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Energies 2018, 11(2), 445;

Ensemble-Based Data Assimilation in Reservoir Characterization: A Review

E&P Business Division, SK Innovation, Seoul 03188, Korea
Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea
Department of Energy and Resources Engineering, Kangwon National University, Chuncheon 24341, Kangwon, Korea
Department of Energy Systems Engineering, Seoul National University, Seoul 03080, Korea
Author to whom correspondence should be addressed.
Received: 9 January 2018 / Revised: 7 February 2018 / Accepted: 9 February 2018 / Published: 17 February 2018
(This article belongs to the Section Energy Sources)
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This paper presents a review of ensemble-based data assimilation for strongly nonlinear problems on the characterization of heterogeneous reservoirs with different production histories. It concentrates on ensemble Kalman filter (EnKF) and ensemble smoother (ES) as representative frameworks, discusses their pros and cons, and investigates recent progress to overcome their drawbacks. The typical weaknesses of ensemble-based methods are non-Gaussian parameters, improper prior ensembles and finite population size. Three categorized approaches, to mitigate these limitations, are reviewed with recent accomplishments; improvement of Kalman gains, add-on of transformation functions, and independent evaluation of observed data. The data assimilation in heterogeneous reservoirs, applying the improved ensemble methods, is discussed on predicting unknown dynamic data in reservoir characterization. View Full-Text
Keywords: ensemble-based method; ensemble Kalman filter; ensemble smoother; data assimilation; heterogeneous reservoir ensemble-based method; ensemble Kalman filter; ensemble smoother; data assimilation; heterogeneous reservoir

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Jung, S.; Lee, K.; Park, C.; Choe, J. Ensemble-Based Data Assimilation in Reservoir Characterization: A Review. Energies 2018, 11, 445.

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