Leveraging stable diffusion to reconstruct missing core intervals.

Core-based data provide critical insights into subsurface reservoir characterization by offering direct observations of rock formations, including lithology, sedimentary structures, and minerals. However, core analysis is frequently compromised by missing intervals resulting from extraction damage, handling losses, and the common practice of drilling inch-size plugs for laboratory analysis. These gaps create significant interpretation challenges, particularly in heterogeneous formations where spatial continuity is essential for accurate reservoir modeling. Given the high operational costs and strategic value of core data, developing reliable methods for reconstructing missing core sections represents a crucial advancement in reservoir evaluation. This study investigates the application of Stable Diffusion Model (SDM) for core interval/plug reconstruction, building on its proven success in conditioned image inpainting tasks. Through systematic evaluation across diverse depositional environments, we demonstrate SDM's effectiveness in reconstructing homogeneous intervals and gradual facies transition zones, while testing its limitations in handling complex heterogeneous zones with abrupt facies changes. The model shows strengths in restoring sedimentary textures and bedding architectures but faces challenges in accurately predicting the locations of sharp facies boundaries. Our approach enhances the utility of incomplete core images, and the findings along with the proposed potential improvement methods may provide insights for other researchers to further reduce reservoir assessment uncertainties.