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SCRF 26 th Annual Meeting May 8-9 2013

This event showcases leading research in quantitative reservoir modeling, emphasizing data integration and uncertainty assessment. Explore topics like modeling geological heterogeneity, uncertainty, and 3D/4D models incorporating vast data sets. Discover seismic reservoir characterization, statistical rock physics, and seismic data interpretation. Engage with SCRF professors, staff, and students, advancing collaboration with various research groups. Benefit from research reports, theses, and software access with SCRF membership. Unveil research insights such as Distance Kernel Methods, Multidimensional Scaling, and Uncertain Geologic Scenario Modeling. Witness groundbreaking projects like Joint Inversion of Production and Time-lapse Seismic Data. Connect with SCRF's affiliate members and their long-term research goals. Join this research-focused annual meeting to delve into the cutting-edge of reservoir forecasting.

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SCRF 26 th Annual Meeting May 8-9 2013

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  1. Annual Meeting 2013 Stanford Center for Reservoir Forecasting SCRF 26th Annual Meeting May 8-9 2013

  2. SCRF 26th Annual Meeting • SCRF Overview • 2013 Research Highlights

  3. SCRF Overview SCRF Mission Leading research in quantitative reservoir modeling with a focus on data integration and assessing uncertainty

  4. SCRF: Overview • Quantitative modeling of geological heterogeneity • Modeling uncertainty • Building 3D/4D models accounting for scale and accuracy of geological, geophysical and reservoir engineering data

  5. SCRF: Research topics • Modeling uncertainty • Modeling integrated uncertainty in metric space • Distance-Kernel Method • Quantifying geological scenario uncertainty • Multiple-point geostatistics • Stochastic simulation of (geo)patterns • Design of fast and robust geostatistical algorithms • Application to actual reservoirs, carbonate and clastic • Hybridization with surface and object-based methods

  6. SCRF: Research topics • Seismic reservoir characterization • Statistical Rock physics • Interpretation of facies from seismic data • Dealing with sub-seismic scale • Integrating different types of geophysical data • Seismic constraints for Basin Modeling • Time-lapse seismic and history matching • Geologically consistent HM • Workflows for integrating 4D seismic • Streamline-based HM • Value of Information • Decision driven modeling of uncertainty

  7. SCRF: Students, Staff, and Faculty Graduate students (~17) Post-docs Andre Jung, Pejman Tahmasebi Research Staff Celine Scheidt Staff Thuy Nguyen, Joleen Castro Faculty Jef Caers Tapan Mukerji Alexandre Boucher Work closely with other research groups in the School of Earth Sciences

  8. SCRF: Stanford Collaborations • SRB • Rock Physics • SUPRI/Smart Fields • Flow simulation • SEP • Seismic Imaging • SPODDS • Deep Water Systems • BPSM • Basin Modeling

  9. SCRF: Affiliate Members Long-term research goals are made possible through continuous funding of most major oil, service and software companies ~20 affiliate members

  10. SCRF: Membership Benefits • Graduates • Facilitated access to research • Reports • Theses • Software • Annual Meeting • Visits • Research collaborations

  11. SCRF 26th Annual Meeting 2013 Research and Results: Highlights

  12. 1. Modeling Uncertainty

  13. 1. Modeling Uncertainty • Distance Kernel Methods • Generalized Sensitivity Analysis (D-GSA)

  14. 1. Multidimensional Scaling (MDS) Caers et al., 2009 Map a set of N earth models using a pair wise distance between them.

  15. 1. Distance based sensitivity analysis Fenwick, Scheidt, Caers

  16. 1. Distance based sensitivity analysis - applications - reservoir modeling - basin and petroleum system modeling - seismic interpretation - 4-D seismic

  17. 1. Distance based sensitivity analysis Addy Satija Not sensitive parameters Fix to what value?

  18. 1. Distance based modeling of uncertain geologic scenarios O Scenario 1 OScenario 2 P( geologic scenario | data) Updating geologic scenario * data 18

  19. 1. Andre Jung Distance based scenario analysis for fractured reservoirs Spatial patterns of dual porosity effective properties

  20. 1. Orhun Aydin, Celine Scheidt Distance Based Modeling of Uncertainty Distance between shapes and patterns

  21. 1. Lewis Li, Jef Caers Modeling Uncertainty A possible alternative to probability?

  22. 2. Multiple Point Pattern Simulation Algorithms

  23. 2. MS-CCSIM Pejman Tahmasebi Multi-scale cross-correlation simulation

  24. 3. Integrating Geophysical Data 24

  25. 3. Core Well logs Seismic data • Data Integration

  26. 3. Integrating geophysical data Quantitative seismic interpretation Seismic inversion for facies and fluids 26

  27. 3. Spatial model Perturb the initial model Seismic inversion for litho-fluid facies Simultaneous or single-loop approach 27

  28. 3. Iterative Adaptive Spatial Resampling Applied to Seismic Inversion for facies Cheolkyun Jeong Gregoire Mariethoz 28

  29. 3. Iterative Spatial Resampling (ISR) Markov chain Monte Carlo (McMC): perturbs realizations of a spatially dependent variable while preserving its spatial structure. Gregoire Mariethoz et al.

  30. 3. Cheolkyun Jeong Adaptive spatial resampling in 3D well Reference Posterior sample Seismic impedance

  31. 3. Seismic time-lapse inversion Dario Grana Changes in fluid saturations and pressure Time-lapse seismic difference Near, mid and far angle 31

  32. 3. Seismic History Matching Production data Time-lapse seismic data 32

  33. 3. Integration of production and time lapse seismic data: Norne field Amit Suman

  34. 3. Southern part of Norwegian sea Norne Field Segment E

  35. 3. • Well logs • Horizons • Well data • - Oil , gas and water flow rate • - BHP (Bottom hole pressure) • Time-lapse seismic data

  36. 3. Predicted flow and seismic response Observed flow and seismic response Joint Inversion Loop Model Reservoir

  37. 3. What are the sensitive parameters in joint time-lapse and production inversion loop? • Flow response • Seismic response

  38. 3. AmitSuman, Ph.D. dissertation JOINT INVERSION OF PRODUCTION AND TIME-LAPSE SEISMIC DATA: APPLICATION TO NORNE FIELD

  39. 3. Jaehoon Lee Integrating seismic and electromagnetic time-lapse data Well-Log scale Field scale Scaling distributions

  40. 4. Hybrid Geomodeling

  41. 4. Hybrid Geomodeling • Surface based models • Generalized cellular automata • Quantitative geologic models

  42. 4. Bertoncello et al. Two points Multiple points Geological realism Object based Surface based Process based Conditioning capabilities

  43. 4. Prof. Chris Paola St. Anthony Falls Lab (UMN) Tank Experiment

  44. Statistical Similarity between Stacking Patterns: Linking Tank Experiments to Field Scale Extract morphometrics From tank data 4. Siyao Xu 44

  45. 4. Modeling channelized systems Yinan Wang Generalized cellular model Topography Avulsion

  46. 5. Software

  47. 5. Alex Boucher Lewis Li • C++ toolkit for Multiple Point Simulation • SGEMS-UQ • SGEMS plug-in • efficient workflow for performing distance-based uncertainty quantification code and tutorial example available from http://github.com/SCRFpublic/SGEMS-UQ.

  48. 2013 Research Highlights • Modeling Uncertainty • -Distance-based generalized sensitivity analysis • -Scenario uncertainty and updating • Multiple-point pattern simulation • -MS-CCSIM • Integrating geophysical data • -Seismic reservoir characterization • -Time-lapse data • Hybrid geomodeling • Tank experiment analysis • Modeling channelized systems • Software – SGEMS-UQ

  49. Guest Speaker Professor Roussos Dimitrakopoulos

  50. Research Report Digital annual report with papers Ph.D. Theses Presentations: http://scrf.stanford.edu

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