Automated Workflow for Detecting Fractures and Bedding Planes
M Quamer Nasim, Tannistha Maiti, Nader Mosavat, P. V. Grech, J. Ali, T. Singh, Pronoy Roy · 2024
Summary In geoscience, manually picking fractures and bedding from Formation Micro-Imager (FMI) logs is labour-intensive, requiring substantial time and effort. This study addresses the critical need for an automated solution to streamline this picking and identification process, emphasizing the significance of expediting reservoir characterization and decision-making in the petroleum industry. The model presented here aims to provide a quick turnaround of results delivery while significantly reducing the manual workload by automating the identification of fractures and beddings. The study leverages a comprehensive dataset comprising FMI logs from 14 vertical wells in Oman. The methodology adopts an advanced Fracture Detection Model based on the Detection Transformer architecture, customized to the unique requirements of the study. Precision, Recall, and F1-Score are key evaluation metrics computed through a tailored confusion matrix and depth thresholding for nuanced predictions. Our results are validated through different depth thresholds for fractures and beds. Sensitivity tests reveals that 8cm threshold generates a recall ∼85% in comparison to 4cm threshold of ∼75%. Visual analyses, performance metrics, and comparative plots underscore the model’s proficiency in accurately identifying subsurface features. This Fracture Detection Model, validated through testing on several wells, stands out as an efficient and accurate automated tool for reservoir characterization.