An iterative stochastic ensemble method for parameter estimation of subsurface flow models

Handle URI:
http://hdl.handle.net/10754/562779
Title:
An iterative stochastic ensemble method for parameter estimation of subsurface flow models
Authors:
Elsheikh, Ahmed H.; Wheeler, Mary Fanett; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Parameter estimation for subsurface flow models is an essential step for maximizing the value of numerical simulations for future prediction and the development of effective control strategies. We propose the iterative stochastic ensemble method (ISEM) as a general method for parameter estimation based on stochastic estimation of gradients using an ensemble of directional derivatives. ISEM eliminates the need for adjoint coding and deals with the numerical simulator as a blackbox. The proposed method employs directional derivatives within a Gauss-Newton iteration. The update equation in ISEM resembles the update step in ensemble Kalman filter, however the inverse of the output covariance matrix in ISEM is regularized using standard truncated singular value decomposition or Tikhonov regularization. We also investigate the performance of a set of shrinkage based covariance estimators within ISEM. The proposed method is successfully applied on several nonlinear parameter estimation problems for subsurface flow models. The efficiency of the proposed algorithm is demonstrated by the small size of utilized ensembles and in terms of error convergence rates. © 2013 Elsevier Inc.
KAUST Department:
Earth Science and Engineering Program; Applied Mathematics and Computational Science Program; Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Earth Fluid Modeling and Prediction Group
Publisher:
Elsevier
Journal:
Journal of Computational Physics
Issue Date:
Jun-2013
DOI:
10.1016/j.jcp.2013.01.047
Type:
Article
ISSN:
00219991
Appears in Collections:
Articles; Environmental Science and Engineering Program; Applied Mathematics and Computational Science Program; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorElsheikh, Ahmed H.en
dc.contributor.authorWheeler, Mary Fanetten
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-08-03T11:05:24Zen
dc.date.available2015-08-03T11:05:24Zen
dc.date.issued2013-06en
dc.identifier.issn00219991en
dc.identifier.doi10.1016/j.jcp.2013.01.047en
dc.identifier.urihttp://hdl.handle.net/10754/562779en
dc.description.abstractParameter estimation for subsurface flow models is an essential step for maximizing the value of numerical simulations for future prediction and the development of effective control strategies. We propose the iterative stochastic ensemble method (ISEM) as a general method for parameter estimation based on stochastic estimation of gradients using an ensemble of directional derivatives. ISEM eliminates the need for adjoint coding and deals with the numerical simulator as a blackbox. The proposed method employs directional derivatives within a Gauss-Newton iteration. The update equation in ISEM resembles the update step in ensemble Kalman filter, however the inverse of the output covariance matrix in ISEM is regularized using standard truncated singular value decomposition or Tikhonov regularization. We also investigate the performance of a set of shrinkage based covariance estimators within ISEM. The proposed method is successfully applied on several nonlinear parameter estimation problems for subsurface flow models. The efficiency of the proposed algorithm is demonstrated by the small size of utilized ensembles and in terms of error convergence rates. © 2013 Elsevier Inc.en
dc.publisherElsevieren
dc.subjectIterative stochastic ensemble methoden
dc.subjectParameter estimationen
dc.subjectRegularizationen
dc.subjectSubsurface flow modelsen
dc.titleAn iterative stochastic ensemble method for parameter estimation of subsurface flow modelsen
dc.typeArticleen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentEarth Fluid Modeling and Prediction Groupen
dc.identifier.journalJournal of Computational Physicsen
dc.contributor.institutionCenter for Subsurface Modeling (CSM), Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX, United Statesen
kaust.authorHoteit, Ibrahimen
kaust.authorElsheikh, Ahmed H.en
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