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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampSoares, Ricardo Vasconcellos-
dc.contributor.authorunicampMaschio, Célio-
dc.contributor.authorunicampSchiozer, Denis José-
dc.typeArtigopt_BR
dc.titleApplying a localization technique to Kalman Gain and assessing the influence on the variability of models in history matchingpt_BR
dc.contributor.authorSoares, Ricardo Vasconcellos-
dc.contributor.authorMaschio, Célio-
dc.contributor.authorSchiozer, Denis José-
dc.subjectLocalizaçãopt_BR
dc.subject.otherlanguageLocationpt_BR
dc.description.abstractHistory matching (HM) is an important process that considers dynamic data to reduce uncertainties of parameters. As an ill-posed inverse problem, different combinations of uncertainties can result in matched models and, as the real response is unknown, methodologies for HM must be capable of representing all possible answers in a certain search space, mitigating the risk of convergence to a local minimum that may not represent the real answer. This work presents a study of an ensemble-based method, derived from the Kalman Filter (KF), the Ensemble Smoother with Multiple Data Assimilation (ES-MDA), in conjunction with a localization technique applied to a benchmark model with a known response, seeking to evaluate the final variability of the models and potential exclusion of better models in a HM problem. We used three different approaches of the same model aiming to identify the main applications and limitations of the method: the first approach uses ES-MDA without localization and the other two use ES-MDA with localization under distinct approaches. Results showed that ES-MDA without localization generated an ensemble with excessive uncertainty reduction. The localization technique was able to deal with this issue. However, the different approaches with localization presented different answers, suggesting that careful analysis is required. In addition, key parameters, such as the number of models and iterations also influence the resultspt_BR
dc.relation.ispartofJournal of petroleum science and engineeringpt_BR
dc.relation.ispartofabbreviationJ. pet. sci. eng.pt_BR
dc.publisher.cityAmsterdampt_BR
dc.publisher.countryPaíses Baixospt_BR
dc.publisherElsevierpt_BR
dc.date.issued2018-
dc.date.monthofcirculationOct.pt_BR
dc.language.isoengpt_BR
dc.description.volume169pt_BR
dc.description.firstpage110pt_BR
dc.description.lastpage125pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn0920-4105pt_BR
dc.identifier.eissn1873-4715pt_BR
dc.identifier.doi10.1016/j.petrol.2018.05.059pt_BR
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0920410518304534pt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsordocumentnumber134658/2015-9pt_BR
dc.date.available2020-07-20T15:58:04Z-
dc.date.accessioned2020-07-20T15:58:04Z-
dc.description.provenanceSubmitted by Susilene Barbosa da Silva (susilene@unicamp.br) on 2020-07-20T15:58:04Z No. of bitstreams: 0. Added 1 bitstream(s) on 2021-01-07T20:41:02Z : No. of bitstreams: 1 000440428800011.pdf: 6843794 bytes, checksum: 134b6647c0dad2949582dc624aabd7aa (MD5) Bitstreams deleted on 2021-01-08T14:10:59Z: 000440428800011.pdf,. Added 1 bitstream(s) on 2021-01-08T14:14:36Z : No. of bitstreams: 1 000440428800011.pdf: 6843794 bytes, checksum: 134b6647c0dad2949582dc624aabd7aa (MD5) Bitstreams deleted on 2021-01-13T13:27:32Z: 000440428800011.pdf,. Added 1 bitstream(s) on 2021-01-13T13:30:29Z : No. of bitstreams: 1 000440428800011.pdf: 6843798 bytes, checksum: c652a89c32e40b95b18e77f906c0c65d (MD5)en
dc.description.provenanceMade available in DSpace on 2020-07-20T15:58:04Z (GMT). No. of bitstreams: 0 Previous issue date: 2018en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/345764-
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentDepartamento de Engenharia de Petróleopt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.subject.keywordHistory matchingpt_BR
dc.subject.keywordEnsemble Kalman filterpt_BR
dc.subject.keywordEnsemble smoother with multiplept_BR
dc.subject.keywordData assimilationpt_BR
dc.identifier.source000440428800011pt_BR
dc.creator.orcid0000-0002-9069-8064pt_BR
dc.creator.orcidsem informaçãopt_BR
dc.creator.orcid0000-0001-6702-104Xpt_BR
dc.type.formArtigopt_BR
dc.description.sponsorNoteThe authors would like to thank PETROBRAS (grant number 0050.0100204.16.9), the National Agency of Petroleum, Natural Gas and Biofuels (ANP), the Center of Petroleum Studies (CEPETRO), UNISIM research group, University of Campinas, and the National Council for Scientific and Technological Development (CNPq) (grant number 134658/2015-9), for supporting this research, and Computer Modelling Group (CMG) for software licensespt_BR
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