Modeling interfacial tension of methane-brine systems at high pressure and high salinity conditions

Abstract

Natural gas which consists mainly of methane (usually more than 90% in volume), is becoming increasingly an important and efficient source of energy because of the lower greenhouse gas emissions and air pollution. Achieving satisfactory recovery factors in gas reservoirs is sensitive to the methane-brine/water interfaces induced by the interfacial tension (IFT) between these systems. Accordingly, accurate determination of IFT of the systems methane-brine/water is extremely important for natural gas production. In this paper, several intelligent models were implemented to accurately estimate interfacial tension (IFT) of the systems brine/pure water-methane under wide temperature, pressure and salinity ranges of (278.1–477.59 K), (0.01–260 MPa) and (0–200,000 ppm), respectively. The established models were based on an extensive databank including 879 experimental measurements. The implemented intelligent models in this study were Extreme Learning Machine (ELM), Radial Basis Function (RBF) neural network, Multilayer Perceptron (MLP), Least Square Vector Machine (LSSVM), and Generalized Regression Neural Network (GRNN). Various optimization algorithms were applied for improving the learning phase of these models. Furthermore, a Committee Machine Intelligent System (CMIS) scheme was proposed by linking the best-found paradigm under a linear single model. The results showed that all the developed intelligent-based paradigms exhibit reliable prediction abilities. In addition, it was found that CMIS and GRNN are the fittest paradigms with overall absolute average percent relative error (AAPRE) values of 1.117% and 1.003%, respectively. Besides, the performance assessment revealed that our best paradigms outperform the existing approaches. Finally, the sensitivity analysis revealed that salinity has a slight impact on IFT.