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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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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Predicting ultimate hydrogen production and residual volume during cyclic underground hydrogen storage in porous media using machine learning2025
  2. 2Data-driven optimization of underground hydrogen storage: A multi-metric machine learning framework for operational planning and control2026
  3. 3Machine learning models for the prediction of hydrogen solubility in aqueous systems2025 · 10 citations
  4. 4Analysis of the influence mechanisms of key operational parameters on underground hydrogen storage2026
  5. 5Application of machine learning in modelling gas dispersion coefficients for hydrogen storage in porous media2025