Cross-covariances and localization for EnKF in multiphase flow data assimilation

By Yan Chen, Dean S. Oliver. 2010

157 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/e604b35fc733ae1cb60b9e7bd66c21b10ffd8724

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Citations in the corpus

Evaluation of Gaussian approximations for data assimilation in reservoir models (2012, unknown): “Nevertheless, due to its ease of implementation and low computational cost, ensemble Kalman filter-type methods are arguably the only feasible techniques for online data assimilation in subsurface applications.”
Improving the Ensemble Estimate of the Kalman Gain by Bootstrap Sampling (2010, background): “Chen and Oliver (2010) point out that the region of non-zero cross-covariance cannot be determined from the region of sensitivity alone but the contribution of the prior covariance must also be considered.”
Inverse methods in hydrogeology: Evolution and recent trends (2014, background): “…problem, several regularization strategies are proposed such as501 covariance inflation (Anderson, 2007), distance-based covariance localization (Chen and Oliver, 2010; Nan502 and Wu, 2011), adaptive localiztion with wavelets (Chen and Oliver, 2012), the use of damping factors503 (Hendricks…”
Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications (2011, methodology): “Chen and Oliver [ 4 ] concluded that for reservoir problems, the cross-covariance between data and state variables, C f”
Bayesian Reservoir History Matching Considering Model and Parameter Uncertainties (2012, background): “Techniques to solve this problem include covariance localization (Gaspari and Cohn 1999; Houtekamer and Mitchell 2001; Chen and Oliver 2010), covariance inflation (Anderson and Anderson 1999; Anderson 2001) and reliable estimation of the covariance matrix (Zhang and Oliver 2010).”
Localization and regularization for iterative ensemble smoothers (2017, methodology): “As an aside, much better data matches were achieved at the final iteration with all the localization methods using the iterative ensemble smoother than those obtained using the ensemble Kalman filter (EnKF) with covariance localization shown in Chen and Oliver [3].”