Generative emulation and uncertainty quantification of geological CO2 storage with conditional diffusion models

Carbon capture and storage (CCS) has emerged as a pivotal technology for reaching climate-neutrality targets. Safe and effective deployment of CCS requires reliable predictions of pressure buildup and CO${}_2$ plume migration under geological uncertainties. However, traditional numerical simulations are limited by computational inefficiency, while machine learning methods face bottlenecks in predictive accuracy and uncertainty. Here we introduce a generative emulation framework named DiffMF for efficient prediction of multiphase flows in geological CO${}_2$ storage. The framework treats flow prediction as conditional generation processes and employs cutting-edge diffusion models to produce the temporal–spatial evolution of pressure and CO${}_2$ saturation fields under varying geological property conditions. Unlike existing approaches that focus primarily on point estimation, the probabilistic nature of DiffMF allows for generating multiple predictions that align with the statistics of the underlying dynamics, thereby facilitating effective quantification of predictive uncertainty. Comprehensive evaluations on diverse CO${}_2$ storage cases show that DiffMF achieves up to 52.6% lower CO${}_2$ saturation error compared to leading baseline models while maintaining high accuracy even under increased geological heterogeneity. Furthermore, we interpret the black-box model via visual analysis, providing insights into the generation process of DiffMF. Finally, the application to uncertainty quantification and propagation task for a field-scale storage system demonstrates that DiffMF yields statistics of the system responses in close agreement with those derived from high-fidelity simulations while executing 100 times faster, underscoring its promising potential in practical applications. The proposed generative emulation paradigm enables real-time prediction and probabilistic modeling that can foster informed decision-making for CCS deployment.