Clustered iterative stochastic ensemble method for multi-modal calibration of subsurface flow models

Handle URI:
http://hdl.handle.net/10754/562748
Title:
Clustered iterative stochastic ensemble method for multi-modal calibration of subsurface flow models
Authors:
Elsheikh, Ahmed H.; Wheeler, Mary Fanett; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
A novel multi-modal parameter estimation algorithm is introduced. Parameter estimation is an ill-posed inverse problem that might admit many different solutions. This is attributed to the limited amount of measured data used to constrain the inverse problem. The proposed multi-modal model calibration algorithm uses an iterative stochastic ensemble method (ISEM) for parameter estimation. ISEM employs an ensemble of directional derivatives within a Gauss-Newton iteration for nonlinear parameter estimation. ISEM is augmented with a clustering step based on k-means algorithm to form sub-ensembles. These sub-ensembles are used to explore different parts of the search space. Clusters are updated at regular intervals of the algorithm to allow merging of close clusters approaching the same local minima. Numerical testing demonstrates the potential of the proposed algorithm in dealing with multi-modal nonlinear parameter estimation for subsurface flow models. © 2013 Elsevier B.V.
KAUST Department:
Applied Mathematics and Computational Science Program; Earth Science and Engineering Program; Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Earth Fluid Modeling and Prediction Group
Publisher:
Elsevier
Journal:
Journal of Hydrology
Issue Date:
May-2013
DOI:
10.1016/j.jhydrol.2013.03.037
Type:
Article
ISSN:
00221694
Sponsors:
The authors thank the two anonymous reviewers for their insightful and constructive comments. We are particularly indebted to one of the reviewers for his/her extensive and insightful comments which resulted in considerable improvements to this manuscript. A.H. Elsheikh performed initial investigation of this research as part of the activities of the Qatar Carbonates and Carbon Storage Research Centre (QCCSRC) at Imperial College London. He gratefully acknowledges the funding of QCCSRC provided jointly by Qatar Petroleum, Shell and the Qatar Science and Technology Park.
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.accessioned2015-08-03T11:04:14Zen
dc.date.available2015-08-03T11:04:14Zen
dc.date.issued2013-05en
dc.identifier.issn00221694en
dc.identifier.doi10.1016/j.jhydrol.2013.03.037en
dc.identifier.urihttp://hdl.handle.net/10754/562748en
dc.description.abstractA novel multi-modal parameter estimation algorithm is introduced. Parameter estimation is an ill-posed inverse problem that might admit many different solutions. This is attributed to the limited amount of measured data used to constrain the inverse problem. The proposed multi-modal model calibration algorithm uses an iterative stochastic ensemble method (ISEM) for parameter estimation. ISEM employs an ensemble of directional derivatives within a Gauss-Newton iteration for nonlinear parameter estimation. ISEM is augmented with a clustering step based on k-means algorithm to form sub-ensembles. These sub-ensembles are used to explore different parts of the search space. Clusters are updated at regular intervals of the algorithm to allow merging of close clusters approaching the same local minima. Numerical testing demonstrates the potential of the proposed algorithm in dealing with multi-modal nonlinear parameter estimation for subsurface flow models. © 2013 Elsevier B.V.en
dc.description.sponsorshipThe authors thank the two anonymous reviewers for their insightful and constructive comments. We are particularly indebted to one of the reviewers for his/her extensive and insightful comments which resulted in considerable improvements to this manuscript. A.H. Elsheikh performed initial investigation of this research as part of the activities of the Qatar Carbonates and Carbon Storage Research Centre (QCCSRC) at Imperial College London. He gratefully acknowledges the funding of QCCSRC provided jointly by Qatar Petroleum, Shell and the Qatar Science and Technology Park.en
dc.publisherElsevieren
dc.subjectK-means Clusteringen
dc.subjectMulti-modal Optimizationen
dc.subjectParameter estimationen
dc.subjectRegularizationen
dc.subjectSubsurface Flow Modelsen
dc.titleClustered iterative stochastic ensemble method for multi-modal calibration of subsurface flow modelsen
dc.typeArticleen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentEarth Science and Engineering 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 Hydrologyen
dc.contributor.institutionInstitute for Computational Engineering and Sciences (ICES), University of Texas, Austin, TX, United Statesen
kaust.authorHoteit, Ibrahimen
kaust.authorElsheikh, Ahmed H.en
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