State-of-the-Art Least Square Support Vector Machine Application for Accurate Determination of Natural Gas Viscosity

Abstract

Estimation of the viscosity of naturally occurring petroleum gases is essential to provide more accurate analysis of gas reservoir engineering problems. In this study, a new soft computing approach, namely, least square support vector machine (LSSVM) modeling, optimized with a coupled simulated annealing technique was applied for estimation of the natural gas viscosities at different temperature and pressure conditions. This model was developed based on 2485 viscosity data sets of 22 gas mixtures. The model predictions showed an average absolute relative error of 0.26% and a correlation coefficient of 0.99. The results of the proposed model were also compared with the well-known predictive models/correlations available in the literature. It has been observed that the proposed model correctly captures the physical trend of changing the natural gas viscosity as a function of the temperature and pressure. Finally, sensitivity analysis was performed to assess the effect of the gas viscosity uncertainty on the cumulative gas production for a synthetic natural gas reservoir, using a numerical reservoir simulation. Results revealed that applications of LSSVM modeling can lead to a more accurate and reliable estimation of the gas viscosity over a wide range of reservoir conditions.