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dc.contributor.authorunicampMeira, Luis Augusto Angelotti-
dc.contributor.authorunicampCoelho, Guilherme Palermo-
dc.contributor.authorunicampSilva, Celmar Guimarães da-
dc.contributor.authorunicampAbreu, João L. A.-
dc.contributor.authorunicampSantos, Antonio Alberto de Souza dos-
dc.contributor.authorunicampSchiozer, Denis José-
dc.titleImproving representativeness in a scenario reduction process to aid decision making in petroleum fieldspt_BR
dc.contributor.authorMeira, Luis A. A.-
dc.contributor.authorCoelho, Guilherme P.-
dc.contributor.authorSilva, Celmar G. da-
dc.contributor.authorAbreu, Joao L. A.-
dc.contributor.authorSantos, Antonio A. S.-
dc.contributor.authorSchiozer, Denis J.-
dc.subjectCampos petroliferos - Administraçãopt_BR
dc.subject.otherlanguageOil fields - Managementpt_BR
dc.description.abstractThis paper presents an extension of the RMFinder technique, previously proposed for scenario reduction within the decision-making process in oil fields. As there are several uncertainties associated with this process, a large number of scenarios should be analyzed so that high-quality production strategies can be defined. Such broad analysis is very time-consuming so techniques to automatically identify representative models (RMs) are particularly relevant. In this context, traditional approaches are often based on the selection of three representative models: pessimistic, optimistic and most likely, according only to the most relevant variable of the problem. Here, the RMs are selected to (i) guarantee representativeness in tens of variables of the problem simultaneously; (ii) maintain the original distribution of the uncertain variables; (iii) preserve a good distribution in the scatterplots (cross-plots) of the main output variables of the problem; and (iv) allow a specialist to adjust the relative importance among the considered variables. Therefore, we modeled such a scenario reduction problem as a multi-criteria optimization problem assisted by an expert. We applied the proposed methodology to the OLYMPUS benchmark model and to two reservoir models based on real-world Brazilian fields: (i) UNISIM-I-D, a benchmark case based on the sandstone Namorado field; and (ii) UNISIM-II-D, a benchmark case based on a highly fractured pre-salt carbonate reservoir. In each experiment, the number of RMs varied from 1 to 25. We verified that, the larger the number of RMs, the smaller the bias will be with respect to the risk curves. The obtained sets of RMs were analyzed by petroleum engineers and considered appropriate for the problems studied, and they were adopted as the standard models in the following steps of the decision-making process to define the production strategies under uncertaintiespt_BR
dc.relation.ispartofJournal of petroleum science and engineeringpt_BR
dc.publisher.cityAmsterdam pt_BR
dc.description.provenanceSubmitted by Thais de Brito Barroso ( on 2020-03-30T19:13:15Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-07-20T14:20:08Z : No. of bitstreams: 1 000501599800077.pdf: 7833301 bytes, checksum: 727893794e15ed763b91b8d29e2f6bb5 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-03-30T19:13:15Z (GMT). No. of bitstreams: 0 Previous issue date: 2020en
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentDepartamento de Engenharia de Petróleopt_BR
dc.contributor.unidadeFaculdade de Tecnologiapt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.contributor.unidadeCentro de Estudos de Petróleopt_BR
dc.subject.keywordRepresentative model selectionpt_BR
dc.subject.keywordRepresentative model selectionpt_BR
dc.subject.keywordUncertain geological modelspt_BR
dc.creator.orcidsem informaçãopt_BR
dc.creator.orcidsem informaçãopt_BR
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