Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/345751
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampMoreno, Rosângela Barros Zanoni Lopes-
dc.contributor.authorunicampAvansi, Guilherme Daniel-
dc.typeArtigopt_BR
dc.titleEmulation of reservoir production forecast considering variation in petrophysical propertiespt_BR
dc.contributor.authorMoreno, R.-
dc.contributor.authorAvansi, Close G.-
dc.contributor.authorSchiozer, D.-
dc.contributor.authorVernon, I.-
dc.contributor.authorGoldstein, M.-
dc.contributor.authorCaiado, C.-
dc.subjectPetrofísicapt_BR
dc.subjectReservatóriospt_BR
dc.subjectSimulaçãopt_BR
dc.subjectRiscopt_BR
dc.subject.otherlanguageRiskpt_BR
dc.subject.otherlanguagePetrophysicspt_BR
dc.subject.otherlanguageReservoirspt_BR
dc.subject.otherlanguageSimulationpt_BR
dc.description.abstractImplementation of proxy models, such as emulators might reduce the computational time required in a variety of reservoir simulation studies. By definition, an emulator uses reservoir properties as input parameters in a statistical model constructed from simulator outputs. However, incorporation of petrophysical properties distributions in all model grid-blocks implies too many input parameters for direct emulation. Currently, most employments of emulation only consider single-value parameterization of reservoir properties. In this work, we propose a methodology to consider spatially-distributed properties, such as porosity and permeability, in reservoir emulation technique. First, we present the process of finding a procedure to deal with geostatistical realizations in the emulator and then implement it in a risk quantification application. Construction of an emulator in a probabilistic approach involved: selection of a base model, definition of uncertain inputs, selection of outputs to be emulated, sampling inputs to generate scenarios, simulation of scenarios, and building the emulator. As an application, we used emulators to generate risk curves at the final production time of a synthetic reservoir model. By implementing the proposed procedure, we showed that emulators can provide reliable results during risk analysis in oilfield development. Furthermore, with emulators it is possible to generate risk curves that reproduce simulations results at a lower computational cost. It can be expected that parameterization of petrophysical properties will boost the applicability of the reservoir emulation technique. For instance, emulators can significantly reduce both the time and computational resources demanded in various reservoir studies for high heterogeneity and complex reservoir models such as found in the Brazilian pre-salt areapt_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.monthofcirculationJunept_BR
dc.language.isoengpt_BR
dc.description.volume165pt_BR
dc.description.firstpage711pt_BR
dc.description.lastpage725pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn0920-4105pt_BR
dc.identifier.eissn1873-4715pt_BR
dc.identifier.doi10.1016/j.petrol.2018.02.056pt_BR
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0920410518301621pt_BR
dc.description.sponsorshipCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESpt_BR
dc.description.sponsordocumentnumberNão tempt_BR
dc.date.available2020-07-20T14:58:46Z-
dc.date.accessioned2020-07-20T14:58:46Z-
dc.description.provenanceSubmitted by Susilene Barbosa da Silva (susilene@unicamp.br) on 2020-07-20T14:58:46Z No. of bitstreams: 0. Added 1 bitstream(s) on 2021-01-07T20:41:01Z : No. of bitstreams: 1 000429896600064.pdf: 4483791 bytes, checksum: 8d9087a3a2a0027c582489c10075d67d (MD5) Bitstreams deleted on 2021-01-08T14:10:59Z: 000429896600064.pdf,. Added 1 bitstream(s) on 2021-01-08T14:14:35Z : No. of bitstreams: 1 000429896600064.pdf: 4483791 bytes, checksum: 8d9087a3a2a0027c582489c10075d67d (MD5) Bitstreams deleted on 2021-01-13T13:27:32Z: 000429896600064.pdf,. Added 1 bitstream(s) on 2021-01-13T13:30:29Z : No. of bitstreams: 1 000429896600064.pdf: 4483791 bytes, checksum: 8d9087a3a2a0027c582489c10075d67d (MD5)en
dc.description.provenanceMade available in DSpace on 2020-07-20T14:58:46Z (GMT). No. of bitstreams: 0 Previous issue date: 2018en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/345751-
dc.contributor.departmentDepartamento de Engenharia de Petróleopt_BR
dc.contributor.departmentDepartamento de Energiapt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.subject.keywordProxy modelpt_BR
dc.identifier.source000429896600064pt_BR
dc.creator.orcid0000-0001-5216-4638pt_BR
dc.creator.orcid0000-0003-1720-5080pt_BR
dc.type.formArtigopt_BR
dc.description.sponsorNoteThis work was carried out in association with the ongoing Project registered as "BG-32 – Análise de Risco para o Desenvolvimento e Gerenciamento de Campos de Petróleo e Potencial uso de Emuladores" (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 CAPES for supporting this work and CMG, Emerson and Schlumberger for software licensespt_BR
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