Conditional Generative Adversarial Networks for 2D core grayscale image reconstruction from pore parameters

Abstract

Digital cores are of great significance for reservoir structure simulation, oil and gas exploration and development. Most existing digital core reconstruction methods only generate binary cores with complicated implementation processes, among other problems. To address these problems, this study proposed a combination of core pore parameters and conditional generative adversarial network (CGAN) to realize the 2D reconstruction of core grayscale images from only pore parameters (namely, text-to-image synthesis). The current text-to-image synthesis approaches still have many difficulties in generating fine images, but the technologies of image-to-image generation have improved drastically in recent years. Therefore, the proposed method involves two stages to avoid the difficulty of directly generating core grayscale images from pore parameters. In stage I, we preprocessed core sample images to obtain binary-grayscale image pairs, and then used the CGAN to learn the mapping from core binary images to real sample images. At the same time, the pores in the binary images were segmented and extracted to construct the pore component library. In stage II, on the basis of the given pore parameters, the corresponding pores were randomly extracted from the pore component library to generate binary images, and then the generated binary images were used as input for the trained CGAN model to produce core grayscale images. The experimental results showed that the core grayscale images reconstructed by the proposed method meet the pore conditions and reflect the basic characteristics of real cores. • The reconstruction by our method is based on pore parameters rather than images. • The reconstruction is decomposed into two stages: “pore parameters to binary core” and “binary core to grayscale core”. • The method has a general validity and can reconstruct different types of cores according to the research requirements.