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

Abstract

Accurate carbon dioxide (CO₂) phase prediction at the bottomhole of injection wells is essential for ensuring safe and efficient CO₂ storage and enhanced gas recovery (EGR). Phase misclassification can cause operational inefficiencies, equipment failure, and compromised storage integrity, posing significant risks to CO₂ injection projects. While previous studies have contributed to CO₂ 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 CO₂ 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. CO₂ phases were classified into supercritical and dense categories, reflecting prevailing flow conditions. To enhance practical applicability, we incorporated real-field wellbore data, ensuring alignment with actual injection environments. The standard AFN model achieved an F1-score of 0.94, with data augmentation further improving performance by reducing false predictions by 50 % and increasing the F1-score to 0.97. Rigorous validation demonstrated the model's robustness for optimizing wellhead temperature to achieve the desired CO₂ phase transition. By explicitly considering well geometry effects and real-field conditions, this study advances data-driven CO₂ injection modeling, providing a scalable, high-accuracy framework for evaluating CO₂ storage and EGR feasibility.