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

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), Multiobjective 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 x 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.