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

Byeongcheol Kang, Sungil Kim, Hyungsik Jung, Jonggeun Choe and Kyungbook Lee
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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

Energies, 2019, vol. 12, issue 10, 1-24

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.

Keywords: distance-based clustering; reservoir uncertainty assessment; distance; dimension reduction; clustering (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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