Enhancing resolution of micro-CT images of reservoir rocks using super resolution

Current hardware configuration of micro-CT detectors puts a lower limit on voxel size that can be acquired while maintaining a sufficiently large field of view. This limits the degree to which rock pores can be resolved in a micro-CT image and thus restricting the application envelope of Digital Rock technology. Super resolution techniques can refine voxel size while maintaining a sufficiently large field of view using pairs of low- and high-resolution images for training. However, for interpretation of quality of Digital Rock results, image quality is not determined by voxel size alone but by the degree to which a feature such as pore throat is resolved, which depends on both the physical size of the feature and voxel size. Furthermore, artificially down-sampling finer voxel size images to obtain images of coarser voxel size for training deep learning networks is not sufficient to capture the mapping between images acquired at different resolutions. This is especially true for reservoir rocks because the noise and artifacts introduced during imaging and reconstruction are more complex than that captured by simple down-sampling operation. We overcome these two limitations, by (1) using the ratio of pore throat size and voxel size (N) to group training dataset instead of voxel size and (2) using pairs of registered micro-CT images acquired using a state-of-the-art detector instead of synthetically down-sampled images. We show that combination of these two techniques produced images with better sharpness and contrast and enabled us to refine voxel size significantly beyond what is possible using the current imaging technology while maintaining the field of view. •Generative Adversarial Networks are trained to enhance micro-CT image resolution.•Registered real low- and high-resolution micro-CT images are used instead of synthetically down-sampled images.•The ratio between pore throat size and voxel size is used as a measurement of image quality to group training dataset.