CMG GEM Simulation Datasets for Underground Hydrogen Storage

The dataset consists of 3,300 paired inputs and outputs, designed to serve as a benchmark subsurface multiphase flow problem for scientific machine‑learning models training in the context of underground hydrogen storage. Each input includes both field features and scalar parameters, and each output...

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Detalles Bibliográficos

Publicado en:	Texas Digital Library (University of Texas)
Autor Principal:	Tao Wang
Formato:	Dataset
Publicado:	The University of Texas at Austin 2026
Subjects:	Reservoir Engineering and Simulation Methods CO2 Sequestration and Geologic Interactions Model Reduction and Neural Networks underground hydrogen storage multiphase flow gas saturation pressure buildup machine learning benchmark permeability porosity relative permeability injection rate reservoir simulation
Acceso en liña:	https://doi.org/10.18738/t8/svsxdd
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