
Hydrostatic Cyclic Loading of Rock: Experimental Insights for Constitutive Modeling of Underground Hydrogen Storage
Aram Yakoby, Yossef H. Hatzor, Shmulik Pinkert
(1) Department of Structural Engineering, Ben Gurion University of the Negev, Beer-Sheva 84105
(2) Department of Earth and Environmental Sciences, Ben Gurion University of the Negev, Beer Sheva 84105
(3) Department of Structural Engineering, Ben Gurion University of the Negev, Beer-Sheva 84105
(5) Please notice for affiliation 1 and 3, the Department has changed its name from "Department of structural engineering" to "Department of civil and environmental engineering"
Underground hydrogen storage (UHS), also known as Hydrogen Geological Storage, is a promising solution for large-scale green energy storage, in order to balance supply and demand. The idea is to store the Hydrogen in suitable geological formations, such as depleted gas reservoirs, aquifers, and salt caverns, which undergo cyclic loading due to gas injection and extraction. This process induces pore pressure variations, leading to hydrostatic stress fluctuations that affect the mechanical response and long-term integrity of the reservoir rock.
The presentation focuses on the combined experimental and modeling efforts undertaken to develop and calibrate a constitutive model for the mechanical response of the host rock. To this aim, Berea sandstone has been chosen as a host rock for this study. Cyclic hydrostatic test results indicate that the rock mechanical behavior under moderate hydrostatic loading is typically non-linear, elasto-plastic and time dependent. To enable efficient model calibration, these phenomena must be examined separately, prompting the design of a sequential loading experiment. A mechanical analog model approach was chosen for its ability to capture all these behaviors. Analytical analysis is used to derive characteristic parameters that facilitate experimental calibration, followed by numerical analysis to predict the rock response. The proposed model demonstrates strong predictive capability for rock mechanical response, and this framework can be further extended for advanced research and future engineering applications.