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SPE EUROPEC/EAGE Annual Conference and Exhibition, 2010
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 ...
Alexandre A. Emerick, Albert C. Reynolds
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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 ...
Alexandre A. Emerick, Albert C. Reynolds
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Statistics and Computing, 2006
We propose to combine two quite powerful ideas that have recently appeared in the Markov chain Monte Carlo literature: adaptive Metropolis samplers and delayed rejection. The ergodicity of the resulting non-Markovian sampler is proved, and the efficiency of the combination is demonstrated with various examples.
H. HAARIO +3 more
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We propose to combine two quite powerful ideas that have recently appeared in the Markov chain Monte Carlo literature: adaptive Metropolis samplers and delayed rejection. The ergodicity of the resulting non-Markovian sampler is proved, and the efficiency of the combination is demonstrated with various examples.
H. HAARIO +3 more
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2004
We maintain that the analysis and synthesis of random fields is much faster in a hierarchical setting. In particular, complicated long-range interactions at a fine scale become progressively more local (and therefore more efficient) at coarser levels. The key to effective coarse-scale activity is the proper model definition at those scales. This can be
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We maintain that the analysis and synthesis of random fields is much faster in a hierarchical setting. In particular, complicated long-range interactions at a fine scale become progressively more local (and therefore more efficient) at coarser levels. The key to effective coarse-scale activity is the proper model definition at those scales. This can be
openaire +1 more source
2003
Markov chain Monte Carlo (MCMC) algorithms are now widely used in virtually all areas of statistics. In particular, spatial applications featured very prominently in the early development of the methodology (Geman & Geman 1984), and they still provide some of the most challenging problems for MCMC techniques.
Petros Dellaportas, Gareth O. Roberts
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Markov chain Monte Carlo (MCMC) algorithms are now widely used in virtually all areas of statistics. In particular, spatial applications featured very prominently in the early development of the methodology (Geman & Geman 1984), and they still provide some of the most challenging problems for MCMC techniques.
Petros Dellaportas, Gareth O. Roberts
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MCMC algorithms for Subset Simulation
Probabilistic Engineering Mechanics, 2015Iason Papaioannou +2 more
exaly