Prediction of carbon dioxide phase at bottomhole by adaptive factorization network considering well geometry

Accurate carbon dioxide (CO 2 ) phase prediction at the bottomhole of injection wells is essential for ensuring safe and efficient CO 2 storage and enhanced gas recovery (EGR). Phase misclassification can cause operational inefficiencies, equipment failure, and compromised storage integrity, posing significant risks to CO 2 injection projects. While previous studies have contributed to CO 2 phase prediction, they have overlooked well geometry effects, which can impact reliability in real-world applications. This study addresses these challenges by introducing a deep learning framework based on the adaptive factorization network (AFN), which enhances CO 2 phase prediction accuracy by leveraging feature interactions. The AFN model was trained on ∼43,000 wells across seven major North American shale gas basins, covering a wide range of well geometries and injection conditions. CO 2 phases w…