Applications of Physically Accurate Deep Learning for Processing Digital Rock Images

Digital rock analysis aims to improve our understanding of the fluid flow properties of reservoir rocks, which are important for enhanced oil recovery, hydrogen storage, carbonate dioxide storage, and groundwater management. X-ray microcomputed tomography (micro-CT) is the primary approach to capturing the structure of porous rock samples for digital rock analysis. Initially, the obtained micro-CT images are processed using image-based techniques, such as registration, denoising, and segmentation depending on various requirements. Numerical simulations are then conducted on the digital models for petrophysical prediction. The accuracy of the numerical simulation highly depends on the quality of the micro-CT images. Therefore, image processing is a critical step for digital rock analysis. Recent advances in deep learning have surpassed conventional methods for image processing. Herein, the utility of convolutional neural networks (CNN) and generative adversarial networks (GAN) are assessed in regard to various applications in digital rock image processing, such as segmentation, super-resolution, and denoising. To obtain training data, different sandstone and carbonate samples were scanned using various micro-CT facilities. After that, validation images previously unseen by the trained neural networks are utilised to evaluate the performance and robustness of the proposed deep learning techniques. Various threshold scenarios are applied to segment the reconstructed digital rock images for sensitivity analyses. Then, quantitative petrophysical analyses, such as porosity, absolute/relative permeability, and pore size distribution, are implemented to estimate the physical accuracy of the digital rock data with the corresponding ground truth data. The results show that both CNN and GAN deep learning methods can provide physically accurate digital rock images with less user bias than traditional approaches. These results unlock new pathways for various applications related to the reservoir characterisation of porous reservoir rocks

Image super-resolution using deep belief networks

Conference Name:6th International Conference on Internet Multimedia Computing and Service, ICIMCS 2014. Conference Address: Xiamen, China. Time:July 10, 2014 - July 12, 2014.National Natural Foundation of China; SIGMM China Chapter; Xiamen UniversityIn this paper, we aim at using Deep Belief Networks (DBNs) to solve the problem of image super-resolution (SR). We exploit the hierarchical structure of the DBNs to capture the non-linear mapping from low-resolution (LR) patches to their high-resolution (HR) counterpart. When a query LR image is input, we divide it into a list of patches, then we put each patch into a forward propagation network which is a trained deep belief network. The output is the predicted HR patches. Finally, we combine the HR patches into expected HR images. We evaluate our approach on a popular dataset which is used in other super-resolution literature. Experimental results demonstrate the performance of our method is superior to several state-of-the-art super-resolution methods both quantitatively and perceptually. Copyright 2014 ACM