NDLI: Large-scale history matching with quadratic interpolation models

Content Provider	Springer Nature Link
Author	Zhao, Hui Li, Gaoming Reylds, Albert C. Yao, Jun
Copyright Year	2012
Abstract	Due to the limited availability of adjoint code in commercial reservoir simulators for gradient calculations, there is a need to explore the applicability of derivative-free optimization algorithms for large-scale history matching. This paper tests the utility of three derivative-free optimization algorithms (stochastic Gaussian search direction (SGSD), new unconstrained optimization algorithm (NEWUOA), and quadratic interpolation model-based algorithm guided by approximate gradient (QIM-AG)) for history matching. The SGSD method uses a negative stochastic gradient which is obtained by simultaneously perturbing all the model parameters using a Gaussian random vector. For a continuous objective function and a sufficiently small perturbation size, the stochastic gradient is always uphill and the expectation of the stochastic gradient converges to the true gradient as the perturbation size goes to zero. NEWUOA is a quadratic interpolation model-based optimization algorithm. At each iteration, the objective function is first approximated by a quadratic interpolation model. The quadratic model is then minimized to obtain an updated reservoir description for the next iteration. The number of interpolation points (reservoir simulation runs) required by NEWUOA must be larger than the dimension of reservoir model parameter space in order to construct the initial quadratic model. QIM-AG reduces the required number of interpolation points by replacing the first-order coefficients that appear in the quadratic model by an approximate gradient. The approximate gradient used in this study is an average of several stochastic gradients from SGSD. To reduce the dimension of the optimization problem, a simple parameterization method based on the prior covariance matrix is applied. The prior covariance matrix is approximated using an ensemble of unconditional realizations. The parameterization avoids the calculation of the inverse of the prior covariance matrix during optimization and may further regularize the ill-posed inverse problem.
Starting Page	117
Ending Page	138
Page Count	22
File Format	PDF
ISSN	14200597
Journal	Computational Geosciences
Volume Number	17
Issue Number	1
e-ISSN	15731499
Language	English
Publisher	Springer Netherlands
Publisher Date	2012-10-19
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	SGSD NEWUOA Parameterization History matching Uncertainty quantification Earth Sciences Geotechnical Engineering & Applied Earth Sciences Hydrogeology Mathematical Modeling and Industrial Mathematics 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

Uncertainty quantification of reservoir performance predictions using a stochastic optimization algorithm

Population MCMC methods for history matching and uncertainty quantification

Recent progress on reservoir history matching: a review

Local adaptive parameterization for the history matching of 3D seismic data

History matching and uncertainty quantification of facies models with multiple geological interpretations

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

History matching of statistically anisotropic fields using the Karhunen-Loeve expansion-based global parameterization technique

Direct forecasting of reservoir performance using production data without history matching

Distance parameterization for efficient seismic history matching with the ensemble Kalman Filter

Large-scale history matching with quadratic interpolation models

Similar Documents

Uncertainty quantification of reservoir performance predictions using a stochastic optimization algorithm

Population MCMC methods for history matching and uncertainty quantification

Recent progress on reservoir history matching: a review

Local adaptive parameterization for the history matching of 3D seismic data

History matching and uncertainty quantification of facies models with multiple geological interpretations

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

History matching of statistically anisotropic fields using the Karhunen-Loeve expansion-based global parameterization technique

Direct forecasting of reservoir performance using production data without history matching

Distance parameterization for efficient seismic history matching with the ensemble Kalman Filter

Large-scale history matching with quadratic interpolation models