Sensitivity Analysis of CCUS Development Parameters in High-Temperature Oil Reservoirs Based on a Backpropagation Neural Network Proxy Model

Parameter sensitivity analysis is a critical step in the numerical simulation and parameter design of CCUS models for high-temperature oil reservoirs. Owing to the complexity of these models, a single simulation run is computationally expensive, and sensitivity analysis typically requires numerous model evaluations, making the process both time-consuming and labor-intensive. Moreover, conventional sensitivity analysis methods often fail to identify which parameter most significantly influences development performance when multiple parameters are considered simultaneously. To address these challenges, the paper focuses on high-temperature oil reservoirs and establishes a comprehensive numerical model. Meanwhile, the BP neural network is employed to construct the proxy model, and the Sobol global sensitivity analysis method is used to perform sensitivity analysis. In the analysis, multiple parameters, including reservoir heterogeneity, CO2 injection rate, liquid production rate, and CO2 injection purity, were simultaneously evaluated to assess their impact on oil recovery, CO2 geological storage capacity, and CO2 heat recovery. The results indicate that the CO2 injection rate, liquid production rate, and CO2 injection purity exert the greatest influence on CCUS development performance in high-temperature reservoirs. Compared with traditional parameter sensitivity analysis techniques, the combination of the BP neural network proxy model and the Sobol method delivers high-precision sensitivity results while reducing computation time to only 40.7% of that required by conventional approaches, thus significantly saving both computational effort and time.