Multiscale characterization of micro fracture connectivity and gas migration in volcanic reservoirs using µCT and hybrid learning segmentation

Volcanic reservoirs contain highly heterogeneous and extremely tight pore and fracture systems that exert a primary control on natural gas storage and migration. Accurate identification of micro fractures in µCT images remains challenging because fracture apertures are small, morphologies are complex, and grayscale contrast is low. To address this issue, this study develops an integrated framework that combines ensemble learning with a 2.5D deep learning model to achieve efficient and precise segmentation of volcanic micro fractures. A Random Forest model is first used to provide rapid pre segmentation, followed by a U Net plus plus model that captures through plane continuity and improves fracture boundary recognition. A semi-automatic label as you train strategy reduces annotation requirements while maintaining high accuracy. The model achieves a Dice coefficient of 0.902 within ten epochs. Based on the segmentation results, three dimensional reconstruction, pore throat network modeling and gas flow simulations were conducted to quantify fracture connectivity and reveal gas migration behavior. The results show that highly connected fractures create axial flow channels and lateral micro pathways that enhance seepage efficiency and govern the overall migration process of natural gas. Samples with larger pore throat radii and stronger multiscale connectivity present significantly higher permeabilities and more continuous flow paths. This study provides a practical and accurate fracture segmentation workflow and quantitatively demonstrates how micro fracture connectivity regulates gas transport in tight volcanic reservoirs. The findings offer theoretical and methodological support for digital rock analysis, reservoir evaluation and multiscale modeling in geomechanics.