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

In the present study, two white-box correlative models, namely gene expression programming (GEP) and group method of data handling (GMDH), were implemented to predict the interfacial tension (IFT) of brine-methane system. To achieve this, 790 data points were collected from literature, with pressure, temperature, and salinity as inputs and IFT as the output. The range of salinity, pressure, and temperature is from 0 to 107000 ppm, 0.01–260 MPa, and 278.1–477.59 K, respectively. The results show that the GMDH model with an R 2 of 0.9321 and an average absolute percent relative error (AAPRE) of 3.68% outperforms the GEP, which has an AAPRE of 5.08% and an R 2 of 0.8943. Based on our investigation, GMDH and GEP models follow the expected trend of IFT with the variation of pressure. It has been found that the GMDH model exhibits better performance compared to the previously developed correlations in the literature. Furthermore, the sensitivity analysis indicated that temperature has the greatest effect on IFT. By using the leverage approach, we proved that the GMDH model is statistically valid. • GMDH and GEP, predicted IFT in methane-brine systems for petroleum and chemical processes. • Pressure, temperature, and salinity were inputs to develop the models. • GMDH demonstrated superior performance and AAPRE outperformed the GEP model. • Temperature was found the most influential factor on IFT in industrial applications.