Smart phase behavior modeling of asphaltene precipitation using advanced computational frameworks: ENN, GMDH, and MPMR

Abstract

Asphaltene precipitation is the reason behind some of the most destructive issues in the oil industry such as wettability alteration, formation plugging, and reduction of the relative permeability. There are different experimental and modeling methods to study the asphaltene phase behavior. However, those approaches are either time-consuming or costly. In addition, the prediction of asphaltene precipitation is challenging due to the nonlinear dependence. Therefore, in this study, three advanced computational algorithms including group method of data handling (GMDH), emotional neural network (ENN), and minimax probability machine regression (MPMR) are developed and applied to the comprehensive dataset to estimate the amount of asphaltene precipitation as the function of temperature, dilution ratio, and the molecular weight of the n-alkanes. Many different performance metrics are used to evaluate the predictive performance of intelligent algorithms. Obtained results of the modeling reveal that intelligent computational algorithms have a great ability to mimic the nonlinear relationships between the target variable and its influential variables. The results indicate MPMR as the best predictive model with the highest R-squared value on both training and testing datasets with values of R 2 = 0.992 and R 2 = 0.991, respectively. In addition, MPMR is compared and outperformed empirical correlations.