SPE Distinguished Lecturer · PDF fileSPE Distinguished Lecturer Program ... • Geoscience to SimulationGeoscience to Simulation ... unreali ti i t tlistic reservoir compartments

SPE Distinguished Lecturer Program

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Society of Petroleum Engineers Distinguished Lecturer Programwww.spe.org/dl

Let’s Model It!3D Geoscience Modeling – Implications for

R E ti ti d Fi ldReserves Estimation and Field Development Planning

Doug PeacockGaffney, Cline & Associates

Society of Petroleum Engineers Society of Petroleum Engineers Distinguished Lecturer Programwww.spe.org/dl

Presentation Outline• Development of 3D modeling techniques• Current Problems and Issues• Geoscience to Simulation• Geoscience to Simulation• Solutions and Best Practices• Future Developments• Summary & ConclusionsSummary & Conclusions

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Why did 3D modeling become such l d t h i ?a commonly used technique?

• It’s 3D real world is 3D not 2D• It s 3D – real world is 3D, not 2D• Consistency of horizons, faults, picks etc into

i l f ka single framework – no more overlapping horizons, strange faults,

li ti i t tunrealistic reservoir compartments • Common view of reservoir for all disciplines:

– Shared Earth Model concept• Can be used as a basis for field activity

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– appraisal, FDP, development, and updated

Why did 3D modeling become such l d t h i ?a commonly used technique?

• Allows use of geostatistics, facies algorithmsAllows use of geostatistics, facies algorithms – Evaluate heterogeneity in inter-well areas– Analyze full range of uncertainty

• More meaningful volumetrics• Dovetails the static / dynamic elements• Allows iterative improvements• It’s addictive• Biggest changes in 3D modeling have been

– Increased speed, detailI d i i di i li

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– Increased integration across disciplines

Classical Modeling Workflowg

Well Correlation Mapping Structural Model

6Facies ModelPetrophysical

ModelSimulation Model


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Gross Rock Volume• GRV is typically the largest single factor in

STOIIP uncertaintySTOIIP uncertainty– Often modeled only in relation to uncertainty in

Top ReservoirTop Reservoir– What about: interpretation, isopachs, depth

conversion, fault presence/position/ throw etcconversion, fault presence/position/ throw etc• It requires more effort to model these

uncertainties so it is easy to neglect themuncertainties so it is easy to neglect them• Especially important in the early / appraisal

stage of field life when facilities design8

stage of field life when facilities design (capacity, lifespan) are being considered

Structural Issues

Oil W COil Water Contact

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Fault PositionLost

VolumeVolume


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Fault Angleg


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Number of Faults

Oil W t C t tOil Water Contact

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Contacts

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Problems and Issues with Modeling Techniqueswith Modeling Techniques

• Predictions are extrapolative rather than interpretive

• Stochastic models alone do not utilize the skill and experience of the geologist– Statistics (GSS) vs. Geology (Object Model)( ) gy ( j )

• Assumptions usually have a large effect• Results depend on experiences and p p

preferences of modeler– Although experienced geomodelers understand

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g p gthe effect of these assumptions

Modeling Assumptionsg p• Data available is never enough to provide full

understanding of the subsurfaceunderstanding of the subsurface

DirectionalWhich

Directional Variograms?

Algorithm?

Seismic Attributes? Stationarity?

Trends?Vertical

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e caProportions?

Algorithm Assumptionsg pWhich

Al ith ?Algorithm?

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Moving Average Gaussian Sequential Simulation

Variogram Assumptionsg p• Data sampling is rarely sufficient to well define a

variogram – need to rely on experience, analogy, i i d t t i l & ?seismic data, trial & error?

Directional Variograms?

17Weak N-S Strong NE-SW

From the same data set…….Which Algorithm?

Variogram length and direction

Trends / Seismic Data

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All models will match the input data; differences come from the decisions that are made about how to build the model


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Geoscience to Simulation

• Scale issues are still problematic

• Better History Matches may be achieved by correctly identifying contributing rock and honoring scale

• Feedback still required but earlier is betterFeedback still required but earlier is better

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Scale Issues• Better History Matches may be

achieved by correctly identifying contributing rock and honoring scalecontributing rock and honoring scale

• Scale issues are still problematic– Relationships developed at a core or log p p g

scale are applied at a grid cell scale– Log core geo cell sim cell

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Log, core, geo cell, sim cell

Scales of Measurement

Well ProductionCore Log

Seismic

Production

Geo ModelGeo Model

Sim ModelMissing Scale

10-5 10-4 10-3 10-2 10-1 100 101 102 103 104

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Measurement Volume m3

log φ vs. log K at different scalesg φ gCore scaleCore scale GridGrid--cell scalecell scale

log (Permeability, mD) at core scale log (Permeability, mD) at grid-cell scale10000 10000

1000

100

1000

100

10

1

10

11

0.1

1

0.1

0 10 0 16 0 20 0 25 0 32 0 400 13 0 10 0 16 0 20 0 25 0 32 0 400 13

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log (Porosity) at core scalelog (Porosity) at grid-cell scale

Modified from: Worthington 2004

0.10 0.16 0.20 0.25 0.32 0.400.13 0.10 0.16 0.20 0.25 0.32 0.400.13


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Model Problems• Too big, Too complex• Too long to build• Delivered lateDelivered late• Don’t meet business needs

Diffi lt t d t• Difficult to update• Difficult to History Match• Homogenously Heterogeneous• Don’t necessarily give good predictions

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Don t necessarily give good predictions

DefinitionsScenario

• Different structural orRealization

• One of a number ofDifferent structural or geological concept e.g.– Fault Configuration

One of a number of outputs from stochastic modeling e.g. Gaussian g

– Depositional Setting Sequential Simulation

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Scenario MethodLow Best High

Scenario A Scenario B Scenario C

2 31 …2 31 … 2 31 …Realizations

• Scenarios may be variously definedW ll it d t l fi ld lif

Realizations

• Well suited to early field life• Later field life may require fewer history-

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matched models

Scenario Method

80

90

100ProbabilisticDeterministic

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50

60

70m

Pro

babi

lity

0

10

20

30Cum

• Uncertainty range often greater between scenarios than

00 50 100 150 200

STOIIP

• Uncertainty range often greater between scenarios than within them

• Risk of under-estimating range of uncertainty

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• Modeling hundreds of realizations doesn’t mean that all the uncertainty has been captured!

What makes a good model?g• Geologically Reasonable

R t l i l d t di– Represents geological understanding– Honors available data

• Allows fast and accurate history match– Assisted by accurate net pay, honoring scale (K,

Sw vs h)• Gives good predictions

– Of geology (in new wells, one by one removal)– Of reservoir performance

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p• Fit for Purpose

Fit for Purposep• Meets Business Needs

– e.g. time, budget, resources, technical• Range of Uncertaintiesg

– e.g. for Development Planning, Reserves• Best Technical CaseBest Technical Case

– e.g. for well planningIf simulation is involved discuss all issues• If simulation is involved, discuss all issues with reservoir engineer:

A l li it O i t ti C ll Si L i30

– Areal limits, Orientation, Cell Size, Layering, Upscaling, Feedback, Key Issues, etc

Possible Solutions and Best Practices• “Top Down” or “First Pass” modeling

C t k t i ti ith ll b f– Capture key uncertainties with small number of simpler models – detail added laterSupports scenario modeling allowing different– Supports scenario modeling allowing different concepts & methods – not just uncertainty

• First Pass / Top Down models allow• First Pass / Top Down models allow– Data Validation, Identify Data Gaps

Early Results and Early Feedback– Early Results and Early Feedback– Quantify Main Uncertainties and Risks

Provide focus for more detailed modeling

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– Provide focus for more detailed modeling• Business requirements define model purpose

Simulation Results can be AmbiguousgA B


Simulation Results indicate that Well A should have less

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Pore Volume and Well B should have more pore volume

Different StructureA BDifferent

interpretation and/or depthand/or depth conversion


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Thicker SandA BDifferent net pay

cut-off results in inclusion of lowerinclusion of lower quality sands


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Better Properties pA B

Different property modeling assumptions e.g. channel width, depositional environment etc


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Different ContactsA B

Deeper contact e.g. different contacts in different faultdifferent fault blocks, contact not observed, uncertainty on pressure depthpressure depth plots etc


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Move FaultA B

Different i t t tiinterpretation and/or depth conversion


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Staffing Issuesg• Software tools are becoming increasingly complex• Software tools are becoming easier to use• Software tools are becoming easier to use• Risk of becoming “Nintendo GeoEngineers”• Specialists required to build a “good” model?• Specialists required to build a good model?• Encourage generalists or specialists?• Many large companies do have specialist• Many large companies do have specialist

geomodelers, with appropriate skills – i.e. Software, Geology, Geostatistics, Experience ……….i.e. Software, Geology, Geostatistics, Experience ……….– Dedicated group and outsourced to assets– Or spread throughout assets

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• Companies with limited staff, resources, time?


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Technological Developments• Better, faster, easier to use software• Grid cell “arms race”• More integration between data and disciplines• New Methods and ToolsNew Methods and Tools

– Grid creation (unstructured, ‘easy gridding’)– Small scale bedding impact on large scale flowSmall scale bedding impact on large scale flow – Multi-point geostatistics– Inversion Loopsp– Discrete Fracture Networks (DFN),Geomechanics

• Increased use of digital & outcrop analogues,

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c eased use o d g a & ou c op a a ogues,– Potential for industry/academia collaboration

Limitations of Traditional Geostatisticsar

ianc

eS

emi-V

a

Lag Distance

1 2 3

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2-point correlation is not enough to characterize connectivitySource: Caers

Multi-Point Geostatistics

• Offers a way of including Training Image

more geology• Training Images requiredTraining Images required• Could be based on digital

analogue/outcrop data Final Modelanalogue/outcrop data• Still depends on

selection of anselection of an appropriate training image

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image

Tetzlaff et al 2005

The Future……….• Effective modeling in the future will require a

blend of technology and processgy p• Technology will continue to evolve

– More detail = better models?– More detail = better models?– Multi-point statistics, better use of seismic, better

workflows automated history match noworkflows, automated history match, no upscaling, etc ………….

• Process and smarter working practices mayProcess and smarter working practices may deliver greater benefits

Better understanding of uncertainty

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– Better understanding of uncertainty– Fit for Purpose models

The Future ?Easy Gridding

Input

G l i lGeological ModelValidation

Validation

Inversion Loops

Automatic History Match

MPS / Digital

Input

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MPS / Digital Analog

Summary & Conclusions• 3D Geoscience modeling will continue to be a

widely used and indispensible technique• Modeling techniques have implicit assumptions built

in to themK h t th d h t ff t th h– Know what they are and what affect they have

• Modeling methods are continuously evolving– Only expert modelers may be able to keep up to dateOnly expert modelers may be able to keep up to date

• Do not be seduced by “Nintendo GeoEngineering” • It is still good practice to:It is still good practice to:

– Think about the purpose of modeling (Fit for Purpose)– Understand data, data validity, data limitations

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– Define main assumptions and continue to challenge them

Let’s Model It!3D Geoscience Modeling – Implications for

Reserves Estimation and Field Development Planning

Doug PeacockGaffney, Cline & Associates

Society of Petroleum Engineers

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Society of Petroleum Engineers Distinguished Lecturer Programwww.spe.org/dl

ReferencesWorthington, P.F., 2004, The Effect of Scale on the

Petrophysical Estimation of Intergranular Permeability:Petrophysical Estimation of Intergranular Permeability: Petrophysics, vol 45, no 1

Caers, J., 2002, Stochastic inverse modeling under realistic gprior model constraints using multiple-point geostatistics. Invited presentation for the IAM2002 Workshop on ""Quantifying uncertainty and multiscale phenomena inQuantifying uncertainty and multiscale phenomena in subsurface processes, Minneapolis, Minnesota, Jan 7-11

Tetzlaff et al, 2005, Application of multipoint geostatistics to honor multiple attribute constraints applied to a deepwater outcrop analog, Tanqua Karoo Basin, South Africa: SEG Expanded Abstracts vol 24, 1370,

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Expanded Abstracts vol 24, 1370,

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SPE Distinguished Lecturer · PDF fileSPE Distinguished Lecturer Program ... • Geoscience to SimulationGeoscience to Simulation ... unreali ti i t tlistic reservoir compartments