Evaluating Pressure in Deepwater Gas Pipeline for the Prediction of Natural Gas Hydrate Formation

A. Abbasi, F. Mohd Hashim


This paper proposes the prediction of hydrate formation pressure in deepwater pipeline with an approach of intelligent optimization. The proposed novel correlation of hydrate formation is using the function of ordinary differential equation. The developed optimization prediction model was founded on the constant coefficients which were examined by a multiple set of experimental data of methane (CH4), ethane (C2H6), propane (C3H8), iso-butane (iC4), nitrogen (N), Carbon Dioxide (CO2) and hydrogen sulfide (H2S) hydrates. The consequences of this research are highly optimistic for the natural gas production industry.


intelligent optimization techniques; hydrate formation conditions; natural gases; ordinary differential equation

Full Text:



E. G. Hammerschmidt, “Formation of gas hydrates in natural gas transmission lines”, Industrial & Engineering Chemistry, Vol. 26, No. 8, pp. 851-855, 1934

J. Carroll, Natural gas hydrate: A guide for engineers, Gulf Professional Publishing, 2003

A. Abbasi, F. M. Hashim, “Hydrate formation prediction model for binary gases of methane+ethane and methane+propane by using optimization algorithm”, Petroleum Science and Technology, available at: www.tandfonline.com/doi/abs/10.1080/10916466.2019.1655447, pp. 1-7, 2019

R. G. Kobayashi, K. Y. Song, E. D. Sloan, “Phase behavior of water/hydrocarbon systems”, in: Petroleum Engineering Handbook, pp. 25-28, Society of Petroleum Engineers, 1987

A. Abbasi, F. M. Hashim, “A prediction model for the natural gas hydrate formation pressure into transmission line”, Petroleum Science and Technology, Vol. 34, No. 9, pp. 824-831, 2016

A. Abbasi, F. M. Hashim, “Development of a hydrate formation prediction model for sub-sea pipeline”, Petroleum Science and Technology, Vol. 35, No. 5, pp. 443-450, 2017

D. Mahlke, A. Martin, S. Moritz, “A simulated annealing algorithm for transient optimization in gas networks”, Mathematical Methods of Operations Research, Vol. 66, No. 1, pp. 99-115, 2007

E. D. Fatnes, Numerical simulations of the flow and plugging behaviour of hydrate particles, University of Bergen, 2010

W. Duch, J. Korczak, Optimization and global minimization methods suitable for neural networks, Neural Computing Surveys, 1998

Z. Cui, X. Gu, “An improved discrete artificial bee colony algorithm to minimize the makespan on hybrid flow shop problems”, Neurocomputing, Vol. 148, pp. 248-259, 2015

R. Z. R. Mercado, C. B. Sanchez, “Optimization problems in natural gas transportation systems: A state-of-the-art review”, Applied Energy, Vol. 147, pp. 536-555, 2015

C. B. Sanchez, Optimization methods for pipeline transportation of natural gas, University of Bergen, 2010

H. Kang, F. Chen, Y. Li, J. Deng, Z. Yang, “Knot calculation for spline fitting via sparse optimization”, Computer-Aided Design, Vol. 58, pp. 179-188, 2015

D. N. Truong, V. T. Bui, “Hybrid PSO-optimized ANFIS-based model to improve dynamic voltage stability”, Engineering, Technology & Applied Science Research, Vol. 9, No. 4, pp. 4384-4388, 2019

K. Soleimani, J. Mazloum, “Designing a GA-based robust controller for load frequency control (LFC)”, Engineering, Technology & Applied Science Research, Vol. 8, No. 2, pp. 2633-2639, 2018

A. Bahadori, H. B. Vuthaluru, “A novel correlation for estimation of hydrate forming condition of natural gases”, Journal of Natural Gas Chemistry, Vol. 18, No. 4, pp. 453-457, 2009

M. M. Ghiasi, A. Bahadori, S. Zendehboudi, “Estimation of triethylene glycol (TEG) purity in natural gas dehydration units using fuzzy neural network”, Journal of Natural Gas Science and Engineering, Vol. 17, pp. 26-32, 2014

S. Rashid, A. Fayazi, B. Harimi, E. Hamidpour, S. Younesi, “Evolving a robust approach for accurate prediction of methane hydrate formation temperature in the presence of salt inhibitor”, Journal of Natural Gas Science and Engineering, Vol. 18, pp. 194-204, 2014

M. Ghavipour, 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, Vol. 91, No. 2, pp. 264-273, 2013

M. Farzad, H. Tahersima, H. Khaloozadeh, “Predicting the mackey glass chaotic time series using genetic algorithm”, SICE-ICASE International Joint Conference, Busan, South Korea, October 18-21, 2006

A. Goshtasby, W. D. Oneill, “Curve fitting by a sum of gaussians”, CVGIP: Graphical Models and Image Processing, Vol. 56, No. 4, pp. 281-288, 1994

D. C. Montgomery, Design and analysis of experiments, John Wiley & Sons, 2008

M. M. Ghiasi, A. Bahadori, S. Zendehboudi, A. Jamili, S. R. Gomari, “Novel methods predict equilibrium vapor methanol content during gas hydrate inhibition”, Journal of Natural Gas Science and Engineering, Vol. 15, pp. 69-75, 2013

J. Foroozesh, A. Khosravani, A. Mohsenzadeh, A. H. Mesbahi, “Application of artificial intelligence (AI) in kinetic modeling of methane gas hydrate formation”, Journal of the Taiwan Institute of Chemical Engineers, Vol. 45, No. 5, pp. 2258-2264, 2014

E. Khamehchi, E. Shamohammadi, H. Yousefi, “Predicting the hydrate formation temperature by a new correlation and neural network”, Gas Processing Journal, Vol. 1, No. 1, pp. 41-50, 2013

M. M. Ghiasi, A. Bahadori, S. Zendehboudi, “Estimation of the water content of natural gas dried by solid calcium chloride dehydrator units”, Fuel, Vol. 117, Part A, pp. 33-42, 2014

E. D. Sloan, C. A. Koh, Clathrate Hydrates of Natural Gases, CRC Press, 2007

eISSN: 1792-8036     pISSN: 2241-4487