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June 13, 2026DaedalusOpen Access

Scaling Physics Intelligence for the Earth's Subsurface

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Authors

GWGege Wen

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Overview

Randomized trial evaluates AI-powered subsurface predictions in energy systems, implying a faster energy transition.

Key Points

  • The aim is to integrate AI with subsurface physics to improve energy systems supporting AI development.
  • Developed predictive models to simulate gases and fluids in porous media.
  • Created an accessible AI web application for carbon dioxide storage predictions, ccsnet.ai.
  • Utilized synthetic simulations and real-world datasets to enhance modeling accuracy.
  • The application enabled rapid predictions, taking seconds instead of days for reservoir simulations.
  • Democratized access for approximately two hundred users globally, excluding Antarctica.
  • Forecasted optimal locations for carbon storage and geothermal wells significantly accelerating project design.

Cite This Study

Gege Wen (2026) studied this question.

synapsesocial.com/papers/6a2cf3b4faef96ed7f0561bbhttps://doi.org/10.1162/daed.a.995
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  5. 5Accelerating CCUs Storage Site Screening Using AI/ML: A Data-Driven Approach to Early-Stage Subsurface Evaluation2025