Efficient Algorithm for the Prediction of Pressure–Volume–Temperature Properties of Crude Oils Using the Perturbed-Chain Statistical Associating Fluid Theory Equation of State

A new simplified approach is presented for characterizing crude oils using the perturbed-chain version of the statistical associating fluid theory equation of state (PC-SAFT EoS). The new approach models the liquid phase in crude oil as one pseudocomponent called the “single liquid fraction” (SLF). The SLF approach requires a single fitting parameter called aromaticity (γ SLF ) which is fitted to the experimental bubble point and density at saturation. Simulation results for 10 light crudes from the Middle East are presented in this work and compared to 2078 data points for the predictions of constant composition expansion (CCE), differential liberation (DL), separator test, and swell test experiments. It is found that the model predictions of density are the most accurate, with an average absolute percent deviation (AAPD) of 0.5% in the CCE, 0.7% in the DL, 0.8% in the separator test, and 2.1% in the swell test. The swell test study included modeling of blends of live oil with different gases such as lean and rich hydrocarbon gases, CO 2, N 2, and H 2 S, with injection up to 71.43 mol %. The new model can predict the bubble pressure of these blends with an AAPD of 3.4%.