Modelo multimodal para pronóstico de producción de pozos petroleros

ilustraciones, diagramas

Autores:: Bello Angulo, David Esneyder

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Modelo multimodal para pronóstico de producción de pozos petroleros
dc.title.translated.eng.fl_str_mv	Multi-modal model for production forecasting in oil wells
title	Modelo multimodal para pronóstico de producción de pozos petroleros
spellingShingle	Modelo multimodal para pronóstico de producción de pozos petroleros 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Clasificación de series de tiempo Pronósticos de series de tiempo Multimodal Redes neuronales Aprendizaje automático Aprendizaje profundo Timeseries classification Timeseries forecasting Multimodal Neural networks Machine learning Deep learning Industria petrolera Análisis de datos Petroleum industry Data analysis pronóstico inteligencia artificial forecasting artificial intelligence
title_short	Modelo multimodal para pronóstico de producción de pozos petroleros
title_full	Modelo multimodal para pronóstico de producción de pozos petroleros
title_fullStr	Modelo multimodal para pronóstico de producción de pozos petroleros
title_full_unstemmed	Modelo multimodal para pronóstico de producción de pozos petroleros
title_sort	Modelo multimodal para pronóstico de producción de pozos petroleros
dc.creator.fl_str_mv	Bello Angulo, David Esneyder
dc.contributor.advisor.spa.fl_str_mv	León Guzmán, Elizabeth
dc.contributor.author.spa.fl_str_mv	Bello Angulo, David Esneyder
dc.contributor.researchgroup.spa.fl_str_mv	Midas: Grupo de Investigación en Minería de Datos
dc.contributor.orcid.spa.fl_str_mv	Bello Angulo, David Esneyder [https://orcid.org/0009-0007-4142-1441]
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Clasificación de series de tiempo Pronósticos de series de tiempo Multimodal Redes neuronales Aprendizaje automático Aprendizaje profundo Timeseries classification Timeseries forecasting Multimodal Neural networks Machine learning Deep learning Industria petrolera Análisis de datos Petroleum industry Data analysis pronóstico inteligencia artificial forecasting artificial intelligence
dc.subject.proposal.spa.fl_str_mv	Clasificación de series de tiempo Pronósticos de series de tiempo Multimodal Redes neuronales Aprendizaje automático Aprendizaje profundo
dc.subject.proposal.eng.fl_str_mv	Timeseries classification Timeseries forecasting Multimodal Neural networks Machine learning Deep learning
dc.subject.unesco.spa.fl_str_mv	Industria petrolera Análisis de datos
dc.subject.unesco.eng.fl_str_mv	Petroleum industry Data analysis
dc.subject.wikidata.spa.fl_str_mv	pronóstico inteligencia artificial
dc.subject.wikidata.eng.fl_str_mv	forecasting artificial intelligence
description	ilustraciones, diagramas
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-07-17T19:44:49Z
dc.date.available.none.fl_str_mv	2024-07-17T19:44:49Z
dc.date.issued.none.fl_str_mv	2024
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/86543
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/86543 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Abadi, Martín; Agarwal, Ashish; Barham, Paul; Brevdo, Eugene; Chen, Zhifeng; Citro, Craig; Corrado, Greg S.; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Goodfellow, Ian; Harp, Andrew; Irving, Geoffrey; Isard, Michael; Jia, Yangqing; Jozefowicz, Rafal; Kaiser, Lukasz; Kudlur, Manjunath; Levenberg, Josh; Mané, Dandelion; Monga, Rajat; Moore, Sherry; Murray, Derek; Olah, Chris; Schuster, Mike; Shlens, Jonathon; Steiner, Benoit; Sutskever, Ilya; Talwar, Kunal; Tucker, Paul; Vanhoucke, Vincent; Vasudevan, Vijay; Viégas, Fernanda; Vinyals, Oriol; Warden, Pete; Wattenberg, Martin; Wicke, Martin; Yu, Yuan; Zheng, Xiaoqiang: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. 2015. – Software available from tensorflow.org Aizenberg, Igor; Sheremetov, Leonid; Villa-Vargas, Luis; Martinez-Muñoz, Jorge: Multilayer Neural Network with Multi-Valued Neurons in time series forecasting of oil production. 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En: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings (2014), 12 Kostas, Demetres ; Aroca-Ouellette, Stéphane ; Rudzicz, Frank: BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data. En: Frontiers in Human Neuroscience 15 (2021), 1. – ISSN 16625161 Li, X. ; Chan, C.W. ; Nguyen, H.H.: Application of the Neural Decision Tree approach for prediction of petroleum production. En: Journal of Petroleum Science and Engineering 104 (2013), apr, p. 11–16. – ISSN 0920–4105 Li, Yunan ; Han, Yifu: Decline Curve Analysis for Production Forecasting Based on Machine Learning. En: SPE Symposium: Production Enhancement and Cost Optimisation, Society of Petroleum Engineers, nov 2017. – ISBN 978–1–61399–561–7 Lin, Jessica ; Khade, Rohan ; Li, Yuan: Rotation-invariant similarity in time series using bag-of-patterns representation. 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En: SPE Hydraulic Fracturing Technology Conference, Society of Petroleum Engineers, feb 2016. – ISBN 978–1–61399–438–2 Ma, Zhiwei ; Liu, Yaqi ; Leung, Juliana Y. ; Zanon, Stefan: Practical Data Mining and Artificial Neural Network Modeling for SAGD Production Analysis. En: SPE Canada Heavy Oil Technical Conference, Society of Petroleum Engineers, jun 2015. – ISBN 978–1–61399–402–3 Malhotra, Pankaj ; Vishnu, T. V. ; Vig, Lovekesh ; Agarwal, Puneet ; Shroff, Gautam: TimeNet: Pre-trained deep recurrent neural network for time series classification. En: ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2017), 6, p. 607–612. ISBN 9782875870391 Martin, Eileen ; Wills, Peter ; Hohl, Detlef ; Lopez, Jorge L.: Using Machine Learning to Predict Production at a Peace River Thermal EOR Site. 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ISBN 9781509061815 Wu, Kan ; Peng, Houwen ; Chen, Minghao ; Fu, Jianlong ; Chao, Hongyang: Rethinking and Improving Relative Position Encoding for Vision Transformer. En: Proceedings of the IEEE International Conference on Computer Vision (2021), 7, p. 10013–10021. – ISBN 9781665428125 Xian, Qingyu ; Liang, Wenxuan: A Multi-modal Time Series Intelligent Prediction Model. En: Lecture Notes in Electrical Engineering 942 LNEE (2022), p. 1150–1157. – ISBN 9789811924552 Xue, Wang ; Zhou, Tian ; Wen, Qingsong ; Gao, Jinyang ; Ding, Bolin ; Jin, Rong: Make Transformer Great Again for Time Series Forecasting: Channel Aligned Robust Dual Transformer. (2023), 5 Zerveas, George ; Jayaraman, Srideepika ; Patel, Dhaval ; Bhamidipaty, Anuradha ; Eickhoff, Carsten: A Transformer-based Framework for Multivariate Time Series Representation Learning. En: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 11 (2021), 8, p. 2114–2124. ISBN 9781450383325 Zheng, Jingwen ; Leung, Juliana Y. ; Sawatzky, Ronald P. ; Alvarez, Jose M.: A Proxy Model for Predicting SAGD Production from Reservoirs Containing Shale Barriers. En: SPE Canada Heavy Oil Technical Conference, Society of Petroleum Engineers, jun 2016. – ISBN 978–1–61399–471–9 Zhou, Peng ; Sang, Huiyan ; Jin, Liuyi ; Lee, W. J.: Application of Statistical Methods to Predict Production From Liquid-Rich Shale Reservoirs. En: Proceedings of the 5th Unconventional Resources Technology Conference. Tulsa, OK, USA : American Association of Petroleum Geologists, jul 2017. – ISBN 978–0–9912144–4–0 Zhu, Qiding ; Zhang, Shukui ; Zhang, Yang ; Yu, Chunqing ; Dang, Mengli ; Zhang, Li: Multimodal time series data fusion based on SSAE and LSTM. En: IEEE Wireless Communications and Networking Conference, WCNC 2021-March (2021). – ISBN 9781728195056
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2León Guzmán, Elizabeth779ab1fc47e98dc4709dec375b4bafa4Bello Angulo, David Esneyder4e657d135456c80be90c44798dfe8055600Midas: Grupo de Investigación en Minería de DatosBello Angulo, David Esneyder [https://orcid.org/0009-0007-4142-1441]2024-07-17T19:44:49Z2024-07-17T19:44:49Z2024https://repositorio.unal.edu.co/handle/unal/86543Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEl 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).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.MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónMinería de Datos - Clasificación y pronóstico de series de tiempo multimodalesxvii, 116 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaClasificación de series de tiempoPronósticos de series de tiempoMultimodalRedes neuronalesAprendizaje automáticoAprendizaje profundoTimeseries classificationTimeseries forecastingMultimodalNeural networksMachine learningDeep learningIndustria petroleraAnálisis de datosPetroleum industryData analysispronósticointeligencia artificialforecastingartificial intelligenceModelo multimodal para pronóstico de producción de pozos petrolerosMulti-modal model for production forecasting in oil wellsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAbadi, Martín; Agarwal, Ashish; Barham, Paul; Brevdo, Eugene; Chen, Zhifeng; Citro, Craig; Corrado, Greg S.; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Goodfellow, Ian; Harp, Andrew; Irving, Geoffrey; Isard, Michael; Jia, Yangqing; Jozefowicz, Rafal; Kaiser, Lukasz; Kudlur, Manjunath; Levenberg, Josh; Mané, Dandelion; Monga, Rajat; Moore, Sherry; Murray, Derek; Olah, Chris; Schuster, Mike; Shlens, Jonathon; Steiner, Benoit; Sutskever, Ilya; Talwar, Kunal; Tucker, Paul; Vanhoucke, Vincent; Vasudevan, Vijay; Viégas, Fernanda; Vinyals, Oriol; Warden, Pete; Wattenberg, Martin; Wicke, Martin; Yu, Yuan; Zheng, Xiaoqiang: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. 2015. – Software available from tensorflow.orgAizenberg, Igor; Sheremetov, Leonid; Villa-Vargas, Luis; Martinez-Muñoz, Jorge: Multilayer Neural Network with Multi-Valued Neurons in time series forecasting of oil production. 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En: IEEE Wireless Communications and Networking Conference, WCNC 2021-March (2021). – ISBN 9781728195056EstudiantesInvestigadoresMaestrosORIGINAL1099213032.2024.pdf1099213032.2024.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemas y Computaciónapplication/pdf5804043https://repositorio.unal.edu.co/bitstream/unal/86543/2/1099213032.2024.pdfec40d6e30c7b0d953b0a54c5b1ee2a1bMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86543/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53THUMBNAIL1099213032.2024.pdf.jpg1099213032.2024.pdf.jpgGenerated Thumbnailimage/jpeg4641https://repositorio.unal.edu.co/bitstream/unal/86543/4/1099213032.2024.pdf.jpg1502cea2bf97669504de041695a12640MD54unal/86543oai:repositorio.unal.edu.co:unal/865432024-08-26 23:10:35.139Repositorio Institucional Universidad Nacional de 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