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    Evaluation of Gaussian approximations for data assimilation in reservoir models

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    Type
    Article
    Authors
    Iglesias, Marco A.
    Law, Kody J H
    Stuart, Andrew M.
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2013-07-14
    Online Publication Date
    2013-07-14
    Print Publication Date
    2013-10
    Permanent link to this record
    http://hdl.handle.net/10754/594222
    
    Metadata
    Show full item record
    Abstract
    The Bayesian framework is the standard approach for data assimilation in reservoir modeling. This framework involves characterizing the posterior distribution of geological parameters in terms of a given prior distribution and data from the reservoir dynamics, together with a forward model connecting the space of geological parameters to the data space. Since the posterior distribution quantifies the uncertainty in the geologic parameters of the reservoir, the characterization of the posterior is fundamental for the optimal management of reservoirs. Unfortunately, due to the large-scale highly nonlinear properties of standard reservoir models, characterizing the posterior is computationally prohibitive. Instead, more affordable ad hoc techniques, based on Gaussian approximations, are often used for characterizing the posterior distribution. Evaluating the performance of those Gaussian approximations is typically conducted by assessing their ability at reproducing the truth within the confidence interval provided by the ad hoc technique under consideration. This has the disadvantage of mixing up the approximation properties of the history matching algorithm employed with the information content of the particular observations used, making it hard to evaluate the effect of the ad hoc approximations alone. In this paper, we avoid this disadvantage by comparing the ad hoc techniques with a fully resolved state-of-the-art probing of the Bayesian posterior distribution. The ad hoc techniques whose performance we assess are based on (1) linearization around the maximum a posteriori estimate, (2) randomized maximum likelihood, and (3) ensemble Kalman filter-type methods. In order to fully resolve the posterior distribution, we implement a state-of-the art Markov chain Monte Carlo (MCMC) method that scales well with respect to the dimension of the parameter space, enabling us to study realistic forward models, in two space dimensions, at a high level of grid refinement. Our implementation of the MCMC method provides the gold standard against which the aforementioned Gaussian approximations are assessed. We present numerical synthetic experiments where we quantify the capability of each of the ad hoc Gaussian approximation in reproducing the mean and the variance of the posterior distribution (characterized via MCMC) associated to a data assimilation problem. Both single-phase and two-phase (oil-water) reservoir models are considered so that fundamental differences in the resulting forward operators are highlighted. The main objective of our controlled experiments was to exhibit the substantial discrepancies of the approximation properties of standard ad hoc Gaussian approximations. Numerical investigations of the type we present here will lead to the greater understanding of the cost-efficient, but ad hoc, Bayesian techniques used for data assimilation in petroleum reservoirs and hence ultimately to improved techniques with more accurate uncertainty quantification. © 2013 Springer Science+Business Media Dordrecht.
    Citation
    Iglesias MA, Law KJH, Stuart AM (2013) Evaluation of Gaussian approximations for data assimilation in reservoir models. Computational Geosciences 17: 851–885. Available: http://dx.doi.org/10.1007/s10596-013-9359-x.
    Sponsors
    MI, KJHL, and AMS gratefully acknowledge the support of EPSRC, ERC, ESA, and ONR for various aspects of this work.
    Publisher
    Springer Nature
    Journal
    Computational Geosciences
    DOI
    10.1007/s10596-013-9359-x
    arXiv
    1212.1779
    ae974a485f413a2113503eed53cd6c53
    10.1007/s10596-013-9359-x
    Scopus Count
    Collections
    Articles; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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