Automated end-to-end fracture identification, classification, localization, and parameter estimation for enabling rapid risk management and CO₂ storage optimization in CCUS applications

The success of Carbon Capture, Utilization, and Storage (CCUS) projects heavily depends on understanding subsurface fluid flow behaviour particularly through fracture networks. Fractures play a dual role in such operations: they can enhance reservoir injectivity and storage capacity by providing pathways for CO₂ injection, but they also pose risks by potentially compromising caprock integrity, increasing the risk of structural storage failure thereby enabling CO₂ leakage. Accurate fracture detection and characterization is essential for optimizing injection strategies, ensuring effective containment, and mitigating environmental risks. Fractures influence critical processes such as trapping mechanisms and pressure distribution within the reservoir. Furthermore, understanding their orientation and density is vital for designing safe and efficient CO₂ injection operations. These factors highlight the importance of robust, non-bias, automated, and scalable fracture detection methods. Traditional fracture identification methods rely heavily on manual interpretation, which is time-intensive, subjective, and challenging to scale for large fields with several wells. This study proposes a scalable automated methodology employing advanced deep-learning techniques to detect fractures from borehole imaging tools such as FMI, CMI, and ThruBit logs. The proposed approach uses detection transformers which eliminates the need for manual mask creation and post-processing steps by adopting an end-to-end framework, which not only identifies the presence of fractures but also estimates their orientation and density. Custom evaluation metrics were developed to measure the model's performance (in comparison with expert’s fracture analysis) in handling diverse geological and well conditions, including vertical and horizontal well orientations. The automated workflow facilitates speedy assessment of fracture networks which in turn can offer speedy actionable insights for CO₂ injection optimization, caprock stability assessment, and risk management. The model demonstrated an interpretation speed of less than one minute per 2 meters, with an ~80% F1 score (6 cm depth error margin), ~91% accuracy in dip picking (3° error margin), and ~93% accuracy in dip estimation (15° dip margin). By utilizing the proposed automated fracture detection model based on transformers, CCUS project planning and designing can be accelerated. Furthermore, integrating MLOps into the workflow ensures the scalability, maintainability, and adaptability of these models for practical deployment. While this methodology is tailored to CCUS, its versatility extends to a much wider range of applications, including geothermal energy, mining, and other subsurface characterization domains.