SCRF 23 Annual Meeting May 5 2010 - Stanford Earth
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SCRF 23 rd Annual Meeting May 5‐6 2010 Stanford Center for Reservoir Forecasting Stanford Center for Reservoir Forecasting Annual Meeting 2010
Transcript of SCRF 23 Annual Meeting May 5 2010 - Stanford Earth
SCRF 23rd Annual Meeting May 5‐6 2010
Stanford Center for Reservoir ForecastingStanford Center for Reservoir Forecasting
Annual Meeting 2010
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Research and Results: Highlights
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Research and Results: Highlights
Five Themes
Ferguson
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ONE
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1.Modeling Uncertainty and Inverse Problems
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1.Challenges
Very high dimensions
Non‐linear
Non‐unique
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1.Reformulation of non‐linear inverse problems in metric space
Kwangwon Park et al.
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1.Modeling Uncertainty and Inverse Problems
Distance‐based methods
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1.Modeling Uncertainty using distance based methods
Celine Scheidt et al.
Construct fine scale modelsUsing coarse scalesimulations
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1.Modeling Uncertainty using distance based methods
Addy Satija
Structural uncertainty
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1.Challenges
Very high dimensions
Non‐linear
Non‐unique
ApproachesMcMC samplingOptimization
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1.Iterative Spatial Resampling
Applied to History Matching
Gregoire Mariethoz et al.
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1.Iterative Spatial Resampling
Conditioning to soft data
Matz Haugen et al.
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1.Challenges
Very high dimensions
Non‐linear
Non‐unique
ApproachesDimension reduction and choice of base
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1.Geological Bases and Uncertainty
Fernandez‐Martinez et al.
Basis vectors
original reconstruction
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1.Challenges
Very high dimensions
Non‐linear
Non‐unique
ApproachesData Integration
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1.
Data Integration Cheolkyun JeongAmit Suman
CoreWell logsSeismic data
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1.Modeling Uncertainty and Inverse Problems
‐ Sensitivity
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1.Uncertainty in Basin and Petroleum System Modeling
‐ SensitivityBin Jia
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TWO
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2.Particle Swarm Optimization
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2.Particle Swarm Optimization
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2.Particle Swarm Optimization
Spring‐mass system
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2.Particle Swarm OptimizationStochastic differential equationParticle trajectory = stochastic processes
Analysis of stability
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2.Particle Swarm OptimizationThough stochastic PSO is not heuristic
Design of algorithm
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2.PSO: Advances and Applications in Seismic History Matching
Fernandez‐Martinez et al.
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2.Integration of production and time lapse Seismic data: Norne field
Amit Suman et al.
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THREE
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3.Hybrid Geomodeling
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3.Hybrid Geomodeling
Holly Michael et al. 2008/2009
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3.Hybrid Geomodeling
Leiva, 2009
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3.Hybrid Geomodeling
‐ conditioningAntoine Bertoncello et al.
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3.Hybrid Geomodeling
‐ Sensitivity analysis using distances
Antoine Bertoncello et al.
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3.Hybrid Geomodeling
Cellular Automata flow models
Siyao Xu
Salles et al. 2008 (CSIRO/IFP)
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FOUR
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4.Software
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4.Metrel
Petrel plug‐in for modeling uncertainty in metric space
Kwangwon Park et al.
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4.• New features in SGeMS
RegionsPartition a grid into active and inactive cells
Commands to add, delete and merge regions
Rahul Gupta et al.
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4.• New features in SGeMS
Ease of Use• Quick access to common operations
• Data Transforms
• Access to user defined python scripts
Rahul Gupta et al.
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4.Tetris Alex Boucher et al.
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4.Tetris Alex Boucher et al.
Next generation training image generator for SGeMS
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4.Tetris Alex Boucher et al.
Recursive Composition Technique
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4.DISPAT Mehrdad Honarkhah
Distance based pattern simulation
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FIVE
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5.Value of Information
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5.Spatial modeling and VOI
‐ data reliability and workflow
Trainor‐Guitton et al.
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5.Value of Information
‐ History matching
Bratvold and Mukerji
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History Matching, rms
Bratvold
Success of history matching (Bos, 2002)
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5.6
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00.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
Fore
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Stop?
History Matching, rms
Bratvold
Success of history matching (Bos, 2002)
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5.Value of Information
‐ Decision driven history matching
Bratvold
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Guest SpeakersProf. Reidar Bratvold
University of Stavanger
Prof. Sanjay Srinivasan
University of Texas, Austin
Dr. Faruk O. Alpak, Shell
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Research and Results: Highlights
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Research Report
Digital annual report with papers
Presentations: http://scrf.stanford.edu
SCRF 23rd Annual Meeting May 5‐6 2010
Stanford Center for Reservoir ForecastingStanford Center for Reservoir Forecasting
Annual Meeting 2010
