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Identificación de patrones de flujo bifásico de petróleo y agua en tuberías verticales mediante redes de memoria a corto y largo plazo

dc.contributor.advisor
dc.contributor.authorRuiz Diaz, Carlos Mauricio
dc.contributor.authorQuispe Suarez, Brayan
dc.contributor.authorGonzález Estrada, Octavio Andrés
dc.coverage.spatialColombiaen_US
dc.date.accessioned2026-04-23T16:34:04Z
dc.date.available2026-04-23T16:34:04Z
dc.date.issued2024-02-12
dc.identifier.issn2522-574Xen_US
dc.identifier.urihttps://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/6235
dc.description.abstractAccurate biphasic flow pattern recognition is essential in the design of coatings for the oil and gas sector because it enables engineers to create materials that are tailored to specific flow conditions. This results in enhanced corrosion protection, erosion resistance, flow efficiency, and overall performance of equipment and infrastructure in the challenging environments of the oil and gas industry. The development of flow maps has been based on empirical correlations that incorpórate characteristics such as superficial velocities, volume fractions, and physical properties such as the density and viscosity of the analyzed substances. In addition, geometric parameters such as the inclination and the internal diameter of the pipes are considered. However, due to the difficult working conditions on offshore platforms and the limitations in monitoring internal flow patterns, technological advances have been implemented to improve this process using artificial intelligence techniques. In this context, this study proposes using a long-term memory (LSTM) recurrent neural network to predict the flow patterns generated in vertical pipes. Esta red LSTM se entrenó y validó utilizando datos obtenidos de una base de datos bibliográfica. Los resultados obtenidos mostraron que el modelo presenta un error de predicción inferior al 1 %. Estos avances tecnológicos suponen un paso importante hacia la optimización del proceso de identificación de patrones de flujo en la industria de los hidrocarburos. Al aprovechar las capacidades de la inteligencia artificial, se pueden obtener previsiones más precisas y fiables, lo que permite tomar decisiones fundamentadas y mejorar la eficiencia y la seguridad de las operaciones.en_US
dc.language.isoengen_US
dc.relationhttps://link.springer.com/article/10.1007/s42247-024-00631-2en_US
dc.titleIdentificación de patrones de flujo bifásico de petróleo y agua en tuberías verticales mediante redes de memoria a corto y largo plazoen_US
dc.title.translatedTwo-phase oil and water flow pattern identification in vertical pipes applying long short-term memory networksen_US
dc.contributor.cvlacCarlos Mauricio Ruiz Diaz [0001772117]en_US
dc.contributor.cvlacOctavio Andrés González Estrada [0001406575]en_US
dc.contributor.googlescholarCarlos Mauricio Ruiz Diaz [x10qI0UAAAAJ]en_US
dc.contributor.googlescholarOctavio Andrés González Estrada [Z4YyFpgAAAAJ]en_US
dc.contributor.orcidCarlos Mauricio Ruiz Diaz [0000-0003-3836-9332]en_US
dc.contributor.orcidOctavio Andrés González Estrada [0000-0002-2778-3389]en_US
dc.contributor.researchgateCarlos Mauricio Ruiz Diaz [Carlos-Mauricio-Ruiz-Diaz]en_US
dc.contributor.researchgateOctavio Andrés González Estrada [Octavio-Gonzalez-Estrada]en_US
dc.contributor.scopusCarlos Mauricio Ruiz Diaz [57326268000]en_US
dc.contributor.scopusOctavio Andrés González Estrada [25927116600]en_US
dc.description.abstractenglishAccurate biphasic flow pattern recognition is essential in the design of coatings for the oil and gas sector because it enables engineers to create materials that are tailored to specific flow conditions. This results in enhanced corrosion protection, erosion resistance, flow efficiency, and overall performance of equipment and infrastructure in the challenging environments of the oil and gas industry. The development of flow maps has been based on empirical correlations that incorpórate characteristics such as superficial velocities, volume fractions, and physical properties such as the density and viscosity of the analyzed substances. In addition, geometric parameters such as the inclination and the internal diameter of the pipes are considered. However, due to the difficult working conditions on offshore platforms and the limitations in monitoring internal flow patterns, technological advances have been implemented to improve this process using artificial intelligence techniques. In this context, this study proposes using a long-term memory (LSTM) recurrent neural network to predict the flow patterns generated in vertical pipes. This LSTM network was trained and validated using data obtained from a literature database. The results obtained showed that the model has a prediction error of less than 1%. These technological advances represent an important step towards optimizing the flow pattern identification process in the hydrocarbons industry. By leveraging the capabilities of artificial intelligence, more accurate and reliable forecasts can be obtained, enabling informed decisions and improving the efficiency and safety of operations.en_US
dc.subject.proposalLSTMen_US
dc.subject.proposalRedes neuronales recurrentesen_US
dc.subject.proposalFlujo bifásico líquido-líquidoen_US
dc.subject.proposalIdentificación de patrones de flujoen_US
dc.subject.keywordsLSTMen_US
dc.subject.keywordsRecurrent neural networksen_US
dc.subject.keywordsLiquid–liquid two-phase flowen_US
dc.subject.keywordsFlow pattern identificationen_US
dc.publisher.facultyFacultad ingenieríasen_US
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTen_US
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dc.identifier.DOIhttps://doi.org/10.1007/s42247-024-00631-2en_US


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