Accurate determination of natural gas compressibility factor by measuring temperature, pressure and Joule-Thomson coefficient: Artificial neural network approach

Natural Gas (NG) compressibility factor as important property at any NG industrial applications determined by utilizing an intelligent approach precisely. Three thermodynamic properties include pressure, temperature and Joule-Thomson (JT) coefficient are selected as input parameters. These properties are chosen due to the measurement capabilities of available sensors. Unlike the traditional approaches, the current approach does not require NG compositions as input. The current intelligent approach is developed based on an Artificial Neural Network (ANN) method. Real-time measurement capability and very low cost are two main advantages of the developed approach. Big data sets of NG thermodynamic properties are created considering 30,000 random compositions for training, testing and validating the ANN. The GERG-2008 is utilized (as the most recent equation of state) to calculate thermodynamic properties to train the ANN. Validation of the developed ANN method compared to experimental data shows the Average Absolute Percent Deviation (AAPD) is about 0.33%. To show the accuracy of the developed approach, four different NG compositions are selected as case studies. The compressibility factor and JT coefficient are computed for various pressure and temperature range using the traditional approach. Then, the compressibility factor is determined using the intelligent approach when only pressure, temperature and JT coefficient are known. The AAPD of NG compressibility factor calculations for various natural gases show 0.385% for pure methane, 0.45% for the Khangiran gas, 0.58% for the Kangan gas, 0.78% for the Pars gas and is 1.12% for the Bidboland gas. The comparing results show that overall AAPD is less than 0.7% that shows the high accuracy of the intelligent approach.

Automated summary

The study focuses on accurately determining important properties of natural gas using an intelligent approach, without the need for inputting gas composition, reducing costs and improving accuracy.