crossrefProcesses2025-08-21Cited by 2
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
Eirini Maria Kanakaki, Sofianos Panagiotis Fotias, Vassilis Gaganis
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 ap…