Prediction of sour gas compressibility factor using an intelligent approach

Compressibility factor (z-factor) values of natural gasses are essential in most petroleum and chemical engineering calculations. The most common sources of z-factor values are laboratory experiments, empirical correlations and equations of state methods. Necessity arises when there is no available experimental data for the required composition, pressure and temperature conditions. Introduced here is a technique to predict z-factor values of natural gasses, sour reservoir gasses and pure substances. In this communication, a novel mathematical-based approach was proposed to develop reliable model for prediction of compressibility factor of sour and natural gas. A robust soft computing approach namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization tool was proposed. To evaluate the performance and accuracy of this model, statistical and graphical error analyses have been used simultaneously. Moreover, comparative studies have been conducted between this model and nine empirical correlations and equations of state. The obtained results demonstrated that the proposed CSA-LSSVM model is more robust, reliable and efficient than the existing correlations and equations of state for prediction of z-factor of sour and natural gasses.