Population MCMC methods for history matching and uncertainty quantification

Mohamed, L., Calderhead, B., Filippone, M., Christie, M. and Girolami, M. (2012) Population MCMC methods for history matching and uncertainty quantification. Computational Geosciences, 16(2), pp. 423-436. (doi: 10.1007/s10596-011-9232-8)

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Abstract

This paper presents the application of a population Markov Chain Monte Carlo (MCMC) technique to generate history-matched models. The technique has been developed and successfully adopted in challenging domains such as computational biology but has not yet seen application in reservoir modelling. In population MCMC, multiple Markov chains are run on a set of response surfaces that form a bridge from the prior to posterior. These response surfaces are formed from the product of the prior with the likelihood raised to a varying power less than one. The chains exchange positions, with the probability of a swap being governed by a standard Metropolis accept/reject step, which allows for large steps to be taken with high probability. We show results of Population MCMC on the IC Fault Model—a simple three-parameter model that is known to have a highly irregular misfit surface and hence be difficult to match. Our results show that population MCMC is able to generate samples from the complex, multi-modal posterior probability distribution of the IC Fault model very effectively. By comparison, previous results from stochastic sampling algorithms often focus on only part of the region of high posterior probability depending on algorithm settings and starting points.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Filippone, Dr Maurizio and Girolami, Prof Mark
Authors: Mohamed, L., Calderhead, B., Filippone, M., Christie, M., and Girolami, M.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Computational Geosciences
Publisher:Springer Netherlands
ISSN:1420-0597
ISSN (Online):1573-1499

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
456071Advancing machine learning methodology for new classes of prediction problemsMark GirolamiEngineering & Physical Sciences Research Council (EPSRC)EP/F009429/1COM - COMPUTING SCIENCE
450691The integration of probabilistic prediction and mechanistic modelling within a computational and systems biology contextMark GirolamiEngineering & Physical Sciences Research Council (EPSRC)EP/E052029/1COM - COMPUTING SCIENCE
431861Simplicity, Complexity And Modelling (SCAM)Stephen SennEngineering & Physical Sciences Research Council (EPSRC)EP/E018173/1M&S - STATISTICS