An ensemble based nonlinear orthogonal matching pursuit algorithm for sparse history matching of reservoir models

Handle URI:
http://hdl.handle.net/10754/564646
Title:
An ensemble based nonlinear orthogonal matching pursuit algorithm for sparse history matching of reservoir models
Authors:
Fsheikh, Ahmed H.; Wheeler, Mary Fanett; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
A nonlinear orthogonal matching pursuit (NOMP) for sparse calibration of reservoir models is presented. Sparse calibration is a challenging problem as the unknowns are both the non-zero components of the solution and their associated weights. NOMP is a greedy algorithm that discovers at each iteration the most correlated components of the basis functions with the residual. The discovered basis (aka support) is augmented across the nonlinear iterations. Once the basis functions are selected from the dictionary, the solution is obtained by applying Tikhonov regularization. The proposed algorithm relies on approximate gradient estimation using an iterative stochastic ensemble method (ISEM). ISEM utilizes an ensemble of directional derivatives to efficiently approximate gradients. In the current study, the search space is parameterized using an overcomplete dictionary of basis functions built using the K-SVD algorithm.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Earth Fluid Modeling and Prediction Group
Publisher:
Society of Petroleum Engineers (SPE)
Journal:
SPE Reservoir Simulation Symposium
Conference/Event name:
SPE Reservoir Simulation Symposium 2013
Issue Date:
2013
DOI:
10.2118/163582-ms
Type:
Conference Paper
ISBN:
9781627480246
Appears in Collections:
Conference Papers; Environmental Science and Engineering Program; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorFsheikh, Ahmed H.en
dc.contributor.authorWheeler, Mary Fanetten
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-08-04T07:10:46Zen
dc.date.available2015-08-04T07:10:46Zen
dc.date.issued2013en
dc.identifier.isbn9781627480246en
dc.identifier.doi10.2118/163582-msen
dc.identifier.urihttp://hdl.handle.net/10754/564646en
dc.description.abstractA nonlinear orthogonal matching pursuit (NOMP) for sparse calibration of reservoir models is presented. Sparse calibration is a challenging problem as the unknowns are both the non-zero components of the solution and their associated weights. NOMP is a greedy algorithm that discovers at each iteration the most correlated components of the basis functions with the residual. The discovered basis (aka support) is augmented across the nonlinear iterations. Once the basis functions are selected from the dictionary, the solution is obtained by applying Tikhonov regularization. The proposed algorithm relies on approximate gradient estimation using an iterative stochastic ensemble method (ISEM). ISEM utilizes an ensemble of directional derivatives to efficiently approximate gradients. In the current study, the search space is parameterized using an overcomplete dictionary of basis functions built using the K-SVD algorithm.en
dc.publisherSociety of Petroleum Engineers (SPE)en
dc.titleAn ensemble based nonlinear orthogonal matching pursuit algorithm for sparse history matching of reservoir modelsen
dc.typeConference Paperen
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.journalSPE Reservoir Simulation Symposiumen
dc.conference.date18 February 2013 through 20 February 2013en
dc.conference.nameSPE Reservoir Simulation Symposium 2013en
dc.conference.locationThe Woodlands, TXen
dc.contributor.institutionUniversity of Texas at Austin, United Statesen
kaust.authorHoteit, Ibrahimen
kaust.authorFsheikh, Ahmed H.en
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