Toward a predictive model for predicting viscosity of natural and hydrocarbon gases

Abstract

Accurate knowledge of pure hydrocarbon and natural gas viscosity is essential for reliable reservoir characterization and simulation as well as economic design of natural gas processing and transport units. The most trustable sources of pure hydrocarbon and natural gas viscosity values are laboratory experiments. When there is no available experimental data for the required composition, pressure, and temperature conditions, the use of predictive methods becomes important. In this communication, a novel approach was proposed to develop for prediction of viscosity of pure hydrocarbons as well as gas mixtures containing heavy hydrocarbon components and impurities such as carbon dioxide, nitrogen, helium, and hydrogen sulfide, using over 3800 data sets. A robust soft computing approach namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization technique was proposed (CSA-LSSVM model). Moreover, comparative studies between the CSA-LSSVM model and fourteen empirical correlations were done. To this end, statistical and graphical error analyses have been used simultaneously. Obtained results illustrated that the “two-parameter” CSA-LSSVM model is more robust, reliable, and consistent than the existing correlations for the prediction of pure and natural gas viscosity. Moreover, the relevancy factor illustrated that the molecular weight of gas has the greatest impact on gas viscosity.