Development of an Advanced Approach for Next-Generation, High-Resolution, Integrated Reservoir...

Development of an Advanced Development of an Advanced Approach for Next-Generation, Approach for Next-Generation,

High-Resolution, Integrated High-Resolution, Integrated Reservoir CharacterizationReservoir Characterization

Performed by:

Advanced Resources InternationalAdvanced Resources InternationalHouston, Texas

September 9, 2003

Tulsa, Oklahoma

DOE Award No. DE-FC26-01BC15357

ADVANCED RESOURCES INTERNATIONAL

SP09092003-22722

Presentation OutlinePresentation Outline

Background and Project Background and Project Description Description

Data Availability and Preliminary Data Availability and Preliminary ProcessingProcessing

Model BuildingModel Building Accomplishments & Next StepsAccomplishments & Next Steps


SP09092003-22723

The Industry NeedThe Industry Need

Effective oilfield management requires 3-Effective oilfield management requires 3-D, high resolution reservoir D, high resolution reservoir characterization to identify reservoir characterization to identify reservoir heterogeneity.heterogeneity.

SOR/IORSOR/IOR EOREOR COCO22 sequestration sequestration

• Surface seismic is the most cost-efficient Surface seismic is the most cost-efficient method to obtain inter-well volumetric method to obtain inter-well volumetric reservoir information, but vertical reservoir information, but vertical resolution is insufficient (>50 ft) for resolution is insufficient (>50 ft) for optimized injection management.optimized injection management.


SP09092003-22724

ARI’s Technology Development ARI’s Technology Development GoalsGoals

Using existing data acquisition Using existing data acquisition capabilities, improve vertical resolution capabilities, improve vertical resolution and reduce uncertainty of reservoir and reduce uncertainty of reservoir characterization.characterization.

Present result in engineering terms Present result in engineering terms required for flow modeling and required for flow modeling and performance forecasting (performance forecasting (, k)., k).

Reduce time/cost requirements.Reduce time/cost requirements. ““Better, faster, cheaper”.Better, faster, cheaper”.


SP09092003-22725

ApproachApproach Apply advanced pattern recognition Apply advanced pattern recognition

technologies (artificial neural networks) technologies (artificial neural networks) to integrate multi-scale data (cores, logs, to integrate multi-scale data (cores, logs, seismic).seismic).

SimpleSimple Data-DrivenData-Driven DeterministicDeterministic

Generate high-frequency reservoir Generate high-frequency reservoir description at each 3-D seismic trace description at each 3-D seismic trace location.location.

Incorporate intermediate-scale data Incorporate intermediate-scale data (crosswell seismic) to bridge resolution (crosswell seismic) to bridge resolution gap and reduce uncertainty.gap and reduce uncertainty.


SP09092003-22726

Proposed Pathway to High-Resolution Proposed Pathway to High-Resolution Reservoir CharacterizationReservoir Characterization

Borehole Seismic(X-Well, VSP)

Conventional Well Logs

3-D Surface SeismicHigh-Resolution

Reservoir Description(Core, MRI, etc.)

Objective

Model #3

Model #2

Model #1


SP09092003-22727

Benefits over InversionBenefits over Inversion

Directly predicts permeability in Directly predicts permeability in addition to porosity (more robustly addition to porosity (more robustly than than /k relationship)./k relationship).

More deterministic outcome (not More deterministic outcome (not series of equi-probable outcomes).series of equi-probable outcomes).

Simple & fast.Simple & fast. Data-driven modeling, not analytic Data-driven modeling, not analytic

modeling.modeling.


SP09092003-22728

Project ObjectiveProject Objective

Demonstrate and validate the Demonstrate and validate the integrated (virtual integrated (virtual intelligence) procedure at a intelligence) procedure at a single field.single field.


SP09092003-22729

Analytic Work FlowAnalytic Work FlowAcquire and QC Data

•3D Seismic•X-well, VSP

•Log•Core

•Well Completion•Production

Clustering•Logs

•(Seismic)•(Log/seismic)

•(Log/core)

Seismic Processing•Depth/time•3D/borehole

•Attribute extraction

Rock Physics Modeling•Critical attributes

Engineering Model•Log/core

, k

Broadband Transform Function•3D/X-Well/Log

Validation•Simulation?•Statistical?

•Uncertainty analysis?

Acquire Site•McElroy Field

•ChevronTexaco


SP09092003-227210


Background and Project DescriptionBackground and Project Description Data Availability and Preliminary Data Availability and Preliminary

ProcessingProcessing Model BuildingModel Building AccomplishmentsAccomplishments


SP09092003-227211

McElroy Field, West TexasMcElroy Field, West Texas

Advanced Resources International

Data Coverage Map

High ResolutionReservoir Characterization Project

Advanced Resources International

Data Coverage Map

High ResolutionReservoir Characterization Project

Wells with Good LogsWells with Poor LogsWells with No LogsCoresSonic LogsImage Logs

Legend



Legend

Good W ell Suites

1000 0 1000 2000 3000 ft

Seismic Survey

CrosswellProfiles

Study Area N


SP09092003-227212

Summary of Data ReceivedSummary of Data Received

Data Type Amount Available

Well Locations 192

Seismic Survey, 3D P-P 2.5 sq. mi. (2000, post-steam) migrated stacked time

Crosswell Profiles 8 crosswell profile data files (1997, pre-steam)

Well Logs Complete modern log suites for 59 wells (1984 – 2001)

Sonic Logs 84 sonic logs over survey area

Formation Tops Interpreted formation tops (5) in 150 wells

Image Logs 8 image log files within the survey area

Core Logs Core analysis logs for 13 cored wells in survey: approx. 325 ft. of whole core each with core porosity, saturation, and permeability measurements on ½ foot intervals.


SP09092003-227213

Sample Surface Seismic LineSample Surface Seismic Line

~ 2.25 mi.

W E

Inline 165


SP09092003-227214

Comparison of Surface & X-well DataComparison of Surface & X-well Data

~ 430 ms.

~ 457 ms.

X-line 1295

X-well Profile

S N

S N

•Illustrates need for intermediate-scale crosswell data!


SP09092003-227215

Three Main “Data Processing” Three Main “Data Processing” ElementsElements

Rock Physics ModelingRock Physics ModelingSeismic Processing & Attribute Seismic Processing & Attribute

ExtractionExtractionLog ClusteringLog Clustering


SP09092003-227216

Rock Physics Modeling - Rock Physics Modeling - ObjectivesObjectives

Identify and prioritize seismic Identify and prioritize seismic attributes most likely to be attributes most likely to be influenced by reservoir properties influenced by reservoir properties of interest (of interest (,seismic facies ,seismic facies thickness).thickness).

Results will be used to select Results will be used to select attributes to include in seismic attributes to include in seismic models.models.

See Topical Report.See Topical Report.


SP09092003-227217

Rock Physics WorkflowRock Physics Workflow Build stratigraphic models of varying vertical Build stratigraphic models of varying vertical

resolution based on McElroy dataresolution based on McElroy data Conduct seismic experimentConduct seismic experiment

Generate suites of synthetic seismic as investigation Generate suites of synthetic seismic as investigation layer properties varylayer properties vary

Lithology (over range encountered at McElroy)Lithology (over range encountered at McElroy) Fluid content (saturations, type)Fluid content (saturations, type) Seismic Facies Thickness (over range Seismic Facies Thickness (over range

encountered at McElroy)encountered at McElroy) PorosityPorosity

Use results to guide selection of seismic Use results to guide selection of seismic attributes most affected by reservoir attributes most affected by reservoir parameters of interest at McElroy.parameters of interest at McElroy.


SP09092003-227218

Example Seismic Facies ModelsExample Seismic Facies Models

Model 1Model 12 Model 8Model 17 Model 4

3 Layers 23 Layers 50 Layers 114 Layers 250 Layers


SP09092003-227219

Ranking of Attributes Affected by Ranking of Attributes Affected by Biot-Gassmann Layer ThicknessBiot-Gassmann Layer Thickness


SP09092003-227220

““High-Priority” AttributesHigh-Priority” Attributes Trace DifferentiationTrace Differentiation Hilbert Transform (complex part of the analytic Hilbert Transform (complex part of the analytic

tract)tract) Perigram (zero mean of the complex amplitude of Perigram (zero mean of the complex amplitude of

the trace)the trace) Cosine of Phase (cosine of the instantaneous phase)Cosine of Phase (cosine of the instantaneous phase) Perigram * Cosine of Phase (product of these two Perigram * Cosine of Phase (product of these two

attributes)attributes) Instantaneous PhaseInstantaneous Phase Instantaneous Frequency (time derivative of Instantaneous Frequency (time derivative of

instantaneous phase)instantaneous phase) Median Smoother (3 point)Median Smoother (3 point) Absolute Value of TraceAbsolute Value of Trace Response Phase (instantaneous phase at the trace Response Phase (instantaneous phase at the trace

envelope peaks in degrees)envelope peaks in degrees) Seismic AmplitudeSeismic Amplitude


SP09092003-227221

Seismic Processing and Attribute Seismic Processing and Attribute Extraction - ObjectivesExtraction - Objectives

Data QC, time/depth conversion, Data QC, time/depth conversion, tie (collocate) surface & X-well tie (collocate) surface & X-well traces.traces.

Calculate and extract “high Calculate and extract “high priority” attributes from depth priority” attributes from depth seismic. seismic.



SP09092003-227222

Seismic Data QCSeismic Data QC

Two datasets to reprocessTwo datasets to reprocess 2000 vintage surface P-P seismic cube2000 vintage surface P-P seismic cube

~2.5 square mile reflection seismic survey~2.5 square mile reflection seismic survey 176 inlines, 176 crosslines176 inlines, 176 crosslines 55 ft. bin spacing CMP gathers55 ft. bin spacing CMP gathers Central frequency 65 Hz.Central frequency 65 Hz. Zero to 2 sec. data @ 2 ms. samplingZero to 2 sec. data @ 2 ms. sampling

1997 vintage X-well surveys1997 vintage X-well surveys 8 Crosswell seismic surveys8 Crosswell seismic surveys Lengths from 443 ft to 758 ft. in six surveysLengths from 443 ft to 758 ft. in six surveys Two surveys approx. 1,250 ft. very poor qualityTwo surveys approx. 1,250 ft. very poor quality Shot depths approx 1700-2950 ft.Shot depths approx 1700-2950 ft. Receiver depths 2200-2900 ft.Receiver depths 2200-2900 ft. Sample interval 0.15 ms.Sample interval 0.15 ms. Varying Data QualityVarying Data Quality


SP09092003-227223

Seismic Frequency SpectraSeismic Frequency Spectra

Surface SeismicSurface Seismic Good S/N 20 to 100 HzGood S/N 20 to 100 Hz Implied vertical resolution Implied vertical resolution

~40 ft.~40 ft.

Crosswell SeismicCrosswell Seismic Source Sweep 100-2,000 Hz.Source Sweep 100-2,000 Hz. Signal to Noise as high as 20 db.Signal to Noise as high as 20 db. Falloff above 2000 Hz. – Poor Falloff above 2000 Hz. – Poor

data qualitydata quality Implied vertical resolution ~ 5 Implied vertical resolution ~ 5

ft.ft.

Overall Good Quality Surface DataOverall Good Quality Surface Data Signal to Noise Difficult in CrosswellSignal to Noise Difficult in Crosswell

Ten Traces 0 Freq. Hz 3000

No

rma

lize

d S

ign

al

Db

Ten Traces Freq. Hz0 3000

No

rma

lize

d S

ign

al

Db

S/N Ratio Poor Above 2 KHz.


SP09092003-227224

Crosswell DataCrosswell Data

Most data of poor quality.Most data of poor quality. Tube-wave noiseTube-wave noise

Two profiles suitable for analysis.Two profiles suitable for analysis. Well DY0386 – B03826 (CM319 – CM423)Well DY0386 – B03826 (CM319 – CM423) Well B03826 – DY4441 (CM423 – CM314)Well B03826 – DY4441 (CM423 – CM314)

DY0386

B03826

DY4441


SP09092003-227225

Clustering - ObjectivesClustering - Objectives

Identify trends in log data; adds an Identify trends in log data; adds an important input parameter to ANN important input parameter to ANN analysis.analysis.

Facies definitionFacies definition To be used in log-core model.To be used in log-core model. See Topical Report.See Topical Report.


SP09092003-227226

The Log Clustering ProcessThe Log Clustering Process

Clustering uses a technique known as Clustering uses a technique known as Self Organizing Maps, or unsupervised Self Organizing Maps, or unsupervised neural networksneural networks

The object is to group data with The object is to group data with similar characteristics into bins, or similar characteristics into bins, or “clusters”“clusters”

Determining what each cluster Determining what each cluster represents (e.g., facies) requires a represents (e.g., facies) requires a priori knowledgepriori knowledge

Overlap can exist between adjacent Overlap can exist between adjacent clustersclusters


SP09092003-227227

Frequency Distribution Curves for Frequency Distribution Curves for LogsLogs


SP09092003-227228

Example Multi-Dimensional Example Multi-Dimensional CrossplotCrossplot


SP09092003-227229

Clustering Facies Type Clustering Facies Type CodesCodes

Facies Code Interpretation

Grain Density (g/cc)

Gamma Ray (API

Units)

PE

(barns/m2)

Porosity (%)

Permeability (md)

% of all Samples

Reservoir Quality Rank Remarks

1 Sandstone 2.627 36 3.320 13.4 78 0.5 9 (Black) Poorly defined.

2 Anhy, w/Gypsum 2.908 12 2.827 3.6 38 7.9 10 (Purple) Very low porosity.

3 Dolomite, w/Gypsum 2.813 20 3.277 9.6 29 26.4 2 (Dk Pink) High porosity.

4 Dolomite, w/Clay 2.830 53 3.165 7.0 8.9 3.8 8 (Med Gray) Low porosity.

5 Dolomite, w/Clay ? 2.839 43 3.165 14.1 35.8 5.0 7 (Lt Gray)High CNL due to

clay?

6 Dolomite 2.855 28 3.341 4.9 14.1 12.5 5 (Lt Blue)Intermediate

porosity.

7 Dolomite w/Gypsum 2.806 13 3.102 7.1 57 7.2 3 (Lt Pink)Intermediate

porosity.

8 Dolomite w/Gypsum 2.815 24 3.241 11.3 87 5.7 4 (Brown)High porosity

(poorly defined).

9 Dolomite w/Gypsum 2.762 28 3.106 17.7 62 18 1 (Red) Very high porosity.

10 Dolomite 2.869 21 3.642 5.5 35.2 13 6 (Blue) Low porosity.


SP09092003-227230

Comparison to Core DataComparison to Core DataDY0534 (CM315C)DY0534 (CM315C)

Facies Porosity

Permeability


SP09092003-227231


Background and Project DescriptionBackground and Project Description Data Availability and Preliminary Data Availability and Preliminary

ProcessingProcessing Model BuildingModel Building Accomplishments & Next StepsAccomplishments & Next Steps


SP09092003-227232

Three ModelsThree Models

Log-to-Core (complete)Log-to-Core (complete) X-well-to-Log (underway)X-well-to-Log (underway) Surface-to-X-well (underway)Surface-to-X-well (underway)


SP09092003-227233

Log-to-Core ModelLog-to-Core Model +/- 6500 datapoints; (10 wells x 325 ft (@ +/- 6500 datapoints; (10 wells x 325 ft (@

½ ft increments).½ ft increments). 6 logs as input; core porosity and 6 logs as input; core porosity and

permeability as output.permeability as output. Applied “depth windowing” to account for Applied “depth windowing” to account for

uncertainty introduced by different sample uncertainty introduced by different sample intervals.intervals.

Use 60% data for training, 20% for testing, Use 60% data for training, 20% for testing, 20% for validation.20% for validation.



SP09092003-227234

28-14-2 Network Architecture28-14-2 Network Architecture

Inputs

6 logs x 3 depth windows

10 fuzzy facies codes


SP09092003-227235

Actual vs. Predicted Crossplots for Porosity & Actual vs. Predicted Crossplots for Porosity & Permeability (Training/Testing Data)Permeability (Training/Testing Data)

Results show model tends to “smooth” extreme values.Results show model tends to “smooth” extreme values.

This is an expected outcome.This is an expected outcome.

Predicted vs. Actual Porosity After Network Training with Clusters

y = 0.7625x + 2.859

R2 = 0.7446

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35 40

Actual Porosity, %

Pre

dic

ted

Po

rosi

ty,

%

Predicted vs. Actual Log(Perm) After Network Training with Clusters

y = 0.7373x + 0.1705

R2 = 0.7106

-4

-3

-2

-1

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3 4

Actual Log(Perm)

Pre

dic

ted

Lo

g(P

erm

)

Unit

Slope

Unit

Slope


SP09092003-227236

Actual vs. Predicted Porosity & Permeability Actual vs. Predicted Porosity & Permeability Logs (Training/Testing Data)Logs (Training/Testing Data)

DY0534 Actual and Predicted Porosity Profile

2600

2650

2700

2750

2800

2850

2900

2950

3000

3050

0 10 20 30 40

Porosity, %D

epth

, ft

.

Actual Predicted

DY0534 Actual and Predicted Permeability Profile

2600

2650

2700

2750

2800

2850

2900

2950

3000

3050

0.01 1 100 10000

Permeability, md.

Dep

th,

ft.

Actual Predicted

“Smoothing”

Porosity/

Permeability

“Streaks”


SP09092003-227237

Benefits of ANN ModelBenefits of ANN Model

Predictor Correlation R2

Porosity PermeabilityCNL log 0.54 0.09

GR log 0.02 <0.01

LLD log 0.10 0.01

PE log 0.24 0.02

RHOB log 0.58 0.13

DP log 0.67 0.17

ANN Model 0.74 0.71


SP09092003-227238


Background and Project DescriptionBackground and Project Description Data availability and Preliminary Data availability and Preliminary

ProcessingProcessing Model BuildingModel Building Accomplishments & Next StepsAccomplishments & Next Steps


SP09092003-227239

Accomplishments/ProgressAccomplishments/ProgressQC Data

•3D Seismic•X-well, VSP

•Log•Core

•Well Completion•Production

Clustering•Logs

•(Seismic)•(Log/seismic)

•(Log/core)

Seismic Processing•Depth/time•3D/borehole

•Attribute extraction

Engineering Model•Log/core

, k

Broadband Transform Function•3D/X-Well/Log

Validation•Simulation?•Statistical?

•Uncertainty analysis?

DONE

DONE

DONE

DONEDONE

DONEDONE

DONE

Acquire Site•McElroy Field

•ChevronTexacoDONE

DONE

Rock Physics Modeling•Critical attributes

DONE

DONE


SP09092003-227240

Current FocusCurrent Focus

X-well-to-Log ModelX-well-to-Log Model Surface-to-X-well ModelSurface-to-X-well Model Automate Predictive WorkflowAutomate Predictive Workflow Generate Hi Res 3D Ø and k Generate Hi Res 3D Ø and k

Volumes.Volumes.


SP09092003-227241

X-well-to-Log ModelX-well-to-Log Model

+/- 2,000 datapoints (3 wells x 325 ft/well x +/- 2,000 datapoints (3 wells x 325 ft/well x ½ ft sample interval).½ ft sample interval).

Increasing to almost 5,000 by using multiple traces.Increasing to almost 5,000 by using multiple traces.

11 X-well attributes as input; 6 logs as 11 X-well attributes as input; 6 logs as output.output.

Will evaluate applicability of “depth windowing”.Will evaluate applicability of “depth windowing”.

60% for training, 20% for testing, 20% for 60% for training, 20% for testing, 20% for validation.validation.


SP09092003-227242

Preliminary ResultsPreliminary Results

Actual vs. PredictedActual vs. Predicted

(all logs)(all logs)Sample Density LogSample Density Log


SP09092003-227243

Surface-to-X-well ModelSurface-to-X-well Model

+/- 180,000 data points (135 +/- 180,000 data points (135 traces/line x 2 lines x 325 ft/trace x ½ traces/line x 2 lines x 325 ft/trace x ½ ft sample interval).ft sample interval).

11 surface seismic attributes as input; 11 surface seismic attributes as input; 11 X-well seismic attributes as output.11 X-well seismic attributes as output.

Will evaluate application of “depth windowing”.Will evaluate application of “depth windowing”.

60% for training, 20% for testing, 20% 60% for training, 20% for testing, 20% for validation.for validation.


SP09092003-227244

Workflow AutomationWorkflow Automation Need to generate 37 million prediction valuesNeed to generate 37 million prediction values

Data cube is 1.8 mi x 1.8 mi x 300 ftData cube is 1.8 mi x 1.8 mi x 300 ft 55 ft horizontal resolution (bin spacing)55 ft horizontal resolution (bin spacing) ½ ft vertical resolution (core sampling interval)½ ft vertical resolution (core sampling interval)

176 inlines x 176 crosslines (≈ 31,000 traces)176 inlines x 176 crosslines (≈ 31,000 traces) 300 ft interval300 ft interval ½ ft increments½ ft increments Ø, kØ, k

Scripts being used to feed each 3D seismic trace Scripts being used to feed each 3D seismic trace through each model:through each model:

11 seismic attributes 11 seismic attributes → 11 X-well attributes→ 11 X-well attributes 11 X-well attributes → 6 logs11 X-well attributes → 6 logs 6 logs → 2 core values (Ø, k)6 logs → 2 core values (Ø, k)

Timing: Expect to be finished and have 3D Ø/k cube by Timing: Expect to be finished and have 3D Ø/k cube by end of September.end of September.


SP09092003-227245

ValidationValidation Compare predictions to two cored wells not Compare predictions to two cored wells not

used in study.used in study. Compare predictions to all logs/core, and as Compare predictions to all logs/core, and as

a function of distance from central study a function of distance from central study area.area.

Compare predictions to results of time-Compare predictions to results of time-lapse crosswell survey (pre- vs. post-steam)lapse crosswell survey (pre- vs. post-steam)

Compare predictions to ChevronTexaco’s Compare predictions to ChevronTexaco’s existing inversion model.existing inversion model.

Error Analysis.Error Analysis.

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