رضا آذین

Accurate recognition of the fluid behavior is one of the basic and also key part to identify and manage hydrocarbon reservoirs. Among the various hydrocarbon fluids, the lean gas condensate fluids have a complex and unique behavior. Different approaches have been used in this study for better identification of the fluid phase behavior using 6 samples of lean gas condensate. To this end, different PVT calculations are validated by the utilization of commercial software: WinProp and PVTi. Reliable data including CCE experiment data, saturation pressure, and liquid density in the stock tank are used for tuning the equation of state (EOS). Due to the nature of the lean gas condensate fluids, occurring errors in the laboratory CVD experimental data is inevitable because the formation of even a condensate drop in the output lines of the CVD experiment cell causes drastic changes in the fluid properties. Genetic optimization algorithm is used to tune the EOS. The results of this study demonstrate that there is no need to use CVD experimental data in the process of EOS tuning for simulation of lean gas condensate fluid. Therefore, the usage of this data in the process of EOS tuning is unnecessary for lean gas condensate fluid, and it leads to reduce not only the error in the simulation process but also laboratory costs. Furthermore, the tuning strategy in this study demonstrates that molecular weight and the volume shift parameters of the heaveast component can adjust the EOS in the best way. In addition to using the least number of tuning parameters, this strategy achieves reasonable and acceptable results to improve the simulation results after the tuning process. Moreover, the unreliable data are identified and corrected for CVD experiment by the tuned EOS.