Bayesian uncertainty quantification for flows in heterogeneous porous media using reversible jump Markov chain Monte Carlo methods

Handle URI:
http://hdl.handle.net/10754/597660
Title:
Bayesian uncertainty quantification for flows in heterogeneous porous media using reversible jump Markov chain Monte Carlo methods
Authors:
Mondal, A.; Efendiev, Y.; Mallick, B.; Datta-Gupta, A.
Abstract:
In this paper, we study the uncertainty quantification in inverse problems for flows in heterogeneous porous media. Reversible jump Markov chain Monte Carlo algorithms (MCMC) are used for hierarchical modeling of channelized permeability fields. Within each channel, the permeability is assumed to have a lognormal distribution. Uncertainty quantification in history matching is carried out hierarchically by constructing geologic facies boundaries as well as permeability fields within each facies using dynamic data such as production data. The search with Metropolis-Hastings algorithm results in very low acceptance rate, and consequently, the computations are CPU demanding. To speed-up the computations, we use a two-stage MCMC that utilizes upscaled models to screen the proposals. In our numerical results, we assume that the channels intersect the wells and the intersection locations are known. Our results show that the proposed algorithms are capable of capturing the channel boundaries and describe the permeability variations within the channels using dynamic production history at the wells. © 2009 Elsevier Ltd. All rights reserved.
Citation:
Mondal A, Efendiev Y, Mallick B, Datta-Gupta A (2010) Bayesian uncertainty quantification for flows in heterogeneous porous media using reversible jump Markov chain Monte Carlo methods. Advances in Water Resources 33: 241–256. Available: http://dx.doi.org/10.1016/j.advwatres.2009.10.010.
Publisher:
Elsevier BV
Journal:
Advances in Water Resources
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Mar-2010
DOI:
10.1016/j.advwatres.2009.10.010
Type:
Article
ISSN:
0309-1708
Sponsors:
We would like to acknowledge NSF CMG 0724704 This work is partly supported by Award Number KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorMondal, A.en
dc.contributor.authorEfendiev, Y.en
dc.contributor.authorMallick, B.en
dc.contributor.authorDatta-Gupta, A.en
dc.date.accessioned2016-02-25T12:43:55Zen
dc.date.available2016-02-25T12:43:55Zen
dc.date.issued2010-03en
dc.identifier.citationMondal A, Efendiev Y, Mallick B, Datta-Gupta A (2010) Bayesian uncertainty quantification for flows in heterogeneous porous media using reversible jump Markov chain Monte Carlo methods. Advances in Water Resources 33: 241–256. Available: http://dx.doi.org/10.1016/j.advwatres.2009.10.010.en
dc.identifier.issn0309-1708en
dc.identifier.doi10.1016/j.advwatres.2009.10.010en
dc.identifier.urihttp://hdl.handle.net/10754/597660en
dc.description.abstractIn this paper, we study the uncertainty quantification in inverse problems for flows in heterogeneous porous media. Reversible jump Markov chain Monte Carlo algorithms (MCMC) are used for hierarchical modeling of channelized permeability fields. Within each channel, the permeability is assumed to have a lognormal distribution. Uncertainty quantification in history matching is carried out hierarchically by constructing geologic facies boundaries as well as permeability fields within each facies using dynamic data such as production data. The search with Metropolis-Hastings algorithm results in very low acceptance rate, and consequently, the computations are CPU demanding. To speed-up the computations, we use a two-stage MCMC that utilizes upscaled models to screen the proposals. In our numerical results, we assume that the channels intersect the wells and the intersection locations are known. Our results show that the proposed algorithms are capable of capturing the channel boundaries and describe the permeability variations within the channels using dynamic production history at the wells. © 2009 Elsevier Ltd. All rights reserved.en
dc.description.sponsorshipWe would like to acknowledge NSF CMG 0724704 This work is partly supported by Award Number KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).en
dc.publisherElsevier BVen
dc.subjectFaciesen
dc.subjectLevel setsen
dc.subjectMCMCen
dc.subjectMultiscaleen
dc.subjectPorous mediaen
dc.subjectReversible jumpen
dc.subjectUpscalingen
dc.titleBayesian uncertainty quantification for flows in heterogeneous porous media using reversible jump Markov chain Monte Carlo methodsen
dc.typeArticleen
dc.identifier.journalAdvances in Water Resourcesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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