Wax precipitation modelling using Perturbed Chain Statistical Associating Fluid Theory (PC-SAFT)

Wax precipitation is one of the most challenging flow assurance problems because its ability to create restrictions inside wellbores, pipelines, and some production facilities. Inaccuracy in predictions wax appearance temperature (WAT) amount precipitated makes it necessary reassess existing thermodynamic models. In addition, current models require accurate description composition from expensive PNA analysis. Here we propose substitute traditional cubic equations-of-state with Perturbed Chain form Statistical Associating Fluid Theory (PC-SAFT). The advantage PC-SAFT mainly accuracy estimation fugacities heavy components vapor liquid mixtures. These are important they define equilibrium between wax, phases. novelty this research was approach for fluid characterization. Such as multi-solid or solution-solid were not used. Instead, phase represented taken inexpensive cross-polarized microscopy data. Therefore, did need To be able accurately predict component, reservoir divided into sectors determine parameters component calibration each sector separately. Later, when a new well drilled, content can determined experiment same that (they obtained before). This data alone enough at any conditions high accuracy. First, validated experimental PVT such bubble point pressure, gas-to-oil ratio (GOR) oil formation volume factor (Bo) compared results Peng Robinson EoS. fields South Caspian Basin. first validation itself, however, performed on literature. model calibrated sample (composition lab experiment) field Finally, verified rest wells field. prove method show costs analysis avoided if available.