intelligent reservoir characterization

8/6/2019 Intelligent Reservoir Characterization

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Well Log

Characterization

Lithological

Patterns

Seismic Syntax

Patterns

Seismic

Attributes

SeismicStratigraphy

Seismic

DepositionalPatterns

Depositional Sequences

Seismic SyntaxPatterns

Spectral Anal

Segmentation

Lithologies

Markov Chain

Sequence Stratigraphy Patterns

Seismic – Well Pattern Semantics

Geological Grammar for TranslationPattern Classification &

Characterization

3D Markov

Models

Geostats

Re

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gCalibration Prediction

Validation

High-level Methodology and Cycles



Detailed Methodology



Spectral Analysis for Determination of Sequence boundaries

T er t i ar yn o t d i f f er en t i a t e d

D e t a

i l e d

z on a t i on

W e l l d e v e l o p e d c y c l e s w i t h h i g h - r e s o l u t i o n

c o r r e l a t i o n p o t e n t i a l

A m a l g a m a t e d c y c l e s



Segmentation



Lithofacies From Logs

S t a n d a r d L o g A n a l y s i s –

A n a l o g

L i t h o l o g y C l a s s

e s

S p e c i a l I m a g e L o g s a n d D e r i v e d L i t h o I n d i c a t o r s

S t a n d a r d L o g S u i t e ( T o D o S

p e c t r a l + S e g m )

Lithfacies from Conventional Logs using Brigg’s Triangles. Calibrated

with Core and used. Requires Field level tuning

V e r y h i g h v e r t i c a l r e s o l u t i o n a n d c l a s s i f i c a t i o n a c h i e v e

d

S

e q u e n c e s t r a

t i g r a p h y c o n f i n e d M a r k o v i a n S t r i n g s ?



Seismic Character and Scale-of-Support issue

Top of Reservoir

Changing Patterns of the tracespoint to lateral variations in thereservoir character.

Also, the vertical litho/fluid patternseen at the well is reflected by theseismic trace at the well-location.

And the seismic is responding inpatterns

Most analysis is limited to thereservoir zone, while the patterncalibration and classification

should consider entire loggedinterval

Scale-of-Support issue fromseismic will be addressed by thisapproach



Classification & Categorization

• Such Patterns Exist in Real data

• The Various StructuredParameters Can be :

– On each Trace Separately

– Based on adjacent traces eitherside on 3D (1 look up= 4 traces)

– Based on Multiple adjacent

traces (2-5 lookup – How to do?)

While all of these refer to same reservoir – their characters are different



Structural Elements from Seismic Traces of Reservoir

Does this litholog match here?



Workflow steps with Well

Logs

There is no unique deterministic

solution to the depositional facies

implied in log data of a basin or field

• Data driven methods are essential to openthe possibilities (4th Paradigm)

• Log data structure is well established

• Conventional log analysis works on

acquisition, environmental and borehole

corrections

• Log analysis using standard empirical

equations and models (multi-mineral,

Thomas-Steiber etc.)

• These become INPUT to this WF

• A set of pathways will create multiple,

consistent and repeatable solutions and its

characters

Objective: consistentalgorithmic depofacies

1. Demarcate

Sequences

2. Segment logs in

Sequences

3. Petrofacies using

AI methods4. Determine

steady/unsteady

Markovian str.

5. Arrive at a

sedimentary unit

description &

character6. Determine

Depositional

Facies by

Classification



Adjacent Trace Structures

1. Univariate occurs from using one Seismic Attribute and

Multivariate may originate from multiple attribute patterns2. Formal Method

a. Similarity index (For each trace) to be derived from earlier Structure

b. Classify the trace shapes

c. Distance of each trace from the FUZZY/ CLUSTER center of the group

d. Use to define the similarity/ dissimilarity between traces

e. Time Series Coherency

3. Syntactic method? Can a grammar defined?

4. Graphical method?

5. Geostatistical method?



A Possible Workflow to Solution (Seismic Part)

• Use both reservoir and non-reservoir intervals to work with sparse wells

1. Select the AOI and the number of primary Seismic Attribute volumes

2. Obtain basis geological interpretation and model for convergence of result

3. From well’s facies logs – identify the pattern features in seismic

4. Select 3-4 units for analysis

1. Extract the (single) Seismic Trace Structure(STS) for each unit

2. Extract the seismic coherency structure(SCS) for trace-to-trace variability

3. Classify the (STS, SCS) into groups and map Check spatial meaning geologically

4. Converge for individual attribute volume and together providing the various spatial patterns andtheir spatial co-existence

5. Select STS, SCS and Converged Map for further integration with wells

1. Check for Anisotropy, Non-Stationarity of the patterns

6. Using well data – check the patterns implied at well location and match to Seismicsignature

1. Calibrate the Lithological signature and Fluid signature on seismic Patterns

7. Convert Seismic to Lithologies with allowable markovian variations8. Create Fine-scale geological models to map the lithological variations and understand

geological consistency

9. Forward model to create synthetic seismic to compare resulting patterns

10. Validate at blind wells if available – Recycle the workflow to improve

intelligent reservoir characterization

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