Data-driven comparison between solid model and PC-SAFT for modeling asphaltene precipitation

Abstract Selecting an appropriate equation of state (EOS) to model asphaltene precipitation in compositional wellbore and reservoir simulators is still unclear in the literature. Recent studies have shown that the PC-SAFT model is more appropriate for modeling asphaltene precipitation compared to the commonly used solid model. The main objective of this paper is to compare the solid and PC-SAFT models in both static and dynamic asphaltene modeling. Through fluid characterization, the capabilities of both models are compared to reproduce precipitation experimental data. The results show that both solid and PC-SAFT models are capable of predicting the amount of asphaltene precipitation with high accuracy. Although the matching process using the PC-SAFT model is much easier, the properly tuned solid model is also able to reproduce the experimental data with the same quality as the PC-SAFT model. The simulation results show that the PC-SAFT model is superior to the solid model in terms of the extrapolation accuracy when the experimental data are not available for the simulation conditions (i.e., variation in temperature, pressure, and fluid composition in the reservoir/wellbore). However, both models are applicable for interpolation when the experimental data cover the entire range of the simulation condition. The wellbore simulations show that although the trend of asphaltene deposition is similar for both models, the solid model using Peng-Robinson EOS overestimates the amount of asphaltene precipitation and deposition in the wellbore compared to the PC-SAFT model. On the other hand, the simulation procedure using the PC-SAFT model takes much more computational time as this model uses an iterative solution to obtain the density roots and the phase equilibrium calculation.