Controlling oil production in smart wells by MPC strategy with reinforcement learning

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dc.contributor.authorTalavera, Alvaro
dc.contributor.authorTúpac Valdivia, Yván Jesús
dc.contributor.authorVellasco, Marley
dc.date.accessioned2019-01-29T22:19:56Z
dc.date.available2019-01-29T22:19:56Z
dc.date.issued2010
dc.description.abstractThis work presents the modeling and development of a methodology based on Model Predictive Control - MPC that uses a machine learning model, based on Reinforcement Learning, as the method for searching the optimal control policy, and a neural network as a proxy, for modeling the nonlinear plant. The neural network model was developed to predict the following variables: average pressure of the reservoir, the daily production of oil, gas, water and water cut in the production well, for three consecutive values, to perform the predictive control. This model is applied as a strategy to control the oil production in an oil reservoir with existing producer and injector wells. The experiments were carried out on a synthetic oil reservoir model that consists in a reservoir with three layers with different permeability and one producer well and one injector well, both completed in the three layers. There are three valves located into the injector well, one for each completion, which are the handling variables of the model. The oil production of the producer well is the controlled variable. The experiments performed have considered various set points and also the impact of disturbances on the production well. The obtained results indicate that the proposed model is capable of controlling oil production even with disturbances in the producing well, for different reference values for oil production and supporting some features of the petroleum reservoir systems such as: strong non-linearity, long delay in the system response, and multivariate characteristic. Copyright 2010, Society of Petroleum Engineers.es_PE
dc.description.uriTrabajo académicoes_PE
dc.identifier.doihttps://doi.org/10.2118/139299-mses_PE
dc.identifier.isbnurn:isbn:9781617821837es_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12590/15897
dc.language.isoenges_PE
dc.publisherScopuses_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-79952923914&partnerID=40&md5=b77bc00299d3b754cef65047df282276es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectControlled variableses_PE
dc.subjectDaily productiones_PE
dc.subjectInjector wellses_PE
dc.subjectMachine-learninges_PE
dc.subjectNeural network modeles_PE
dc.subjectNon-Linearityes_PE
dc.subjectNonlinear plantes_PE
dc.subjectOil productiones_PE
dc.subjectOil reservoirses_PE
dc.subjectOptimal control policyes_PE
dc.subjectPredictive controles_PE
dc.subjectProduction wellses_PE
dc.subjectReference valueses_PE
dc.subjectSet-point; Smart wellses_PE
dc.subjectSynthetic oiles_PE
dc.subjectSystem responsees_PE
dc.subjectThree-layeres_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.00es_PE
dc.titleControlling oil production in smart wells by MPC strategy with reinforcement learninges_PE
dc.typeinfo:eu-repo/semantics/conferenceObject
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