Arabian Journal for Science and Engineering

, Volume 43, Issue 11, pp 6627–6638 | Cite as

Application of Adaptive Neuro-Fuzzy Inference System and Optimization Algorithms for Predicting Methane Gas Viscosity at High Pressures and High temperatures Conditions

  • Chico SamboEmail author
  • Yap Yin
  • Ulugbek Djuraev
  • Deva Ghosh
Research Article - Petroleum Engineering


Accurate estimation of methane viscosity is extremely important for petroleum engineers. Methane viscosity as an important property is used to model multiphase fluid flow in porous media. The viscosity of methane is usually presented as a function of pseudoreduced pressure (\({P}_{\mathrm{pr}})\) and pseudoreduced temperature (\({T}_{\mathrm{pr}})\) and can be obtained using correlations and charts. Nevertheless, the prediction of methane viscosity at high pressure and high pressure (HPHT) by correlations is associated with some level of uncertainties. Moreover, in the available charts, the methane viscosities are not presented at HPHT conditions. Therefore, having an accurate model that can predict the viscosity of methane at HPHT conditions is beneficial. Therefore, in this study, adaptive neuro-fuzzy inference system (ANFIS) as a powerful intelligent tool is used to predict the viscosity of methane at HPHT using literature experimental data. Two hybrid ANFIS-based models are developed. In the first model, particle swarm optimization (PSO) is employed to find the optimum ANFIS model parameters (ANFIS-PSO), while in the second model, the genetic algorithm (GA) optimization is applied (ANFIS-GA). The results show a better prediction of methane viscosity by the ANFIS models compared to those by models and correlations from the literature. Moreover, ANFIS-GA model shows slightly better prediction than the ANFIS-PSO model. In fact, ANFIS-GA model possesses the lowest average absolute relative deviation, the lowest mean squared error, and the highest correlation coefficient. The findings from the present work demonstrate that the proposed ANFIS-GA model can be easily implemented in any reservoir simulation software, and it provides superior accuracy and performance in reservoir simulators.


Genetic algorithm Adaptive neuro fuzzy inference system Particle swarm optimization Viscosity of gas Artificial neural networks ANFIS-GA ANFIS-PSO Viscosity correlations 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



We would like to thank the support provided by the Centre of Seismic Imaging (CSI) and University Technology PETRONAS. We would also like to show our gratitude to two anonymous reviewers for their comments on an earlier version of the manuscript.


  1. 1.
    Davani, E.; Falcone, G.; Teodoriu, C.; McCain Jr., W.D.: HPHT viscosities measurements of mixtures of methane/nitrogen and methane/carbon dioxide. J. Nat. Gas Sci. Eng. 12, 43–55 (2013)CrossRefGoogle Scholar
  2. 2.
    Sun, C.-Y.; Liu, H.; Yan, K.-L.; Ma, Q.-L.; Liu, B.; Chen, G.-J.; et al.: Experiments and modeling of volumetric properties and phase behavior for condensate gas under ultra-high-pressure conditions. Ind. Eng. Chem. Res. 51, 6916–6925 (2012)CrossRefGoogle Scholar
  3. 3.
    Heidaryan, E.; Moghadasi, J.; Salarabadi, A.: A new and reliable model for predicting methane viscosity at high pressures and high temperatures. J. Nat. Gas Chem. 19, 552–556 (2010)CrossRefGoogle Scholar
  4. 4.
    AlQuraishi, A.A.; Shokir, E.M.: Artificial neural networks modeling for hydrocarbon gas viscosity and density estimation. J. King Saud Univ. Eng. Sci. 23, 123–129 (2011)Google Scholar
  5. 5.
    Comings, E.W.; Mayland, B.J.; Egly, R.S.: The Viscosity of Gases at High Pressures. University of Illinois at Urbana Champaign, College of Engineering. Engineering Experiment Station (1944)Google Scholar
  6. 6.
    Carr, N.L.; Kobayashi, R.; Burrows, D.B.: Viscosity of hydrocarbon gases under pressure. J. Petrol. Technol. 6, 47–55 (1954)CrossRefGoogle Scholar
  7. 7.
    Londono, F.E.; Archer, R.A.; Blasingame, T.A.: Correlations for hydrocarbon gas viscosity and gas density-validation and correlation of behavior using a large-scale database. SPE Reserv. Eval. Eng. 8, 561–572 (2005)Google Scholar
  8. 8.
    Lee, A.L.; Gonzalez, M.H.; Eakin, B.E.: The viscosity of natural gases. J. Petrol. Technol. 18, 997–1,000 (1966)CrossRefGoogle Scholar
  9. 9.
    Jossi, J.A.; Stiel, L.I.; Thodos, G.: The viscosity of pure substances in the dense gaseous and liquid phases. AIChE J. 8, 59–63 (1962)CrossRefGoogle Scholar
  10. 10.
    Standing, M.B.: Volumetric and Phase Behavior of Oil Field Hydrocarbon Systems: PVT for Engineers. California Research Corp., California (1951)Google Scholar
  11. 11.
    Davani, E.; Kegang, L.; Teodoriu, C.; McCain, W.D.; Falcone, G.: Inaccurate gas viscosity at HP/HT conditions and its effect on unconventional gas reserves estimation. In: Latin American and Caribbean Petroleum Engineering Conference (2009)Google Scholar
  12. 12.
    Ghiasi, M.M.; Shahdi, A.; Barati, P.; Arabloo, M.: Robust modeling approach for estimation of compressibility factor in retrograde gas condensate systems. Ind. Eng. Chem. Res. 53, 12872–12887 (2014)CrossRefGoogle Scholar
  13. 13.
    Arabloo, M.; Shokrollahi, A.; Gharagheizi, F.; Mohammadi, A.H.: Toward a predictive model for estimating dew point pressure in gas condensate systems. Fuel Process. Technol. 116, 317–324 (2013)CrossRefGoogle Scholar
  14. 14.
    Rafiee-Taghanaki, S.; Arabloo, M.; Chamkalani, A.; Amani, M.; Zargari, M.H.; Adelzadeh, M.R.: Implementation of SVM framework to estimate PVT properties of reservoir oil. Fluid Phase Equilib. 346, 25–32 (2013)CrossRefGoogle Scholar
  15. 15.
    MohamadiBaghmolaei, M.; Mahmoudy, M.; Jafari, D.; MohamadiBaghmolaei, R.; Tabkhi, F.: Assessing and optimization of pipeline system performance using intelligent systems. J. Nat. Gas Sci. Eng. 18, 64–76 (2014)CrossRefGoogle Scholar
  16. 16.
    Ahmadi, M.A.; Soleimani, R.; Bahadori, A.: A computational intelligence scheme for prediction equilibrium water dew point of natural gas in TEG dehydration systems. Fuel 137, 145–154 (2014)CrossRefGoogle Scholar
  17. 17.
    Fayazi, A.; Arabloo, M.; Shokrollahi, A.; Zargari, M.H.; Ghazanfari, M.H.: State-of-the-art least square support vector machine application for accurate determination of natural gas viscosity. Ind. Eng. Chem. Res. 53, 945–958 (2013)CrossRefGoogle Scholar
  18. 18.
    Marjani, A.; Baghmolai, A.: Analytical and numerical modeling of non-isothermal and steady-state gas transportation network and the comparison with the results of artificial neural network (ANN) and fuzzy inference system (FIS). J. Nat. Gas Sci. Eng. 36, 1–12 (2016)CrossRefGoogle Scholar
  19. 19.
    Zendehboudi, A.: Implementation of GA-LSSVM modelling approach for estimating the performance of solid desiccant wheels. Energy Convers. Manage 127, 245–255 (2016)CrossRefGoogle Scholar
  20. 20.
    Foroozesh, J.; Khosravani, A.; Mohsenzadeh, A.; Mesbahi, A.H.: Application of artificial intelligence (AI) in kinetic modeling of methane gas hydrate formation. J. Taiwan Inst. Chem. Eng. 45, 2258–2264 (2014)CrossRefGoogle Scholar
  21. 21.
    Park, T.K.; Joo, H.G.; Kim, C.H.: Multicycle fuel loading pattern optimization by multiobjective simulated annealing employing adaptively constrained discontinuous penalty function. Nucl. Sci. Eng. 176, 226–239 (2014)CrossRefGoogle Scholar
  22. 22.
    Anemangely, M.; Ramezanzadeh, A.; Tokhmechi, B.: Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: a case study from Ab-Teymour oilfield. J. Nat. Gas Sci. Eng. 38, 373–387 (2017)CrossRefGoogle Scholar
  23. 23.
    Zadeh, L.A.: Fuzzy sets. In: Zadeh, L.A. (ed.) Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers, pp. 394–432. World Scientific, Singapore (1996)CrossRefGoogle Scholar
  24. 24.
    Jang, J.-S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993)CrossRefGoogle Scholar
  25. 25.
    Maniezzo, A.: Distributed optimization by ant colonies. In: Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, p. 134 (1992)Google Scholar
  26. 26.
    Geem, Z.W.; Kim, J.H.; Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76, 60–68 (2001)CrossRefGoogle Scholar
  27. 27.
    Glover, F.: Tabu search–part I. ORSA J. Comput. 1, 190–206 (1989)CrossRefGoogle Scholar
  28. 28.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence, pp. 439–444. University of Michigan Press, Ann Arbor (1975)Google Scholar
  29. 29.
    Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning. Springer, Boston, MA, pp. 760–766 (2011)Google Scholar
  30. 30.
    Zamani, H.A.; Rafiee-Taghanaki, S.; Karimi, M.; Arabloo, M.; Dadashi, A.: Implementing ANFIS for prediction of reservoir oil solution gas–oil ratio. J. Nat. Gas Sci. Eng. 25, 325–334 (2015)CrossRefGoogle Scholar
  31. 31.
    Tahmasebi, P.; Hezarkhani, A.: A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Comput. Geosci. 42, 18–27 (2012)CrossRefGoogle Scholar
  32. 32.
    Onwunalu, J.E.; Durlofsky, L.J.: Application of a particle swarm optimization algorithm for determining optimum well location and type. Comput. Geosci. 14, 183–198 (2010)CrossRefGoogle Scholar
  33. 33.
    Ling, K.: Gas Viscosity at High Pressure and High Temperature. Texas A & M University, College Station (2012)Google Scholar
  34. 34.
    Mohammadi, A.H.; Eslamimanesh, A.; Richon, D.; Gharagheizi, F.; Yazdizadeh, M.; Javanmardi, J.; et al.: Gas hydrate phase equilibrium in porous media: mathematical modeling and correlation. Ind. Eng. Chem. Res. 51, 1062–1072 (2011)CrossRefGoogle Scholar
  35. 35.
    Chen, P.-H.: Particle swarm optimization for power dispatch with pumped hydro. In: Lazinica A. (ed.) Particle Swarm Optimization. Department of Electrical Engineering, St. John’s University Taiwan. InTech (2009)Google Scholar
  36. 36.
    Kennedy, J.: The behavior of particles. In: International Conference on Evolutionary Programming, pp. 579–589 (1998)Google Scholar
  37. 37.
    Dempsey, M.E.: Pathways of enzymic synthesis and conversion to cholesterol of \(\Delta \)5,7,24-cholestatrien-3\(\beta \)-ol and other naturally occurring sterols. J. Biol. Chem. 240, 4176–4188 (1965)Google Scholar
  38. 38.
    Sanjari, E.; Lay, E.N.; Peymani, M.: An accurate empirical correlation for predicting natural gas viscosity. J. Nat. Gas Chem. 20, 654–658 (2011)CrossRefGoogle Scholar
  39. 39.
    Elsharkawy, A.M.: Efficient methods for calculations of compressibility, density and viscosity of natural gases. Fluid Phase Equilib. 218, 1–13 (2004)CrossRefGoogle Scholar
  40. 40.
    Heidaryan, E.; Esmaeilzadeh, F.; Moghadasi, J.: Natural gas viscosity estimation through corresponding states based models. Fluid Phase Equilib. 354, 80–88 (2013)CrossRefGoogle Scholar
  41. 41.
    Nazari, A.; Safarnejad, M.G.: Prediction early age compressive strength of OPC-based geopolymers with different alkali activators and seashell powder by gene expression programming. Ceram. Int. 39, 1433–1442 (2013)CrossRefGoogle Scholar
  42. 42.
    Mousavi, S.M.; Mostafavi, E.S.; Hosseinpour, F.: Gene expression programming as a basis for new generation of electricity demand prediction models. Comput. Ind. Eng. 74, 120–128 (2014)CrossRefGoogle Scholar
  43. 43.
    Shiri, J.; Sadraddini, A.A.; Nazemi, A.H.; Kisi, O.; Landeras, G.; Fard, A.F.; et al.: Generalizability of gene expression programming-based approaches for estimating daily reference evapotranspiration in coastal stations of Iran. J. Hydrol. 508, 1–11 (2014)CrossRefGoogle Scholar
  44. 44.
    Tagaki, T.; Sugeno, M.: Fuzzy identification of systems and its application to modelling and control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985)CrossRefGoogle Scholar
  45. 45.
    Sambo, C.H.; Hematpour, H.; Danaei, S.; Herman, M.; Ghosh, D.P.; Abass, A.; Elraies, K.A.: An Integrated Reservoir Modelling and Evolutionary Algorithm for Optimizing Field Development in a Mature Fractured Reservoir. Society of Petroleum Engineers. (2016).
  46. 46.
    Sambo, C.H.; Hermana, M.; Babasari, A.; Janjuhah, H.T.; Ghosh, D.P.: Application of artificial intelligence methods for predicting water saturation from new seismic attributes. In: Offshore Technology Conference. (2018).

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Chico Sambo
    • 1
    Email author
  • Yap Yin
    • 1
  • Ulugbek Djuraev
    • 1
  • Deva Ghosh
    • 1
  1. 1.Centre of Seismic Imaging and Hydrocarbon Prediction (CSI)Universiti Teknologi PETRONASSeri IskandarMalaysia

Personalised recommendations