Application of an Automated Machine Learning-Driven Grid Block Classification Framework to a Realistic Deep Saline Aquifer Model for Accelerating Numerical Simulations of CO2 Geological Storage

Numerical simulations are essential for optimizing CO2 geological storage in deep saline aquifers; however, their substantial computational demands pose a significant challenge. This study introduces an automated machine learning (ML)-driven grid block classification framework applied to a realistic deep saline aquifer model to accelerate numerical simulations while maintaining accuracy. The methodology employs an ML and interquartile range-based classifier to distinguish grid blocks as either fast- or slow-varying. ML-based proxy models are applied exclusively to slow-varying regions, while traditional iterative methods handle dynamic, fast-varying regions. Results confirm a considerable reduction in computational costs without compromising predictive accuracy. Validated under realistic reservoir conditions, the approach demonstrates scalability and robustness, supporting efficient, accurate large-scale CO2 storage simulations and advancing sustainable subsurface sequestration strategies.