Abstract
One of the essential properties of natural gas is its compressibility factor (z-factor), which is required for the efficient design of natural gas pipelines, storage facilities, gas well testing, gas reserve estimation, etc. Its importance has led to the development of several approaches involving new laboratory methods, equations of state (EOS), empirical correlations, and artificial intelligence for estimating gas compressibility factors. Most of the developed Z factor models have a limited range of applicability. They are unsuitable for predicting Z factors of highly pressurized gas reservoirs and natural gas systems with pseudo-reduced temperatures less than 1. Where such models exist, they are scarce and less accurate. In this study, three machine learning models, including the Gradient Boosted Decision Tree (GBDT), Support Vector Regression (SVR), and Radial Basis Function-Neural Network (RBF-NN), were developed for predicting the z-factor of natural gas mixtures with a range of Ppr and Tpr of 0–30 and 0.92–3.0, respectively. The results showed that the Gradient Boosted Decision Tree (GBDT) model outperformed other selected machine learning algorithms and published correlations. The proposed model gave a superior coefficient of determination (R2 score), and root mean square (RMSE) of 0.99962 and 0.01033, respectively. Also, the variation of the Z factors from the GBDT model with pseudo-reduced pressures at different pseudo-reduced temperatures using the isotherm plot was found to be adequate. Hence, the GBDT model in this study is a reliable method for predicting Z factors of natural gas mixtures with Ppr and Tpr of 0–30 and 0.92–3.0, respectively. The plot revealed that the GBDT model performed extremely well in predicting compressibility factor with an MAPE of about 1%. The findings of this study shows that the proposed intelligent model can be utilized in predicting the gas Z-factor.
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