Ensemble-Based Data Assimilation in Reservoir Characterization: A Review

Seungpil Jung, Kyungbook Lee, Changhyup Park and Jonggeun Choe
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Seungpil Jung: E&P Business Division, SK Innovation, Seoul 03188, Korea
Kyungbook Lee: Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea
Changhyup Park: Department of Energy and Resources Engineering, Kangwon National University, Chuncheon 24341, Kangwon, Korea
Jonggeun Choe: Department of Energy Systems Engineering, Seoul National University, Seoul 03080, Korea

Energies, 2018, vol. 11, issue 2, 1-23

Abstract: 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.

Keywords: ensemble-based method; ensemble Kalman filter; ensemble smoother; data assimilation; heterogeneous reservoir (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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