Research Paper (Special Issue)
Andrä, H., Combaret, N., Dvorkin, J., Glatt, E., Han, J., Kabel, M., ... and Zhan, X., 2013. Digital rock physics benchmarks—Part I: Imaging and segmentation, Computers & Geosciences, 50, p.25-32.
10.1016/j.cageo.2012.09.005Andrew, M., 2018. A quantified study of segmentation techniques on synthetic geological XRM and FIB-SEM images, Computational Geosciences, 22(6), p.1503-1512.
10.1007/s10596-018-9768-yAndrew, M., 2020. Comparing organic-hosted and intergranular pore networks: topography and topology in grains, gaps and bubbles, Geological Society, London, Special Publications, 484(1), p.241-253.
10.1144/SP484.4Arns, C.H., Knackstedt, M.A., Pinczewski, W.V., and Martys, N.S., 2004. Virtual permeametry on microtomographic images, Journal of Petroleum Science and Engineering, 45(1-2), p.41-46.
10.1016/j.petrol.2004.05.001Balcewicz, M., Siegert, M., Gurris, M., Ruf, M., Krach, D., Steeb, H., and Saenger, E.H., 2021. Digital rock physics: A geological driven workflow for the segmentation of anisotropic Ruhr sandstone, Frontiers in Earth Science, 9, 673753.
10.3389/feart.2021.673753Belkin, M. and Niyogi, P., 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering, Advances in Neural Information Processing Systems, 14.
10.7551/mitpress/1120.003.0080Belkin, M. and Niyogi, P., 2003. Laplacian eigenmaps for dimensionality reduction and data representation, Neural Computation, 15(6), p.1373-1396.
10.1162/089976603321780317British Geological Survey (BGS), 2000. Final Report of the SACS 1 project – Saline Aquifer CO2 Storage: A Demonstration Project at the Sleipner Field. Work Area 1 – Geology, BGS Technical Report WH/2000/21C, British Geological Survey, Keyworth, UK, p.4.
Chernyaev, E., 1995. Marching cubes 33: Construction of topologically correct isosurfaces (No. CERN-CN-95-17).
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., and Bharath, A.A., 2018. Generative adversarial networks: An overview, IEEE signal processing magazine, 35(1), p.53-65.
10.1109/MSP.2017.2765202Gabrieli, R., Schiavi, A., and Baino, F., 2024. Determining the permeability of porous bioceramic scaffolds: significance, overview of current methods and challenges ahead, Materials, 17(22), 5522.
10.3390/ma1722552239597346PMC11595756Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... and Bengio, Y., 2014. Generative adversarial nets, Advances in Neural Information Processing Systems, 27.
Gostick, J.T., 2017. Versatile and efficient pore network extraction method using marker-based watershed segmentation.
10.1103/PhysRevE.96.023307Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A.C., 2017. Improved training of wasserstein gans, Advances in Neural Information Processing Systems, 30.
International Energy Agency (IEA), 2023. Net zero roadmap a global pathway to keep the 1.5°C goal in reach, IEA, Paris, France, 226p.
Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A., 2017. Image-to-image translation with conditional adversarial networks, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p.1125-1134.
10.1109/CVPR.2017.632Jo, H., Santos, J.E., and Pyrcz, M.J., 2020. Conditioning well data to rule-based lobe model by machine learning with a generative adversarial network, Energy Exploration & Exploitation, 38(6), p.2558-2578.
10.1177/0144598720937524Jones, S.A., Van Der Bent, V., Farajzadeh, R., Rossen, W.R., and Vincent-Bonnieu, S., 2016. Surfactant screening for foam EOR: Correlation between bulk and core-flood experiments, Colloids and Surfaces A: Physicochemical and Engineering Aspects, 500, p.166-176.
10.1016/j.colsurfa.2016.03.072Kench, S. and Cooper, S.J., 2021. Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion, Nature Machine Intelligence, 3(4), p.299-305.
10.1038/s42256-021-00322-1Ko, J., 2013. Review of site selection criteria for geological storage of carbon dioxide, Journal of the Korean Society of Mineral and Energy Resources Engineers, 50(5), p.732-749.
10.32390/ksmer.2013.50.5.732LeCun, Y. and Bengio, Y., 1995. Convolutional networks for images, speech, and time series, The Handbook of Brain Theory and Neural Networks, 3361(10), p.255-258.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P., 2002. Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86(11), p.2278-2324.
10.1109/5.726791Lee, K.H. and Yun, G.J., 2024. Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling, npj Computational Materials, 10(1), 99p.
10.1038/s41524-024-01280-zLiu, L., Chang, B., Prodanović, M., and Pyrcz, M.J., 2025. AI‐based digital rocks augmentation and assessment metrics, Water Resources Research, 61(5), e2024WR037939.
10.1029/2024WR037939Liu, Z., Herring, A., Robins, V., and Armstrong, R.T., 2017. Prediction of permeability from Euler characteristic of 3D images, Proceedings of the International Symposium of the Society of Core Analysts, Society of Core Analysts, Vienna, Austria, p.1-12.
Loucks, R.G., Reed, R.M., Ruppel, S.C., and Hammes, U., 2012. Spectrum of pore types and networks in mudrocks and a descriptive classification for matrix-related mudrock pores, AAPG bulletin, 96(6), p.1071-1098.
10.1306/08171111061Madonna, C., Almqvist, B.S., and Saenger, E.H., 2012. Digital rock physics: Numerical prediction of pressure-dependent ultrasonic velocities using micro-CT imaging, Geophysical Journal International, 189(3), p.1475-1482.
10.1111/j.1365-246X.2012.05437.xMcPhee, C., 2012. The core analysis elephant in the formation evaluation room, In SPE Annual Technical Conference and Exhibition, p.SPE-158087.
10.2118/158087-MSMinistry of Trade, Industry and Energy (MOTIE), 2024. Selection of a preliminary feasibility study target project for a CCS demonstration project using the Donghae Gas Field, Press Release 2024.01.05., Sejong, Korea, p.1.
Mosser, L., Dubrule, O., and Blunt, M.J., 2017. Reconstruction of three-dimensional porous media using generative adversarial neural networks, Physical Review E, 96(4), 043309.
10.1103/PhysRevE.96.043309Nekouie, H., Cao, J., James, L., and Johansen, T., 2016. Analytical gas-oil relative permeability interpretation method for immiscible flooding experiments under constant differential pressure conditions, In Proceedings of the International Symposium of the Society of Core Analysts, Snow Mass, Colorado.
Park, H. and Jin, J., 2023. Carbon neutrality/green growth national strategy and the first national basic plan and carbon neutrality in the urban sector, Urban planners, 10(3), p.5-8.
Pinto, T.S., Lima, O.A.D., and Leal Filho, L.D.S., 2009. Sphericity of apatite particles determined by gas permeability through packed beds. Mining, Metallurgy & Exploration, 26(2), p.105-108.
10.1007/BF03403426Ramstad, T., Idowu, N., Nardi, C., and Øren, P.E., 2012. Relative permeability calculations from two-phase flow simulations directly on digital images of porous rocks, Transport in Porous Media, 94(2), p.487-504.
10.1007/s11242-011-9877-8Robb, E., Chu, W.S., Kumar, A., and Huang, J.B., 2020. Few-shot adaptation of generative adversarial networks, arXiv preprint arXiv:2010.11943.
Sengar, S.S., Hasan, A.B., Kumar, S., and Carroll, F., 2025. Generative artificial intelligence: a systematic review and applications, Multimedia Tools and Applications, 84(21), p.23661-23700.
10.1007/s11042-024-20016-1Shaham, T.R., Dekel, T., and Michaeli, T., 2019. Singan: Learning a generative model from a single natural image, In Proceedings of the IEEE/CVF international conference on computer vision, p.4570-4580.
10.1109/ICCV.2019.00467Sondergeld, C.H., Newsham, K.E., Comisky, J.T., Rice, M.C., and Rai, C.S., 2010. Petrophysical considerations in evaluating and producing shale gas resources, In SPE Unconventional Resources Conference/Gas Technology Symposium, p.SPE-131768.
10.2523/131768-MSSong, S., Ding, Q., and Wei, J., 2019. Improved algorithm for estimating pore size distribution from pore space images of porous media, Physical Review E, 100(5), 053314.
10.1103/PhysRevE.100.053314Sun, W. and Wong, T.F., 2018. Prediction of permeability and formation factor of sandstone with hybrid lattice Boltzmann/finite element simulation on microtomographic images, International Journal of Rock Mechanics and Mining Sciences, 106, p.269-277.
10.1016/j.ijrmms.2018.04.020- Publisher :The Korean Society of Mineral and Energy Resources Engineers
- Publisher(Ko) :한국자원공학회
- Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
- Journal Title(Ko) :한국자원공학회지
- Volume : 63
- No :1
- Pages :73-86
- Received Date : 2026-01-08
- Revised Date : 2026-02-06
- Accepted Date : 2026-02-09
- DOI :https://doi.org/10.32390/ksmer.2026.63.1.073


Journal of the Korean Society of Mineral and Energy Resources Engineers







