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Enhanced oil recovery assignment using a new strategy for clustering oil reservoirs: Application of fuzzy logics
Author(s) -
Khojastehmehr Mohsen,
Naderifar Abbas,
Aminshahidy Babak
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3046
Subject(s) - enhanced oil recovery , cluster analysis , petroleum engineering , process (computing) , oil field , computer science , data mining , fuzzy logic , cluster (spacecraft) , petroleum reservoir , geology , artificial intelligence , programming language , operating system
Abstract Enhanced Oil Recovery (EOR) aims at producing more oil from low productive reservoirs. EOR is a costly process due to the time and expenses required to allocate a suitable EOR method to an oil reservoir. For this purpose, this work presents a systematic approach to incorporate past experiences into a compact system to assign the EOR process to oil reservoirs. Because the number of clusters within the reservoirs data is not known, the method proposes a novel validity index to calculate optimal number of clusters c opt . Then, the data are clustered to c opt compact clusters according to their similarities, dissimilarities, rock, and fluid properties using fuzzy clustering algorithm. To decide on the EOR process to be applied to a new reservoir, this reservoir is assigned to one of the c opt clusters based on similarity, and then the EOR method is chosen based on that cluster. This approach is used for available reservoirs data from different oil fields, and its effectiveness and superiority over previous methods are confirmed.