Prediction of natural gas density using only three measurable properties: intelligence and mathematical approaches

ABSTRACT

Accurate determination of natural gas (NG) density is a very important issue in the NG custody transfer. In the conventional methods for density calculation such as laboratory methods and equation of state (EoS), the temperature, pressure, and NG composition are required as input parameters. However, measuring NG composition is a complicated and costly procedure. To overcome this problem, two novel approaches are proposed to calculate density without the need to measure NG composition. In these approaches, speed of sound, pressure, and temperature as three simple measurable properties are introduced as input variables. The main approach is developed based on the artificial neural network (ANN). Moreover, a mathematical correlation is also developed as the alternative approach. The results of these two approaches are validated by comparing them with experimental data. The validation results show that the average absolute percent deviation (AAPD) and root mean square error (RMSE) is 1.94% and 2.88 for the ANN approach and are 2.54% and 3.82 for the correlation approach. The results show that the ANN approach has high precision and the correlation approach has acceptable accuracy. On the other hand, the density calculations using these approaches have a significant error at low temperature and high pressure.

KEYWORDS:

• Natural gas density
• GERG-2008 EoS
• artificial neural network
• mathematical correlations
• measurable properties

Acknowledgments

This research is supported by the Ferdowsi University of Mashhad. The third author would like to thank the support from the Ferdowsi University of Mashhad.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Ferdowsi University of Mashhad.

Notes on contributors

Mahmood Farzaneh-Gord

Prof Mahmood Farzaneh-Gord is currently serving as a Full Professor in the Division of Energy Conversion, Department of Mechanical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad. His main teaching contribution for undergraduate students in engineering thermodynamics and postgraduate students is advanced engineering thermodynamics. He has been awarded several times as a distinguished instructor/researcher for his teaching/research contributions at Ferdowsi University. His research has been resulted in over 300 papers in international journals and conferences. He has also been heavily involved in industrial projects mainly for oil and natural gas companies. He has already carried out several projects for these industries.

Morteza Baghestani

Morteza Baghestani is currently a Master’s Degree student in the Division of Energy Conversion, Department of Mechanical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad. He has worked on the application of artificial neural networks for the calculation of natural gas thermodynamic properties as a Master's thesis.

Hamid Reza Rahbari

Dr. Hamid Reza Rahbari is currently serving as a Postdoctoral Researcher in the Division of Energy Conversion, Department of Mechanical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad. His main research domain is thermodynamics and the application of intelligence methods in the oil and natural gas industries. He has been published more than 33 papers in international journals.