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

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 techni...

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Bibliographic Details
Published inEnergies (Basel) Vol. 12; no. 10; p. 1859
Main Authors Kang, Byeongcheol, Kim, Sungil, Jung, Hyungsik, Choe, Jonggeun, Lee, Kyungbook
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 15.05.2019
Subjects
Algorithms
Centroids
Clustering
Computer simulation
Data points
dimension reduction
distance
distance-based clustering
Elbow
Enhanced oil recovery
Kalman filters
Learning algorithms
Oil recovery
Permeability
Petroleum engineering
Petroleum production
Principal components analysis
reservoir uncertainty assessment
Reservoirs
Reviews
Scientific papers
Sensitivity analysis
Simulation
Static models
Stochasticity
Trends
Uncertainty
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12101859