Frontiers in Big Data (Dec 2023)

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

  • Brendan Hoover,
  • Brendan Hoover,
  • Brendan Hoover,
  • Dakota Zaengle,
  • Dakota Zaengle,
  • MacKenzie Mark-Moser,
  • MacKenzie Mark-Moser,
  • Patrick Wingo,
  • Patrick Wingo,
  • Anuj Suhag,
  • Kelly Rose

DOI
https://doi.org/10.3389/fdata.2023.1227189
Journal volume & issue
Vol. 6

Abstract

Read online

Subsurface interpretations and models rely on knowledge from subject matter experts who utilize unstructured information from images, maps, cross sections, and other products to provide context to measured data (e. g., cores, well logs, seismic surveys). To enhance such knowledge discovery, we advanced the National Energy Technology Laboratory's (NETL) Subsurface Trend Analysis (STA) workflow with an artificial intelligence (AI) deep learning approach for image embedding. NETL's STA method offers a validated science-based approach of combining geologic systems knowledge, statistical modeling, and datasets to improve predictions of subsurface properties. The STA image embedding tool quickly extracts images from unstructured knowledge products like publications, maps, websites, and presentations; categorically labels the images; and creates a repository for geologic domain postulation. Via a case study on geographic and subsurface literature of the Gulf of Mexico (GOM), results show the STA image embedding tool extracts images and correctly labels them with ~90 to ~95% accuracy.

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