Ensemble Kalman filter (EnKF) is one of the widely used optimization methods in petroleum engineering. It uses multiple reservoir models, known as ensemble, for quantifying uncertainty ranges, and model parameters are updated using observation data repetitively. However, it requires a large number of ensemble members to get stable results, causing huge simulation time. In this study, we propose a sampling method using principal component analysis (PCA) and K-means clustering. It excludes poor ensemble with different geological trends to the reference so we can improve both speed and reliability of future predictions. A representative model, which is selected from candidate models of each cluster, has a role to choose proper ensemble for EnKF. For applying EnKF to channelized reservoirs, we compare cases with using 400, randomly picked 100, sampled 100 using Hausdorff distance, and sampled 100 by the proposed method. The proposed method shows improvements over the other cases compared. It gives stable uncertainty ranges and well-updated reservoir parameters after the assimilations. Randomly selected 100 ensemble members predict wrong reservoir performances, and 400 ensemble members exhibit too large uncertainty ranges with long simulation times. Even though more ensemble members are utilized, they provide worse results due to disturbance by improperly designed models. We confirm our sampling strategy in a real field case, PUNQ-S3, and it reduces simulation time as well as improves the future predictions for efficient and reliable history matching.
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Research-Article
Ensemble Kalman Filter With Principal Component Analysis Assisted Sampling for Channelized Reservoir Characterization
Byeongcheol Kang,
Byeongcheol Kang
Department of Energy Systems Engineering,
Seoul National University,
Seoul 08826, South Korea
e-mail: qudcjf@snu.ac.kr
Seoul National University,
Seoul 08826, South Korea
e-mail: qudcjf@snu.ac.kr
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Hyungjun Yang,
Hyungjun Yang
Department of Energy Systems Engineering,
Seoul National University,
Seoul 08826, South Korea
e-mail: yang4697@snu.ac.kr
Seoul National University,
Seoul 08826, South Korea
e-mail: yang4697@snu.ac.kr
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Kyungbook Lee,
Kyungbook Lee
Petroleum and Marine Research Division,
Korea Institute of Geoscience
and Mineral Resources,
Daejeon 34132, South Korea
e-mail: kblee@kigam.re.kr
Korea Institute of Geoscience
and Mineral Resources,
Daejeon 34132, South Korea
e-mail: kblee@kigam.re.kr
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Jonggeun Choe
Jonggeun Choe
Department of Energy Resources Engineering,
Seoul National University,
Seoul 08826, South Korea
e-mail: johnchoe@snu.ac.kr
Seoul National University,
Seoul 08826, South Korea
e-mail: johnchoe@snu.ac.kr
Search for other works by this author on:
Byeongcheol Kang
Department of Energy Systems Engineering,
Seoul National University,
Seoul 08826, South Korea
e-mail: qudcjf@snu.ac.kr
Seoul National University,
Seoul 08826, South Korea
e-mail: qudcjf@snu.ac.kr
Hyungjun Yang
Department of Energy Systems Engineering,
Seoul National University,
Seoul 08826, South Korea
e-mail: yang4697@snu.ac.kr
Seoul National University,
Seoul 08826, South Korea
e-mail: yang4697@snu.ac.kr
Kyungbook Lee
Petroleum and Marine Research Division,
Korea Institute of Geoscience
and Mineral Resources,
Daejeon 34132, South Korea
e-mail: kblee@kigam.re.kr
Korea Institute of Geoscience
and Mineral Resources,
Daejeon 34132, South Korea
e-mail: kblee@kigam.re.kr
Jonggeun Choe
Department of Energy Resources Engineering,
Seoul National University,
Seoul 08826, South Korea
e-mail: johnchoe@snu.ac.kr
Seoul National University,
Seoul 08826, South Korea
e-mail: johnchoe@snu.ac.kr
1Corresponding author.
Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received May 17, 2016; final manuscript received January 6, 2017; published online February 6, 2017. Editor: Hameed Metghalchi.
J. Energy Resour. Technol. May 2017, 139(3): 032907 (12 pages)
Published Online: February 6, 2017
Article history
Received:
May 17, 2016
Revised:
January 6, 2017
Citation
Kang, B., Yang, H., Lee, K., and Choe, J. (February 6, 2017). "Ensemble Kalman Filter With Principal Component Analysis Assisted Sampling for Channelized Reservoir Characterization." ASME. J. Energy Resour. Technol. May 2017; 139(3): 032907. https://doi.org/10.1115/1.4035747
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