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

1
Department of Energy Systems Engineering, Seoul National University, Seoul 08826, Korea
2
Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea
*
Author to whom correspondence should be addressed.
Energies 2019, 12(10), 1859; https://doi.org/10.3390/en12101859
Received: 14 April 2019 / Revised: 9 May 2019 / Accepted: 10 May 2019 / Published: 15 May 2019
(This article belongs to the Section Energy Sources)
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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. View Full-Text
Keywords: distance-based clustering; reservoir uncertainty assessment; distance; dimension reduction; clustering distance-based clustering; reservoir uncertainty assessment; distance; dimension reduction; clustering
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kang, B.; Kim, S.; Jung, H.; Choe, J.; Lee, K. Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review. Energies 2019, 12, 1859.

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