Modelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learning

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dc.contributor.advisorOchoa Gutierrez, Luis Hernan
dc.contributor.advisorCundar Paredes, Cristiam David
dc.contributor.authorGuerrero Benavides, Christian David
dc.date.accessioned2021-09-28T19:41:33Z
dc.date.available2021-09-28T19:41:33Z
dc.date.issued2020-02-14
dc.descriptionimágenes, ilustraciones, tablasspa
dc.description.abstractLa viscosidad es una propiedad física importante para la simulación del flujo en el medio poroso, producción, transporte y refinación de hidrocarburos. La medición directa de la viscosidad es obtenida mediante pruebas de laboratorio a una muestra de crudo de fondo de pozo. Estas muestras son difíciles de adquirir y las pruebas toman tiempo en realizarse. Por ello, existen diferentes técnicas para estimar la viscosidad, una de ellas mediante la relación empírica con el registro de Resonancia Magnética Nuclear. Este trabajo presenta una metodología para el desarrollo de un modelo predictivo de viscosidad representativo, de acuerdo con las condiciones del yacimiento a partir de mediciones de laboratorio y registros de pozo usando el aprendizaje de máquina. (Texto tomado de la fuente)spa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ciencias - Geofísicaspa
dc.description.notesViscosity is a very important physical property to simulate how the fluid flows thru the porous space, hydrocarbon production, oil pipe transport and refination. The direct value of viscosity is measure thru lab test to an oil sample from bottom hole. That is why these samples are difficult to get and test takes time to perform; so, there are different techniques to estimate viscosity; one of them is by an empirical relationship of nuclear magnetic resonance log. This research presents a methodology to develop a predictive model to get a representative viscosity value at reservoir conditions from lab measures and petrophysical well logs using Machine Learning methods.eng
dc.format.extentxvi, 89 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/80331
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Geocienciasspa
dc.publisher.facultyFacultad de Cienciasspa
dc.publisher.placeBogotá - Colombiaspa
dc.publisher.programBogotá - Ciencias - Maestría en Ciencias - Geofísicaspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.ddc530 - Física::532 - Mecánica de fluidosspa
dc.subject.ddc550 - Ciencias de la tierraspa
dc.subject.proposalViscosidadspa
dc.subject.proposalPetrofisicaspa
dc.subject.proposalPVTspa
dc.subject.proposalMachine Learningeng
dc.subject.proposalNuclear Magneticeng
dc.subject.proposalRegressioneng
dc.subject.proposalNuclear Magnetic Resonanceeng
dc.subject.proposalViscosityeng
dc.titleModelo de predicción de Viscosidad a partir del registro de Resonancia magnética Nuclear usando Machine Learningspa
dc.title.translatedViscosity predicted model from Resonance Magnetic Log using Machine Learningeng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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