Vol. 19 Núm. 2 (2021): Revista Fuentes, el reventón energético Volumen 19 n° 2
Artículos

Pronóstico de estabilidad de asfaltenos en petróleo crudo con base en análisis SARA mediante redes neuronales artificiales

Tomás Darío Marín-Velásquez
Universidad de Oriente, Nucleo de Monagas

Publicado 2021-12-10

Palabras clave

  • Asfaltenos,
  • estabilidad,
  • estadística multivariante,
  • petróleo crudo,
  • SARA

Cómo citar

Marín-Velásquez, T. D. (2021). Pronóstico de estabilidad de asfaltenos en petróleo crudo con base en análisis SARA mediante redes neuronales artificiales. Revista Fuentes, El reventón energético, 19(2), 19–33. Recuperado a partir de https://revistas.uis.edu.co/index.php/revistafuentes/article/view/12949

Resumen

La estabilidad del petróleo o su tendencia a producir precipitación de asfaltenos debe ser estimada, debido a su importancia para predecir problemas de obstrucciones de tuberías y equipos de procesos. A partir de las fracciones de hidrocarburos componentes del petróleo, denominadas fracciones SARA (Saturados, Aromáticos, Resinas y Asfaltenos) se han generado índices para estimar la condición de estabilidad con base en las relaciones de solubilidad e insolubilidad de los asfaltenos respecto a las otras fracciones a partir de estudios de laboratorio y análisis matemático. En la presente investigación se analiza la aplicabilidad de pruebas estadísticas multivariantes por Redes Neuronales Artificiales (RNA) para pronosticar la condición de estabilidad determinada a partir de dos índices, la Inestabilidad Coloidal (CII) y el Índice de Estabilidad (IE), además se proponen modificaciones de rangos con base en los resultados y un índice basado en la solubilidad/insolubilidad (IIS). Se utilizó como muestra de estudio 193 análisis SARA de petróleos de diferentes países obtenidas de artículos publicados en revistas científicas para la creación de la RNA, con la que se pronosticó el porcentaje de clasificación correcta basada en la interacción y tendencia de relaciones entre las cuatro fracciones en su conjunto. Adicionalmente se utilizaron 11 muestras externas a las utilizadas en el modelo RNA para validar el mismo. Se obtuvo que laRNA clasificó correctamente el 92,75% de la condición de estabilidad determinada con el CII y 88,60% respecto al IE. El ajuste de los rangos de estabilidad mejoró el pronóstico a 97,41% respecto al CII y 96,89% con el IE. El uso del IIS demostró un menor ajuste según la RNA con 98,45% de casos correctamente clasificados. Se demostró la aplicabilidad de la metodología de RNA para clasificar la condición de estabilidad de muestras de petróleo con base en sus fracciones SARA.

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