Stanford Center for Reservoir Forecasting

Slides:

Advertisements

Similar presentations

Uncertainty in reservoirs

Advertisements

A multi-scale, pattern-based approach to sequential simulation annual scrf meeting, may 2003 stanford university burc arpat ( coaching provided by jef.

1 (from Optimization of Advanced Well Type and Performance Louis J. Durlofsky Department of Petroleum Engineering, Stanford University.

I DENTIFICATION OF main flow structures for highly CHANNELED FLOW IN FRACTURED MEDIA by solving the inverse problem R. Le Goc (1)(2), J.-R. de Dreuzy (1)

Classification: Internal Status: Draft Using the EnKF for combined state and parameter estimation Geir Evensen.

Projective Texture Atlas for 3D Photography Jonas Sossai Júnior Luiz Velho IMPA.

16 th Annual Meeting Stanford Center for Reservoir Forecasting Stanford Center for Reservoir Forecasting.

Uncertainty Maps for Seismic Images through Geostatistical Model Randomization Lewis Li, Paul Sava, & Jef Caers 27 th SCRF Affiliates’ Meeting May 8-9.

Markov Random Fields Probabilistic Models for Images

Bayesian Reasoning: Tempering & Sampling A/Prof Geraint F. Lewis Rm 560:

Stochastic inverse modeling under realistic prior model constraints with multiple-point geostatistics Jef Caers Petroleum Engineering Department Stanford.

Céline Scheidt and Jef Caers SCRF Affiliate Meeting– April 30, 2009.

Quality of model and Error Analysis in Variational Data Assimilation François-Xavier LE DIMET Victor SHUTYAEV Université Joseph Fourier+INRIA Projet IDOPT,

Remarks on the TAU grid adaptation Thomas Gerhold.

Adaptive Spatial Resampling as a McMC Method for Uncertainty Quantification in Seismic Reservoir Modeling Cheolkyun Jeong*, Tapan Mukerji, and Gregoire.

Dr. Monia Santini University of Tuscia and CMCC CMCC Annual Meeting

History Matching Flowmeter Data in the Ghawar Field

Hyucksoo Park, Céline Scheidt and Jef Caers Stanford University Scenario Uncertainty from Production Data: Methodology and Case Study.

A General Approach to Sensitivity Analysis Darryl Fenwick, Streamsim Technologies Céline Scheidt, Stanford University.

Retrospective Production Optimization Under Uncertainty Using Kernel Clustering Mehrdad Gharib Shirangi and Tapan Mukerji Department of Energy Resources.

Stanford Center for Reservoir Forecasting The Stanford VI-E Reservoir: A Synthetic Data Set for Joint Seismic-EM Time- lapse Monitoring Algorithms Jaehoon.

Hybrid Bayesian Linearized Acoustic Inversion Methodology PhD in Petroleum Engineering Fernando Bordignon Introduction Seismic inversion.

SEISMIC ATTRIBUTES FOR RESERVOIR CHARACTERIZATION

Geostatistical History Matching Methodology using Block-DSS for Multi-Scale Consistent Models PHD PROGRAM IN PETROLUM ENGINEERING CATARINA MARQUES

Measurement and Control of Reservoir Flow

21st Mediterranean Conference on Control and Automation

Yao Tong, Tapan Mukerji Stanford University

Thin sub-resolution shaly-sands

Maria Volkova, Mikhail Perepechkin, Evgeniy Kovalevskiy*

Amit Suman and Tapan Mukerji

Farthest Point Seeding for Efficient Placement of Streamlines

Cheolkyun Jeong, Céline Scheidt, Jef Caers, and Tapan Mukerji

A strategy for managing uncertainty

Computer Vision Lecture 12: Image Segmentation II

Jef Caers, Xiaojin Tan and Pejman Tahmasebi Stanford University, USA

Automatic Picking of First Arrivals

Addy Satija and Jef Caers Department of Energy Resources Engineering

SCRF 26th Annual Meeting May

Yongduk Shin and Tapan Mukerji May 8th, 2014

Pejman Tahmasebi and Jef Caers

Jincong He, Louis Durlofsky, Pallav Sarma (Chevron ETC)

S-GEMS-UQ: An Uncertainty Quantification Toolkit for SGEMS

New Software Tools for Geostatistics: GsTL and Simulacre Nicolas Remy

Jef Caers, Céline Scheidt and Pejman Tahmasebi

Pejman Tahmasebi, Thomas Hossler and Jef Caers

Modeling sub-seismic depositional lobes using spatial statistics

Céline Scheidt, Jef Caers and Philippe Renard

Assessing uncertainties on production forecasting based on production Profile reconstruction from a few Dynamic simulations Gaétan Bardy – PhD Student.

Haim Kaplan and Uri Zwick

Fast Pattern Simulation Using Multi‐Scale Search

Efficient Distribution-based Feature Search in Multi-field Datasets Ohio State University (Shen) Problem: How to efficiently search for distribution-based.

Problem statement Given: a set of unknown parameters

Upscaling of 4D Seismic Data

Céline Scheidt, Jef Caers and Philippe Renard

Jaehoon Lee, Tapan Mukerji, Michael Tompkins

Céline Scheidt, Pejman Tahmasebi and Jef Caers

A Multimodel Drought Nowcast and Forecast Approach for the Continental U.S. Dennis P. Lettenmaier Department of Civil and Environmental Engineering University.

Brent Lowry & Jef Caers Stanford University, USA

Yao Tong, Tapan Mukerji Stanford University

Siyao Xu Earth, Energy and Environmental Sciences (EEES)

Stanford Center for Reservoir Forecasting

Semi-Numerical Simulations of

SCRF High-order stochastic simulations and some effects on flow through heterogeneous media Roussos Dimitrakopoulos COSMO – Stochastic Mine Planning.

SPDA-1-3-OBS Software Upgrade

Energy Resources Engineering Department Stanford University, CA, USA

Paper No. SPE MS Low-dimensional tensor representations for the estimation of petrophysical reservoir parameters Edwin Insuasty , Eindhoven University.

MCMC Inference over Latent Diffeomorphisms

Siyao Xu, Andre Jung Tapan Mukerji and Jef Caers

Yalchin Efendiev Texas A&M University

Stochastic Methods.

Presentation transcript:

Stanford Center for Reservoir Forecasting Direct updating of geostatistical reservoir models using iterative resampling with DISPAT Xiaojin Tan & Jef Caers SCRF 2012 SCRF 2012

Motivation Question addressed: How to update a single legacy reservoir model with new production data? Example Single legacy model matches history but there is No geostat input (variograms, TI) No parameterization No software for model updating

Basic idea Use the current existing reservoir model as a training image in a non-stationary geostatistical algorithm termed dispat. Using iterative spatial resampling (ISR) to update the current legacy model with the additional production data

DisPat Why Dispat? Every single real legacy model has non-stationary elements Conditioned to wells Conditioned to seismic Imposed layering and trends (vertical/horizontal) CPU-efficient for large models

Mariethoz et al., 2010 m1 m2 m3 Sampling Sampling r1 r2 1.only one parameter required 2.keep the same spatial continuity Mariethoz et al., 2010

Metropolis Sampling Current model mi proposal model m* Perturb using ISR proposal model current model flow simulation flow simulation Water Rate Water Rate target Accept with p=L(m*)/ L(mi) target days days

Updating with Dispat and ISR Summary Current reservoir = TI, Current reservoir = mi Start Sampler ISR proposes m* Run the flow simulator to obtain L(m*). Accept/rejection according to the Metropolis criterion The training image remains the same

Results Study properties of resampling with dispat apply ISR to realizations generated by dispat Updating with regions freeze a part of domain Flow modeling the influence of the amount of perturbation on a flow response

Properties of resampling with dispat Training image Single realization Data extracted dispat The only input required with dispat a base case to create perturbation with different amount of data extracted regular grids coarse grid locations avoid discontinuity near data locations

Conditioning with resampled points

Effect on ensemble average # resampled points =121 # resampled points =361 more perturbation blurry less perturbation crispy

Updating with regions # resampled points =18 # resampled points =128 fix the bottom part and perturb the top part

Updating with regions # resampled points =18 # resampled points =128

Effect on Ensemble Average # resampled points =18 # resampled points =128 no discontinuity at the region boundary more perturbation less perturbation

Flow modeling Investigate the influence of the amount of perturbation on a flow response Water Rate Water rate Time, days Producer well Injector well

Flow modeling Investigate the influence of the amount of perturbation on a flow response Large perturbation (small # resampled points) Large perturbation (small # resampled points) Large perturbation (small # resampled points) Water Rate Water Rate Water Rate Water Rate Black = base case Black = base case Black = base case Black = base case Small perturbation (large # resampled points) days

Reservoir updating: proof of concept A simple illustrative example: Legacy model new data Water rate Forecast with current model History data Producer well days Injector well

Reservoir updating proof of concept Updated with Metropolis a single legacy reservoir model updated model Water Rate target data days

Conclusions What is the appeal of the idea ? Practical No model parameterization No need for ensemble construction Applications envisioned Mature fields 4D seismic