Modified PC-SAFT characterization technique for modeling asphaltenic crude oil phase behavior

The perturbed-chain version of Statistical Association Fluid Theory (PC-SAFT) emerged as a powerful tool to study the phase behavior of complex fluids such as polymers and asphaltene. In spite of this, it has not gained widespread acceptance in the industry because of its sophisticated oil characterization procedure. In fact, the predictive performance of the current characterization methods is limited and not all-inclusive as they use a large number of fitting parameters and/or a non-integrated procedure. In this study, a convenient and easy-to-use procedure for crude oil characterization is proposed, assuming asphaltene as a combination of poly-nuclear aromatics, benzene derivatives, and saturates. To achieve the best match between measured and model predictions, a simple and robust Trust-Region based optimization method is used. A set of weighting factors is also used based on uncertainties in experimental data. Additionally, it is showed that the poor agreement between the measured amounts of precipitated asphaltene (from filtration test) and thermodynamic model predictions -tuned toward experimental asphaltene onset pressure data- is because of the inherent difference between the accuracy of the experimental methods. The performance of the proposed method is evaluated against reliable literature data of three frequently used crude oils which have been previously characterized over wide range of temperature, pressure, and compositions, and a reservoir fluid sample taken from a southwestern Iranian reservoir. The results are encouraging if compared to the available characterization procedures in the literature. (C) 2020 The Authors. Published by Elsevier B.V.