SCRF 24th Annual Meeting May 4‐5 2011

Stanford Center for Reservoir Forecasting

Annual Meeting 2011

SCRF 2011 2

SCRF 24th Annual Meeting

•SCRF Overview

•2011 Research Highlights

SCRF 2011 3

SCRF Mission

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

SCRF Overview

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• Quantitative modeling of geological heterogeneity

• Modeling uncertainty

• Building 3D/4D models accounting for scale and accuracy of geological, geophysical and reservoir engineering data

SCRF Overview

SCRF 2011 5

SCRF Research topics

• Modeling uncertainty• Uncertainty of global reservoir parameters• Modeling integrated uncertainty in metric space• Distance‐Kernel Method

• Multiple‐point geostatistics • How to quantify geological concepts / Training images ?• 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 2011 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

• History matching & 4D seismic• Geologically consistent HM• Workflows for integrating 4D seismic• Streamline‐based HM

• Value of Information• Decision driven modeling of uncertainty

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SCRF: Students, Staff, and Faculty• Graduate students (~10)

• Research Associates, Post‐docsCeline 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

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•SRBRock Physics

•SUPRI/Smart FieldsFlow simulation

•SEP Seismic Imaging

•SPODDSDeep Water Systems

•BPSMBasin Modeling

SCRF: Stanford Collaborations 

SCRF: Affiliate Members

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Long‐term research goals are made possible throughcontinuous funding of most major oil, service and software companies

New sponsors:BPGDF SuezUniversite de Neuchatel

SCRF: Membership Benefits

• Graduates

• Facilitated access to research• Reports

• Theses

• Software

• Annual Meeting

• Visits

• Research collaborations

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SCRF 2011 11

SCRF 24th Annual Meeting

2011 Research and Results: Highlights

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1.Modeling Uncertainty in Metric Space and 

Distance Kernel Methods

Multidimensional Scaling (MDS)Caers et al., 2009

Map a set of N earth models using a pair wise distance between them.

1.

Scheidt & Caers

1.

1.Kwangwon Park, Ph.D. Thesis

Modeling Uncertainty in Metric Space

Reformulation of non‐linear inverse problems in metric space

SCRF 2011 15

1.

SCRF 2011 16

Scheidt & Caers

How many models?

1.

SCRF 2011 17

Time

Water produ

ction

Scheidt & Caers

P25‐P75 Interval

Adaptive sequential space filling design for geostatistical simulations

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1.Distance based sensitivity analysis for fractured reservoirs

Fenwick & Park

SCRF 2011 19

1.Distance based modeling of structural uncertainty

Addy Satija

Faults and well placement uncertainty 

SCRF 2011 20

1.Distance based modeling of uncertain geologic scenarios

Park and Caers

P( geologic scenario | data) 

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2.Multiple Point Geostatistics

Algorithms

SCRF 2011 22

2.Mehrdad Honarkhah, Ph.D. thesis

Stochastic Simulation of Patterns using Distance‐Based Pattern Modeling

DISPAT

SCRF 2011 23

2.Mehrdad Honarkhah, Ph.D. thesisModeling non‐stationarity

2‐D Gabor filter

SCRF 2011 24

2.

Mehrdad Honarkhah, Ph.D. thesis

Modeling non‐stationarity

Gabor filter bank

orientation

freq

uency

SCRF 2011 25

2.Mehrdad Honarkhah, Ph.D. thesis

Convolution with Gabor filter

Extracted features used for clustering and automatic segmentation

SCRF 2011 26

2.Mehrdad Honarkhah, Ph.D. thesisMPS with non‐stationarity

SCRF 2011 27

3.Seismic Reservoir Characterization

Quantitative seismic interpretation

Seismic inversion for facies and fluids

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3.

Data Integration

CoreWell logsSeismic data

3.

Seismic Impedance, Molasse Basin, Austria

Stright et al.

Multi‐attribute, multi‐scale calibration for reservoir characterization

3.Stright et al.

3.Stright et al.

Tetris/SGeMS

Geostatistical modeling of sub‐seismic scale sandstone packages.

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3.Seismic inversion for facies and fluids

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3.

Cascaded or multi‐step approach

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3.

Cascaded or multi‐step approach

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3.

Geostats

Cascaded or multi‐step approach

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3.

Geostats

Simultaneous or single‐loop approach

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3.

Geostats

Simultaneous or single‐loop approach

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3.

Geostats

Simultaneous or single‐loop approach

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3.

Perturb the initial modelGeostats

Simultaneous or single‐loop approach

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3.Dario Grana

Two angle stacks:

• Near = 20°• Far    = 44°

Well data

Single‐loop inversion using probability perturbation method

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3.Dario Grana

Litho‐fluid classes

Single‐loop inversion using probability perturbation method

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3.

Iterative Spatial ResamplingApplied to Single Loop Seismic Inversion

Cheolkyun JeongGregoire Mariethoz

SCRF 2011 43

3.Iterative Spatial Resampling (ISR)

Gregoire Mariethoz et al.

ISR perturbs realizations of a spatially dependent variable while preserving its spatial structure.

SCRF 2011 44

3.Adaptive Spatial Resampling (ASR) Cheolkyun Jeong

Gregoire Mariethoz

SCRF 2011 45

Log1

0RM

SE

Iterations

3.ASR optimization

Reference model

Cheolkyun JeongGregoire Mariethoz

SCRF 2011 46

4.Seismic History Matching

Production dataTime‐lapse seismic data

SCRF 2011 47

Integration of production and time lapse Seismic data: Norne field Amit Suman

4.

Southern part of Norwegian sea

C

D

EG

C

D

EG

Norne Field Segment E

4.

• Well logs

• Horizons

• Well data

‐ Oil , gas and water flow rate

‐ BHP (Bottom hole pressure)

4.

Time Lapse Seismic Data

Near, Mid , Far and Full stack 3D seismic Q – Marine Seismic

2001 2003 2004 2006

4.

Generatemultiplemodels

Evaluatemisfit

.

Reservoir

Model

Observed flow and seismic response

Predicted flow and seismic response

Joint Inversion Loop

4.

What are the sensitive parameters in joint time‐lapse and production inversion loop?

– Flow response

– Seismic response

4.

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5.Hybrid Geomodeling–Surface based models

–Generalized cellular automata

Two points

Multiple points

Object based

Surface based

Process based

Conditioning capabilities

Geological realism

Bertoncello et al. 

5.

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5.Hybrid Geomodeling

Holly Michael et al. 2008/2009Leiva, 2008

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5.Conditioning surface based models to log data

Lobe‐by‐lobe optimization using genetic algorithm and Nelder‐Mead

Bertoncello et al.

SCRF 2011 57

5.Conditioning surface based models to log data: Application to Karoo dataset 

Bertoncello et al.

9 wells2 sediment sources

Flow physics

Bed surface

processes

Fluid mechanics

(Physics equations)

Generalized cellular automata (Rule‐based)

Decomposition of the problem

5.Hybrid Geomodeling Siyao Xu

A coupled scheme

Generalized Cellular Automata Bed Surface Model

Local Flow EnergyLocal Bed Surface

Analytical Flow Equations

Lattice Boltzmann Flow Model

Discretize

Siyao Xu

5.

SCRF 2011 60

Research and Results: Highlights

1.Modeling Uncertainty ‐ Distance‐Kernel Methods

2.Multiple‐point simulation algorithms

3.Seismic reservoir characterization

4.Time‐lapse seismic history matching

5.Hybrid geomodeling

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Guest Speakers

Lisa StrightUniversity of Utah

Gregoire MariethozUniversity of Texas, Austin

Darryl FenwickStreamsim

SCRF 2011 62

Research Report

Digital annual report with papers

Ph.D. Theses

Presentations: http://scrf.stanford.edu

SCRF 24th Annual Meeting May 4‐5 2011

Stanford Center for Reservoir Forecasting

Annual Meeting 2011