A modified thermodynamic modeling of wax precipitation in crude oil based on PC-SAFT model

Wax precipitation may occur in production or transportation of crude oil form field which is a serious problem in petroleum industry. Flow assurance issues concerning wax precipitation make it necessary to develop a precise thermodynamic model to predict the wax appearance temperature and amount of precipitation at different conditions. In this work a new procedure has been proposed to characterize crude oil based on the SARA test considering the wax and asphaltene as single pseudo components. Two scenarios have been investigated for the survey of the crude oil characterization, with and without asphaltene pseudo component. Also, in this work, the Perturbed Chain form of the Statistical Associating Fluid Theory, PC-SAFT, has been developed to evaluate its ability for modeling of wax precipitation prediction. It is demonstrated that the developed PC-SAFT model can correlate the wax precipitation amount better than basic models (multiple solid, solid solution) typically used in the industry. The results obtained with the proposed model show a remarkable matching with the experimental data for wax precipitation values. The obtained results are very promising in providing better approach to model wax precipitation. The effect of asphaltene molecules on wax precipitation has been investigated by sensitivity analysis using Monte Carlo algorithm and the Artificial Neural Network as the base model. In this work, a three layer FFBP neural network has been constructed using the Levenberg-Marquardt training method to predict the wax precipitation amount at different conditions which are the network input parameters. Positive effect of asphaltene on wax precipitation confirmed that asphaltene molecules act like the nucleation sites for wax crystals. The obtained results in this work show that the asphaltene content of crude oil should be considered in wax precipitation models.