Performance evaluation and improvement of PC-SAFT equation of state for the asphaltene precipitation modeling during mixing with various fluid types

One of the significant problems of the upstream flow assurance community is precipitation or deposition of asphaltene that leads to affect the economics of oil production seriously. Knowledge of asphaltene onset pressure (AOP) and amount of asphaltene precipitation in the change of temperature, pressure, and composition is the essential information for preventing this problem. Recent studies have shown that thermodynamic models based on the solubility nature of asphaltenes using an equation of state (EOS) are the most effective approach to asphaltene behavior modeling. In this research, the common version of the SAFT equation, known as the Perturbed Chain form of Statistical Association Fluid Theory (PC-SAFT) EOS without using association terms, has been employed and evaluated during the pure gas and other fluid types injection or mixing. In the case of pure gas injection (single components, for instance, N-2 or CO2 injection), the model results have shown that using the reference values of the PC-SAFT parameters ( m, sigma, epsilon/k), those are reported in the literature, may not predict the weight percentage of the asphaltene precipitation accurately. It will be shown that considering these parameters as the adjustable variables can improve the model prediction for the asphaltene precipitation behavior. Furthermore, a new methodology has been suggested to characterize the injection fluid while its SARA (Saturates-Aromatics-Resins-Asphaltenes) analysis is not reported. This methodology is established in accordant with the material balance concept; in fact, the distribution of SARA fractions for the injection fluid will be estimated from the characterized crude oil. Six systems of crude oil and injection fluid, including pure N-2 , pure methane, pure CO2, wet gas, and liquid condensate, have been taken under study to evaluate the accuracy and flexibility of the proposed methodology by comparing to the available asphaltene experimental data. (c) 2021 Elsevier B.V. All rights reserved.