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Analyzing X-Ray CT Images from Unconventional Reservoirs Using Deep Generative Models


 Characterization of rock samples is relevant to hydrocarbon production, geothermal energy, hydrogen storage, waste storage, and carbon sequestration. Image resolution plays a key role in both property estimation and image analysis. However, poor resolution may lead to underestimation of rock properties such as porosity and permeability. Therefore, improving the image resolution is paramount. This study shows the workflow for 2D image super-resolution processes using a Convolutional Neural Network (CNN) method. The rock samples used to test the networks were three unfractured Wolfcamp shales, a Bentheimer sandstone (Guan et al., 2019), and a Vaca Muerta (Frouté et al., 2020) shale. These samples were imaged with a clinical Computed Tomography (CT) scanner (100's µm resolution) as well a microCT scanner (10's µm resolution). This established training, validation, and test data sets. The deep learning architectures were implemented in Matlab 2021b. The network performance is calculated using two metrics: i) pixel signal to noise ratio (PSNR) and ii) structural similarity index method (SSIM). In addition, porosity values on the image data sets are presented to illustrate their relevance. Training options and different strategies for network tuning are also discussed in the results section. Results illustrate the potential for AI to improve the resolution of CT images by at least a factor of 4. This level of improvement is essential for resolving fractures, other permeable conduits in impermeable shale samples, and shale fabric features. We outline a pathway to greater improvement of resolution.