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  • October Technical Meeting: Dr. Heather Bedle

October Technical Meeting: Dr. Heather Bedle

  • 20 Oct 2025
  • 11:30 AM - 1:00 PM
  • Devon Energy Center 333 West Sheridan Ave. Oklahoma City, OK 73102 Visualization Room
  • 28

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Dr. Heather Bedle, OU Geophysics Professor and Inaugural Director of the Sustainable Energy Systems Program

AAPG Distinguished Lecture: Characterizing Subsurface Systems for Energy and Sustainability Applications

Accurate subsurface characterization remains a fundamental challenge across the energy industry, where traditional interpretation methods often fail to capture critical reservoir details needed for both conventional and emerging energy solutions. This presentation showcases how advanced seismic attributes combined with machine learning techniques can significantly enhance reservoir characterization while reducing subsurface uncertainty and improving project risk assessment. Based on research conducted with the Attribute Assisted Seismic Processing and Interpretation (AASPI) consortium at the University of Oklahoma, I present integrated workflows that demonstrably improve seismic facies classification and fault detection accuracy. Using case studies in a variety of geologic settings, I illustrate how these methods provide valuable insights applicable not only to conventional hydrocarbon systems but also to sustainable energy initiatives including carbon storage and geothermal development. Attendees will gain practical understanding of strategic attribute selection and effective machine learning approaches that reveal subtle subsurface features essential for assessing reservoir quality and mitigating subsurface risk. These techniques empower geoscientists to contribute more effectively to reservoir characterization across the spectrum of energy applications, bridging traditional expertise with emerging sustainability goals.

Key Points:

  • Seismic attributes that provide powerful tools for lithofacies and fluid prediction as well as fault identification for reservoir risk assessment.
  • Using case studies, show how machine learning techniques can be combined with seismic attribute analysis to enhance accuracy and efficiency in subsurface characterization workflows.
  • Case studies demonstrating successful applications across diverse energy sectors including hydrocarbon exploration, geothermal development, and carbon storage projects, from onshore to offshore.
  • Examples from both sandstone and carbonate reservoir systems that illustrate the versatility of attribute-based characterization methods across different geological settings. 

Talk 1: Characterizing subsurface systems for energy and sustainability applications



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