Improved 3D Fracture Reconstruction Method Based on Superpixel Segmentation and Convolutional Neural Network

The morphology and connectivity of subsurface fracture networks are critical factors controlling wellbore stability and hydraulic fracturing efficiency. Accurate characterization of the three-dimensional complexity of fractures holds significant importance for engineering safety and performance enhancement. A novel image segmentation model is established in this study. It enhances the iterative threshold method by incorporating simple linear iterative clustering superpixels, ResNet50, and a Gaussian mixture model. The model first divides complex computed tomography images into numerous superpixel images using simple linear iterative clustering superpixel segmentation. Subsequently, ResNet50 is employed to classify these superpixel images. Based on the classification results, the iterative threshold segmentation method is applied to segment different categories of superpixel images accordingly. Following preliminary image segmentation, Gaussian mixture module is used for denoising the segmented fracture images, resulting in high-precision segmented images. The two-dimensional segmented images are then reconstructed in three-dimensional space, and the three-dimensional distribution characteristics of fractures are analyzed. This study concludes that the new fracture segmentation method enables high-precision extraction of fracture regions. Compared with threshold segmentation, the morphological analysis noise value in the two-dimensional images segmented by the method proposed in this study was reduced from 0.21% to 0.08%. Fracture distribution in three-dimensional space is complex, and areas with larger fracture networks exhibit greater complexity in their three-dimensional distribution.