Modeling of Gas Hydrate Formation in the Presence of Inhibitors by Intelligent Systems

Document Type : Research Paper

Authors

1 Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

2 Young Researchers Society, Shahid Bahonar University of Kerman, Kerman, Iran

3 Department of Mining Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

4 Department of Chemical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

5 Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract

Gas hydrate formation in production and transmission pipelines and consequent plugging of these lines have been a major flow-assurance concern of the oil and gas industry for the last 75 years. Gas hydrate formation rate is one of the most important topics related to the kinetics of the process of gas hydrate crystallization. The main purpose of this study is investigating phenomenon of gas hydrate formation with the Presence of kinetic Inhibitors in operation gas transmission, and prediction of gas hydrate formation rate in the pipeline. In this regard, by using experimental data and Intelligent Systems (Artificial neural networks and adaptive neural–fuzzy system), two different high efficient and accurate models were designed to predict hydrate formation rate of , , , and i- . It was found that such models can be used as powerful tools, for prediction of gas hydrate formation rate with total average of absolute deviation less than 6%.

Keywords


[1] Sloan, E. D., (1997)."Clathrate hydrates of natural gases. New York: Marcel Dekker."
[2] Talaghat, M. R. (1988). "Experimental Investigation of Natural Gas Components During Gas hydrate formation in Presence or Absence of the L-Tyrosine as a Kinetic Inhibitor in a Flow Mini-loop Apparatus." J Chem Petroleum Eng, University of Tehran, Vol. 45, No.2, December 2011, PP. 153-166
[3] Vysniauskas, J. W., and Bishnoi, P. R. (1983). "A kinetic study of methane hydrate formation." Chem EngSci, No. 38, PP. 1061–1072.
[4] Kamari, E., Mohammadi, S., Ghozatloo, A., and Shariaty-Niassar, Mo. "Development of Hydrate Formation Phase Envelope: An Experimental Approach in One of the Iranian Gas Reservoirs". J Chem Petroleum Eng, University of Tehran, Vol. 47, No. 2, PP. 115-127.
[5] Skovborg, P., and Rasmussen, P. (1994). "A mass transport limited model for the growth of methane and ethane gas hydrates" Chem Eng Sci, No. 49, PP. 1131–1143.
[6] Kashchiev, D., and Firoozabadi, A. (2003). "Induction time in crystallization of gas hydrate" J Crystal Growth ,No. 250, PP. 499–515.
[7] Mokhatab, S., Wilkens, R. J., and Leontaritis, K. J. (2007). "A review of strategies for solving as-hydrate problems in subsea pipelines, recovery utilization and environmental effects". Energy Sources part A. No. 29, PP. 39-45.
[8] Zhang, C. S., Fan, S. S., Liang, D. Q., and Guo, K. H .(2004).”Effect of additives on formation of natural gas hydrate". Fuel, No. 83, PP. 2115-2121.
[9] Talaghat, M. R., Esmaeilzadeh, F., and Fathikalajahi, J. (2010). "Experimental and theoretical investigation of simple gas hydrate formation in presence of kinetic inhibitors in a flow mini-loop apparatus". Fluid Phase Equilibria, No. 279, PP. 28–40.
[10] Blusari, A. B. (1995). "Neural networks for chemical engineers". Amsterdam: Elsevier Science Press.
[11] Graupe, D. (2007). "Principles of artificial neural networks." Singapore: World Scientific Publishing Co.
[12] Shadravanan, R., Schaffie, M., and Ranjbar, M. (2010). "Prediction of Hydrate Formation Rate in the presence of inhibitors". J Energy Sources, Part A.
[13] Jang, J. S. R. (1993). "ANFIS: Adaptive-network-based fuzzy inference system". IEEE Trans. Syst. ManCyber. No. 23, PP. 665–685.