[1]. Alreshedan, F. and Kantzas, A. (2016). Sensitivity Analysis of the Effect of Pore Structure and Geometry on Petrophysical and Electrical Properties of Tight Media: Random Network Modeling. Journal of Petroleum Science and Technology, 6(1), 28-45. doi: 10.22078/jpst.2016.566.##
[2]. Dong, H. and Blunt, M.J., (2009). Pore-network extraction from micro-computerized-tomography images. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 80(3), 036307. doi: 10.1103/PhysRevE.80.036307.##
[3]. Zoeir, A., Tabatabaei Nejad, S. A., Qajar, J. (2019). An Estimation of Multiphase Relative Permeabilities in Reservoir Cores from Micro-CT Data, Journal of Petroleum Science and Technology, 9(2), 18-29. doi: 10.22078/jpst.2018.3237.1516.##
[4]. Blunt, M.J., Bijeljic, B., Dong, H., Gharbi, O., Iglauer, S., Mostaghimi, P., Paluszny, A. and Pentland, C., (2013). Pore-scale imaging and modelling. Advances in Water resources, 51, 197-216. doi: 10.1016/j.advwatres.2012.03.003.##
[5]. Carman, P.C., (1939). Permeability of saturated sands, soils and clays. The Journal of Agricultural Science, 29(2), pp.262-273. doi: 10.1017/S0021859600051789.##
[6]. Wu, Y., Tahmasebi, P., Lin, C., Munawar, M.J. and Cnudde, V., (2019). Effects of micropores on geometric, topological and transport properties of pore systems for low-permeability porous media. Journal of Hydrology, 575, pp.327-342. doi: 10.1016/j.jhydrol.2019.05.014.##
[7]. Wu, X., Wang, F., Xiao, Z., Zhang, Y., Zhao, J., Fang, C. and Wei, B., (2024). Multiscale pore network modeling and flow property analysis for tight sandstone: a case study. Journal of Geophysics and Engineering, 21(1), pp.47-59. doi: 10.1093/jge/gxad092.##
[8]. Liu, Y., Guo, C., Cao, J., Cheng, Z., Ding, X., Lv, L., Li, F. and Gong, M., (2020). A new resolution enhancement method for sandstone thin-section images using perceptual GAN. Journal of Petroleum Science and Engineering, 195, p.107921. doi: 10.1016/j.petrol.2020.107921.##
[9]. Singh, K. and Kapoor, R., (2014). Image enhancement using exposure based sub image histogram equalization. Pattern Recognition Letters, 36, pp.10-14. doi: 10.1016/j.patrec.2013.08.024.##
[10]. Zhao, Z., Gao, W., Chang, J., Chen, Y., Zhang, Q., Wang, B. (2022). An Analysis of Deep Neural Network Model in Recognition of Mud Cuttings Image for Practical Applications, Journal of Petroleum Science and Technology, 12(4), pp. 53-61. doi: 10.22078/jpst.2023.4852.1812.##
[11]. Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F. and Zheng, Y., (2019). Recent progress on generative adversarial networks (GANs): A survey. IEEE access, 7, pp.36322-36333. DOI: 10.1109/ACCESS.2019.2905015.##
[12]. Krichen, M., (2023), July. Generative adversarial networks. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. doi: 10.1109/ICCCNT56998.2023.10306417.##
[13]. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y., (2014). Generative adversarial nets. Advances in neural information processing systems, 27. doi: 10.1145/3422622.##
[14]. Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z. Gorelick S., and Shi, W., (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4681-4690). doi: 10.48550/arXiv.1609.04802.##
[15]. Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y. and Change Loy, C., (2018). Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the European conference on computer vision (ECCV) workshops (pp. 0-0). doi: 10.48550/arXiv.1809.00219.##
[16]. Wang, X., Xie, L., Dong, C. and Shan, Y., (2022). Realesrgan: Training real-world blind super-resolution with pure synthetic data supplementary material. Computer Vision Foundation open access, 1(2), 2. doi: 10.1109/ICCVW54120.2021.00217.##
[17]. Shams, R., Masihi, M., Boozarjomehry, R.B. and Blunt, M.J., (2020). Coupled generative adversarial and auto-encoder neural networks to reconstruct three-dimensional multi-scale porous media. Journal of Petroleum Science and Engineering, 186, p.106794. doi: 10.1016/j.petrol.2019.106794.##
[18]. Shams, R., Masihi, M., Boozarjomehry, R.B. and Blunt, M.J., (2021). A hybrid of statistical and conditional generative adversarial neural network approaches for reconstruction of 3D porous media (ST-CGAN). Advances in Water Resources, 158, 104064. doi: j.advwatres.2021.104064.##
[19]. Liang, Y., Wang, S., Feng, Q., Zhang, M., Cao, X. and Wang, X., (2024). Ultrahigh-resolution reconstruction of shale digital rocks from FIB-SEM images using deep learning. SPE J., 29(3), 1434-1450. doi: 10.2118/218397-PA.##
