Efficient estimation of natural gas compressibility factor using a rigorous method

The compressibility factor (Z-factor) of natural gases is necessary in many gas reservoir engineering calculations. Accurate determination of this parameter is of crucial need and challenges a large number of used simulators in petroleum engineering. Although numerous studies for prediction of gas compressibility factor have been reported in the literature, the accurate prediction of this parameter has been a topic of debate in the literature. For this purpose, a new soft computing approach namely, least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing optimization technique is implemented. The model is developed and tested using a large database consisting of more than 2200 samples of sour and sweet gas compositions. The developed model can predict the natural gas compressibility factor as a function of the gas composition (mole percent of C1–C7+, H2S, CO2, and N2), molecular weight of the C7+, pressure and temperature. The calculated Z-factor values by developed intelligent model are also compared with predictions of other well-known empirical correlations. Statistical error analysis shows that the developed LSSVM model outperforms all existing predictive models with average absolute relative error of 0.19% and correlation coefficient of 0.999. Results from present study show that implementation of LSSVM can lead to more accurate and reliable estimation of natural gas compressibility factor.