Super-Resolution Reconstruction of Reservoir Core CT Images via GAN: Advancing Energy Extraction Accuracy

Accurate characterization of reservoir core porosity and morphology from core CT images is essential for oil and gas exploration, and super-resolution offers a cost-effective means to enhance image clarity. To overcome the limitation of existing pixel-level algorithms in distinguishing high- from low-frequency information, we propose the Multi-scale Fusion Attention Mechanisms of Generative Adversarial Networks (MFAGAN). MFAGAN integrates a residual-in-residual fusion attention module to improve throat structure discrimination, a multi-scale discriminator to stabilize training and boost generator performance, and a perceptual loss to sharpen edges and textures. Comparative experiments demonstrate that MFAGAN achieves superior performance over GAN-based baselines in terms of learned perceptual image patch similarity (LPIPS), structural similarity index (SSIM), subjective image quality, and generalization ability. However, its peak signal-to-noise ratio (PSNR) is slightly lower than that of methods such as EDSR. When tested within the distribution of the DeepRock-SR dataset, MFAGAN demonstrated stable and consistent objective and subjective performance. However, its out-of-distribution generalization ability has not yet been evaluated, which is a clear limitation of the current study. In our future work, we will build upon MFAGAN to further explore 3D super-resolution reconstruction techniques for core CT images, with the aim of advancing the practical application of super-resolution methods in oil and gas exploration and development.