Articles | Volume 67
https://doi.org/10.5194/adgeo-67-79-2025
https://doi.org/10.5194/adgeo-67-79-2025
03 Dec 2025
 | 03 Dec 2025

Quantifying carbonate microstructure using classical segmentation pipelines for CCS and radioactive waste applications

Wurood S. Alwan, Omar Choudhry, Paul Glover, Louey Tliba, and Richard Collier

Cited articles

Al Khalifah, H., Glover, P., and Lorinczi, P.: Permeability prediction and diagenesis in tight carbonates using machine learning techniques, Mar. Petrol. Geol., 112, 104096, https://doi.org/10.1016/j.marpetgeo.2019.104096, 2020. 
Alqahtani, N. J., Niu, Y., Wang, Y. D., Chung, T., Lanetc, Z., Zhuravljov, A., Armstrong, R. T., and Mostaghimi, P.: Super-resolved segmentation of X-ray images of carbonate rocks using deep learning, Transp. Porous Media, 143, 497–525, https://doi.org/10.1007/s11242-022-01781-9, 2022. 
Al-Zainaldin, S., Glover, P. W., and Lorinczi, P.: Synthetic fractal modelling of heterogeneous and anisotropic reservoirs for use in simulation studies: implications on their hydrocarbon recovery prediction, Transp. Porous Media, 116, 181–212, https://doi.org/10.1007/s11242-016-0770-3, 2017. 
Bangaru, S. S., Wang, C., Zhou, X., and Hassan, M.: Scanning electron microscopy (SEM) image segmentation for microstructure analysis of concrete using U-net convolutional neural network, Autom. Constr., 144, 104602, https://doi.org/10.1016/j.autcon.2022.104602, 2022. 
Bentley, M. and Ringrose, P.: The rock model, in: Reservoir Model Design: A Practitioner's Guide, 2nd edn., Springer, Cham, 11–63, https://doi.org/10.1007/978-3-030-70163-5, 2021. 
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Short summary
We mapped pore networks in carbonate scanning-electron-microscope (SEM) images to aid safe CO2 storage and radioactive-waste isolation. An open 29 000 × 23 000-pixel mosaic was split into 100 fully hand-labelled tiles. Eight classic computer-vision pipelines were benchmarked: Watershed gave the best recall–precision balance, and a simple Hybrid-Voting of three filters halved false alarms. The data, masks and code offer an immediate reference set for training and testing future 3-D deep-learning models.
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