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Title: An Overview of Evolutionary Computing for Interpretation in the Oil and Gas Industry
Authors: Rui Lourenço Lopes
Hamed Nikhalat Jahromi
Alípio Jorge
Issue Date: 2016
Abstract: The Oil and Gas Exploration & Production (E&P) field deals with high-dimensional heterogeneous data, collected at different stages of the E&P activities from various sources. Over the years different soft-computing algorithms have been proposed for data-driven oil and gas applications. The most popular by far are Artificial Neural Networks, but there are applications of Fuzzy Logic systems, Support Vector Machines, and Evolutionary Algorithms (EAs) as well. This article provides an overview of the applications of EAs in the oil and gas E&P industry. The relevant literature is reviewed and categorised, showing an increasing interest amongst the geoscience community. © 2016 ACM.
metadata.dc.type: conferenceObject
Appears in Collections:LIAAD - Articles in International Conferences

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