LAUSR

Abstract Drill cores provide the most reliable fracture information in subsurface formations as they present a clear and direct view of fractures. Core observation and image log interpretation are usually integrated for fracture analysis of underground layers. There has been a strong move towards developing automated fracture detection methods, however, the focus has been on extracting fracture information from log images, such as acoustic or resistivity image logs. Such efforts using core images are significantly less. This study presents a machine learning-based approach for automatic fracture recognition from unwrapped drill-core images. The proposed method applies a state-of-the-art convolutional neural network for object identification and segmentation. The study also investigates the feasibility of using synthetic fracture images for training the machine learning model. This can provide an alternative to real data, and thus address data availability issues common for supervised machine learning applications. We first create two types of synthetic data by using masks of real fractures and creating sinusoidal shaped fractures. The trained model is evaluated on real core images from two boreholes, which provided an average precision of approximately 95%. The identified fractures are further analyzed and compared to the manually segmented fractures in terms of fracture dip angle and dip direction, which achieved average absolute errors of around 2° and 11°, respectively. Overall, the study presents a novel application of an advanced machine learning algorithm for fracture detection and analysis from unwrapped core images.