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

Chinese Journal of Chemical Engineering ›› 2021, Vol. 32 ›› Issue (4): 431-445.DOI: 10.1016/j.cjche.2020.07.008

• Energy, Resources and Environmental Technology • Previous Articles Next Articles

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:2021-06-19 Published:2021-04-28
Contact: 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

通讯作者: Abdolhossein Hemmati-Sarapardeh

Abstract

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

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

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]. Chinese Journal of Chemical Engineering, 2021, 32(4): 431-445.

References

[1] E. Davani, G. Falcone, C. Teodoriu, D. McCain Jr, HPHT viscosities measurements of mixtures of methane/nitrogen and methane/carbon dioxide, J. Nat. Gas Sci. Eng. 12(2013) 43-55.
[2] M. Atilhan, S. Aparicio, R. Alcalde, G. Iglesias-Silva, M. El-Halwagi, K. Hall, Viscosity measurements and data correlation for two synthetic natural gas mixtures, J. Chem. Eng. Data 55(7) (2010) 2498-2504.
[3] F.E. Londono, R.A. Archer, T.A. Blasingame, Correlations for hydrocarbon gas viscosity and gas density-validation and correlation of behavior using a largescale database, SPE Reserv. Eval. Eng. 8(06) (2005) 561-572.
[4] E. Sanjari, E.N. Lay, M. Peymani, An accurate empirical correlation for predicting natural gas viscosity, J. Nat. Gas Chem. 20(6) (2011) 654-658.
[5] Z.F. Shan, W.J. Guang, Advances in chemical viscosity-reducing methods and techniques for viscous crude oils, Oilfield Chem. 3(2001) 024.
[6] E.M.E.M. Shokir, H.N. Dmour, Genetic programming (GP)-based model for the viscosity of pure and hydrocarbon gas mixtures, Energy Fuel 23(7) (2009) 3632-3636.
[7] A. Fayazi, M. Arabloo, A. Shokrollahi, M. Hadi Zargari, M. Ghazanfari, State-ofthe-art least square support vector machine application for accurate determination of natural gas viscosity, Ind. Eng. Chem. Res. 53(2) (2013) 945-958.
[8] F. Gharagheizi, A. Eslamimanesh, M. Sattari, A.H. Mohammadi, D. Richon, Corresponding states method for determination of the viscosity of gases at atmospheric pressure, Ind. Eng. Chem. Res. 51(7) (2012) 3179-3185.
[9] K. Ling, W. McCain, E. Davani, G. Falcone, Measurement of gas viscosity at high pressures and high temperatures, in:International Petroleum Technology Conference, Doha, Qatar, 2009.
[10] N.L. Carr, R. Kobayashi, D.B. Burrows, Viscosity of hydrocarbon gases under pressure, J. Pet. Technol. 6(10) (1954) 47-55.
[11] J. Lohrenz, B.G. Bray, and C.R. Clark, Calculating viscosities of reservoir fluids from their compositions. J. Pet. Technol.. 16(10)(1964)1,171-1,176.
[12] J.A. Jossi, L.I. Stiel, G. Thodos, The viscosity of pure substances in the dense gaseous and liquid phases, AIChE J. 8(1) (1962) 59-63.
[13] D.E. Dean, L.I. Stiel, The viscosity of nonpolar gas mixtures at moderate and high pressures, AIChE J. 11(3) (1965) 526-532.
[14] A.L. Lee, M.H. Gonzalez, B.E. Eakin, The viscosity of natural gases, J. Pet. Technol. 18(08) (1966) 997-1,000.
[15] M.B. Standing, Volumetric and Phase Behavior of Oil Field Hydrocarbon Systems, Soc. Pet. Eng. AIME, Dallas, Texas, 1977.
[16] M.E. Dempsey, Pathways of enzymic synthesis and conversion to cholesterol of Δ5, 7, 24-cholestatrien-3b-ol and other naturally occurring sterols, J. Biol. Chem. 240(11) (1965) 4176-4188.
[17] K. Lucas, The pressure dependence of the viscosity of liquids-a simple estimate, Chemie Ingenieur Technik 53(12) (1981) 959-960.
[18] Z. Chen, D. Ruth, On viscosity correlations of natural gas in Annual Technical Meeting, in:Petroleum Society of Canada, 1993.
[19] R. Gurbanov, A.M. Dadash-Zade, Calculations of natural gas viscosity under pressure and tempereture, 2, Azerbaijani oil ind, 1986, 44-44.
[20] R.P. Sutton, Fundamental PVT calculations for associated and gas-condensate natural gas systems in SPE annual technical conference and exhibition, in:Society of Petroleum Engineers, 2005.
[21] E. Heidaryan, J. Moghadasi, M. Rahimi, New correlations to predict natural gas viscosity and compressibility factor, J. Pet. Sci. Eng. 73(1-2) (2010) 67-72.
[22] E. Heidaryan, J. Moghadasi, A. Salarabadi, A new and reliable model for predicting methane viscosity at high pressures and high temperatures, J. Nat. Gas Chem. 19(5) (2010) 552-556.
[23] S. Hajirezaie, A. Hemmati-Sarapardeh, A.H. Mohammadi, M. Pournik, A. Kamari, A smooth model for the estimation of gas/vapor viscosity of hydrocarbon fluids, J. Nat. Gas Sci. Eng. 26(2015) 1452-1459.
[24] A. Dargahi-Zarandi, A. Hemmati-Sarapardeh, S. Hajirezaie, A. Dabir, Atashrouz, Modeling gas/vapor viscosity of hydrocarbon fluids using a hybrid GMDH-type neural network system, J. Mol. Liq. 236(2017) 162-171.
[25] A. Rostami, A. Hemmati-Sarapardeh, S. Shamshirband, Rigorous prognostication of natural gas viscosity:smart modeling and comparative study, Fuel. 222(2018) 766-778.
[26] C. Sambo, Y. Yin, U. Djuraev, D. Ghosh, Application of adaptive neuro-fuzzy inference system and optimization algorithms for predicting methane gas viscosity at high pressures and high temperatures conditions, Arab. J. Sci. Eng. 43(11) (2018) 6627-6638.
[27] A.L. Lee, Viscosity of light hydrocarbons, in:American Petroleum Institute Monograph on API Research Project, 1965.
[28] K. Ling, Gas viscosity at High Pressure and High Temperature. PhD Thesis, A&M, Texas, 2010.
[29] G. Krotov, A.G. Ivakhnenko, V. Visotsky, Identification of the mathematical model of a complex system by the self-organization method, in:Theoretical Systems Ecology:Advances and Case Studies, Academic Press, New York, 1979.
[30] R. Shankar, The Group Method of Data Handling (PhD Thesis), University of Delaware, 1972.
[31] Y. Sawaragi, T. Soeda, H. Tamura, T. Yoshimura, S. Ohe, Y. Chujo, H. Ishihara, Statistical prediction of air pollution levels using non-physical models, Automatica. 15(4) (1979) 441-451.
[32] Farlow, S., Self-organizing methods in modeling, statistics:Textbooks and monographs. New York and Basel:Marcel Dekker Inc. 54(1984).
[33] A. Ivakhnenko, G. Krotov, Multiplicative and additive nonlinear gmdh algorithm with factor degree optimization, Avtomatika. 3(1984) 13-18.
[34] A. Ivakhnenko, J. Yurachkovsky, Modeling of Complex Systems by Experimental Data. radio i svyaz publishing house, Moscow, 120 p. Ива хнеко АГ, Юрачковский ЮП Моделированйе слжных ситем по экспериментальным. М.:Радио и свяэь,1987.120 c, 1987
[35] H.R. Madala, A.G. Ivakhnenko, Inductive Learning Algorithms for Complex Systems Modeling, vol. 368, CRC Press, Boca Raton, 1994.
[36] A.G. Ivakhnenko, Polynomial theory of complex systems, IEEE Trans. Syst. Man Cyber. (4) (1971) 364-378.
[37] S. Atashrouz, G. Pazuki, S.S. Kakhki, A GMDH-type neural network for prediction of water activity in glycol and poly (ethylene glycol) solutions, J. Mol. Liq. 202(2015) 95-100.
[38] S. Atashrouz, E. Amini, G. Pazuki, Modeling of surface tension for ionic liquids using group method of data handling, Ionics. 21(6) (2015) 1595-1603.
[39] S. Atashrouz, M. Mozaffarian, G. Pazuki, Modeling the thermal conductivity of ionic liquids and ionanofluids based on a group method of data handling and modified Maxwell model, Ind. Eng. Chem. Res. 54(34) (2015) 8600-8610.
[40] S. Atashrouz, G. Pazuki, Y. Alimoradi, Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system, Fluid Phase Equilib. 372(2014) 43-48.
[41] C. Ferreira, Gene expression programming:A new adaptive algorithm for solving problems, in:arXiv preprint cs/0102027, 2001.
[42] L.J. Fogel, A.J. Owens, M.J. Walsh, Artificial Intelligence through Simulated Evolution, John Willey & Sons. Inc., New York, 1966.
[43] J. Holland, Adaptation in natural and artificial systems:an introductory analysis with application to biology. Control Artif. Intelligence, 1975.
[44] J.R. Koza, J.R. Koza, Genetic Programming:On the Programming of Computers by Means of Natural Selection, vol. 1, MIT Press, Cambridge, 1992.
[45] H.P. Schwefel, Numerical Optimization of Computer Models, John Wiley & Sons, Inc., 1981.
[46] C. Ferreira, Gene Expression Programming:Mathematical Modeling by an Artificial Intelligence, vol. 21, Springer, Berlin Heidelelberg, 2006.
[47] C. Ferreira, U. Gepsoft, What Is Gene Expression Programming, Idea Group Publishing, London, UK, 2008.
[48] N. Ryan, D. Hibler, Robust gene expression programming, Proc. Comput. Sci. 6(2011) 165-170.
[49] L. Teodorescu, D. Sherwood, High energy physics event selection with gene expression programming, Comput. Phys. Commun. 178(6) (2008) 409-419.
[50] C.R. Goodall, 13 Computation Using the QR Decomposition, 1993.
[51] R. Ershadnia, M-A Amooie, R. Shams, S. Hajirezaie, Y. Liu, S. Jamshidi, M-R Soltanian, Non-Newtonian fluid flow dynamics in rotating annular media:Physics-based and data-driven modeling, J. Pet. Sci. Eng. 185(2020) 106641.
[52] A. Hemmati-Sarapardeh, F. Ameli, B. Dabir, M. Ahmadi, A.H. Mohammadi, On the evaluation of asphaltene precipitation titration data:Modeling and data assessment, Fluid Phase Equilib. 415(2016) 88-100.
[53] A.M. Leroy, P.J. Rousseeuw, Robust Regression and Outlier Detection. Wiley Series in Probability and Mathematical Statistics, Wiley, New York, 1987.
[54] A.H. Mohammadi, A. Eslamimanesh, F. Gharagheizi, D. Richon, A novel method for evaluation of asphaltene precipitation titration data, Chem. Eng. Sci. 78(2012) 181-185.

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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