Deep learning-assisted optimization for enhanced oil recovery and CO2 sequestration considering gas channeling constraints

Carbon dioxide Enhanced Oil Recovery (CO₂-EOR) technology guarantees substantial underground CO₂ sequestration while simultaneously boosting the production capacity of subsurface hydrocarbons (oil and gas). However, unreasonable CO₂-EOR strategies, encompassing well placement and well control parameters, will lead to premature gas channeling in production wells, resulting in large amounts of CO₂ escape without any beneficial effect. Due to the lack of prediction and optimization tools that integrate complex geological and engineering information for the widely used CO₂-EOR technology in promising industries, it is imperative to conduct thorough process simulations and optimization evaluations of CO₂-EOR technology. In this paper, a novel optimization workflow that couples the AST-GraphTrans-based proxy model (Attention-based Spatio-temporal Graph Transformer) and multi-objective optimization algorithm MOPSO (Multi-objective Particle Swarm Optimization) is established to optimize CO₂-EOR strategies. The workflow consists of two outstanding components. The AST-GraphTrans-based proxy model is utilized to forecast the dynamics of CO₂ flooding and sequestration, which includes cumulative oil production, CO₂ sequestration volume, and CO₂ plume front. And the MOPSO algorithm is employed for achieving maximum oil production and maximum sequestration volume by coordinating well placement and well control parameters with the containment of gas channeling. By the collaborative coordination of the two aforementioned components, the AST-GraphTrans proxy-assisted optimization workflow overcomes the limitations of rapid optimization in CO₂-EOR technology, which cannot consider high-dimensional spatio-temporal information. The effectiveness of the proposed workflow is validated on a 2D synthetic model and a 3D field-scale reservoir model. The proposed workflow yields optimizations that lead to a significant increase in cumulative oil production by 87% and 49%, and CO₂ sequestration volume enhancement by 78% and 50% across various reservoirs. These findings underscore the superior stability and generalization capabilities of the AST-GraphTrans proxy-assisted framework. The contribution of this study is to provide a more efficient prediction and optimization tool that maximizes CO₂ sequestration and oil recovery while mitigating CO₂ gas channeling, thereby ensuring cleaner oil production.