Journal of Petroleum Research

Journal of Petroleum Research

Investigating the effect of the color spectrum of input images in the process of increasing image quality using a new method based on adversarial generative networks

Document Type : Research Paper

Authors
Alireza ElahiKhaledi 1
Saeid Jamshidi 2
Mohsen Masihi 2
1 Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran
2 Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran.
10.22078/pr.2025.5735.3550
Abstract
Reconstructing high-resolution images from poor-quality images is one of the basic challenges in image processing. In modeling porous media using images obtained from computed microtomography (Micro-CT), high-quality images of these media are usually unavailable for different reasons, including the cost and computational complexity of high-quality imaging. With the advancement in the development of multiscale networks, it is possible to use more image details in these networks, and there is a further need for high-quality images. Today, adversarial generative networks are used as a practical tool in increasing image quality. These networks are trained using pairs of high-quality and low-quality images and then, they produce high-quality images by taking low-quality images as input. The final images used in modeling the porous media are binary images, but using binary images as input in the image quality enhancement process may result in detail loss. For this purpose, this study investigates the effect of using grayscale or binary images as input in the model training process. Grayscale images of a Brea sandstone sample are taken as the main input and binary images are generated using grayscale images and the realization of the real porosity of the rock sample. In this research, a new model called RealESRGAN is used to enhance the quality of the images.
Keywords
Generative Adversarial Network (GAN)
Real ESRGAN Model
Grayscale Image
Binary Image
Pore Network Modeling
Subjects
 
[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.##
 
Journal of Petroleum Research
Volume 36, Issue 1 - Serial Number 146
May and June 2026
Pages 50-66