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

SCRF 26th Annual Meeting SCRF Overview 2013 Research Highlights

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

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

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

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

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

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

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

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

SCRF 26th Annual Meeting 2013 Research and Results: Highlights

1. Modeling Uncertainty

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

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

1. Fenwick, Scheidt, Caers Distance based sensitivity analysis

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

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

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

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

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

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

2. Multiple Point Pattern Simulation Algorithms

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

3. Integrating Geophysical Data 24

3. Core Well logs Seismic data Data Integration

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

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

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

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.

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

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

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

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

3. Southern part of Norwegian sea Norne Field Segment E

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

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

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

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

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

4. Hybrid Geomodeling

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

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

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

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

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

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

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 http://github.com/SCRFpublic/SGEMS-UQ.

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

Guest Speaker Professor Roussos Dimitrakopoulos

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

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