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SPE Distinguished Lecturer Program
Primary funding is provided by
The SPE Foundation through member donations and a contribution from Offshore Europe
The Society is grateful to those companies that allow their professionals to serve as lecturers
Additional support provided by AIME
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Society of Petroleum Engineers Distinguished Lecturer Programwww.spe.org/dl
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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
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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
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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
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Classical Modeling Workflowg
Well Correlation Mapping Structural Model
6Facies ModelPetrophysical
ModelSimulation Model
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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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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
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Structural Issues
Oil W COil Water Contact
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Fault PositionLost
VolumeVolume
Oil W COil Water Contact
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Fault Angleg
Oil W COil Water Contact
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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
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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?
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Algorithm Assumptionsg pWhich
Al ith ?Algorithm?
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Moving Average Gaussian Sequential Simulation
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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
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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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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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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
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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
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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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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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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
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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
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Scenario Method
80
90
100ProbabilisticDeterministic
40
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!
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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
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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
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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
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Simulation Results can be AmbiguousgA B
Oil W COil Water Contact
Simulation Results indicate that Well A should have less
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Pore Volume and Well B should have more pore volume
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Different StructureA BDifferent
interpretation and/or depthand/or depth conversion
Simulation Results indicate that Well A should have less
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Pore Volume and Well B should have more pore volume
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Thicker SandA BDifferent net pay
cut-off results in inclusion of lowerinclusion of lower quality sands
Simulation Results indicate that Well A should have less
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Pore Volume and Well B should have more pore volume
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Better Properties pA B
Different property modeling assumptions e.g. channel width, depositional environment etc
Simulation Results indicate that Well A should have less
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Pore Volume and Well B should have more pore volume
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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
Simulation Results indicate that Well A should have less
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Pore Volume and Well B should have more pore volume
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Move FaultA B
Different i t t tiinterpretation and/or depth conversion
Simulation Results indicate that Well A should have less
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Pore Volume and Well B should have more pore volume
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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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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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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
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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
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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
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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
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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
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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
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Let’s Model It!3D Geoscience Modeling – Implications for
Reserves Estimation and Field Development Planning
Doug PeacockGaffney, Cline & Associates
Society of Petroleum Engineers
46
Society of Petroleum Engineers Distinguished Lecturer Programwww.spe.org/dl
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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,