Precise calculation of natural gas sound speed using neural networks: An application in flow meter calibration

Abstract

The natural gas speed of sound (SoS) is needed for flowmeters calibration using sonic nozzles. To determine SoS of real gases, the compressibility factor (sometimes called Z-factor) should be calculated firstly. Historically, natural gas thermodynamic properties are determined by employing AGA8 equation of state (EOS). Although, AGA8 EOS could predict natural gas thermodynamic properties precisely but has a few limitations. The acceptable range of natural gas composition is the main limitation of employing AGA8 EOS. In this work, to overcome this limitation and also speed up the thermodynamic calculation procedure, the Artificial Neural Network (ANN) has been utilized to predict the compressibility factor of natural gas and SoS. The ANN training is performed by measured values of gas mixture and also training data obtained from AGA8 equation of state. The validity of the ANN methodology is carried out by comparing results with experimental values of natural gas mixtures. Analyzing the results indicate that the percentage of the relative difference for Z-factor and SoS prediction is almost within ± 5% (and even in some cases, much less than this amount). This shows a very high accuracy of the proposed method for calculating natural gas thermodynamic properties. Finally, the application of the calculations for natural gas flowmeter calibrating is applied to the different gas fields in the world as case studies.