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dc.contributor.CRUESPUNIVERSIDADE DE ESTADUAL DE CAMPINASpt_BR
dc.identifier.isbnpt
dc.typeArtigo de periódicopt_BR
dc.titleApplication Of Artificial Neural Networks In A History Matching Processpt_BR
dc.contributor.authorCosta L.A.N.pt_BR
dc.contributor.authorMaschio C.pt_BR
dc.contributor.authorJose Schiozer D.pt_BR
unicamp.authorCosta, L.A.N., DEP/FEM/CEPETRO/UNICAMP, Caixa Postal 6122Campinas, São Paulo, Brazilpt_BR
unicamp.authorMaschio, C., DEP/FEM/CEPETRO/UNICAMP, Caixa Postal 6122Campinas, São Paulo, Brazilpt_BR
unicamp.authorJosé Schiozer, D., DEP/FEM/CEPETRO/UNICAMP, Caixa Postal 6122Campinas, São Paulo, Brazilpt_BR
dc.description.abstractReservoir simulation is an important tool for reservoir studies because it enables the testing of production strategies and to perform forecasts. To obtain a reliable production prediction, the reservoir model must reproduce results similar to the observed data. This is accomplished through a history matching process, which basically consists of modifying the reservoir parameters until this condition is reached. Usually the process is complex, demanding great time and computational effort and, thus, has been the object of several studies, such as the use of proxy models to substitute the flow simulator in some stages of the history matching process to reduce the number of simulations required to achieve an acceptable match. In this work, the application of proxy models generated through Artificial Neural Networks (ANN) as a substitute for the flow simulator in the history matching process was assessed, showing that the ANN can efficiently capture the nonlinearities of the problems. A synthetic reservoir with real characteristics was used to test the methodology. The results showed that the application of the ANN as a proxy model is promising and that a good match can be achieved with fewer simulations.en
dc.relation.ispartofJournal of Petroleum Science and Engineeringpt_BR
dc.publisherElsevierpt_BR
dc.date.issued2014pt_BR
dc.identifier.citationJournal Of Petroleum Science And Engineering. Elsevier, v. 123, n. , p. 30 - 45, 2014.pt_BR
dc.language.isoenpt_BR
dc.description.volume123pt_BR
dc.description.issuenumberpt_BR
dc.description.initialpage30pt_BR
dc.description.lastpage45pt_BR
dc.rightsfechadopt_BR
dc.sourceScopuspt_BR
dc.identifier.issn9204105pt_BR
dc.identifier.doi10.1016/j.petrol.2014.06.004pt_BR
dc.identifier.urlhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84911125549&partnerID=40&md5=37518bd72137ce8508870cf200bf9721pt_BR
dc.date.available2015-06-25T17:49:47Z
dc.date.available2015-11-26T15:25:33Z-
dc.date.accessioned2015-06-25T17:49:47Z
dc.date.accessioned2015-11-26T15:25:33Z-
dc.description.provenanceMade available in DSpace on 2015-06-25T17:49:47Z (GMT). No. of bitstreams: 1 2-s2.0-84911125549.pdf: 3692630 bytes, checksum: 491f96772018b70e709c6f00db269bfd (MD5) Previous issue date: 2014en
dc.description.provenanceMade available in DSpace on 2015-11-26T15:25:33Z (GMT). No. of bitstreams: 2 2-s2.0-84911125549.pdf: 3692630 bytes, checksum: 491f96772018b70e709c6f00db269bfd (MD5) 2-s2.0-84911125549.pdf.txt: 70501 bytes, checksum: 8dd0efc033293d29a17217f92d8094d5 (MD5) Previous issue date: 2014en
dc.identifier.urihttp://www.repositorio.unicamp.br/handle/REPOSIP/85713
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/85713-
dc.identifier.idScopus2-s2.0-84911125549pt_BR
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