Data-Driven Site Selection Based on CO2 Injectivity in the San Juan Basin

CO2 injection success hinges on the injectivity index, a major determinant of storage feasibility. This study develops a machine learning (ML)-driven framework optimized for CO2 injectivity prediction, benchmarking its robustness and real-world applicability against an empirical correlation developed in the literature. The framework is applied to the Entrada Formation in the San Juan Basin, a laterally extensive sandstone unit with limited structural complexity across most of the basin, except for localized uplift in the Hogback region. A numerical model was calibrated to perform sensitivity analysis to identify the dominant parameters influencing injectivity. A dataset of these parameters generated through experimental design informs the development of several ML-based proxies and the best model is selected based on error metrics. These metrics include coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). The effective permeability-thickness product was obtained by the Peaceman’s well model, fractional flow slope, and Dykstra–Parsons coefficient were identified as the most influential parameters impacting the objective function. Train–test and blind test validation identified the Ridge model as the best, achieving an R2 ≈ 0.994. The Ridge model which was used to map the Entrada Formation closely matches field-based correlations in the literature, confirming both its physical validity and the Entrada Formation’s strong injectivity potential, with slight deviations explained by the inclusion of additional parameters. This study reduces dependence on computationally intensive simulations while improving prediction accuracy. By benchmarking against established correlations, it enhances model reliability across diverse reservoir conditions. The proposed framework enables rapid, data-driven well placement and feasibility evaluations, streamlining decision-making for CO2 storage projects.