A smooth model for the estimation of gas/vapor viscosity of hydrocarbon fluids

Precise evaluation of pure hydrocarbon and natural gas viscosity is vital for reliable reservoir characterization, simulation, transportation and optimum consumption. The most trustable sources of pure hydrocarbon and natural gas viscosity values are laboratory experiments. The need of new methods becomes important when there is not enough experimental data for specific composition, pressure, and temperature conditions. In this study, a promising approach is utilized for the prediction of viscosities of pure hydrocarbons as well as gas mixtures containing heavy hydrocarbon components and impurities such as carbon dioxide, nitrogen, helium, and hydrocarbon sulfide using over 3800 data sets. Gene Expression Programming (GEP) is employed to develop a general model for pure and natural gas viscosity. The proposed model is a function of pseudo reduced pressure, pseudo reduced temperature, molecular weight and density. In addition, comparative studies are performed between the results obtained by the GEP model and previously published empirical correlations. To this end, statistical and graphical error analyses are used simultaneously. The results obtained show a value of 4.9% for average absolute percent relative error which is a measure of relative absolute deviation from the experimental data. The results also propose that standard deviation as a sign of data scattering is only 0.0870. These observations illustrate that the GEP model is more robust, reliable and consistent than the existing correlations for prediction of pure and natural gas viscosity. Finally, the relevancy factor shows that molecular weight has the greatest effect on gas viscosity.

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