Nested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration

Handle URI:
http://hdl.handle.net/10754/552161
Title:
Nested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration
Authors:
Elsheikh, A. H.; Wheeler, M. F.; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans, and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known as nested sampling (NS), which can simultaneously sample the posterior distribution for uncertainty quantification, and estimate the Bayesian evidence for model selection. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different levels of complexities. In this work, we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems. The estimated Bayesian evidence by the NS algorithm is used to weight different parameterizations of the subsurface flow models (prior model selection). The results of the numerical evaluation implicitly enforced Occam's razor where simpler models with fewer number of parameters are favored over complex models. The proper level of model complexity was automatically determined based on the information content of the calibration data and the data mismatch of the calibrated model.
KAUST Department:
Earth Science and Engineering Program
Citation:
Nested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration 2013, 49 (12):8383 Water Resources Research
Journal:
Water Resources Research
Issue Date:
Dec-2013
DOI:
10.1002/2012WR013406
Type:
Article
ISSN:
00431397
Additional Links:
http://doi.wiley.com/10.1002/2012WR013406
Appears in Collections:
Articles; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorElsheikh, A. H.en
dc.contributor.authorWheeler, M. F.en
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-05-04T16:12:40Zen
dc.date.available2015-05-04T16:12:40Zen
dc.date.issued2013-12en
dc.identifier.citationNested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration 2013, 49 (12):8383 Water Resources Researchen
dc.identifier.issn00431397en
dc.identifier.doi10.1002/2012WR013406en
dc.identifier.urihttp://hdl.handle.net/10754/552161en
dc.description.abstractCalibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans, and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known as nested sampling (NS), which can simultaneously sample the posterior distribution for uncertainty quantification, and estimate the Bayesian evidence for model selection. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different levels of complexities. In this work, we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems. The estimated Bayesian evidence by the NS algorithm is used to weight different parameterizations of the subsurface flow models (prior model selection). The results of the numerical evaluation implicitly enforced Occam's razor where simpler models with fewer number of parameters are favored over complex models. The proper level of model complexity was automatically determined based on the information content of the calibration data and the data mismatch of the calibrated model.en
dc.relation.urlhttp://doi.wiley.com/10.1002/2012WR013406en
dc.rightsArchived with thanks to Water Resources Researchen
dc.titleNested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibrationen
dc.typeArticleen
dc.contributor.departmentEarth Science and Engineering Programen
dc.identifier.journalWater Resources Researchen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionCenter for Subsurface Modeling; Institute for Computational Engineering and Sciences; University of Texas at Austin; Austin Texas USAen
dc.contributor.institutionCenter for Subsurface Modeling; Institute for Computational Engineering and Sciences; University of Texas at Austin; Austin Texas USAen
dc.contributor.institutionInstitute of Petroleum Engineering, Heriot–Watt University, Edin- burgh, UKen
kaust.authorHoteit, Ibrahimen
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