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 R-2 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. (C) 2013 Elsevier B.V. All rights reserved.