Maximizing oil recovery is a challenging task for the oil industry worldwide, mainly in the presence of dynamic technical and economical constraints. To achieve this target, a number of enhanced oil recovery technologies are being applied, and one of the most successful and used methods is water alternating gas injection (WAG). The estimation of the optimal operating parameters of the WAG process is a complex problem which requires considerable number of time-consuming runs. Therefore, developing a faster alternative tool without scarifying the precision of the numerical simulators becomes essential. Proxy models that are user-friendly mathematical models based on machine learning and pattern recognition, have a noticeable ability to deal with highly complex problems, such as the outcomes of the numerical simulators in reasonable time.

The present work aims at establishing various dynamic proxy models for optimizing a constrained WAG project applied to real field data from “Gullfaks” in the North Sea. Two types of artificial neural network (ANN), namely multi-layer perceptron (MLP) and radial basis function neural network (RBFNN) were taught for predicting all the needed parameters for the formulated optimization problem. Levenberg–Marquardt (LM) algorithm was applied for optimizing the MLP model, while genetic algorithm (GA) and ant colony optimization (ACO) were applied for the proper selection of the RBFNN control parameters. Furthermore, the best proxy model found was coupled with GA and ACO for resolving the WAG optimization problems.

The results showed that the established proxies are robust, practical and effective in mimicking the performance of numerical reservoir model. In addition, the results demonstrated the effectiveness of GA and ACO in optimizing the parameters of WAG process for the real field data used in this study. The findings of this investigation contribute to the knowledge of the mathematics of oil recovery in various perspectives, namely the establishment of cheap and accurate time-dependent proxy models for real cases, the optimization of WAG process in the presence of various types of constraints and also the robustness of nature-inspired algorithms for resolving the optimization problems related to enhanced oil recovery.


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  1. Afzali, S., Ghamartale, A., Rezaei, N., Zendehboudi, S.
    [2020] Mathematical modeling and simulation of water-alternating-gas (WAG) process by incorporating capillary pressure and hysteresis effects. Fuel, 263, 116362.
    [Google Scholar]
  2. Afzali, S., Rezaei, N., Zendehboudi, S.
    [2018] A comprehensive review on enhanced oil recovery by water alternating gas (WAG) injection. Fuel, 227, 218–246.
    [Google Scholar]
  3. Ahmadi, M.A., Shadizadeh, S.R.
    [2013] Implementation of a high-performance surfactant for enhanced oil recovery from carbonate reservoirs. Journal of Petroleum Science and Engineering, 110, 66–73.
    [Google Scholar]
  4. Ahmed, T.
    [2018] Reservoir engineering handbook. Gulf Professional Publishing.
    [Google Scholar]
  5. Blum, C.
    [2005] Ant colony optimization: Introduction and recent trends. Physics of Life reviews, 2, 353–373.
    [Google Scholar]
  6. Chen, S., Cowan, C.F.N., Grant, P.M.
    [1991] Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks, 2, 302–309.
    [Google Scholar]
  7. Christensen, J.R., Stenby, E.H., Skauge, A.
    [1998] Review of WAG field experience. International Petroleum Conference and Exhibition of Mexico, Paper SPE 39883.
    [Google Scholar]
  8. Goldberg, D.E., Holland, J.H.
    [1988] Genetic algorithms and machine learning. Machine Learning, 3, 95–99.
    [Google Scholar]
  9. Haykin, S.
    [2001] Neural Networks and Learning Machines. Third Edition. Angewandte Chemie International Edition. Pearson Upper Saddle River, NJ, USA.
    [Google Scholar]
  10. Hemmati-Sarapardeh, A., Varamesh, A., Husein, M.M., Karan, K.
    [2018] On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment. Renewable and Sustainable Energy Reviews, 81, 313–329.
    [Google Scholar]
  11. Heris, S.M.K., Khaloozadeh, H.
    [2014] Ant colony estimator: an intelligent particle filter based on ACOR. Engineering Applications of Artificial Intelligence, 28, 78–85.
    [Google Scholar]
  12. Holland, J.H.
    [1975] Adaptation in natural and artificial systems. Michigan University Press.
    [Google Scholar]
  13. Killough, J.E., Kossack, C.A.
    [1987] Fifth comparative solution project: evaluation of miscible flood simulators. SPE Symposium on Reservoir Simulatio., Paper SPE 16000.
    [Google Scholar]
  14. Lake, L.W., Johns, R., Rossen, W.R., Pope, G.A.
    [2014] Fundamentals of enhanced oil recovery. Society of Petroleum Engineers.
    [Google Scholar]
  15. Nait Amar, M., Ghriga, M.A., Ouaer, H., Ben Seghier, M.E.A., Pham, B.T., Andersen, P.Ø.
    [2020a] Modeling Viscosity of CO2 at High Temperature and Pressure Conditions. Journal of Natural Gas Science and Engineering, 77, 103271.
    [Google Scholar]
  16. Nait Amar, M., Zeraibi, N.
    [2019] An efficient methodology for multi-objective optimization of water alternating CO2 EOR process. Journal of the Taiwan Institute of Chemical Engineers, 99, 154–165.
    [Google Scholar]
  17. Nait Amar, M., Zeraibi, N., Jahanbani Ghahfarokhi, A.
    [2020b] Applying hybrid support vector regression and genetic algorithm to water alternating CO2 gas EOR. Greenhouse Gases: Science and Technology, doi.org/10.1002/ghg.1982.
    https://doi.org/10.1002/ghg.1982 [Google Scholar]
  18. Nait Amar, M., Zeraibi, N., Redouane, K.
    [2018] Optimization of WAG process using dynamic proxy, genetic algorithm and ant colony optimization. Arabian Journal for Science and Engineering, 43, 6399–6412.
    [Google Scholar]
  19. Ranaee, E., Inzoli, F., Riva, M., Guadagnini, A.
    [2019] Hysteresis effects of three-phase relative permeabilities on black-oil reservoir simulation under WAG injection protocols. Journal of Petroleum Science and Engineering, 176, 1161–1174.
    [Google Scholar]
  20. Shpak, R.
    [2013] Modeling of miscible WAG injection using real geological field data. Master thesis, Institutt for petroleumsteknologi og anvendt geofysikk.
    [Google Scholar]
  21. Siddique, N., Adeli, H.
    [2013] Computational intelligence: synergies of fuzzy logic, neural networks and evolutionary computing. John Wiley & Sons.
    [Google Scholar]
  22. Sivanandam, D., Deepa, S.N.
    [2008] Introduction to genetic algorithms. Springerberlin heidelberg new york.
    [Google Scholar]
  23. Socha, K., Dorigo, M.
    [2008]. Ant colony optimization for continuous domains. European Journal of Operational Research, 185, 1155–1173.
    [Google Scholar]
  24. Tillerson, R.W.
    [2008] Meeting Global Energy Supply And Demand Challenges. 19th World Petroleum Congress, Document ID WPC-19-4967.
    [Google Scholar]
  25. Whitson, C.H., Brulé, M.R.
    [2000] Phase behavior. Henry L. Doherty Memorial Fund of AIME, Society of Petroleum EngineersRichardson, TX.
    [Google Scholar]
  26. Zhao, N., Wen, X., Yang, J., Li, S., Wang, Z.
    [2015] Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks. Powder Technology, 281, 173–183.
    [Google Scholar]

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