Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions

  • NETL advanced the subsurface trend analysis (STA) workflow with an AI-informed image segmentation/embedding model. 
  • The STA method was created to be a foundational technology, capable of assisting any subsurface predictive need.
  • The image embedding tool uses convolutional neural networks (CNNs) to:
    • Extract images from unstructured data
    • Categorically label the images
    • Create a repository for geologic domain postulation
  • A case study on data available for the Gulf of Mexico shows the STA image embedding tool extracts and accurately labels images with 90% to 95% precision.
  • The STA 2D Tool is available on NETL’s Energy Data eXchange® (EDX).