An Efficient Method to Predict Compressibility Factor of Natural Gas Streams

Vassilis Gaganis, Dirar Homouz, Maher Maalouf, Naji Khoury and Kyriaki Polychronopoulou
Additional contact information
Vassilis Gaganis: Mining and Metallurgical Engineering, National Technical University of Athens, Athens 157 73, Greece
Dirar Homouz: Applied Mathematics and Sciences, Khalifa University, Abu Dhabi, P.O. Box 127788, United Arab Emirates
Maher Maalouf: Industrial and Systems Engineering, Khalifa University, Abu Dhabi, P.O. Box 127788, United Arab Emirates
Naji Khoury: Civil and Environmental Engineering, Notre Dame University Louaize, Beirut, P.O. Box 72, Zouk Mikael, Lebanon
Kyriaki Polychronopoulou: Mechanical Engineering, Khalifa University, Abu Dhabi, P.O. Box 127788, United Arab Emirates

Energies, 2019, vol. 12, issue 13, 1-20

Abstract: The gas compressibility factor, also known as the deviation or Z-factor, is one of the most important parameters in the petroleum and chemical industries involving natural gas, as it is directly related to the density of a gas stream, hence its flow rate and isothermal compressibility. Obtaining accurate values of the Z-factor for gas mixtures of hydrocarbons is challenging due to the fact that natural gas is a multicomponent, non-ideal system. Traditionally, the process of estimating the Z-factor involved simple empirical correlations, which often yielded weak results either due to their limited accuracy or due to calculation convergence difficulties. The purpose of this study is to apply a hybrid modeling technique that combines the kernel ridge regression method, in the form of the recently developed Truncated Regularized Kernel Ridge Regression (TR-KRR) algorithm, in conjunction with a simple linear-quadratic interpolation scheme to estimate the Z-factor. The model is developed using a dataset consisting of 5616 data points taken directly from the Standing–Katz chart and validated using the ten-fold cross-validation technique. Results demonstrate an average absolute relative prediction error of 0.04%, whereas the maximum absolute and relative error at near critical conditions are less than 0.01 and 2%, respectively. Most importantly, the obtained results indicate smooth, physically sound predictions of gas compressibility. The developed model can be utilized for the direct calculation of the Z-factor of any hydrocarbon mixture, even in the presence of impurities, such as N 2 , CO 2 , and H 2 S, at a pressure and temperature range that fully covers all upstream operations and most of the downstream ones. The model accuracy combined with the guaranteed continuity of the Z-factor derivatives with respect to pressure and temperature renders it as the perfect tool to predict gas density in all petroleum engineering applications. Such applications include, but are not limited to, hydrocarbon reserves estimation, oil and gas reservoir modeling, fluid flow in the wellbore, the pipeline system, and the surface processing equipment.

Keywords: natural gas stream; compressibility factor; kernel ridge regression; truncated newton method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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