Prediction of Asphaltene Precipitation in Live and Tank Crude Oil Using Gaussian Process Regression

Abstract

The study of asphaltene precipitation properties has been motivated by their propensity to aggregate, flocculate, precipitate, and adsorb onto interfaces. The tendency of asphaltenes to precipitation has posed great challenges for the petroleum industry. Since the nature of asphaltene solubility is yet unknown and several unmodeled dynamics are hidden in the original systems, the existing models may fail in prediction the asphaltene precipitation in crude oil systems. The authors developed some Gaussian process regression models to predict asphaltene precipitation in crude oil systems based on different subsets of properties and components of crude oil. Using feature selection techniques they found some subsets of properties of crude oil that are more predictive of asphaltene precipitation. Then they developed prediction models based on selected feature sets. Results of this research indicate that the proposed predictive models can successfully predict and model asphaltene precipitation in tank and live crude oils with good accuracy.

Keywords:

• asphaltene precipitation
• crude oil
• Gaussian process regression
• crude oil
• machine learning