Experimental Investigation and Estimation of Light Hydrocarbons Gas-Liquid Equilibrium Ratio in Gas Condensate Reservoirs through Artificial Neural Networks

Document Type : Research Article

Authors

1 Department of Petroleum Engineering, Research Institute of Petroleum Industry (RIPI), Tehran, I.R. IRAN

2 Department of Petroleum Geology, Science and Research Branch, Islamic Azad University Tehran, I.R. IRAN

Abstract

Equilibrium ratios for the mixture of different components are very important for many engineering application processes. Different numerical methods were explored and applied to ensure efficient estimation of gas-liquid equilibrium ratio. In this paper, the Artificial Neural Network (ANN) approach along with data of experiments performed on 25 gas condensate reservoirs has been utilized to obtain a relationship of gas-liquid equilibrium ratios in gas condensate reservoirs. The relationship between the gas-liquid equilibrium ratio and parameters of components of a mixture (critical temperature, critical pressure, and acentric factor) has been derived. Finally, the results of ANN have been compared to the proposed correlations in the literature and results of the equation of state. This investigation demonstrated that the result of ANN is more precise than the equation of state and existing empirical correlations. Whereas comparison between experimental data of 3 gas condensate samples by ANN, EOS, and existing empirical correlation show that the average absolute error for ANN was between 7.82 to 13.74% and for others was between 29.99 to 94.99%.

Keywords

Main Subjects

References

[1] Souders, M., Selheimer, C.W., Brown, G.G., Equilibria Between Liquid and Vapor Solutions of Paraffin Hydrocarbons,  Ind. Eng. Chem., 24(5): 517-519 (1932).
[2] Wilson G., A Modified Redlich-Kwong EOS, Application to General Physical Data Calculations, Paper No 15C, Presented at the Aiche 65th National Meeting, May, (1968).
[3] Standing M.B., A Set of Equations for Computing Equilibrium Ratios of a Crude Oil/Natural Gas System at Pressure Below 1000 Psia, Journal of Petroleum Technology, 31(09): 1193-1195 (1979).
[4] Katz D.L., Hachmuth K.H., Vaporization Equilibrium Constants in a Crude Oil–Natural Gas System, Ind. Eng. Chem., 29(9): 1027-1077 (1937).
[5] Hoffmann A.E., Crump J.S., Hocott R.C., Equilibrium Constants for a Gas-Condensate System, Journal of Petroleum Technology, 5(1): 1-10 (1953).
[6] Brinkman F.H., Sicking J.N., Equilibrium Ratios for Reservoir Studies, SPE-1429-G, Published in Petroleum Transactions, AIME, 219: 313-319 (1960).
[7] Kehn D.M., Rapid Analysis of Condensate Systems by Chromatography, Journal of Petroleum Technology, 16(4): 435-440 (1964).
[8] Dykstra H., "=Calculation of Phase Composition and Properties For Lean-or Enriched-Gas Drive, Society of Petroleum Engineers Journal, 5(3): 239-246 (1965).
[9] Soave G., Equilibrium Constant from a Modified Redlich-Kwong Equation of State, Chemical Engineering Science, 27(6): 1197-1203 (1972).
[10] Peng D.Y., Robinson D.B., A New Two-Constant Equation of State, Industrial & Engineering Chemistry Fundamentals, 15(1): 59-64 (1976).
[11] Mcculloch W.S., Pitts W., A Logical Calculus of the Ideas Immanent in Nervous Activity, The Bulletin of Mathematical Biophysics, 5(4): 115-133 (1943).
[12] Hebb D.O., "The Organization of Behavior: A Neuropsychological Theory", John Wiley & Sons, Inc., New York, (1949).  
[13] Hassanvand M., Moradi S., Fattahi M., Zargar G., Kamari M., Estimation of Rock Uniaxial Compressive Strength for an Iranian Carbonate Oil Reservoir: Modeling vs. Artificial Neural Network Application, Petroleum Research, 3(4): 336-345 (2018).
[14] Amini Y., Fattahi M., Khorasheh F., Sahebdelfar S., Neural Network Modeling The Effect of Oxygenate Additives on the Performance of Pt-Sn/ -Al2O3 Catalyst in Propane Dehydrogenation, Applied Petrochemical  Research, 3(1-2): 47-54 (2013).
[15] Fattahi M., Kazemeini M., Khorasheh F., Rashidi A., “Kinetic Modeling of Oxidative Dehydrogenation of Propane (ODHP) Over a Vanadium-Graphene Catalyst: Application of the DOE and ANN Methodologies, Journal of Industrial and Engineering Chemistry, 20(4): 2236-2247 (2014).
[16] Fattahi M., Kazemeini M., Khorasheh F., Rashidi A., An Investigation of the Oxidative Dehydrogenation of Propane Kinetics over a Vanadium-Graphene Catalyst Aiming at Minimizing of the COx Species, Chemical Engineering Journal, 250: 14-24 (2014).
[17] Satake T., Morikawa K., Nakamura N., Neural Network Approach for Minimizing the Makespan of the General Job-Shop, International Journal of Production Economics, 33(1-3): 67-74 (1994).
[18] Bode J., Neural Networks for Cost Estimation: Simulations and Pilot Application, International Journal of Production Research, 38(6): 1231-1254 (2000).
[19] Sabuncuoglu I., Gurgun B., A Neural Network Model for Scheduling Problems, European Journal of Operational Research, 93(2): 288-299 (1996).
[20] Zhu Q.M., A Back Propagation Algorithm to Estimate the Parameters of Non-Linear Dynamic Rational Models, Applied Mathematical Modeling, 27(3): 169-187 (2003).
[21] Olabia A.G., Casalino G., Benyounis K.Y., Hashmi M.S.J., An ANN and Taguchi Algorithms Integrated Approach to the Optimization of CO2 Laser Welding, Advances In Engineering Software, 37(10): 643-648 (2006).
[22] Hadavandi E., Shavandi H., Ghanbari A., Integration of Genetic Fuzzy Systems and Artificial Neural Networks for Stock Price Forecasting, Knowledge-Based Systems, 23(8): 800-808 (2010).
[23] Khosravi A., Nahavandi S., Creighton D., Quantifying Uncertainties of Neural Network Based Electricity Price Forecasts, Applied Energy, 112: 120-129 (2013).
[24] Khotanzad A., Elragal H., Lu T.-L., Combination of Artificial Neural-Network Forecasters for Prediction of Natural Gas Consumption, IEEE Transactions on Neural Networks, 11(2): 464-473 (2000).
[25] Bashir Z.A., El-Hawary M.E., Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks, IEEE Transactions on Power Systems, 24(1): 20-27 (2009).
[26] Xiao Z., Ye S.-J., Zhong B., Sun C.-X., BP Neural Network with Rough Set for Short Term Load Forecasting, Expert Systems with Applications, 36(1): 273-279 (2009).
[27] Olson T.M., “Porosity and Permeability Prediction in Low-Permeability Gas Reservoirs from Well Logs Using Neural Networks", SPE-39964-MS, SPE Rocky Mountain Regional/Low-Permeability Reservoirs Symposium, 5-8 April, Denver, Colorado, (1998).
[28] White A.C., Molnar D., Aminian K., Mohaghegh S., Ameri S., Esposito P., The Application of ANN For Zone Identification in a Complex Reservoir, SPE-30977-MS, SPE Eastern Regional Meeting, 18-20 September, Morgantown, West Virginia, (1995).
[29] Bhatt, A., Helle, H.B., "Determination of Facies from Well Logs Using Modular Neural Networks, Petroleum Geoscience, 8: 217-228 (2002).
[30] Ahmadi M.A., Zendehboudi S., Lohi A., Elkamel A., Chatzis I., Reservoir Permeability Prediction by Neural Networks Combined with Hybrid Genetic Algorithm and Particle Swarm Optimization, Geophysical Prospecting, 61: 582-598 (2013).
[31] Helle H.B., Bhatt A., Fluid Saturation from Well Logs Using Committee Neural Networks, Petroleum Geoscience, 8: 109-118 (2002).
[32] Al-Bulushi N., King P.R., Blunt M.J., Kraaijveld M., Development of Artificial Neural Network for Predicting Water Saturation and Fluid Distribution, Journal of Petroleum Science and Engineering, 68(3-4): 197-208 (2009).
[33] Hurtado J.E., Londoño J.M., Meza M.A., On the Applicability of Neural Networks for Soil Dynamic Amplification Analysis, Soil Dynamics and Earthquake Engineering, 21(7): 579-591 (2001).
[34] Rafiq M.Y., Bugmann G., Easterbrook D.J., Neural Network Design for Engineering Applications, Computers & Structures, 79(17): 1541-1552 (2001).
[35] Bai J., Wild S., Ware J.A., Sabir B.B., Using Neural Networks to Predict Workability of Concrete Incorporating Metakaolin And Fly Ash, Advances in Engineering Software, 34(11-12): 663-669 (2003).
[36] Lee S.J., Lee S.R., Kim Y.S., An Approach to Estimate Unsaturated Shear Strength Using Artificial Neural Network and Hyperbolic Formulation, Computers and Geotechnics, 30(6): 489-503 (2003).
[37] Basma A.A., Kallas N., Modeling Soil Collapse by Artificial Neural Networks, Geotechnical & Geological Engineering, 22(3): 427-438 (2004).
[38] Sinha S.K., Wang M.C., Artificial Neural Network Prediction Models for Soil Compaction and Permeability, Geotechnical and Geological Engineering, 26(1): 47-64 (2007).
[39] Das S.K., Basudhar P., Prediction of Residual Friction Angle of Clays Using Artificial Neural Network, Engineering Geology, 100(3-4): 142-145 (2008).
[40] Kayadelen C., Estimation of Effective Stress Parameter of Unsaturated Soils by Using Artificial Neural Networks, International Journal for Numerical and Analytical Methods In Geomechanics, 32(9): 1087-1106 (2008).
[41] Kahraman S., Gunaydin O., Alber M., Fener M., Evaluating the Strength and Deformability Properties of Misis Fault Breccia Using Artificial Neural Net- Works, Expert Systems with Applications, 36(3): 6874-6878 (2009).
[42] Günaydın O., Estimation of Soil Compaction Parameters by Using Statistical Analyses and Artificial Neural Networks, Environmental Geology, 57(1): 203-215 (2009).
[43] Taskiran T., Prediction of California Bearing Ratio (CBR) of Fine Grained Soils by AI Methods, Advances in Engineering Software, 41(6): 886-892 (2010).
[44] Tiryaki B., Predicting Intact Rock Strength for Mechanical Excavation Using Multivariate Statistics, Artificial Neural Networks, and Regression Trees, Engineering Geology, 99(1-2): 51-60 (2008).
[45] Zorlu K., Gokceoglu C., Ocakoglu F., Nefeslioglu H.A., Acikalin S., Prediction of Uniaxial Compressive Strength of Sandstones Using Petrography-Based Models, Engineering Geology, 96(3-4): 141-158 (2008).
[46] Yagiz S., Gokceoglu C., Sezer E., Iplikci S., Application of Two Non-Linear Prediction Tools to the Estimation of Tunnel Boring Machine Performance, Engineering Applications of Artificial Intelligence, 22(4-5): 808-824 (2009).
[47] Cevik A., Sezer E.A., Cabalar A.F., Gokceoglu C., Modelling of the Uniaxial Compressive Strength of Some Clay-Bearing Rocks Using Neural Network, Applied Soft Computing, 11(2): 2587-2594 (2011).
[48] Samani H.Y., Bafghi A.R.Y., Prediction of the Sawing Quality of Marmarite Stones Using the Capability of Artificial Neural Network, International Journal for Numerical and Analytical Methods in Geomechanics, 36(7): 881-891 (2012).
[49] Yagiz S., Sezer E.A., Gokceoglu C., Artificial Neural Networks and Nonlinear Regression Techniques to Assess the Influence of Slake Durability Cycles on the Prediction of Uniaxial Compressive Strength and Modulus of Elasticity for Carbonate Rocks, International Journal for Numerical and Analytical Methods In Geomechanics, 36(14): 1636-1650 (2012).
[50] Ahmadi M.A., Ebadi M., Shokrollahi A., Majidi S.M.J., Evolving Artificial Neural Network and Imperialist Competitive Algorithm for Prediction Oil Flow Rate of the Reservoir, Applied Soft Computing, 13: 1085-1098 (2013).
[51] Elmabrouk S., Shirif  E., Mayorga R., Artificial Neural Network Modeling for the Prediction of Oil Production, Petroleum Science and Technology, 32: 1123-1130 (2014).
[52] Firoozjaee R.A., Khamehchi E., A Novel Approach to Assist History Matching Using Artificial Intelligence, Chemical Engineering Communications, 202: 513-519 (2015).
[53] Bruyelle J., Guerillot D., Neural Networks and Their Derivatives for History Matching and Reservoir Optimization Problems, Computational Geosciences, 18: 549-561 (2014).
[54] Isaiah J., Schrader S., Reichhardt D., Link C., "Performing Reservoir Simulation with Neural Network Enhanced Data", SPE-163691-MS, SPE Digital Energy Conference, 5-7 March, The Woodlands, Texas, USA, (2013).
[55] Ahmadi M.A., Soleimani R., Lee M., Kashiwao T., Bahadori A., Determination of Oil Well Production Performance Using Artificial Neural Network (ANN) Linked to the Particle Swarm Optimization (PSO) Tool, Petroleum, 1: 118-132 (2015).
[56] Van S.L., Chon B.H., Artificial Neural Network Model for Alkali- Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application, Energies, 9: 1-20 (2016).
[57] Mahdiani M.R., Khamehchi E., A Modified Neural Network Model for Predicting the Crude Oil Price, Intellectual Economics, 10: 71-77 (2017).
[58] Van S.L., Chon B.H., Effective Prediction and Management of A CO2 Flooding Process for Enhancing Oil Recovery Using Artificial Neural Networks, Journal of Energy Resources Technology, 140: 1-14 (2017). 
Iranian Journal of Chemistry and Chemical Engineering
Volume 39, Issue 6 - Serial Number 104
November and December 2020
Pages 163-172

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