Using a machine learning proxy for localization in ensemble data assimilation

DOI10.1007/s10596-020-10031-0zbMath1460.86037OpenAlexW3129387426MaRDI QIDQ2027164

Johann M. Lacerda, Adolfo P. Pires, Alexandre Anozé Emerick

Publication date: 25 May 2021

Published in: Computational Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10596-020-10031-0

zbMATH Keywords

localizationhistory matchingsupport vector regressionensemble smoothersampling errorsensemble data assimilation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Inverse problems in geophysics (86A22) Geostatistics (86A32)

Cites Work

• Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications
• Cross-covariances and localization for EnKF in multiphase flow data assimilation
• Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter vari\-ants
• Improving the ensemble estimate of the Kalman gain by bootstrap sampling
• Investigation of the sampling performance of ensemble-based methods with a simple reservoir model
• Conditioning reservoir models on rate data using ensemble smoothers
• Distributed Gauss-Newton optimization method for history matching problems with multiple best matches

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