Image logs can provide critical information to reduce drilling risk, as they allow for early detection of structures such as faults or fractures. The timeliness of such information is undermined by the time required to manually interpret the data and the subjectivity of interpretations. The proposed methodology is a supervised Deep Learning - based method built on U-Net architecture for segmentaton of image logs acquired while drilling, i.e., automated detection of geological edges in borehole images. The proposed network has been trained on synthetic data and tested on field data. Different learning strategies, namely Curriculum and standard learning (CL,SL), were compared to observe the impact of segmentation process on final results: CL shows greater potential in early fracture detection due to its superior performance in chaotic and heterogeneous intervals
