Comparison of scaling equation with neural network model for prediction of asphaltene precipitation

The precipitation and deposition of crude oil polar fractions such as asphaltenes in petroleum reservoirs reduce considerably the rock permeability and the oil recovery. Therefore, it is of great importance to determine “how much” the asphaltenes precipitate as a function of pressure, temperature and liquid phase composition. Extensive new experimental data for the amount of asphaltene precipitated in an Iranian crude oil has been determined with various solvents at different temperatures and dilution ratios. All experiments were carried out at atmospheric pressure. The experimental data obtained in this study were used to examine the scaling equations proposed by Rassamdana et al. and Hu et al. We introduced a modified version of their proposed scaling equation. Our observation showed that the results obtained from the present scaling equation are more satisfactory. Furthermore, an Artificial Neural Network (ANN) model was also designed and applied to predict the amount of asphaltene precipitation at a given operating condition. The predicted results of asphaltene precipitation from ANN model was also compared with the results of Rassamdana et al., Hu et al. and our proposed scaling equations. It was observed that there is more acceptable quantitative and qualitative agreement between experimental data and predicted amount of asphaltene precipitation through using ANN model and this model can be a more accurate method than scaling equations to predict the asphaltene precipitation.