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Ensemble-Based Data Assimilation in Reservoir Characterization: A Review

Author

Listed:
  • 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)

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.

Suggested Citation

  • Seungpil Jung & Kyungbook Lee & Changhyup Park & Jonggeun Choe, 2018. "Ensemble-Based Data Assimilation in Reservoir Characterization: A Review," Energies, MDPI, vol. 11(2), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:445-:d:132315
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    References listed on IDEAS

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    1. Seungpil Jung, 2017. "Integration of the Production Logging Tool and Production Data for Post-Fracturing Evaluation by the Ensemble Smoother," Energies, MDPI, vol. 10(7), pages 1-12, June.
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    Cited by:

    1. Fathy, Mohammad & Kazemzadeh Haghighi, Foojan & Ahmadi, Mohammad, 2024. "Uncertainty quantification of reservoir performance using machine learning algorithms and structured expert judgment," Energy, Elsevier, vol. 288(C).
    2. Artun Sel & Bilgehan Sel & Cosku Kasnakoglu, 2021. "GLSDC Based Parameter Estimation Algorithm for a PMSM Model," Energies, MDPI, vol. 14(3), pages 1-12, January.
    3. Dongmei Zhang & Yuyang Zhang & Bohou Jiang & Xinwei Jiang & Zhijiang Kang, 2020. "Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching," Energies, MDPI, vol. 13(17), pages 1-15, August.

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    1. Artun Sel & Bilgehan Sel & Cosku Kasnakoglu, 2021. "GLSDC Based Parameter Estimation Algorithm for a PMSM Model," Energies, MDPI, vol. 14(3), pages 1-12, January.

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