Automated Detection of Geological Features: Leveraging Deep Learning for Beddings and Fractures Identification in Image Logs

Fractures and bedding planes within geological formations influence the exploration and development of fields in the petroleum industry, playing a vital role in reservoir characterization. However, manually identifying these features from borehole image logs (BHIs) is labo- rious and time-consuming. This study addresses this challenge by proposing an automated system to streamline the fracture and bedding plane picking and identification process. The model introduced in this study aims to expedite result delivery while significantly reducing the manual workload by automating the detection of fractures and bedding planes. Trained on a data set of BHIs, including formation micro-imager (FMI) and compact micro-imager (CMI) from 14 wells in Oman, the method presented proposes a transformer-based mod- el, named Geological Beddings and Fractures Detection Transformer (GeoBFDT) for the identification, localization, and estimation of geological structural feature parameters. GeoBFDT uses a bipartite matching loss to match the predicted geological feature types and their depth, dip, and azimuth values with the ground truth in an end-to-end manner. Visual analyses, performance metrics, sensitivity tests, and comparative plots highlight the model's capability to identify subsurface features. Validation using various depth thresholds on the test set for fractures and beddings reveals F1-scores of ~65% (fractures) and ~63% (beddings) at a 3-cm depth threshold and ~75% (fractures) and ~69% (beddings) at a 5-cm threshold. Dip accuracy is ~79% (fractures) and ~65% (beddings) at a 1.5° dip threshold and ~90% for both fractures and beddings at a 3° dip threshold. Similarly, azimuth accuracy is at ~83% (fractures) and ~75% ( beddings) at a 7° azi- muth threshold and ~92% (fractures) and ~84% (beddings) for an azimuth threshold of 15°. The ablation study indicates that a smaller backbone yields better performance given the relatively small data set and that increasing the number of wells further enhances model performance. In addition, the dynamic logs of BHIs outperform static logs. The model's adaptability to different well inclinations was also assessed by evaluating its performance on fractures from five horizontal wells, achieving a ~55% score at a 4-cm threshold for fracture detection and localization tasks. Notably, the proposed approach surpasses the objectives of existing deep learning (DL)-based methods by accommodating multiple features in a single image, identifying beddings and fractures in complex geological areas, eliminating mask dependency, and providing an end-to-end strategy. Unlike models such as “you only look once” or mask region-based convolution neural network (R-CNN), which rely on mask generation, the proposed method focuses on different objectives, offering an alternative approach to feature picking. Although trained on limited data and validated through validation and testing sets, the GeoBFDT model offers an alter- native pathway toward an end-to-end automated geological feature picking tool from BHIs. We hope that in the future, this methodology can be further explored to automate the entire feature-picking workflow in an end-to-end fashion.