Dimension-Adaptive Machine Learning for Efficient Uncertainty Quantification in Geological Carbon Storage Models
Seyed Kourosh Mahjour, Ali Saleh, Seyed Saman Mahjour, Seyed Saman Mahjour, Seyed Saman Mahjour
Abstract
Carbon capture and storage (CCS) plays a role in mitigating climate change, but effective implementation requires accurate prediction of CO2 behavior in geological formations. This study introduces a novel machine learning framework for quantifying uncertainty across 2D and 3D carbon storage models. We develop a dimension-adaptive Bayesian neural network architecture that enables efficient knowledge transfer between dimensional representations while maintaining physical consistency. The framework incorporates aleatoric uncertainty from inherent geological variability and epistemic uncertainty from model limitations. Trained on over 5000 high-fidelity simulations across multiple geological scenarios, our approach demonstrates superior computational efficiency, reducing analysis time for 3D models by 87% while maintaining prediction accuracy within 5% of full simulations. The framework effectively captures complex uncertainty patterns in spatiotemporal CO2 plume evolution. It identifies previously unrecognized parameter interdependencies, particularly between vertical permeability anisotropy and capillary entry pressure, which significantly impact plume migration in 3D models but are often overlooked in 2D representations. Compared with traditional Monte Carlo methods, our approach provides more accurate uncertainty bounds and enhanced identification of high-risk scenarios. This multidimensional framework enables rapid assessment of storage capacity and leakage risk under uncertainty, providing a practical tool for CCS site selection and operational decision-making across dimensional scales.