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Multi-objective optimization of water-alternating flue gas process using machine learning and nature-inspired algorithms in a real geological field

Author

Listed:
  • Naghizadeh, Arefeh
  • Jafari, Saeed
  • Norouzi-Apourvari, Saied
  • Schaffie, Mahin
  • Hemmati-Sarapardeh, Abdolhossein

Abstract

Flue gas water-alternating gas (flue gas-WAG) is a promising technique for enhancing oil production and reducing greenhouse gas emissions. The effective utilization of this technique relies on identifying the optimal factors, often determined through numerous numerical simulations. This paper introduces a cost-effective optimization framework that integrates machine learning models and diverse optimization algorithms to identify the best parameters for injecting flue gas-WAG into the "Gullfaks" reservoir. Robust Machine learning models, including Multilayer Perceptron, Cascade Forward Neural Network (CFNN), Radial Basis Function, and Generalized Regression Neural Network, were employed as proxy models. The CFNN model, with satisfactory agreement with actual data (average absolute relative error of 0.4543 % for oil recovery factor (RF) and 1.9366 % for CO2 storage), is selected for optimization through metaheuristic algorithms, including Non-dominated sorting genetic algorithm version II (NSGA-II), Pareto envelope-based selection Algorithm version-II (PESA_II), Multi-objective particle swarm optimization (MOPSO), and Multi-objective gray wolf optimization (MOGWO(. Among the optimization algorithms, MOGWO outperforms others in terms of speed and accuracy, yielding Pareto-optimal solutions with an RF of 0.8284 and a CO2 storage of 1.5314 × 107 (kg-mol). The proposed approach uses Pareto dominance for insightful field development planning, enabling decision-makers to choose flue gas-WAG parameters based on future circumstances.

Suggested Citation

  • Naghizadeh, Arefeh & Jafari, Saeed & Norouzi-Apourvari, Saied & Schaffie, Mahin & Hemmati-Sarapardeh, Abdolhossein, 2024. "Multi-objective optimization of water-alternating flue gas process using machine learning and nature-inspired algorithms in a real geological field," Energy, Elsevier, vol. 293(C).
    • Handle: RePEc:eee:energy:v:293:y:2024:i:c:s0360544224001841
    DOI: 10.1016/j.energy.2024.130413
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    References listed on IDEAS

    as
    1. Vo Thanh, Hung & Sheini Dashtgoli, Danial & Zhang, Hemeng & Min, Baehyun, 2023. "Machine-learning-based prediction of oil recovery factor for experimental CO2-Foam chemical EOR: Implications for carbon utilization projects," Energy, Elsevier, vol. 278(PA).
    2. You, Junyu & Ampomah, William & Sun, Qian, 2020. "Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework," Applied Energy, Elsevier, vol. 279(C).
    3. Maja Arnaut & Domagoj Vulin & Gabriela José García Lamberg & Lucija Jukić, 2021. "Simulation Analysis of CO 2 -EOR Process and Feasibility of CO 2 Storage during EOR," Energies, MDPI, vol. 14(4), pages 1-28, February.
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