Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications

By Alexandre Anozé Emerick, Albert C. Reynolds. 2011

95 citations recorded by the source corpus.

Subfields: Geophysics, Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/41dd82f52a3ad65b512142e06b0a512d456d9735

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Citations in the corpus

Correlation-Based Adaptive Localization With Applications to Ensemble-Based 4D-Seismic History Matching (2018, background): “A possible remedy to this problem is to also introduce localization (see, for example, Chen and Oliver, 2010; Emerick and Reynolds, 2011) to the iterative ensemble smoother.”
Ensemble smoother with multiple data assimilation (2013, background): “…of EnKF for history13 matching can be found in (Skjervheim et al., 2007; Bianco et al., 2007;14 Evensen et al., 2007; Haugen et al., 2008; Emerick and Reynolds, 2011b).15 Two recent review papers (Aanonsen et al., 2009; Oliver and Chen, 2010)16 summarize the main developments and…”
Localization and Sampling Error Correction in Ensemble Kalman Filter Data Assimilation (2012, background): “the one presented here, Emerick and Reynolds (2010) based localization on observation sensitivity matrices in a petroleum reservoir application.”
Evaluation of Gaussian approximations for data assimilation in reservoir models (2012, unknown): “The main goal of [4], however, is to evaluate the ability of the corresponding techniques to provide confidence interval that contains the truth estimate.”
Localization and regularization for iterative ensemble smoothers (2017, methodology): “Emerick and Reynolds [7] suggested setting the critical range for localization to be the sum of the length scale of the prior covariance of model parameters and the length scale of the well drainage area (from streamline simulation) and used Gaspari-Cohn function to compute elements of ρmd and ρdd, ”