure, thickness, porosity, etc.).
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Petroleum System
Source rock
oil
Cap rock
reservoir rock
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Traps
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Interpretation functionalities
Project and data management Data conditioning (interpretive processing)
Scaling, filtering, wavelet processing etc. Integration of various types of data (seismic, well etc.) Calibration (well to seismic tie)
Synthetic seismogram generation and correlation with seismic Visualization Horizon and fault Correlation Generation of time maps Depth conversion and generation of depth maps Special studies
Seismic Attributes Direct hydrocarbon indicators (DHI) and AVO Seismic Inversion Time lapse reservoir monitoring
Integration of structure, attributes, impedance, geologic model etc.
Prospect generation and location identification Reports/proposals
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Pre-requisite of interpretation
Knowledge of geology and geophysical processes Type of data and type of information which can be
extracted Objective of interpretation and amenability of seismic data
Elements of seismic trace data Modes of display Amplitude, time and frequency Role of colours and colour bar
Contrasting and gradational colour scheme Polarity and phase conventions Resolution and detectability Seismic to well tie
Character based matching between synthetic and seismic
Depth to time conversion (T-D curves) Check shots, VSP, Synthetics
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A seismic section showing colour convention and other display elements
Elements of display
Positive amplitude Blue
Negative amplitude Red
Horizon
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Elements of display- max + max
Variable area wiggle display
+/- zero crossing
-/+ zero crossing
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3-D Cube of seismic data
Elements of display
Time slice
In-linecross-line
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Successive time slices depict the anticlinal closures
Time 2430
Time 2390
Elements of display
Vertical section and time slices
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Top and bottom of a gas reservoir (low impedance zone) in (a) American polarity and (b) European polarity
Polarity Convention
American polarity isdescribed as: An increase inimpedance yields positive amplitude normally displayed in blue. A decrease in impedance yields negative amplitude normally displayed in red.
European (or Australian)polarity is described as thereverse, namely: An increase inimpedance yields negative amplitude normally displayed in red. A decrease in impedance yields positive amplitude normally displayed in blue.
American
European
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Phase
The task of tying seismic data and well data together and hence identifying seismic horizons is often oversimplified. The task requires knowledge of velocity, phase, polarity and tuning effects. Zero phase data makes all aspects of interpretation easier and everyone knows that zero phase is desirable. However, zero phase is difficult to accomplish and often it is not achieved in processing. Hence interpreters always need to check the phase and polarity of their data.
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ResolutionThe ability to separate two features that are close togetherThe minimum separation of two bodies before their individual identities are lost on resultant map or cross-sectionThe resolving power of seismic data is always measured in terms of seismic wavelength (λ=V/F) Limit of separability = λ/4The predominant frequency decreases with depth because the higher frequencies in the seismic signal are more quickly attenuated. Wavelength increases with depth.Resolution decreases with depthFor thinner intervals amplitude is progressively attenuated until Limit of visibility=λ/25 is reached when reflection signal becomes obscured by the background noise
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Limit of Separability
Age of rocks Very young
young medium old Very old
Depth Very shallow
shallow medium deep Very deep
Velocity (m/s) 1600 2000 3500 5000 6000
Predominant frequency
70 50 35 25 20
Wavelength 23 40 100 200 300
Separability 6 10 25 50 75
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Limit of visibility
Factors affecting the visibility Impedance contrast of
the geologic layer of interest relative to the embedding material
Random and systematic noise in the data
Phase of the data or shape of seismic wavelet
It may be less than 1 m to more than 40 m
Limit of visibility
S/N Example Limit
Poor Water sand poor data
λ/8
Moderate Wayer or oil sand fairly good data
λ/12
High Gas sand good data
λ/20
Outstanding Gas sand excellent
data
λ/30
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STRUCTURAL INTERPRETATIONCalibration (Well-to-seismic-tie)Correlation (horizons and faults) Map generation (gridding and contouring)
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For geologic information from seismic data, nearby wells are correlated to seismic reflectors. Synthetic seismograms (synthetics) provide this link by converting rock properties from well logs to a synthetic trace. Synthetics make “rocks look like wiggles,” using the convolution model (T = RC * W), which states that traces (T) are the result of convolving (*) the reflection coefficient series (RC) with the wavelet (W). When seismic data are acquired, a source wavelet is sent into the earth, reflected back (convolved) to the surface at geologic boundaries (RC), and recorded as a trace (T). RC is calculated from velocity and density logs
Well to seismic tie Synthetic Seismograms
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RC for normal incidence isRC = (r1v1- r2v2) /(r1v1+ r2v2)where v1, v2 are P-wave velocities (sonic log) and r1, r2 are densities (density log) in the layer above (1) and below (2) the reflecting boundary. The normal incidence assumption is generally valid, except where velocity and density contrasts are very large (gas sands, coal, hard streaks, etc.). When these exceptions are critical to the interpretation, RC needs to be calculated as a function of angle (AVA) from more complex equations and a third parameter, S-wave velocity (shear log).
Well to seismic tie Synthetic Seismograms
(1) r1,v1
(2) r2,v2
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Synthetic to seismic matching before starting the interpretation. Understand geologic elements (model) and their geophysical responses (Trace).
DT RHOB IMP GR LLD RC
Black traces
Seismic
Red traces
synthetic
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Seismic signatures of pay sands in B12-11. Sandstone pays are marked by troughs. Pay1 and Pay3 high negative amplitudes
Overlay of synthetic and logs on seismic sections. Here synthetic is shown by yellow trace
Well to seismic tie Synthetic Seismograms
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Horizon Correlation Identification of sequence boundaries
Reflection configuration and termination pattern, Onlap, Top lap and Down Lap
Tracking Auto and manual mode
Auto dip and correlation 3D tracking
Fault correlation Picking on vertical and horizontal sections Fault plane correlation Computing throw of faults
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Map generation Base map generation
Depicting seismic Well location Cultural data Block boundaries Scale, coordinates and legends
Griding Various method with faults and without
faults Incorporating heaves and throw
Contouring Over lay of attributes Map analysis
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Time map generated from horizon and fault correlation
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Seismic attributes
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Seismic attributes
Attributes are derivatives of basic seismic measurements/Information Seismic attributes extract information from seismic
data that is otherwise hidden in the data These information can be used for predicting,
characterizing, and monitoring hydrocarbon reservoirs Basic information
Time Amplitude Frequency Attenuation Phase
Most attributes are derived from normal stacked and migrated data volume
Can be derived from Pre-stack data (AVO)
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Seismic attributes
Attribute Information
Time-derived Structural information
Amplitude-derived Stratigraphic and reservoir
Frequency-derived Stratigraphic and reservoir
Attenuation Permeability
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Direct hydrocarbon indicator (DHI)
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DHI
Bright spot Water sand has lower
impedance than embedding medium and impedance of gas sand is further reduced.
Top and base reflections show natural pairing
If sand is thick enough, flat spot or fluid contact reflection should be visible between gas sand and water sand
Flat spot will have opposite polarity than bright spot at top
More common in shallower sandstone reservoirs ( Mio-Plio)
High impedance
Low impedance
High impedance
High impedance
Med impedance
High impedance
Pay sands
Water sands
Peak on synthetic
seismogram
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DHI
Low impedance
Med impedance
Low impedance
Low impedance
High impedance
Low impedance
Pay sands
Water sands
Dim spot Water sand has higher
impedance than embedding medium and water is replaced by water impedance is reduced
Contrast is reduced at upper and lower boundaries and reservoir is seen as dim spot
Flat spot can be expected at the point where the dimming occurs.
More common in deeper sandstone reservoirs where shale impedance is lower than sandstone impedance
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DHI Polarity reversal
Water sand is of higher impedance than enclosing medium and gas sand impedance is lower than enclosing medium
The polarity of reflections from water-sand-shale interface and gas-sand-shale interface have opposite sign and thus polarity reversal
Flat spot from GWC may show bright amplitude .
More common in medium depth range sandstone reservoirs
Med impedance
Low impedance
Med impedance
Med impedance
High impedance
Med impedance
Pay sands
Water sands
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Validation of DHI
AVO In many practical cases gas sands show an
increase of amplitude with offset Many difficulties of theoretical and practical
nature Data is pre-stack hence lower S/N
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Pitfalls in DHI
Exploration prospects based on a sound geologic model and supported by seismic amplitude anomalies are highly prospective and are usually assigned a high probability of success. However, a fraction of such prospects, perhaps 10-30%, result in dry holes. Postdrill appraisal can usually assign these results to one or more of the following factors:
• Unusually strong lithologic variations• Fizz water and low gas saturation• Superposition of seismic reflections• Contamination of the seismic signal by multiples or other undesired energy
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In seismic gather reflection coefficient at an incidence angle θ is given by: R(θ ) = Ro + BSin2θ
Where: Ro: RC at zero offset and R (θ) : RC at angle θ B is a gradient term which produces the AVO effect. It is dependent on changes in density, ρ, P-wave velocity, VP, and S-wave velocity, Vs.
Principle of AVO Analysis
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Principle of AVO AnalysisThe AVO response is dependent on the properties of P-wave velocity (VP), S-wave velocity (VS), and density (ρ) in a porous reservoir rock. This involves the matrix material, the porosity, and the fluids filling the pores.
Poisson’s Ratio
K = the bulk modulus, or the reciprocal of compressibility.μ = The shear modulus, modulus of rigidity
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Increase of amplitude with offset
Principle of AVO Analysis
Stack
Gather
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Principle of AVO Analysis
S-wave
P-wave
Water Saturation
Vel
ocit
y (m
/s)
With increase in gas saturation, P-wave velocity drops dramatically, but S-wave velocity only increasesslightly.
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• Rutherford and Williams (1989) derived the following classification scheme for AVO anomalies, with further modifications by Ross and Kinman (1995) and Castagna (1997):
• Class 1: High impedance gas sand with decreasing AVO• Class 2: Near-zero impedance contrast • Class 2p: Same as 2, with polarity change• Class 3: Low impedance gas sand with increasing AVO• Class 4: Low impedance sand with decreasing AVO
Rutherford/Williams Classification
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The generic AVO curves at the top of the gas sand
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Inversion
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Inversion
Inversion is the process of extracting, from the seismic data, the underlying geology which gave rise to that seismic.
Traditionally, inversion has been applied to post-stack seismic data, with the aim of extracting acoustic impedance volumes.
Recently, inversion has been extended to pre-stack seismic data, with the aim of extracting both acoustic and shear impedance volumes. This allows the calculation of pore fluids.
Another recent development is to use inversion results to directly predict lithologic parameters such as porosity and water saturation.
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Inversion Non-Uniqueness
All inversion algorithms suffer from “non-uniqueness”.
There is more than one possible geological model consistent with the seismic data.
The only way to decide\between the possibilities is to use other information, not present in the seismic data.
This other information is usually provided in two ways: The initial guess model constraints on how far the final result may deviate
from the initial guess The final result always depends on the “other
information” as well as the seismic data
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Acoustic Impedance
The definition of the zero-offset reflection coefficient, shown in the figure above. R0 , the reflection coefficient, is the amplitude of the seismic peak shown and represents relative impedance contrast.
1122
11220 VV
VVR
Seismic raypath
Interface at depth = d
r1 V1
r2 V2
t
Reflection at time t = 2d/V1
Geology SeismicSurface
SeismicWavelet
Shale
Gas Sand
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Impedance Reflectivity
Wavelet
Seismic trace
The common forward model for all inversions
General Forward Model for Inversion
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ImpedanceReflectivity
InverseWavelet
Seismic
Inversion tries to reverse the forward model
Inverse Model
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QC plot showing accuracy of inversion process. Error between synthetic generated from real and inverted impedance is insignificant
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Differentiating different lithology types through acoustic impedance inversion
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Impedance profile distinguishing between different rock types
Limestone
Sandstone
Basement
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Thanks