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dc.contributor.authorElsheikh, A. H.
dc.contributor.authorWheeler, M. F.
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2015-05-04T16:12:40Z
dc.date.available2015-05-04T16:12:40Z
dc.date.issued2013-12-16
dc.identifier.citationNested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration 2013, 49 (12):8383 Water Resources Research
dc.identifier.issn00431397
dc.identifier.doi10.1002/2012WR013406
dc.identifier.urihttp://hdl.handle.net/10754/552161
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.
dc.publisherAmerican Geophysical Union (AGU)
dc.relation.urlhttp://doi.wiley.com/10.1002/2012WR013406
dc.rightsArchived with thanks to Water Resources Research
dc.titleNested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration
dc.typeArticle
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalWater Resources Research
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCenter for Subsurface Modeling; Institute for Computational Engineering and Sciences; University of Texas at Austin; Austin Texas USA
dc.contributor.institutionCenter for Subsurface Modeling; Institute for Computational Engineering and Sciences; University of Texas at Austin; Austin Texas USA
dc.contributor.institutionInstitute of Petroleum Engineering, Heriot–Watt University, Edin- burgh, UK
kaust.personHoteit, Ibrahim
refterms.dateFOA2018-06-13T18:11:31Z
dc.date.published-online2013-12-16
dc.date.published-print2013-12


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