Impact of Fracture-Pore Identification Accuracy on Fluid Seepage in Digital Cores

Digital cores play a significant role in the formulation and optimization of decision-making in oil and gas field development. However, existing research predominantly focuses on analyzing internal physical attributes while neglecting the critical impact of scanning resolution on digital core reconstruction. This study evaluates the performance of traditional threshold segmentation and deep learning methods in identifying and segmenting pores, fractures, and matrix using multi-scale core scanning data. Deep learning was employed to achieve accurate segmentation, followed by the construction of a fracture-pore network model based on equivalent radius theory. Comparative analysis reveals that digital cores reconstructed at different resolutions exhibit distinct fluid flow pathways. The accuracy of a digital core depends on whether high-resolution scans resolve micro-fractures and isolated pores and whether low-resolution scans capture intact fractures and pores. Higher resolutions enhance the identification of micro-features, increasing the number of dominant flow channels. By innovatively integrating acidizing simulation with digital rock core technology, the differences in rock permeability result in completely distinct seepage pathways for the acid during the simulation of its flow through the core. In acid flow simulations, permeability differences caused by resolution variations lead to divergent acid migration patterns. High-resolution models, with interconnected channels, achieve greater acid contact efficiency, dissolving most pores, while low-resolution models exhibit isolated pores that remain unacidized, resulting in fundamentally different outcomes. These findings underscore the importance of scanning resolution in guiding oilfield development decisions.