New Correlation for Z-Factor using Genetic Programming Technique

Abstract

Abstract Compressibility factor is a measure of the deviation of a real gas from ideal behavior. Accurate information of z-factor values is necessary in engineering applications such as gas metering, pipeline design, reserves estimation, gas flow rate, and material balance calculations. In addition, z-factor is important in calculating gas properties such as gas formation volume factor, gas isothermal compressibility, viscosity, and density. The most common sources of z-factor values are experimental measurement, but if unavailable, equations of state and empirical correlations are utilized for the gas composition, and pressure and temperature conditions. This paper presents a new model that allow accurate determination of z-factor values both for pure components and gas mixtures including significant amounts of non-hydrocarbon components and rich gas condensates at wide ranges of pressures and temperatures. Large database of experimental z-factor measurement are used. It includes more than 977 samples of worldwide sour and sweet gases. The database consists of gas composition, and z-factor experimental measurements at different pseudo-reduced properties of pressures and temperatures. The new model was developed and tested using linear genetic programming (GP) technique. The proposed model efficiency was compared to four commonly used equations of state (Van der-Walls, Redlech kwong, Peng-Robinson and Lawel-Lake-silberberg) in addition to six empirical correlations (Dranchuk-Purvis-Robinson, Dranchuk-Abu-Kassem, Hall-Yarborough, and Beggs and Brill). Several criteria are used to evaluate the proposed model including the average relative error (ARE), average absolute relative error (AARE), standard deviation (SD), and cross plots. The output of this work indicates the strength of the linear genetic programming technique and the good accuracy and simplicity of the developed model in comparison to the tested commonly utilized models.