Modeling viscosity of methane, nitrogen, and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

doi:10.1016/j.cjche.2020.07.008

中国化学工程学报 ›› 2021, Vol. 32 ›› Issue (4): 431-445.DOI: 10.1016/j.cjche.2020.07.008

• Energy, Resources and Environmental Technology • 上一篇下一篇

Modeling viscosity of methane, nitrogen, and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

Farzaneh Rezaei¹, Saeed Jafari¹, Abdolhossein Hemmati-Sarapardeh^1,2,3, Amir H. Mohammadi^4,5

1 Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran;
2 Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam;
3 Faculty of Environment and Chemical Engineering, Duy Tan University, Da Nang 550000, Vietnam;
4 Institut de Recherche en Génie Chimique et Pétrolier(IRGCP), Paris Cedex, France;
5 Discipline of Chemical Engineering, School of Engineering, University of KwaZulu-Natal, Howard College Campus, King George V Avenue, Durban 4041, South Africa

收稿日期:2020-05-13 修回日期:2020-06-26 出版日期:2021-04-28 发布日期:2021-06-19
通讯作者: Abdolhossein Hemmati-Sarapardeh

Modeling viscosity of methane, nitrogen, and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

Farzaneh Rezaei¹, Saeed Jafari¹, Abdolhossein Hemmati-Sarapardeh^1,2,3, Amir H. Mohammadi^4,5

1 Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran;
2 Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam;
3 Faculty of Environment and Chemical Engineering, Duy Tan University, Da Nang 550000, Vietnam;
4 Institut de Recherche en Génie Chimique et Pétrolier(IRGCP), Paris Cedex, France;
5 Discipline of Chemical Engineering, School of Engineering, University of KwaZulu-Natal, Howard College Campus, King George V Avenue, Durban 4041, South Africa

Received:2020-05-13 Revised:2020-06-26 Online:2021-04-28 Published:2021-06-19
Contact: Abdolhossein Hemmati-Sarapardeh

摘要/Abstract

摘要： Accurate gas viscosity determination is an important issue in the oil and gas industries. Experimental approaches for gas viscosity measurement are time-consuming, expensive and hardly possible at high pressures and high temperatures (HPHT). In this study, a number of correlations were developed to estimate gas viscosity by the use of group method of data handling (GMDH)-type neural network and gene expression programming (GEP) techniques using a large data set containing more than 3000 experimental data points for methane, nitrogen, and hydrocarbon gas mixtures. It is worth mentioning that unlike many of viscosity correlations, the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 K. Also, a comparison was performed between the results of these established models and the results of ten well-known models reported in the literature. Average absolute relative errors of GMDH models were obtained 4.23%, 0.64%, and 0.61% for hydrocarbon gas mixtures, methane, and nitrogen, respectively. In addition, graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT conditions. Also, using leverage technique, valid, suspected and outlier data points were determined. Finally, trends of gas viscosity models at different conditions were evaluated.

关键词: Gas Viscosity, High pressure high temperature, Group method of data handling, Gene expression programming

Abstract: Accurate gas viscosity determination is an important issue in the oil and gas industries. Experimental approaches for gas viscosity measurement are time-consuming, expensive and hardly possible at high pressures and high temperatures (HPHT). In this study, a number of correlations were developed to estimate gas viscosity by the use of group method of data handling (GMDH)-type neural network and gene expression programming (GEP) techniques using a large data set containing more than 3000 experimental data points for methane, nitrogen, and hydrocarbon gas mixtures. It is worth mentioning that unlike many of viscosity correlations, the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 K. Also, a comparison was performed between the results of these established models and the results of ten well-known models reported in the literature. Average absolute relative errors of GMDH models were obtained 4.23%, 0.64%, and 0.61% for hydrocarbon gas mixtures, methane, and nitrogen, respectively. In addition, graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT conditions. Also, using leverage technique, valid, suspected and outlier data points were determined. Finally, trends of gas viscosity models at different conditions were evaluated.

Key words: Gas Viscosity, High pressure high temperature, Group method of data handling, Gene expression programming

Farzaneh Rezaei, Saeed Jafari, Abdolhossein Hemmati-Sarapardeh, Amir H. Mohammadi. Modeling viscosity of methane, nitrogen, and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques[J]. 中国化学工程学报, 2021, 32(4): 431-445.

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Modeling viscosity of methane, nitrogen, and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

Modeling viscosity of methane, nitrogen, and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

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