An Overview of Evolutionary Computing for Interpretation in the Oil and Gas Industry

dc.contributor.author	Rui Lourenço Lopes	en
dc.contributor.author	Hamed Nikhalat Jahromi	en
dc.contributor.author	Alípio Jorge	en
dc.date.accessioned	2017-12-19T18:55:37Z
dc.date.available	2017-12-19T18:55:37Z
dc.date.issued	2016	en
dc.description.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.	en
dc.identifier.uri	http://repositorio.inesctec.pt/handle/123456789/4308
dc.identifier.uri	http://dx.doi.org/10.1145/2948992.2949006	en
dc.language	eng	en
dc.relation	4981	en
dc.relation	6352	en
dc.relation	6502	en
dc.rights	info:eu-repo/semantics/openAccess	en
dc.title	An Overview of Evolutionary Computing for Interpretation in the Oil and Gas Industry	en
dc.type	conferenceObject	en
dc.type	Publication	en

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