4.6 Article

Safe pressure for hydrogen storage in subsurface

Journal

ENERGY REPORTS
Volume 8, Issue -, Pages 15702-15711

Publisher

ELSEVIER
DOI: 10.1016/j.egyr.2022.11.141

Keywords

Underground hydrogen storage; Structural trapping; Artificial Neural Network (ANN); Monte Carlo simulation

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This study determines the safe pressure for hydrogen storage in a gas reservoir and quantifies its uncertainty using Monte Carlo simulation. The study also explores the interfacial tensions of hydrogen systems using an Artificial Neural Network approach. The proposed relation is applied to a field study and presents heat maps of interfacial tensions.
Hydrogen exhibits interesting behavior that has intrigued many researchers in the last few years. It also provides a promising option for reducing carbon dioxide emissions but requires large storage as an energy carrier. In this study, we determine the safe pressure for hydrogen storage in a gas reservoir, so the fugitive and odorless molecules do not leak from the structural trap. For this reason, we propose a relation for estimating the safe pressure and quantify its uncertainty using Monte Carlo simulation. The uncertainty quantification is crucial because of the limited information available in this evolving field. We also adopt an Artificial Neural Network (ANN) approach to present the interfacial tensions of hydrogen systems for various pressures and temperatures. This study applies the proposed relation to the Ann Mag field near Corpus Christi in Texas and discusses complexities that arise in practice. It shows that the structural trap can sustain hydrogen pressure up to 8,438 psi at 10,239 ft and 10,515 psi at 12,020 ft below the surface. Higher pressures may lead to leakage because of fault slippage or fracture propagation. This study presents the heat maps of the interfacial tensions that are convenient tools for analyzing hydrogen transport. The proposed relation also has applications in the safe storage of hydrogen in geological formations. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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