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

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.
Fenwick, Scheidt, Caers
Distance based sensitivity analysis

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

18. 1. Distance based modeling of uncertain geologic scenarios Updating
O Scenario 1
O Scenario 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. Distance Based Modeling of Uncertainty
Orhun Aydin, Celine Scheidt
Distance Based Modeling of Uncertainty
Distance between shapes and patterns

21. 1. Modeling Uncertainty A possible alternative to probability?
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. Perturb the initial model3.
Spatial model
Perturb the initial model
Seismic inversion for litho-fluid facies
Simultaneous or single-loop approach 27

28. 3. Iterative Adaptive Spatial Resampling
Cheolkyun Jeong
Gregoire Mariethoz
Iterative Adaptive Spatial Resampling
Applied to Seismic Inversion for facies 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. Adaptive spatial resampling in 3D well Reference Posterior sample
Cheolkyun Jeong
Adaptive spatial resampling in 3D
well
Reference
Posterior sample
Seismic impedance

31. 3. Seismic time-lapse inversion Changes in fluid saturations
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. Model Reservoir Predicted flow and seismic response
Joint Inversion Loop
Observed flow and seismic response
Model
Reservoir

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

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

39. 3. Integrating seismic and electromagnetic time-lapse data
Jaehoon Lee
Integrating seismic and electromagnetic
time-lapse data
We have two models in the scheme. The flow model, which is governed by flow equaionts, is using lattice boltzmann method to avoid complexity, which will be discussed latter.
The bed is model by cellular automata, which applies rules locally, considering flow and topography
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. Geological realism Conditioning capabilities 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. 4.
Statistical Similarity between Stacking Patterns: Linking Tank Experiments to Field Scale
Extract
morphometrics
From tank data
Siyao Xu 44

45. 4. Modeling channelized systems Generalized cellular model Topography
Yinan Wang
Flow physics, important factor for erosion, but has not been considered yet
Bed surface, important factor for erosion, but also in response to flow
Sediment transport physic, important but correlated to the above two, for the aim of this study, this is not considered yet.
Flow – too complex to be described by rules, and also there are lots of techniques studies flow, so it is modeled by real physics equations
Bed – we try local rules first
Generalized cellular model
Topography
Avulsion

46. 5.
Software
We have two models in the scheme. The flow model, which is governed by flow equaionts, is using lattice boltzmann method to avoid complexity, which will be discussed latter.
The bed is model by cellular automata, which applies rules locally, considering flow and topography

47. 5. C++ toolkit for Multiple Point Simulation SGEMS-UQ SGEMS plug-in
Alex Boucher
Lewis Li
C++ toolkit for Multiple Point Simulation
SGEMS-UQ
SGEMS plug-in
efficient workflow for performing distance-based uncertainty quantification
We have two models in the scheme. The flow model, which is governed by flow equaionts, is using lattice boltzmann method to avoid complexity, which will be discussed latter.
The bed is model by cellular automata, which applies rules locally, considering flow and topography
code and tutorial example available from

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:

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