NDLI: Uncertainty quantification of reservoir performance predictions using a stochastic optimization algorithm

Content Provider	Springer Nature Link
Author	Li, Gaoming Reylds, Albert C.
Copyright Year	2010
Abstract	The simultaneous perturbation stochastic approximation (SPSA) algorithm is modified to obtain a stochastic Gaussian search direction (SGSD) algorithm for automatic history matching. The search direction in the SGSD algorithm is obtained by simultaneously perturbing the reservoir model with unconditional realizations from a Gaussian distribution. This search direction has two nice properties: (1) it is always downhill in the vicinity of the current iterate and (2) the expectation of the stochastic search direction is an approximate quasi-Newton direction with a prior covariance matrix used as the approximate inverse Hessian matrix. For Gaussian reservoir models, we argue and demonstrate that the SGSD algorithm may generate more geologically realistic reservoir description than is obtained with the original SPSA algorithm. It is shown that the SGSD algorithm represents an approximation of the gradual deformation method but its search direction has the desirable properties that are lacking in all gradual deformation methods. The SGSD algorithm is successfully applied to the well-known PUNQ-S3 test case to generate a maximum a posteriori estimate and for uncertainty quantification of reservoir performance predictions using the randomized maximum likelihood method.
Starting Page	451
Ending Page	462
Page Count	12
File Format	PDF
ISSN	14200597
Journal	Computational Geosciences
Volume Number	15
Issue Number	3
e-ISSN	15731499
Language	English
Publisher	Springer Netherlands
Publisher Date	2010-11-03
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	SGSD SPSA RML Uncertainty quantification History matching Quasi-Newton Hydrogeology Mathematical Modeling and Industrial Mathematics Geotechnical Engineering Soil Science & Conservation
Content Type	Text
Resource Type	Article
Subject	Computers in Earth Sciences Computational Theory and Mathematics Computer Science Applications Computational Mathematics

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Large-scale history matching with quadratic interpolation models

Monte Carlo simulation of permeability fields and reservoir performance predictions with SVD parameterization in RML compared with EnKF

Population MCMC methods for history matching and uncertainty quantification

Direct forecasting of reservoir performance using production data without history matching

Bayesian estimation of reservoir properties—effects of uncertainty quantification of 4D seismic data

Reservoir uncertainty tolerant, proactive control of intelligent wells

Efficient reservoir history matching using subspace vectors

A simultaneous perturbation stochastic approximation algorithm for coupled well placement and control optimization under geologic uncertainty

A parallel ensemble-based framework for reservoir history matching and uncertainty characterization

Uncertainty quantification of reservoir performance predictions using a stochastic optimization algorithm

Similar Documents

Large-scale history matching with quadratic interpolation models

Monte Carlo simulation of permeability fields and reservoir performance predictions with SVD parameterization in RML compared with EnKF

Population MCMC methods for history matching and uncertainty quantification

Direct forecasting of reservoir performance using production data without history matching

Bayesian estimation of reservoir properties—effects of uncertainty quantification of 4D seismic data

Reservoir uncertainty tolerant, proactive control of intelligent wells

Efficient reservoir history matching using subspace vectors

A simultaneous perturbation stochastic approximation algorithm for coupled well placement and control optimization under geologic uncertainty

A parallel ensemble-based framework for reservoir history matching and uncertainty characterization

Uncertainty quantification of reservoir performance predictions using a stochastic optimization algorithm