Compressibility factor model of sweet, sour, and condensate gases using genetic programming

摘要

Gas compressibility factor (z-factor) is necessary in most petroleum engineering calculations. The most common sources of z-factor values are experimental measurements, equations of state (EOS) and empirical correlations. There are more than twenty correlations available with two variables for calculating the z-factor from fitting Standing-Katz chart values in an EOS or just through fitting techniques. However, these correlations are too complex, which require initial value and longer computations, and have significant error. This work presents a new model for estimating z-factors of sweet gases, sour gases and gas condensates using genetic programming (GP). The z-factor model was developed using pseudo-reduced pressure, and pseudo-reduced temperature. Moreover, two new models of pseudo-critical pressure and temperature were built as a function of the gas composition (mol percent of C1-C_(7+), H2S, CO2, and N2) and the specific gravity of the C_(7+). The developed new GP-based model yields a more accurate prediction of gas z-factor compared to the commonly used correlations and EOS's.