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Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

Author

Listed:
  • Byeongcheol Kang

    (Department of Energy Systems Engineering, Seoul National University, Seoul 08826, Korea)

  • Sungil Kim

    (Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea)

  • Hyungsik Jung

    (Department of Energy Systems Engineering, Seoul National University, Seoul 08826, Korea)

  • Jonggeun Choe

    (Department of Energy Systems Engineering, Seoul National University, Seoul 08826, Korea)

  • Kyungbook Lee

    (Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea)

Abstract

This paper presents a review of 71 research papers related to a distance-based clustering (DBC) technique for efficiently assessing reservoir uncertainty. The key to DBC is to select a few models that can represent hundreds of possible reservoir models. DBC is defined as a combination of four technical processes: distance definition, distance matrix construction, dimensional reduction, and clustering. In this paper, we review the algorithms employed in each step. For distance calculation, Minkowski distance is recommended with even order due to sign problem. In the case of clustering, K-means algorithm has been commonly used. DBC has been applied to various reservoir types from channel to unconventional reservoirs. DBC is effective for unconventional resources and enhanced oil recovery projects that have a significant advantage of reducing the number of reservoir simulations. Recently, DBC studies have been performed with deep learning algorithms for feature extraction to define a distance and for effective clustering.

Suggested Citation

  • Byeongcheol Kang & Sungil Kim & Hyungsik Jung & Jonggeun Choe & Kyungbook Lee, 2019. "Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review," Energies, MDPI, vol. 12(10), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1859-:d:231496
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    References listed on IDEAS

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    1. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
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    1. Sungil Kim & Byungjoon Yoon & Jung-Tek Lim & Myungsun Kim, 2021. "Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning," Energies, MDPI, vol. 14(5), pages 1-20, March.

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