Toward real-time fracture detection on image logs using deep convolutional neural network YOLOv5

Abstract

Fractures in reservoirs have a profound impact on hydrocarbon production operations. The more accurately fractures can be detected, the better the exploration and production processes can be optimized. Therefore, fracture detection is an essential step in understanding the reservoir’s behavior and the stability of the wellbore. The conventional method for detecting fractures is image logging, which captures images of the borehole and fractures. However, the interpretation of these images is a laborious and subjective process that can lead to errors, inaccuracies, and inconsistencies, even when aided by software. Automating this process is essential for expediting operations, minimizing errors, and increasing efficiency. Although there have been some attempts to automate fracture detection, this paper takes a novel approach by proposing the use of YOLOv5 as a deep-learning (DL) tool to detect fractures automatically. YOLOv5 is unique in that it excels at speed, training, and detection while maintaining high accuracy in fracture detection. We observe that YOLOv5 can detect fractures in near real time with a high mean average precision of 98.2, requiring significantly less training than other DL algorithms. Furthermore, our approach overcomes the shortcomings of other fracture detection methods. Our method has many potential benefits, including reducing manual interpretation errors, decreasing the time required for fracture detection, and improving fracture detection accuracy. Our approach can be used in various reservoir engineering applications, such as hydraulic fracturing design, wellbore stability analysis, and reservoir simulation. By using this technique, the efficiency and accuracy of hydrocarbon exploration and production can be significantly improved.