Design Space Analysis and Performance Trade-Offs of CO2-Water-Alternating-Gas Processes in Low-Permeability Reservoirs

Summary Low-permeability reservoirs are critical frontiers for oil and gas displacement, where CO2 water-alternating-gas (CO2-WAG) injection plays a pivotal role in both enhanced oil recovery (EOR) and carbon capture, utilization, and storage (CCUS). However, development performance is highly sensitive to injection parameters, and conventional optimization methods face significant challenges in efficiently addressing global optimization problems involving multiple parameters—especially the time-varying characteristics of the WAG ratio. To address this challenge, this study proposes an intelligent optimization methodology integrating machine learning (ML) and multiobjective optimization. First, a representative low-permeability reservoir model was established using the Computer Modelling Group (CMG) simulation software, and parameter sensitivity analysis of WAG cycle, injection rate, injection sequence, and WAG ratio identified the WAG ratio as the only parameter with pronounced stage-dependent reversals. Second, the 20-year production period was divided into five distinct stages, with the WAG ratio of each stage treated as a decision variable. A data set of 3,125 samples was generated via full factorial design. Various ML algorithms were trained and compared, and extreme gradient boosting (XGBoost) was ultimately selected to construct high-precision surrogate models for cumulative oil production and CO2 storage, both achieving R2 values exceeding 0.99. Finally, the Nondominated Sorting Genetic Algorithm II (NSGA-II) algorithm was used to perform dual-objective synergistic optimization of continuous WAG ratios across the five stages. The results show that the obtained Pareto optimal frontier clearly illustrates the trade-off between oil production and carbon storage. The optimal WAG ratio sequence exhibits a dynamic evolution pattern: A high WAG ratio is preferred in the early stage to boost reservoir pressure, transitioning to a low ratio in the intermediate stage to facilitate miscible displacement, and adjusting to a moderate ratio in the late stage to maintain efficiency. Compared with fixed WAG ratio schemes, this time-varying strategy significantly enhances synergistic benefits. This research provides an efficient and reliable new approach for the dynamic optimization design of CCUS projects in low-permeability reservoirs. Furthermore, the optimized time-varying WAG strategy derived from the conceptual model is validated against a field-derived heterogeneous model of the Daqing Oil Field. The consistent performance superiority confirms the rationality and engineering applicability of the proposed framework, offering a direct reference for field-scale CCUS project design.