Automated Workflow for Detecting Fractures and Bedding Planes
Event Year
2024
BibTeX / Citation
@inproceedings{Nasim_2024, title={Automated Workflow for Detecting Fractures and Bedding Planes}, url={http://dx.doi.org/10.3997/2214-4609.202410999}, DOI={10.3997/2214-4609.202410999}, booktitle={85th EAGE Annual Conference & Exhibition}, publisher={European Association of Geoscientists & Engineers}, author={Nasim, M.Q. and Maiti, T. and Mosavat, N. and Grech, P. V. and Ali, J. and Singh, T. and Roy, P. Nath Singha}, year={2024}, pages={1–5} }
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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.
Leveraging Transformers for End-To-End Bed and Fractures Identification, Classification, Localization and Parameter Estimation using Image Logs for Subsurface Geological Analysis
Accurate identification, classification, localization, and parameter estimation of subsurface geological features, such as beddings and fractures, are critical for understanding reservoir characteristics. Traditionally, such analyses rely on manual interpretation of borehole image logs, which is labor-intensive and time-consuming. Recent advancements have introduced semi-automated, mask-based segmentation approaches with the intention of assisting manual picking, but these methodologies require manual mask creation and post-processing, limiting their effectiveness in achieving a truly automated workflow. To address these limitations, we propose an end-to-end methodology using a transformer-based model, GeoBFDT, to automatically identify, classify, localize and estimate the parameters of geological features in borehole image logs. The GeoBFDT model eliminates the need for manual mask creation and post-processing by directly identifying whether a feature exists, classifying it as a bedding or fracture, localizing the feature, and estimating its parameters (e.g., dip, azimuth). This streamlined workflow enables efficient and accurate subsurface feature analysis. Several custom evaluation metrics were developed to rigorously evaluate the model's performance. The adaptability of GeoBFDT was validated using data from multiple well orientations (vertical and horizontal) and various imaging tools (FMI, CMI, and ThruBit). While end-to- end methodologies like GeoBFDT are inherently challenging to optimize due to their complexity, the benefits of eliminating manual intervention and enabling fully automated workflows make them highly promising. This study underscores the potential of such approaches for advancing geological feature analysis, particularly in industry, where data is abundant. We envision that the proposed methodology could pave the way for enhanced subsurface feature detection in a truly end-to-end manner, surpassing semi-automated approaches that still rely on manual interpretation.