Applying artificial neural networks to predict Z-factor for natural hydrocarbon gas
Abstract
The natural gas compressibility factor or Z-factor is animportant parameter todetermine thermodynamic properties of gas reservoir, gas density, gas viscosity, gas compression, gas reservoir simulation, calculate the material balance equation, and estimate PVT for oil and gas wells. Based on the data from the Standing-Katz chart, several methods have been developed to calculate the Z-factor [1 - 3]. Based on the method of Mohammadreza Kamyab [4], the authors have calculated and determined the Z-factor using the artificial neural networks (ANN) with input parameters being the pseudo-reduced pressure and temperature of 5,940 experimental data points [5]. The results of research show that this model is able to predict the Z-factor more accurately than other methods and can be applied over the pseudo-reduced temperature range of 1.05 ≤ Tpr ≤ 3 and the pseudo-reduced pressure of 0.2 ≤ Ppr ≤ 15.
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