Population MCMC methods for history matching and uncertainty quantification

Michael Andrew Christie, Lina Mahgoub Yahya Mohamed, B. Calderhead, M. Filippone, M. Girolami

    Research output: Contribution to conferencePaper


    This paper presents the application of a population 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 3 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 surface 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.
    Original languageEnglish
    Number of pages15
    Publication statusPublished - Sept 2010
    Event12th European Conference on the Mathematics of Oil Recovery 2010 - Oxford, United Kingdom
    Duration: 6 Sept 20109 Sept 2010


    Conference12th European Conference on the Mathematics of Oil Recovery 2010
    Abbreviated titleECMOR XII
    Country/TerritoryUnited Kingdom


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