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- W4210361392 abstract "EnKF-MCMC Alexandre A. Emerick; Alexandre A. Emerick University of Tulsa Search for other works by this author on: This Site Google Scholar Albert C. Reynolds Albert C. Reynolds University of Tulsa Search for other works by this author on: This Site Google Scholar Paper presented at the SPE EUROPEC/EAGE Annual Conference and Exhibition, Barcelona, Spain, June 2010. Paper Number: SPE-131375-MS https://doi.org/10.2118/131375-MS Published: June 14 2010 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Emerick, Alexandre A., and Albert C. Reynolds. EnKF-MCMC. Paper presented at the SPE EUROPEC/EAGE Annual Conference and Exhibition, Barcelona, Spain, June 2010. doi: https://doi.org/10.2118/131375-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Europec featured at EAGE Conference and Exhibition Search Advanced Search Abstract The ensemble Kalman filter (EnKF) has recently become a popular history-matching tool largely because of its computational efficiency and ease of implementation. While EnKF has improved a previous history match obtained manually in several field cases, and often appears to give reasonable results for realistic synthetic history matching problems, one cannot theoretically establish that EnKF samples correctly the posterior probability distribution for the reservoir model parameters or correctly assesses the uncertainty in the production forecast unless strong assumptions of Gaussianity and linearity apply. In multiphase flow problems, the relationship between data and reservoir model parameters and the primary simulation variables is highly nonlinear, so the theoretical justification for obtaining a correct assessment of the uncertainty in model parameters and future performance predictions does not hold. On the other hand, it is well known that the Markov chain Monte Carlo (MCMC) method sample correctly in the limit. However its direct application to reservoir problems can be prohibitively expensive computationally. Here we use a variant of EnKF, the ensemble square root filter (EnSRF), to provide an initial sample of the posterior pdf and to propose transitions for the MCMC method. We present a synthetic reservoir case and show that the combined application of EnSRF and MCMC provides improved history-matching results and a better characterization of uncertainty in the predicted reservoir performance. Keywords: procedure, oil rate, ensrf, matrix, model parameter, mcmc, ensemble, covariance localization, history matching, realization Subjects: Reservoir Simulation, History matching Copyright 2010, Society of Petroleum Engineers You can access this article if you purchase or spend a download." @default.
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- W4210361392 date "2010-06-01" @default.
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- W4210361392 title "EnKF-MCMC" @default.
- W4210361392 doi "https://doi.org/10.2523/131375-ms" @default.
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