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.
Highlights
Reservoir uncertainty assessment is an essential process in petroleum exploration and production for the following reasons: long operation term, invisible underground reservoir, lack of geological interpretation, limited available data, oil price fluctuation, extensive early investment costs, and so on
Despite the advantages and disadvantages of each technique, reservoir simulation is commonly employed for uncertainty assessment because various field development plans can be compared and commercial software is well developed based on physical theories
Numerous clustering algorithms exist for data mining, we present three clustering algorithms that are popular in petroleum engineering: K-means clustering, K-medoids clustering, and the self-organizing map (SOM)
Summary
Reservoir uncertainty assessment is an essential process in petroleum exploration and production for the following reasons: long operation term, invisible underground reservoir, lack of geological interpretation, limited available data, oil price fluctuation, extensive early investment costs, and so on. Kriging algorithms can create a single static model for given data and spatial correlation This deterministic approach cannot properly estimate the uncertainty of a static model. The result of this deterministic approach may differ frommay the differ the actual red 1a) lineand in Figure and cannot manage theofuncertainty of a actualfrom production (theproduction red line in(the cannot1a) manage the uncertainty a static model. Techniques is assessment of reservoir using distance-based clustering techniques is to classify reservoir uncertainty similar models as as the the same samegroup. Regardless of the number representative models have 20 field oil production values (the red line). Distance definition is the most important step in DBC to determine the criteria for the (dis)similarity between two reservoir models (Figure 3a). Step in DBCoftodistance determine the static criteria for theand (dis)similarity between two distance reservoirutilizes models a(Figure.
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