Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother

By Yan Chen, Dean S. Oliver. Mathematical Geosciences, 2011

349 citations recorded by the source corpus.

Subfields: Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/2ec9272090b9fb214aa09bed3eb54ec0edee248d

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Citations in the corpus

Localization and regularization for iterative ensemble smoothers (2017, methodology): “Chen and Oliver [4] showed the benefit of truncating small singular values using the Brugge benchmark case [27].”
Gaussian Processes for history-matching: application to an unconventional gas reservoir (2017, methodology): “Different variants of ES such as iterative ES [4], and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) [5] have also been introduced to enhance the performance of data assimilation method in field problems.”
Ensemble smoother with multiple data assimilation (2013, methodology): “…that idea of assim-104 ilating the actual observation multiple times.105 In a paper which appeared after our manuscript was originally submitted,106 Chen and Oliver (2011) proposed using the ensemble randomized maximum107 likelihood (EnRML) method (Gu and Oliver, 2007) as an iterative…”
Ensemble Kalman methods for inverse problems (2012, background): “It is worth pointing out that iterative ensemble methods have been previously developed for the solution of inverse problems [6, 26].”
Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification (2013, methodology): “Equations 2 and 3 form the basis for several iterative forms of the ensemble Kalman filter [9, 10] and iterative ensemble smoothers [5].”
Evaluation of Gaussian approximations for data assimilation in reservoir models (2012, methodology): “Further evaluation of recently proposed methods [7, 12, 22, 31, 32] may be useful to understand and improve the uncertainty quantification properties of these techniques under more general conditions.”