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

Underground hydrogen storage (UHS) is a promising solution for balancing renewable energy supply by storing excess energy as molecular hydrogen (H2) for later use. Large-scale UHS in geological formations requires reservoir simulations of cyclic loading scenarios to optimize storage operations. Such...

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Bibliographic Details

Published in:	ESAIM Proceedings and Surveys
Main Author:	Raymond Mushabe
Other Authors:	Jean Donald Minougou, David Landa-Marbán, Birane Kane, Tor Harald Sandve
Format:	Article
Language:	English
Published:	EDP Sciences 2025
Subjects:	Reservoir Engineering and Simulation Methods CO2 Sequestration and Geologic Interactions Model Reduction and Neural Networks underground hydrogen storage cyclic loading porous media machine learning surrogate models time series neural network reservoir simulation hysteresis capillary pressure hydrogen dissolution computational efficiency
Online Access:	https://doi.org/10.1051/proc/202581145
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