An effective asphaltene precipitation modeling approach using PC-SAFT with detailed fluid descriptions for gas injection conditions

Fluid characterization plays a prominent role in identifying the properties of the crude oil, particularly the equilibrium conditions. It gains more importance, especially in problematic crudes that, for instance, contain precipitating constituents, like Asphaltene. Aside from the pressure depletion, enhanced Asphaltene precipitation during the miscible gas injection causes many problems to the production system. Therefore, the employed characterization procedure must be able to predict the Asphaltene instability conditions effectively. Recently a class of solution techniques using the perturbed chain statistical associating fluid theory (PC-SAFT) and dealing with Asphaltene phase as a liquid-like phase have gained attractions in literature. However, such techniques could not consider the effect of the injected gas composition and structure, because they lumped all the heavy gas components in a single pseudo-component. Hence, in this study, a detailed characterization method is proposed that can account for the heavier gas components. The modeling approach in this work, assumes the liquid-like Asphaltene framework and PC-SAFT for phase behavior calculations. In this work, the three PC-SAFT parameters for Asphaltene and Aromatic + Resin are simultaneously regressed with gas injection, to match the saturation and Asphaltene onset pressures with experimental data. The outcomes of this study are compared with the experimental data and previous characterization models. The results prove the ability of the detailed characterization approach for predicting the bubble point and Asphaltene onset pressures. It not only leads to lower estimation errors in most cases but also can capture the actual trend of bubble and Asphaltene onset curves, which makes it even more reliable for the prediction of instability conditions. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

Fluid characterization is crucial for identifying the properties of crude oil and equilibrium conditions. A detailed characterization method proposed in this study effectively predicts Asphaltene instability conditions, leading to lower estimation errors and capturing the actual trend of bubble and Asphaltene onset curves. This approach proves to be reliable for predicting instability conditions.