Title:	USING REORDERABLE MATRICES TO COMPARE RISK CURVES OF REPRESENTATIVE MODELS IN OIL RESERVOIR DEVELOPMENT AND MANAGEMENT ACTIVITIES
Author(s):	Celmar Guimarães da Silva, Luis Augusto Angelotti Meira, Antonio Alberto S. Santos and Denis José Schiozer
ISBN:	978-989-8704-21-4
Editors:	Yingcai Xiao, Ajith P. Abraham and Jörg Roth
Year:	2020
Edition:	Single
Keywords:	Reorderable Matrix, Heatmaps, Oil Reservoir Model, Cumulative Distribution Functions, Risk Curves, Representative Models
Type:	Full
First Page:	19
Last Page:	26
Language:	English
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Paper Abstract:	Methodologies of oil reservoir development and management demand the creation of a set of reservoir models that represents the uncertainties of a reservoir. This set of uncertainties is often simplified by the use of Representative Models (RMs), i.e., models that represent the full set. Comparison of risk curves (a.k.a. complementary cumulative distribution functions) of reservoir variables is an approach used for helping oil engineers to select a set of RMs. A typical comparison chart of a given variable of interest superposes two risk curves: one of the entire model set, and another from the set of RMs. The level of similarity between the curves in this chart indicates the representativeness of the set of RMs regarding the entire model set. A visualization with some of these charts may help to compare the representativeness of the set of RMs regarding more variables (reservoir properties, production data etc.) or distinct sets of RMs. However, this kind of chart is not enough to provide an overview of these comparisons if the number of variables or sets of RMs increase. This paper shows how the use of reorderable matrices, depicted as heatmaps, can provide an overview of this dataset that can be helpful to engineers to make decisions. We propose to represent the dissimilarity of pairs of risk curves instead of the curves themselves. This solution enables our visualization to increase the number of sets of RMs and the number of variables to represent. We show the usefulness of our proposal in three case studies of oil reservoir benchmarks, and discuss the pattern we found in these cases.
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