Geomechanical Study of Underground Hydrogen Storage

Abstract

With the rise of renewable energy and the drive to achieve net-zero emissions, energy storage has become a crucial component of the energy sector to address the challenges of intermittency. The vast subsurface environment offers significant storage potential, capable of accommodating terawatt-hour (TWh) capacities. One approach to leverage this storage capacity involves converting renewable energy into hydrogen and storing it underground within salt caverns and depleted porous reservoirs. This stored hydrogen can then be utilized as needed. However, this cyclic injection and production of hydrogen will exert repeated stress on the subsurface, resulting in periodic changes in pressure.

One critical aspect that requires investigation for the safe storage of hydrogen (H2) is the field of geomechanics, which becomes essential in both salt caverns and depleted reservoirs. To gain a better understanding of this, a comprehensive review of the geomechanics involved in underground hydrogen storage was conducted to examine existing knowledge and identify research gaps. To delve deeper into the influence of geomechanics, particularly regarding the inelastic creep deformation of rocks in salt caverns and depleted porous reservoirs, numerical simulations were employed. Given the potential costliness of fine-scale simulations, multiscale simulations were carried out using algebraic multiscale methods. Constitutive models were utilized to analyze deformation patterns in and around the reservoir, assessing their impact on subsidence or uplift.

In order to further comprehend the effects of cyclic loading on rocks, constitutive models were developed based on extensive experimental data obtained from sandstone rocks subjected to long-term stress conditions. These models aided in uncovering the underlying physics of rock behavior when exposed to different stress regimes during prolonged cyclic loading. Subsequently, these models were integrated into finite element method (FEM) simulations to observe their impact on field-scale scenarios, with a synthetic Bergermeer case study serving as an example.

To enhance the computational efficiency of multiscale methods, unsupervised machine learning techniques were applied to optimize the formation of computational grids, utilizing graph theory techniques such as Louvain and random walk algorithms. These optimized grids were then compared with the grids generated from METIS to evaluate the computational performance of pressure solvers in a commercial scale simulator.