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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15802
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dc.contributor.authorTúpac Valdivia, Yván Jesús-
dc.contributor.authorTalavera, Álvaro-
dc.contributor.authorRodríguez Rivero, Cristian-
dc.date.accessioned2019-01-29T22:19:51Z-
dc.date.available2019-01-29T22:19:51Z-
dc.date.issued2017-
dc.identifier.isbn9781509025312es_PE
dc.identifier.urihttp://repositorio.ucsp.edu.pe/handle/UCSP/15802-
dc.description.abstractIn this work, a reservoir simulation approximation model (proxy) based on recurrent artificial neural networks is proposed. This model is intended to obtain rates of oil, gas and water production at time t+1 from the respective production rates, average pressure and water cut at t time and the well operation points to be applied in t + 1. Also, this model is able to follow the dynamics of the reservoir system applying online learning from real production observed values. Also, this model allows perform fast and accurate production forecasting for several steps using a recursive mechanism. This model will be inserted into an oil-production control tool to find the optimal operation conditions within a forecast horizon. The obtained outcomes over the approximation tests indicate the methodology is adequate to perform production forecasts. © 2016 IEEE.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.language.isoenges_PE
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85015217277&doi=10.1109%2fANDESCON.2016.7836224&partnerID=40&md5=b0fd3ee7e40c1d167290145723b9a65aes_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectForecastinges_PE
dc.subjectNeural networkses_PE
dc.subjectPetroleum reservoir engineeringes_PE
dc.subjectPetroleum reservoirses_PE
dc.subjectProduction controles_PE
dc.subjectElman neural networkes_PE
dc.subjectOptimal operation conditionses_PE
dc.subjectProduction forecastinges_PE
dc.subjectProduction forecastses_PE
dc.subjectProxyes_PE
dc.subjectRecurrent artificial neural networkses_PE
dc.subjectRecurrent networkses_PE
dc.subjectReservoir simulationes_PE
dc.subjectRecurrent neural networkses_PE
dc.titleDynamic and recursive oil-reservoir proxy using Elman neural networkses_PE
dc.typeinfo:eu-repo/semantics/conferenceObjectes_PE
dc.identifier.doi10.1109/ANDESCON.2016.7836224es_PE
Appears in Collections:Artículos de investigación

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