DiscussionIn recent years, attempts have been made to overcome the trade-off between resolution and FoV for digital rock data using deep learning methods (e.g., CNNs and GANs). However, previous works focused on the super-resolution problem aiming to increase the resolution of micro-CT images by two or four times. They are useful to make the micro-CT images sharper but cannot resolve pore structures at multiscale. Our proposed method provides an effective means to reconstruct rock models that accurately capture multiscale pore structures obtained by different imaging methods (e.g., micro-CT and SEM). Due to the limitation of GPU memory and non-availability of 3-D SEM data, the output high-resolution image is only in 2D and with a size of 1024×1024 and therefore, we need to perform the prediction patch-by-patch by dividing the micro-CT slice into sub-images. The straightforward solution to increase the image size is to use more GPUs, but a more efficient way is to decrease the number of network parameters by model compression (e.g., depthwise separable convolutions, network pruning and knowledge distillation). The potential solution to 3-D simulation is to develop a GAN with the generator in 3-D while the discriminator in 2-D which takes the slices sampled from generated synthetic 3-D images and real SEM data. Those are the research directions that we will investigate in future.
Conclusion
We presented an innovative approach for fusion of multiscale digital rock images, i.e., low-resolution micro-CT and high-resolution SEM data, using StyleGAN2-ADA and CycleGAN. The StyleGAN2-ADA network is effective to overcome the issue of overfitting due to limited number of SEM images, while the CycleGAN network allows for leveraging unpaired training samples of micro-CT and SEM, which is a common challenge in practice. The application to a carbonate dataset reveals that the proposed methodology is a valid and powerful approach for integrating multiscale digital rock data. The reconstructed rock models accurately capture the micro-structures from both low-resolution micro-CT and high-resolution SEM images. Moreover, the computed effective permeabilities are more accurate than the prediction directly from micro-CT data by comparison with the laboratory measurement. We conclude that the proposed method provides an efficient means to reconstruct high-resolution digital rocks with large FoV, which is of great significance for the accurate pore-scale flow simulation and petrophysical properties prediction.