Estimation of Gas Mixture Compressibility Factor and Viscosity based on AI and Experimental DataEstimation of Gas Mixture Compressibility Factor and Viscosity based on AI and Experimental Data

Compressibility factor and viscosity of natural gasses are of great importance in petroleum and chemical engineering. To calculate the natural gas properties in the pipelines, storage systems and reservoirs, the exact values of gas compressibility factor and viscosity are required. A new method that allows accurate determination of compressibility factor and gas viscosity for all types of: sweet, sour, condensate and acid gases in a wide range of pressure and temperature conditions are presented here. The sizable data base of experimental Z factor and experimental gas viscosity measurements is collected from the available related literature. This newly developed method  is tested by implementation of  combined fuzzy inference system and Genetic Algorithm. The natural gas compressibility factor and viscosity as a function of gas composition, pressure, temperature and molecular weight of C7+ can be predicted through this model. The accuracy of this proposed model is compared with some commonly applied empirical correlations. The average absolute relative error  here is 1.28 % and 0.57% for Z factor and gas viscosity, respectively.

Estimation of Gas Mixture Compressibility Factor and Viscosity based on AI and Experimental DataEstimation of Gas Mixture Compressibility Factor and Viscosity based on AI and Experimental Data Keywords:

Natural Gas Viscosity , Gas Compressibility Factor , ANFIS , Genetic Algorithm