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

Abstract

Accurate 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.