Auto-tuning PVT data using multi-objective optimization: Application of NSGA-II algorithm

Abstract

Reservoir simulation is known as perhaps the most widely used, accurate, and reliable method for field development in the petroleum industry. An integral part of a reliable reservoir simulation process is to consider robust and rigorous tuned EOS models. Traditionally, EOS models are tuned iteratively through arduous workflows against experimental PVT data. However, this comes with a number of drawbacks such as forcingly using weight factors, which upon alteration adversely affects the optimization process. The objective of the current work is thus to introduce an auto-tune PVT matching tool using NSGA-II multi-objective optimization. In order to illustrate the robustness of the presented technique, three different PVT samples are used, including two black-oil and one gas condensate sample. We utilize Peng-Robinson EOS during all the manual and auto-tuning processes. Comparison of auto-tuned EOS-generated results with those of experimental and computed statistical error values for these samples clearly show that the proposed method is robust. In addition, the proposed method, contrary to the manual matching process, provides the engineer with several matched solutions, which allows them to select a match based on the engineering background to be best amenable to the problem at hand. In addition, the proposed technique is fast, and can output several solutions within less time compared to the traditional manual matching method.

Keywords

Auto-tuning / PVT / Equation of state / NSGA-II / Multi-objective optimization

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] A. Kumar, R. Okuno, Characterization of reservoir fluids using an EoS based on perturbation from n-alkanes, Fluid Phase Equil. 358 (2013) 250-271.

[2] A. Daryasafar, M. Masoudi, S. Kord, M. Madani, Evaluation of different thermodynamic models in predicting asphaltene precipitation: a comparative study, Fluid Phase Equil. 514 (2020) 112557.

[3] R.A. Almehaideb, A.S. Al-Khanbashi, M. Abdulkarim, M.A. Ali, EOS tuning to model full field crude oil properties using multiple well fluid PVT analysis, J. Petrol. Sci. Eng. 26 (1-4) (2000) 291-300.

[4] I. Ashour, N. Al-Rawahi, A. Fatemi, G. Vakili-Nezhaad, Applications of equations of state in the oil and gas industry, Therm. Kinet. Dyn. Syst. 7 (2011) 165-178.

[5] T.O. Amao, A.M. Amao, Fluid Characterization and EoS Modelling of PVT Experiments. SPE Nigeria Annual International Conference And Exhibition, Society of Petroleum Engineers, 2014.

[6] R. Nazemi, A. Daryasafar, A. Bazyari, S.A. Shafiee Najafi, S. Ashoori, Modeling asphaltene precipitation in live crude oil using cubic plus association (CPA) equation of state, Petrol. Sci. Technol. 38 (3) (2020) 257-265.

[7] A. Daryasafar, A. Ahadi, R. Kharrat, Modeling of steam distillation mechanism during steam injection process using artificial intelligence, Sci. World J. 2014 (2014).

[8] A. Zarifi, A. Daryasafar, Auto-tune of PVT data using an efficient engineering method: application of sensitivity and optimization analyses, Fluid Phase Equil. 473 (2018) 70-79.

[9] O. Redlich, J.N. Kwong,On the thermodynamics of solutions. V. An equation of state. Fugacities of gaseous solutions, Chem. Rev. 44 (1) (1949) 233-244.

[10] G. Soave, Equilibrium constants from a modified Redlich-Kwong equation of state, Chem. Eng. Sci. 27 (6) (1972) 1197-1203.

[11] D.-Y. Peng, D.B. Robinson, A new two-constant equation of state, Ind. Eng. Chem. Fundam. 15 (1) (1976) 59-64.

[12] A. Daryasafar, A. Soleymanzadeh, K. Shahbazi, A New Generalized Auto-Tune Procedure for Modeling Asphaltene Precipitation by Equation of State 82nd EAGE Annual Conference & Exhibition, European Association of Geoscientists & Engineers, 2021, pp. 1-5, 2021.

[13] M.T. Sarvestani, B.S. Sola, F. Rashidi, Genetic algorithm application for matching ordinary black oil PVT data, Petrol. Sci. 9 (2) (2012) 199-211.

[14] S. Leekumjorn, K. Krejbjerg, Phase behavior of reservoir fluids: comparisons of PC-SAFT and cubic EOS simulations, Fluid Phase Equil. 359 (2013) 17-23.

[15] W. Yan, F. Varzandeh, E.H. Stenby, PVT modeling of reservoir fluids using PCSAFT EoS and Soave-BWR EoS, Fluid Phase Equil. 386 (2015) 96-124.

[16] F. Varzandeh, E.H. Stenby, W. Yan, General approach to characterizing reservoir fluids for EoS models using a large PVT database, Fluid Phase Equil. 433 (2017) 97-111.

[17] Y. Khoshnamvand, M. Assareh, B.M. Davoudi, Phase behavior modeling for gas condensate fluids with PC-SAFT and an improved binary interaction coefficient model, Fluid Phase Equil. 444 (2017) 37-46.

[18] K. Coats, G. Smart, Application of a regression-based EOS PVT program to laboratory data, SPE Reservoir Eng. 1 (3) (1986) 277-299.

[19] P. Christensen, Regression to experimental PVT data, J. Can. Petrol. Technol. 38 (13) (1999).

[20] C.H. Whitson, M.R. Brulé, Phase Behavior, Henry L. Doherty Memorial Fund of AIME, Society of Petroleum Engineers, 2000.

[21] M. Gen, R. Cheng, L. Lin, Network Models and Optimization:Multiobjective Genetic Algorithm Approach, Springer Science & Business Media, 2008.

[22] M. Mitchell, An Introduction to Genetic Algorithms, MIT press, 1998.

[23] P. Poon, G.T. Parks, Optimising PWR Reload Cores, 1992.

[24] J. Grefenstette, R. Gopal, B. Rosmaita, D. Van Gucht,Genetic Algorithms for the Traveling Salesman Problem, in:Proceedings of the First International Conference on Genetic Algorithms and Their Applications 160, Lawrence Erlbaum, 1985, pp. 160-168.

[25] I.C. Parmee, A.H. Watson,Preliminary airframe design using coevolutionary multiobjective genetic algorithms, in:Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation 2, 1999, pp. 1657-1665.

[26] M. Madani, M.K. Moraveji, M. Sharifi, Modeling apparent viscosity of waxy crude oils doped with polymeric wax inhibitors, J. Petrol. Sci. Eng. 196 (2021) 108076.

[27] A.B. Shoushtari, S.R. Asadolahpour, M. Madani, Thermodynamic investigation of asphaltene precipitation and deposition profile in wellbore: a case study, J. Mol. Liq. 320 (2020) 114468.

[28] C. Romero, J. Carter, Using genetic algorithms for reservoir characterisation, J. Petrol. Sci. Eng. 31 (2-4) (2001) 113-123.

[29] O. Velez-Langs, Genetic algorithms in oil industry: an overview, J. Petrol. Sci. Eng. 47 (1-2) (2005) 15-22.

[30] C. Coello, A.D. Christiansen, Multiobjective optimization of trusses using genetic algorithms, Comput. Struct. 75 (6) (2000) 647-660.

[31] A. Osyczka, Multicriteria Optimization for Engineering Design. Design Optimization, Elsevier, 1985, pp. 193-227.

[32] C.M. Fonseca, P.J. Fleming, Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization. Icga 93, Citeseer, 1993, pp. 416-423.

[33] C.A.C. Coello, G.B. Lamont, D.A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems, Springer, 2007.

[34] V. Pareto, Cours D’économie Politique, Rouge. Lausanne, Switzerland, 1896.

[35] D.E. Goldberg, Genetic Algorithms in Search. Optimization, and Machine-Learning, 1989.

[36] A. Toffolo, E. Benini, Genetic diversity as an objective in multi-objective evolutionary algorithms, Evol. Comput. 11 (2) (2003) 151-167.

[37] M.M. Etghani, M.H. Shojaeefard, A. Khalkhali, M. Akbari, A hybrid method of modified NSGA-II and TOPSIS to optimize performance and emissions of a diesel engine using biodiesel, Appl. Therm. Eng. 59 (1-2) (2013) 309-315.

[38] J. Yazdi, A. Sadollah, E. Lee, D. Yoo, J.H. Kim, Application of multi-objective evolutionary algorithms for the rehabilitation of storm sewer pipe networks, J. Flood Risk Manag. 10 (3) (2017) 326-338.

[39] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6 (2) (2002) 182-197.

[40] A. Singh, S. Parida, A multiple strategic evaluation for fault detection in electrical power system, Int. J. Electr. Power Energy Syst. 48 (2013) 21-30.

[41] M.B. Shadmand, R.S. Balog, Multi-objective optimization and design of photovoltaic-wind hybrid system for community smart DC microgrid, IEEE Trans. Smart Grid 5 (5) (2014) 2635-2643.

[42] S. Wang, D. Zhao, J. Yuan, H. Li, Y. Gao, Application of NSGA-II Algorithm for fault diagnosis in power system, Elec. Power Syst. Res. 175 (2019) 105893.

[43] K. Deb, S. Agrawal, A. Pratap, T. Meyarivan, A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II,in: International Conference on Parallel Problem Solving from Nature, Springer, 2000, pp. 849-858.

[44] K. Deb, Multi-objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, 2001.

[45] S. Ramesh, S. Kannan, S. Baskar, Application of modified NSGA-II algorithm to multi-objective reactive power planning, Appl. Soft Comput. 12 (2) (2012) 741-753.

[46] W. module Tutorial, Computer Modeling Group, Version, 2020.