Asphaltene formation modeling using vapor-liquid-liquid equilibrium calculations by PC-SAFT for reservoir and surface conditions

Abstract

Asphaltene precipitation can occur during the different stages of oil production, including an underground reservoir, wellbore, pipelines, and surface facilities. An accurate model to predict precipitation in these conditions is still a challenge, which is addressed in this work. In this study, we first utilize the lumped characterization method of Panuganti et al. 1 in a vapor-liquid-liquid equilibrium calculation approach using PC-SAFT, and by making use of a new parameter fitting method and genetic algorithm, we predict the Asphaltene instability conditions due to the pressure, composition and temperature variations in reservoir and surface. Titration experiments of the dead oils have indicated that Asphaltene is comprised of several sub-fractions with different molecular weights that precipitate under different thermodynamic conditions. Therefore, understanding the polydisperse nature of Asphaltene is crucial in order to better estimate the Asphaltene yield during the composition change. In the next step, Asphaltene is split into three sub-fractions according to the titration data of different precipitating agents. The parameters of Asphaltene sub-fraction are then adjusted with respect to the initial monodisperse Asphaltene parameters. In addition, the amount of precipitated Asphaltene at different solvent ratios of titrant to degassed oil is calculated. The results are compared with the model of Buenrostro‐Gonzalez et al. 2 and the experimental data of two Mexican crude oils (C1 and Y3). Regarding the outcomes, from a numerical perspective, this study's approach has resulted in accurate predictions for both the live and dead (degassed) oil. The average estimation error for sample C1 is equal to 3.46% for this study, while this number is 5.13% for the Buenrostro‐Gonzalez et al. 2. Regarding the sample Y3, the sum of errors are nearly at 3.60% and 5.10% for this study and Buenrostro‐Gonzalez et al. 2, respectively.