Aplicaciones de redes neuronales artificiales para optimizar la inyección de agua alternada con gas: una revisión integral
Resumen
La inyección de Agua Alternada con Gas (WAG, por sus siglas en inglés) es un método cíclico que consiste en inyectar gas y agua de manera alternada, repitiendo este proceso a lo largo de múltiples ciclos. Sin embargo, la toma de decisiones durante el proceso WAG suele implicar elecciones críticas que, en ocasiones, no logran proporcionar resultados precisos y eficaces. En los últimos años, el desarrollo de las Redes Neuronales Artificiales (ANN) ha abierto oportunidades prometedoras para transformar los procesos de Recuperación Mejorada de Petróleo (EOR).
Esta revisión integral examina metodologías basadas en ANN para predecir el desempeño de proyectos de inyección WAG. El estudio comienza con una visión general del proceso de inyección WAG, destacando su relevancia en la recuperación mejorada de petróleo. Posteriormente, se explora la arquitectura y complejidad de las ANN, proporcionando una base conceptual sobre su funcionamiento. A continuación, se analiza la aplicación de ANN en el desarrollo de modelos predictivos para la inyección WAG, enfatizando su potencial para mejorar la precisión en el pronóstico del rendimiento del proceso.
Los aspectos clave discutidos incluyen la aplicación de ANN en la inyección WAG considerando diferentes tipos de gas y roca, las funciones utilizadas en el modelado ANN de WAG y los algoritmos empleados en las simulaciones basadas en ANN. Además, esta revisión destaca los desafíos asociados con la implementación de modelos predictivos basados en ANN en proyectos WAG. Al sintetizar investigaciones existentes, este estudio tiene como objetivo proporcionar conocimientos valiosos a los ingenieros petroleros, especialmente para comprender y aplicar modelos ANN destinados a optimizar las estrategias de inyección WAG.
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