Fuentes, el reventón energético

Vol. 19 No. 2 (2021): Revista Fuentes, el reventón energético Volumen 19 n° 2
Articles

Prediction of asphaltene stability in crude oil based on SARA analysis using artificial neural networks

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  • Tomás Darío Marín-Velásquez
Tomás Darío Marín-Velásquez
Universidad de Oriente, Nucleo de Monagas
DOI: https://doi.org/10.18273/revfue.v19n2-2021003

Published 2021-12-10

Keywords

  • Asphaltenes,
  • stability,
  • multivariate statistics,
  • crude oil,
  • SARA

How to Cite

Marín-Velásquez, T. D. (2021). Prediction of asphaltene stability in crude oil based on SARA analysis using artificial neural networks. Fuentes, El reventón energético, 19(2), 19–33. https://doi.org/10.18273/revfue.v19n2-2021003

Copyright (c) 2021 Universidad Industrial de Santander

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The stability of the oil or its tendency to produce asphaltene precipitation must be estimated, due to its importance in predicting problems of obstructions in pipelines and process equipment. From the fractions of hydrocarbon components of oil, called SARA fractions (Saturates, Aromatics, Resins, and Asphaltenes), indexes have been generated to estimate the stability condition based on the solubility and insolubility ratios of the asphaltenes concerning the other fractions from laboratory studies and mathematical analysis. The present research analyzes the applicability of multivariate statistical tests by Artificial Neural Networks (ANN) to predict the stability condition determined from two indexes, the Colloidal Instability (CII) and the Stability Index (SI), also, range modifications are proposed based on the results and an index based on the solubility/insolubility (SII). 193 SARA analyses of oils from different countries obtained from articles published in scientific journals were used as a study sample for the creation of the ANN, with which the percentage of correct classification was predicted based on the interaction and tendency of relationships between the four fractions as a whole. Additionally, 11 samples external to those used in the ANN model were used to validate the model. It was obtained that the ANN correctly classified 92.75% of the stability condition determined with the CII and 88.60% concerning the IE. Adjustment of the stability ranges improved the prognosis to 97.41% concerning the CII and 96.89% for the IE. The use of the IIS showed a lower adjustment according to the ANN with 98.45% of cases correctly classified. The applicability of ANN methodology to classify the stability condition of oil samples based on their SARA fractions was demonstrated.

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