Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature

doi:10.1016/j.cjche.2016.09.007

摘要/Abstract

摘要： Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause. These problems could result in reducing production performance or even production stoppage for a long time. In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature (HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients (R²) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H₂S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model.

关键词: Hydrate formation temperature (HFT), Natural gas, Sour gases, Least squares support vector machine, Outlier diagnostics, Leverage approach

Abstract: Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause. These problems could result in reducing production performance or even production stoppage for a long time. In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature (HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients (R²) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H₂S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model.

Key words: Hydrate formation temperature (HFT), Natural gas, Sour gases, Least squares support vector machine, Outlier diagnostics, Leverage approach

Mohammad Mesbah, Ebrahim Soroush, Mashallah Rezakazemi. Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature[J]. , 2017, 25(9): 1238-1248.

参考文献

[1] E.D. Sloan, Fundamental principles and applications of natural gas hydrates, Nature 426(6964) (2003) 353-363.
[2] A. Chapoy, A.-H. Mohammadi, D. Richon, Predicting the hydrate stability zones of natural gases using artificial neural networks, Oil Gas Sci. Technol. Rev. l'IFP 62(5) (2007) 701-706.
[3] E.D. Sloan, C. Koh, Clathrate Hydrates of Natural Gases, Third edition, Taylor & Francis, 2007.
[4] J. Carroll, Natural Gas Hydrates:A Guide for Engineers, Elsevier Science, 2009.
[5] A.H. Mohammadi, D. Richon, Development of predictive techniques for estimating liquid water-hydrate equilibrium of water-hydrocarbon system, J. Thermodyn. 2009(2009) 1-12.
[6] A.H. Mohammadi, R. Anderson, B. Tohidi, Carbon monoxide clathrate hydrates:Equilibrium data and thermodynamic modeling, AIChE J. 51(10) (2005) 2825-2833.
[7] E. Hammerschmidt, Formation of gas hydrates in natural gas transmission lines, Ind. Eng. Chem. 26(8) (1934) 851-855.
[8] D.L. Katz, Prediction of conditions for hydrate formation in natural gases, Trans. AIME 160(1945) 140-149.
[9] W.I. Wilcox, D. Carson, D. Katz, Natural gas hydrates, Ind. Eng. Chem. 33(5) (1941) 662-665.
[10] C. Baillie, E. Wichert, Chart gives hydrate formation temperature for natural gas, Oil Gas J. 85(14) (1987) 37-39.
[11] S.L. Mann, Vapor-Solid Equilibrium Ratios for Structure I and Ⅱ Natural Gas Hydrates, Gas Processors Association, 1988.
[12] I.U.r.F. Makogon, et al., Hydrates of Natural Gas, PennWell Books, Tulsa, Oklahoma, 1981.
[13] B. Berge, Hydrate predictions on a microcomputer, In:Petroleum Industry Application of Microcomputers, SPE, Colorado, USA, 1986.
[14] R. Kobayashi, K.Y. Song, E.D. Sloan, Phase behavior of water/hydrocarbon systems, Petroleum Engineering Handbook 25(1987) e13.
[15] M. Motiee, Estimate possibility of hydrates, Hydrocarb. Process. 70(7) (1991) 98-99.
[16] B. Towler, S. Mokhatab, Quickly estimate hydrate formation conditions in natural gases, Hydrocarb. Process. 84(4) (2005) 61-62.
[17] A. Bahadori, H.B. Vuthaluru, A novel correlation for estimation of hydrate forming condition of natural gases, J. Nat. Gas Chem. 18(4) (2009) 453-457.
[18] M. Safamirzaei, Predict gas hydrate formation temperature with a simple correlation, in, 2015, http://www.gasprocessingnews.com/features/201508/Predict Gas Hydrate Formation Temperature With a Simple Correlation.aspx. Accessed:18.09.15.
[19] S.O. Salufu, P. Nwakwo, New empirical correlation for predicting hydrate formation conditions, SPE Nigeria Annual International Conference and Exhibition, Society of Petroleum Engineers, 2013.
[20] W.R. Parrish, J.M. Prausnitz, Dissociation pressures of gas hydrates formed by gas mixtures, Ind. Eng. Chem. Process. Des. Dev. 11(1) (1972) 26-35.
[21] H.J. Ng, D.B. Robinson, The measurement and prediction of hydrate formation in liquid hydrocarbon-water systems, Ind. Eng. Chem. Fundam. 15(4) (1976) 293-298.
[22] V. John, K. Papadopoulos, G. Holder, A generalized model for predicting equilibrium conditions for gas hydrates, AIChE J. 31(2) (1985) 252-259.
[23] G.J. Chen, T.M. Guo, Thermodynamic modeling of hydrate formation based on new concepts, Fluid Phase Equilib. 122(1) (1996) 43-65.
[24] G.J. Chen, T.M. Guo, A new approach to gas hydrate modelling, Chem. Eng. J. 71(2) (1998) 145-151.
[25] C.Y. Sun, G.J. Chen, Modelling the hydrate formation condition for sour gas and mixtures, Chem. Eng. Sci. 60(17) (2005) 4879-4885.
[26] A. Elgibaly, A. Elkamel, Optimal hydrate inhibition policies with the aid of neural networks, Energy Fuel 13(1) (1999) 105-113.
[27] A.A. Elgibaly, A.M. Elkamel, A new correlation for predicting hydrate formation conditions for various gas mixtures and inhibitors, Fluid Phase Equilib. 152(1) (1998) 23-42.
[28] G. Zahedi, Z. Karami, H. Yaghoobi, Prediction of hydrate formation temperature by both statistical models and artificial neural network approaches, Energy Convers. Manag. 50(8) (2009) 2052-2059.
[29] A.H. Mohammadi, V. Belandria, D. Richon, Use of an artificial neural network algorithm to predict hydrate dissociation conditions for hydrogen + water and hydrogen + tetra-n-butyl ammonium bromide + water systems, Chem. Eng. Sci. 65(14) (2010) 4302-4305.
[30] M. Ghavipour, M. Chitsazan, S.H. Najibi, S.S. Ghidary, Experimental study of natural gas hydrates and a novel use of neural network to predict hydrate formation conditions, Chemical Engineering Research and Design 91(2013) 264-273.
[31] M. Moradi, K. Nazari, S. Alavi, M. Mohaddesi, Prediction of equilibrium conditions for hydrate formation in binary gaseous systems using artificial neural networks, Energy Technol. 1(2-3) (2013) 171-176.
[32] J. Yang, B. Tohidi, Determination of hydrate inhibitor concentrations by measuring electrical conductivity and acoustic velocity, Energy Fuel 27(2) (2013) 736-742.
[33] A. Eslamimanesh, F. Gharagheizi, M. Illbeigi, A.H. Mohammadi, A. Fazlali, D. Richon, Phase equilibrium modeling of clathrate hydrates of methane, carbon dioxide, nitrogen, and hydrogen + water soluble organic promoters using support vector machine algorithm, Fluid Phase Equilib. 316(2012) 34-45.
[34] C. Cortes, V. Vapnik, Support-vector networks, Mach. Learn. 20(3) (1995) 273-297.
[35] J.A. Suykens, J. Vandewalle, Least squares support vector machine classifiers, Neural. Process. Lett. 9(3) (1999) 293-300.
[36] M. Curilem, G. Acuña, F. Cubillos, E. Vyhmeister, Neural networks and support vector machine models applied to energy consumption optimization in semiautogeneous grinding, Chem. Eng. Trans. 25(2011) 761-766.
[37] E.Soroush,M.Mesbah, A.Shokrollahi, A.Bahadori,M.H.Ghazanfari,Predictionofmethane uptake on different adsorbents in adsorbed natural gas technology using a rigorous model, Energy Fuel 28(10) (2014) 6299-6314.
[38] H. Wang, D. Hu, Comparison of SVM and LS-SVM for regression, 2005 International Conference on Neural Networks and Brain, IEEE, 2005.
[39] J.A. Suykens, J. De Brabanter, L. Lukas, J. Vandewalle, Weighted least squares support vector machines:Robustness and sparse approximation, Neurocomputing 48(1) (2002) 85-105.
[40] K. Pelckmans, J.A. Suykens, T. Van Gestel, J. De Brabanter, L. Lukas, B. Hamers, B. De Moor, J. Vandewalle, LS-SVMlab:A Matlab/c Toolbox for Least Squares Support Vector Machines. Tutorial, KULeuven-ESAT, Leuven, Belgium, 2002.
[41] W. Deaton, E. Frost Jr., Gas Hydrates and Their Relation to the Operation of Naturalgas Pipe Lines, Helium Research Center, Bureau of Mines, Amarillo, TX (USA), 1946.
[42] R. Kobayashi, H. Withrow, G. Williams, D. Katz, Gas hydrate formation with brine and ethanol solutions, Proceeding of the 30th Annual Convention, Natural Gasoline Association of America, 1951.
[43] L.J. Noaker, D.L. Katz, M. Aime, Gas hydrates of hydrogen sulphide-methane mixtures, Trans. Am. Inst. Min. Metall. Pet. Eng. 201(1954) 237-239.
[44] H. McLeod Jr., J. Campbell, 1566-G-natural gas hydrates at pressures to 10,000 psia, J. Pet. Technol. 13(6) (1961) 590-594.
[45] D. Robinson, J. Hutton, Hydrate formation systems containing methane, hydrogen sulphide and carbon dioxide, J. Can. Pet. Technol. 10(1971) 33-35.
[46] S. Adisasmito, R.J. Frank Ⅲ, E.D. Sloan Jr., Hydrates of carbon dioxide and methane mixtures, J. Chem. Eng. Data 36(1) (1991) 68-71.
[47] C.Y. Sun, G.J. Chen, W. Lin, T.M. Guo, Ice, 1995.
[48] E. Kamari, M. Oyarhossein, Experimental determination of hydrate phase equilibrium curve for an Iranian sour gas condensate sample, J. Nat. Gas Sci. Eng. 9(2012) 11-15.
[49] G. Jeffrey, R. McMullan, The clathrate hydrates, Prog. Inorg. Chem. 8(1967) 43-108.
[50] K. De Brabanter, P. Karsmakers, F. Ojeda, C. Alzate, J. De Brabanter, K. Pelckmans, B. De Moor, J. Vandewalle, J. Suykens, LS-SVMlab Toolbox User's Guide, ESAT-SISTA Technical Report, 102011.
[51] C.R. Goodall, 13 computation using the QR decomposition, Handbook of Statistics, 9, 1993, pp. 467-508.
[52] P.J. Rousseeuw, A.M. Leroy, Robust Regression and Outlier Detection, vol. 589, Wiley.com, 2005.
[53] P. Gramatica, Principles of QSAR models validation:Internal and external, QSAR Comb. Sci. 26(5) (2007) 694-701.
[54] A. Kamari, M. Arabloo, A. Shokrollahi, F. Gharagheizi, A.H. Mohammadi, Rapid method to estimate the minimum miscibility pressure (MMP) in live reservoir oil systems during CO₂ flooding, Fuel 153(2015) 310-319.