Permeability Prediction from Well Logs with Machine Learning for CO₂ Storage Formation Assessment in Barrow-Dampier Sub-Basins

The Barrow and Dampier sub-basins of the Northern Carnarvon Basin in Western Australia have emerged as promising regions for geological carbon dioxide (CO2) storage. Permeability, a key petrophysical property for assessing CO2 injectivity, is difficult to measure directly from well logs and typically requires limited core analysis or well testing. This study evaluates the effectiveness of the XGBoost machine learning algorithm in predicting permeability using well logs and interpreted petrophysical properties. A well-based data splitting strategy is adopted to prevent data leakage and to rigorously assess model generalisation across diverse geological settings. Although most training data originated from the Mungaroo formation, the model also performs well on other key CO2 storage formations, suggesting that the variability within the Mungaroo dataset, supplemented by additional data from CO2 storage formations, is broad enough to capture common features across them. Furthermore, integrating petrophysical interpretation results—such as effective porosity and mineral composition—with raw logs significantly enhances prediction accuracy. These findings demonstrate the potential of combining data-driven modelling with domain knowledge to enable reliable, regional-scale permeability prediction in support of CO2 storage formation screening.