ShaleSeg: Deep-learning dataset and models for practical fracture segmentation of large-scale shale CT images

Abstract

Shale gas plays a crucial role as an exploration resource for human life, and its contribution to energy production has been steadily increasing. The success of hydraulic fracturing operations is highly dependent on the understanding of shale fracture propagation at large scales. Currently, the segmentation of large-scale shale fractures is primarily carried out through time-consuming manual or semi-automated methods. The identification of shale fractures is similar to image segmentation, in which deep-learning holds the potential to achieve accurate performance close to human experts. However, its application encounters challenges when dealing with large-scale shale images due to factors such as a lack of datasets, severe artifacts, and limited sample sizes. In this study, we compiled the first publicly available large-scale shale CT dataset, which consists of a total of 4313 scanned images, with over 700 images coming from scanning experiments on 300 cm3 cubic shale samples. We also proposed a new data augmentation method for shale CT images to overcome challenges posed by severe artifacts and small-sample sizes. This method involves simulating image artifacts and reducing high edge gradients along the sample edges, which has proven to be effective in facilitating the efficient training of deep-learning models. We've also conducted several ablation studies to determine the best model design, namely ShaleSeg. ShaleSeg was able to accurately identify multi-scale fractures, achieving a precision of 0.84 and an F-score of 0.81, which is close to the manual results. In the case of minor fractures, precision increased by an increase significantly from 0.191 to 0.513, compared to the traditional algorithm. ShaleSeg significantly improves the accuracy and efficiency of fracture segmentation for large-scale shale images. This advancement will be crucial for a thorough comprehension of the fracture mechanism in shale.