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Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process

Author

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  • Shokufe Afzali

    (Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL A1B 3X5, Canada)

  • Mohamad Mohamadi-Baghmolaei

    (Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL A1B 3X5, Canada)

  • Sohrab Zendehboudi

    (Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL A1B 3X5, Canada)

Abstract

Water alternating gas (WAG) injection has been successfully applied as a tertiary recovery technique. Forecasting WAG flooding performance using fast and robust models is of great importance to attain a better understanding of the process, optimize the operational conditions, and avoid high-cost blind tests in laboratory or pilot scales. In this study, we introduce a novel correlation to determine the performance of the near-miscible WAG flooding in strongly water-wet sandstones. We conduct dimensional analysis with Buckingham’s π theorem technique to generate dimensionless numbers using eight key parameters. Seven dimensionless numbers are employed as the input variables of the desired correlation for predicting the recovery factor of a near-miscible WAG injection. A verified mathematical model is used to generate the required training and testing data for the development of the correlation using a gene expression programming (GEP) algorithm. The provided data points are then separated into two subsets: training (67%) to develop the model and testing (33%) to assess the models’ capability. Conducting error analysis, statistical measures and graphical illustrations are provided to assess the effectiveness of the introduced model. The statistical analysis shows that the developed GEP-based correlation can generate target data with high precision such that the training phase leads to R 2 = 92.85% and MSE = 1.38 × 10 −3 and R 2 = 91.93% and MSE = 4.30 × 10 −3 are attained for the testing phase. The relative importance of the input dimensionless groups is also determined. According to the sensitivity analysis, decreasing the oil–water capillary number results in a significant reduction in RF in all cycles. Increasing the magnitudes of oil to gas viscosity ratio and oil to water viscosity ratio lowers the RF of each cycle. It is found that oil to gas viscosity ratio has a higher impact on RF value compared to oil to water viscosity ratio due to a higher viscosity gap between the gas and oil phases. It is expected that the GEP, as a fast and reliable tool, will be useful to find vital variables including relative permeability in complex transport phenomena such as three-phase flow in porous media.

Suggested Citation

  • Shokufe Afzali & Mohamad Mohamadi-Baghmolaei & Sohrab Zendehboudi, 2021. "Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process," Energies, MDPI, vol. 14(21), pages 1-28, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7131-:d:669720
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    References listed on IDEAS

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    1. Abubakar A. Umar & Ismail M. Saaid & Aliyu A. Sulaimon & Rashidah M. Pilus, 2020. "Predicting the Viscosity of Petroleum Emulsions Using Gene Expression Programming (GEP) and Response Surface Methodology (RSM)," Journal of Applied Mathematics, Hindawi, vol. 2020, pages 1-9, January.
    2. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
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    Cited by:

    1. Jaroslaw Krzywanski, 2022. "Advanced AI Applications in Energy and Environmental Engineering Systems," Energies, MDPI, vol. 15(15), pages 1-3, August.

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