Robust method based on optimized support vector regression for modeling of asphaltene precipitation

Precipitation of asphaltene largely affects the production rate of crude oil owing to the clogging of transportation pipeline as well as formation damage. Therefore, it is imperative to search for a robust method for the modeling of asphaltene precipitation. This paper aims to propose a new mathematical model for computing asphaltene precipitation as a function of titration data including dilution ratio, temperature, and molecular weight of solvent. This model is constructed based on integrating the support vector regression with the imperialist competitive algorithm. Optimization increases the performance of support vector regression by virtue of determining the optimal values of their parameters. The constructed model is applied to experimental data and its performance is compared with scaling equation which is traditionally employed for modeling of asphaltene precipitation. The results of this study show that the integration of imperialist competitive algorithm and support vector regression has a better performance than the scaling equation. This paper offers that the support vector regression optimized by virtue of the imperialist competitive algorithm is a reliable model for modeling asphaltene precipitation.