Modeling viscosity of methane, nitrogen, and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

Abstract

Accurate gas viscosity determination is an important issue in the oil and gas industries. Experimental approaches for gas viscosity measurement are time-consuming, expensive and hardly possible at high pressures and high temperatures (HPHT). In this study, a number of correlations were developed to estimate gas viscosity by the use of group method of data handling (GMDH)-type neural network and gene expression programming (GEP) techniques using a large data set containing more than 3000 experimental data points for methane, nitrogen, and hydrocarbon gas mixtures. It is worth mentioning that unlike many of viscosity correlations, the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 K. Also, a comparison was performed between the results of these established models and the results of ten well-known models reported in the literature. Average absolute relative errors of GMDH models were obtained 4.23%, 0.64%, and 0.61% for hydrocarbon gas mixtures, methane, and nitrogen, respectively. In addition, graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT conditions. Also, using leverage technique, valid, suspected and outlier data points were determined. Finally, trends of gas viscosity models at different conditions were evaluated.