EILnet: An intelligent model for the segmentation of multiple fracture types in karst carbonate reservoirs using electrical image logs

Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs, and electrical image logs are vital data for visualizing and characterizing such fractures. However, the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective. In addition, the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry, which makes it difficult to accurately identify fractures. In this paper, the electrical image logs network (EILnet)—a deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion module—was created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images. Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model. Various image-processing tools, including the bilateral filter, Laplace operator, and Gaussian low-pass filter, were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures. The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models, such as Fully Convolutional Networks (FCN-8s), U-Net, and SegNet, for both the single-channel dataset and the multi-attribute dataset. The EILnet provided significant advantages for the single-channel dataset, and its mean intersection over union (MIoU) and pixel accuracy (PA) were 81.32 % and 89.37 %, respectively. In the case of the multi-attribute dataset, the identification capability of all models improved to varying degrees, with the EILnet achieving the highest MIoU and PA of 83.43 % and 91.11 %, respectively. Further, applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification, thereby indicating its promising potential applications.