Automatic Fracture Detection and Characterization in Borehole Images Using Deep Learning-Based Semantic Segmentation

Fracture analysis represents one of the key investigations that needs to be carried in borehole logs. Identifying fractures, as well as other similar features (like breakouts or foliations) is essential for characterizing the reservoir where the drilling took place. However, identifying and characterizing the fractures from borehole images is a very time and resource consuming task, that require extensive knowledge from geological experts. For this reason, developing semi-automated or automated tools would facilitate and increase the productivity of fracture analysis, since even for one reservoir, experts need to analyze and interpret hundreds of meters of borehole images. This paper presents a deep learning based approach for application of automatic fracture detection and characterization in borehole images, relying on state-of-the-art convolutional neural network for accurate semantic segmentation of fractures. Target images consists of color borehole images, as opposed to acoustic or drill-core images, and uses real world data, both for training the deep learning model and testing the whole system. The system is evaluated by using multiple metrics and the final outputs of the system are the parameters of the sinusoids that define the predicted fractures.