Automated Borehole Image Interpretation Using Computer Vision and Deep Learning

Summary Reservoir characterization is critical for successful oil and gas exploration, heavily reliant on detailed formation analysis from formation microimager (FMI) logs. However, interpreting these complex logs is time-consuming, subjective, and requires expert-level knowledge. This study addresses this challenge by proposing a novel approach that integrates computer vision (CV) and deep learning (DL) for automated and real-time interpretation of FMI logs. Our methodology leverages CV and DL techniques for automated feature extraction and classification from FMI data. A comprehensive training data set encompassing more than 2,500 images is utilized to train the DL model, enabling the identification of more than 50 distinct geological features, including bedding planes, fractures, and mineral variations. In addition, the study explores the creation of local stress maps from drilling-induced fractures to determine the present-day maximum horizontal stress (SHmax) orientation, crucial for optimizing wellbore stability and hydraulic fracturing strategies. This research presents a groundbreaking advancement in reservoir characterization through the synergy of automated FMI interpretation, CV, and DL. The developed model exhibits exceptional accuracy in geological feature classification, significantly surpassing traditional, human-centric interpretations. For instance, the model achieves a remarkable 92% accuracy in classifying ore than 50 geological features, demonstrably outperforming conventional methods. Furthermore, the developed model was applied to actual field cases to predict the stress field. The model was able to accurately predict the minimum horizontal stress (Shmin) and SHmax based on FMI logs, and the results were used to refine the geomechanical modeling and optimize hydraulic fracture orientation for enhanced hydrocarbon recovery. This work establishes a new benchmark for applying artificial intelligence in subsurface analysis, paving the way for future advancements in reservoir management and geomechanics. The implications are far-reaching, offering greater precision in geological interpretations, improved decision-making for production strategies, and ultimately, a more sustainable approach to hydrocarbon extraction. By automating tedious and subjective tasks, this approach not only reduces reliance on experts but also frees up valuable time for more strategic tasks. The ability to extract critical geological information with such accuracy from complex FMI logs translates to significant improvements in reservoir characterization and production efficiency.