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October 12, 2025ESAIM Proceedings and SurveysOpen Access

Predicting ultimate hydrogen production and residual volume during cyclic underground hydrogen storage in porous media using machine learning

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Authors

RMRaymond MushabeJMJean Donald MinougouDLDavid Landa-Marbán

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Overview

This analysis utilizes machine learning to improve predictions of hydrogen production and residual volume in underground hydrogen storage, highlighting significant efficiency gains.

Key Points

  • ML models effectively predict hydrogen production and residual storage with high accuracy, reducing computational time significantly.
  • Results showed that ML techniques achieved a 6773% reduction in computation time compared to traditional reservoir simulations.
  • Neural network models were fine-tuned through hyperparameter optimization and cross-validation for improved performance.
  • This research demonstrates ML's potential to optimize storage operations in underground hydrogen storage systems.

Cite This Study

Mushabe et al. (2025) studied this question.

synapsesocial.com/papers/68ebabe3155248a327effb71https://doi.org/10.1051/proc/202581145
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