Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/345652
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampFormentin, Helena Nandi-
dc.contributor.authorunicampAlmeida, Forlan La Rosa-
dc.contributor.authorunicampAvansi, Guilherme Daniel-
dc.contributor.authorunicampMaschio, Célio-
dc.typeArtigopt_BR
dc.titleGaining more understanding about reservoir behavior through assimilation of breakthrough time and productivity deviation in the history matching processpt_BR
dc.contributor.authorFormentin, Helena Nandi-
dc.contributor.authorAlmeida, Forlan la Rosa-
dc.contributor.authorAvansi, Guilherme Daniel-
dc.contributor.authorMaschio, Célio-
dc.contributor.authorSchiozer, Denis J.-
dc.contributor.authorCaiado, Camila-
dc.contributor.authorVernon, Ian-
dc.contributor.authorGoldstein, Michael-
dc.subjectHipercubopt_BR
dc.subjectReservatóriospt_BR
dc.subject.otherlanguageHypercubept_BR
dc.subject.otherlanguageReservoirspt_BR
dc.description.abstractHistory matching (HM) is an inverse problem where uncertainties in attributes are reduced by comparison with observed dynamic data. Typically, normalized misfit summarizes dissimilarities between observed and simulation data. Especially for long-time series, objective functions (OFs) aggregate multiple events and tendencies relevant to field performance in a single indicator (e.g. water rate and breakthrough time). To capture the attributes influencing the reservoir behavior, we evaluate the assimilation of data series through additional OFs, obtained from splitting time-series data. In this study, two additional OF groups supplement the time-series misfits: Breakthrough Deviation (BD) indicating dissimilarities in water breakthrough time; Productivity Deviation (PD), representing mismatches of the well potential, mainly impacting the transition from history to forecast conditions. The Productivity Deviation (PD) is adapted from previous studies. Instead of simulating the last time of the historical period under forecast conditions, we propose keeping it under historical data. The change is the historical data used as target condition to the simulator: Bottom Hole Pressure (BHP) in place of liquid production and water injection rates; with this, we estimate a mismatch in well productivity, while avoiding the influence of other boundary conditions in the evaluation. Two applications (1 & 2), assimilating different OF quantities, highlight the influence of the additional groups. Application 1 only computes time-series misfit (64 OFs) whereas Application 2 includes the BD and PD (counting 128 OFs). The iterative HM method presents flexibility regarding OFs assimilated and incorporation of uncertain attributes. UNISIM-I-H case allows us to evaluate the HM considering history and forecast data. We examine differences between the 450 scenarios resulting of data assimilation for each application through four perspectives. Application 2 resulted in scenarios with better predictability of the field behavior and smoother transitions between field history and forecast periods. Field cumulative oil production of Application 2 is also forecasted closer to the reference data when compared to Application 1; all forecast periods (1, 5 and 19 years) emphasize this impact. Some wells presented breakthrough time closer to the reference for Application 2. The challenging achievement of exact BD matches leads to the third advantage of the additional indicators. These OFs supply supplementary information to the diagnosis of scenarios, identifying unnoticed problems in the traditional approach. Finally, even with an overall better performance, some of the well OFs presented poorer matches for Application 2. To explain this, we analyzed the relationship between attributes and the OFs used to update the attributes. In conclusion, the improved forecast of the simulation scenarios indicates that superior performance of the HM process is possible by splitting the available dynamic data in relevant additional OFs. This study presents a case application with 11 years of field history, in which additional OFs, derived from dynamic data, add value to the reservoir characterization. They allow the influence of uncertain attributes to be captured for relevant events in reservoir performance. We also show how the increased quantity of OFs assimilated makes the HM harder for some OFspt_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.issued2019-
dc.date.monthofcirculationFeb.pt_BR
dc.language.isoengpt_BR
dc.description.volume173pt_BR
dc.description.firstpage1080pt_BR
dc.description.lastpage1096pt_BR
dc.rightsFechadopt_BR
dc.sourceSCOPUSpt_BR
dc.identifier.issn0920-4105pt_BR
dc.identifier.eissn1873-4715pt_BR
dc.identifier.doi10.1016/j.petrol.2018.10.045pt_BR
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0920410518309136pt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsordocumentnumberNão tempt_BR
dc.date.available2020-07-17T15:54:52Z-
dc.date.accessioned2020-07-17T15:54:52Z-
dc.description.provenanceSubmitted by Susilene Barbosa da Silva (susilene@unicamp.br) on 2020-07-17T15:54:52Z No. of bitstreams: 0. Added 1 bitstream(s) on 2021-01-07T20:41:00Z : No. of bitstreams: 1 2-s2.0-85055890152.pdf: 3923857 bytes, checksum: 31ddd9c780c473a037d7253ee9ff83fe (MD5) Bitstreams deleted on 2021-01-08T14:10:58Z: 2-s2.0-85055890152.pdf,. Added 1 bitstream(s) on 2021-01-08T14:14:34Z : No. of bitstreams: 1 2-s2.0-85055890152.pdf: 3923857 bytes, checksum: 31ddd9c780c473a037d7253ee9ff83fe (MD5) Bitstreams deleted on 2021-01-13T13:27:31Z: 2-s2.0-85055890152.pdf,. Added 1 bitstream(s) on 2021-01-13T13:30:28Z : No. of bitstreams: 1 2-s2.0-85055890152.pdf: 3923861 bytes, checksum: 8c19cbb0cb5942f22944b4a1c1cfc05d (MD5)en
dc.description.provenanceMade available in DSpace on 2020-07-17T15:54:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2019en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/345652-
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.departmentDepartamento de Energiapt_BR
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.subject.keywordHistory matchingpt_BR
dc.subject.keywordIterative discretept_BR
dc.subject.keywordBreakthrough timept_BR
dc.subject.keywordWell productivitypt_BR
dc.identifier.source2-s2.0-85055890152pt_BR
dc.creator.orcid0000-0002-4696-8514pt_BR
dc.creator.orcidSem informaçãopt_BR
dc.creator.orcid0000-0003-1720-5080pt_BR
dc.creator.orcidSem informaçãopt_BR
dc.type.formArtigopt_BR
dc.description.sponsorNoteThis work was conducted with the support of CNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brazil, Energi Simulation and in association with the ongoing Project registered under ANP number 19708–7 as "BG-26 – Fomento à Formação de Recursos Humanos em Gestão de Incertezas e Tomada de Decisão: Um Programa BG Fellowship" (UNICAMP/Shell Brazil/ANP) funded by Shell Brazil, under the ANP R&D levy as "Compromisso de Investimentos com Pesquisa e Desenvolvimento". The authors thank also UNISIM, DE-FEM-UNICAMP, CEPETRO and Department of Mathematical Sciences (Durham University) for supporting this work and CMG, Emerson and Schlumberger for software licensespt_BR
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