A new algorithm is presented for modeling asphaltene phase behavior using the Perturbed Chain version of the Statistical Associating Fluid Theory Equation of State (PC-SAFT EoS). The new algorithm enables a quick and efficient optimization of parameters required to predict asphaltene phase behavior in case of enhanced oil recovery. Such approach is useful especially in cases of parallel optimizations which are otherwise lengthy and complex. Previous work have shown crashing errors of existing thermodynamic simulators. The current paper aims to automate the characterization to be implemented side by side with PC-SAFT EoS without the need to depend on a background simulator. Simulation results for 6 crude oils with different gas injections are presented in the paper.
Ali Al Hammadi received his B.Sc. in Chemical Engineering (CE) from the Petroleum Institute, Abu Dhabi (Honors with Distinction, 2011) and received his PhD from the Department of Chemical and Biomolecular Engineering (CHBE) at Rice University, Houston, TX, U.S.A. (2016). His research is focused mainly on the challenges facing the oil industry as more heavy crude oils are produced; in particular, the clogging of wellbores, chocks, pumps and reservoirs caused by the presence of asphaltene. The effect of asphaltene is an example of how both the thermodynamics and kinetics can contribute to a problem. On the thermodynamic part, it mainly concerns with how asphaltene presence in a mixture or crude oil can affect the phase behavior and PVT properties. On the kinetics part, it mainly emphasizes the precipitation, aggregation and deposition interactions and the prediction of the extent of such deposit.