Utilization of support vector machine to calculate gas compressibility factor

The compressibility factor (Z-factor) is considered as a very important parameter in the petroleum industry because of its broad applications in PVT characteristics. In this study, a meta-learning algorithm called Least Square Support Vector Machine (LSSVM) was developed to predict the compressibility factor. In addition, the proposed technique was examined with previous models, exhibiting an R2 and an MSE of 0.999 and 0.000014, respectively. A significant drawback in the conventional LSSVM is the determination of optimal parameters to attain desired output with a reasonable accuracy. To eliminate this problem, the current study introduced coupled simulated annealing (CSA) algorithm to develop a new model, known as CSA-LSSVM. The proposed algorithm included 4756 datasets to validate the effectiveness of the CSA-LSSVM model using statistical criteria. The new technique can be utilized in chemical and petroleum engineering software packages where the most accurate value of Z-factor is required to predict the behavior of real gas, significantly affecting design aspects of equipment involved in gas processing plants.