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Modelo multimodal para pronóstico de producción de pozos petroleros
dc.rights.license | Reconocimiento 4.0 Internacional |
dc.contributor.advisor | León Guzmán, Elizabeth |
dc.contributor.author | Bello Angulo, David Esneyder |
dc.date.accessioned | 2024-07-17T19:44:49Z |
dc.date.available | 2024-07-17T19:44:49Z |
dc.date.issued | 2024 |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/86543 |
dc.description | ilustraciones, diagramas |
dc.description.abstract | El presente trabajo de investigación presenta un aporte en dos áreas de estudio de series de tiempo en el contexto de la producción de pozos petroleros, siendo estas la clasificación para identificar fallas en los pozos, y los pronósticos de producción. El conjunto de datos utilizado corresponde a la producción de pozos petroleros, incluyendo información multimodal como datos numéricos, imágenes y texto para cada punto temporal. En la clasificación de series de tiempo, se aborda la predicción de fallas en el siguiente paso temporal, logrando una exactitud del 61.3% con un modelo multimodal conectado a una capa LSTM. En pronósticos de series de tiempo, los modelos multimodales con capas LSTM destacan, superando a modelos no multimodales y a implementaciones ARIMA en predicciones trimestrales y bi-anuales, presentando un error porcentual absoluto medio de 8% llegando a 2% en casos específicos. Este trabajo contribuye significativamente a los campos de clasificación y predicción de series de tiempo multimodales, proponiendo una arquitectura de encoder multimodal distribuido en el tiempo que puede ser implementada para series de tiempo multimodales de cualquier área de la industria. (Texto tomado de la fuente). |
dc.description.abstract | This research presents a contribution to two areas of time series study, in the context of oil well production, these are the classification to identify possible failures and the production forecasting. The dataset utilized corresponds to the production of oil wells, consisting in multimodal information such as numerical data, images, and text for each temporal point. In time series classification, the prediction of failures in the subsequent time step is addressed, achieving an accuracy of 61.3% with a multimodal model connected to an LSTM layer. In time series forecasting, multimodal models with LSTM layers excel, outperforming non-multimodal models and ARIMA implementations in quarterly and bi-annual predictions, presenting a mean absolute percentage error of 8%, reaching 2% in specific cases. This work significantly contributes to the fields of multimodal time series classification and prediction, proposing a temporally distributed multimodal encoder architecture that can be implemented for multimodal time series across various industry domains. |
dc.format.extent | xvii, 116 páginas |
dc.format.mimetype | application/pdf |
dc.language.iso | spa |
dc.publisher | Universidad Nacional de Colombia |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ |
dc.subject.ddc | 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
dc.subject.ddc | 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería |
dc.title | Modelo multimodal para pronóstico de producción de pozos petroleros |
dc.type | Trabajo de grado - Maestría |
dc.type.driver | info:eu-repo/semantics/masterThesis |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
dc.publisher.program | Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación |
dc.contributor.researchgroup | Midas: Grupo de Investigación en Minería de Datos |
dc.description.degreelevel | Maestría |
dc.description.degreename | Magíster en Ingeniería - Ingeniería de Sistemas y Computación |
dc.description.researcharea | Minería de Datos - Clasificación y pronóstico de series de tiempo multimodales |
dc.identifier.instname | Universidad Nacional de Colombia |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl | https://repositorio.unal.edu.co/ |
dc.publisher.faculty | Facultad de Ingeniería |
dc.publisher.place | Bogotá, Colombia |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess |
dc.subject.proposal | Clasificación de series de tiempo |
dc.subject.proposal | Pronósticos de series de tiempo |
dc.subject.proposal | Multimodal |
dc.subject.proposal | Redes neuronales |
dc.subject.proposal | Aprendizaje automático |
dc.subject.proposal | Aprendizaje profundo |
dc.subject.proposal | Timeseries classification |
dc.subject.proposal | Timeseries forecasting |
dc.subject.proposal | Multimodal |
dc.subject.proposal | Neural networks |
dc.subject.proposal | Machine learning |
dc.subject.proposal | Deep learning |
dc.subject.unesco | Industria petrolera |
dc.subject.unesco | Petroleum industry |
dc.subject.unesco | Análisis de datos |
dc.subject.unesco | Data analysis |
dc.title.translated | Multi-modal model for production forecasting in oil wells |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa |
dc.type.content | Text |
dc.type.redcol | http://purl.org/redcol/resource_type/TM |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |
dcterms.audience.professionaldevelopment | Estudiantes |
dcterms.audience.professionaldevelopment | Investigadores |
dcterms.audience.professionaldevelopment | Maestros |
dc.contributor.orcid | Bello Angulo, David Esneyder [https://orcid.org/0009-0007-4142-1441] |
dc.subject.wikidata | pronóstico |
dc.subject.wikidata | forecasting |
dc.subject.wikidata | inteligencia artificial |
dc.subject.wikidata | artificial intelligence |
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