Salt cavern simulations involve many numerical challenges that need to be addressed in order to ensure accurate and meaningful results. Firstly, lithological structures and solution-mined salt caverns always present fairly complex shapes, which favors the use of tetrahedral meshes with local refinements for adequate domain discretization. Secondly, salt rocks are known to creep under deviatoric stresses, meaning that deformations take place at constant volume (isochoric). The combination of isochoric deformations with tetrahedral meshes is particularly problematic for low-order finite element formulations. This work presents a stabilized mixed finite element (FE) formulation for linear tetrahedrons, where the mean stress is a primary variable, incorporating all the relevant deformation mechanisms for salt rocks. The stabilization consists of enriching the displacement FE approximation in the mean stress equation by obtaining an approximation for the Laplacian of the displacement that accounts for inelastic strains. This is achieved by using the Physical Influence Scheme (PIS) with the concept of secant Young’s modulus, which promotes local stabilizations where necessary. When combined with a proper calculation of a geometric parameter h[jls-end-space/], this stabilization technique is shown to produce oscillation-free and physically consistent results without any sort of tuning parameter. The proposed technique is analyzed in relevant test cases for salt cavern simulations and the results show the effectiveness of the proposed stabilization to eliminate spurious numerical oscillations with low-order tetrahedral meshes.

This work addresses numerical instabilities that can appear when computing the mean stress in linear elasticity and coupled poroelasticity problems discretized with low-order finite elements. The linear elasticity and coupled poroelasticity models are solved using both primal and mixed finite element formulations. Stabilization is obtained by enriching the finite element approximation with an approximation of the Laplacian of displacements. This Laplacian is then evaluated with the Physical Influence Scheme (PIS) by leveraging the underlying governing equation. A key step in the proposed stabilization is the calculation of a parameter h, often computed in the literature as a characteristic length of the element. In this work, we calculate h by solving an optimization problem at the element level. To avoid the high computational cost associated with this procedure, a machine learning model is proposed to predict the optimal h. The benefit of combining PIS with an appropriate computation of h is that the resulting stabilization scheme does not rely on any type of heuristic or user-specified tuning parameter, as often required in other stabilization methods. The results show that the proposed stabilization strategy can effectively remove both saddle-point and Gibbs mean stress oscillations in linear elasticity. We also report, for the first time, that mean stress oscillations can also appear when solving coupled poroelasticity problems, and, differently from pore pressure oscillations (which naturally vanish with time), mean stress instabilities are persistent throughout the whole simulation time, unless deliberately removed. The proposed stabilized mixed formulation is able to remove both pore pressure and mean stress oscillations in coupled poroelasticity problems. Finally, the calculation of h is shown to be critical for the quality of the stabilization, with the machine learning-based approach providing the best compromise between numerical diffusion and accuracy.

The storage of renewable hydrogen in salt caverns requires fast injection and production rates to cope with the imbalance between energy production and consumption. This raises concerns about the mechanical stability of salt caverns under such operational conditions. The use of appropriate constitutive models for salt mechanics is an important step in investigating this issue, therefore many constitutive models with several parameters have been presented in the literature. However, a robust calibration strategy to reliably determine which model and parameter set represents the given rock, based on stress–strain data sets, remains an unsolved challenge. In this paper, for the first time in the community, we present a multi-step strategy to determine a single parameter set based on many deformation data sets for salt rocks. Towards this end, we first develop a comprehensive constitutive model able to capture all relevant nonlinear deformation physics of transient, reverse, and steady-state creep. The determination of the single set of representative material parameters is then achieved by framing the calibration process as an optimization problem, for which the global Particle Swarm Optimization algorithm is employed. To allow for dynamic data integration, a multi-step calibration strategy is developed for a situation where experiments are included one at a time, as they become available. Additionally, due to the existing mild heterogeneity in the experimental rock samples, our optimization strategy is made flexible to allow for this slight heterogeneity. The devised optimization strategy, based on the multi-physics comprehensive constitutive modeling framework, results in a single set of representative material properties of all the deformation data sets. As a rigorous mathematical analysis for the presented method and the lack of relevant experimental data sets, we consider a wide range of synthetic experimental data sets, inspired by the existing sparse relevant data in the literature. The results of our performance analyses show that the proposed calibration strategy is robust. Moreover, the results become increasingly more accurate as more data sets become available.

Large-scale storage technologies are crucial to balance consumption and intermittent production of renewable energy systems. One of these technologies can be developed by converting the excess energy into compressed air or hydrogen, i.e., compressed gas, and storing it in underground solution-mined salt caverns. Salt caverns are proven seals towards compressed air and hydrogen. However, several challenges, including fast injection/production cycles and operation of systems of caverns, are yet to be resolved to allow for a safe scale-up of energy storage in salt caverns. To address these challenges, it is important to identify key parameters that impact both the safety and efficiency of the operations. For this purpose, the present study conducts sensitivity analyses to show the importance of different parameters on the time-dependent mechanical behavior of salt caverns, individually and in a multi-cavern system. The impact of different deformation mechanisms (e.g. transient and reverse creep), model calibration, cavern shape, presence of interlayers and multi-cavern interactions are investigated in this study. The constitutive model adopted in this work and the mathematical formulation are presented in detail. Additionally, an open-source three-dimensional simulator, named “SafeInCave”, is developed for the numerical solution of the non-linear governing equations. The findings provide insights into improving the reliability of numerical simulations for the safe and efficient operation of salt caverns in energy storage applications.

Successful transition to renewable energy supply depends on the development of cost-effective large-scale energy storage technologies. Renewable energy can be converted to (or produced directly in the form of) green gases, such as hydrogen. Subsurface formations offer feasible solutions to store large-scale compressed hydrogen. These reservoirs act as seasonal storage or buffer to guarantee a reliable supply of green energy in the network. The vital ingredients that need to be considered for safe and efficient underground hydrogen storage include reliable estimations of the in-situ state of the stress, especially to avoid failure, induced seismicity and surface subsidence (or uplift). Geological formations are often highly heterogeneous over their large (km) length scales, and entail complex nonlinear rock deformation physics, especially under cyclic loading. We develop a multiscale simulation strategy to address these challenges and allow for efficient, yet accurate, simulation of nonlinear elastoplastic deformation of rocks under cyclic loading. A coarse-scale system is constructed for the given fine-scale detailed nonlinear deformation model. The multiscale method is developed algebraically to allow for convenient uncertainty quantifications and sensitivity analyses.

Hydrogen is a promising energy carrier for a low-carbon future energy system, as it can be stored on a megaton scale (equivalent to TWh of energy) in subsurface reservoirs. However, safe and efficient underground hydrogen storage requires a thorough understanding of the geomechanics of the host rock under fluid pressure fluctuations. In this context, we summarize the current state of knowledge regarding geomechanics relevant to carbon dioxide and natural gas storage in salt caverns and depleted reservoirs. We further elaborate on how this knowledge can be applied to underground hydrogen storage. The primary focus lies on the mechanical response of rocks under cyclic hydrogen injection and production, fault reactivation, the impact of hydrogen on rock properties, and other associated risks and challenges. In addition, we discuss wellbore integrity from the perspective of underground hydrogen storage. The paper provides insights into the history of energy storage, laboratory scale experiments, and analytical and simulation studies at the field scale. We also emphasize the current knowledge gaps and the necessity to enhance our understanding of the geomechanical aspects of hydrogen storage. This involves developing predictive models coupled with laboratory scale and field-scale testing, along with benchmarking methodologies.

Subsurface geological formations can be utilized to safely store large-scale (TWh) renewable energy in the form of green gases such as hydrogen. Successful implementation of this technology involves estimating feasible storage sites, including rigorous mechanical safety analyses. Geological formations are often highly heterogeneous and entail complex nonlinear inelastic rock deformation physics when utilized for cyclic energy storage. In this work, we present a novel scalable computational framework to analyse the impact of nonlinear deformation of porous reservoirs under cyclic loading. The proposed methodology includes three different time-dependent nonlinear constitutive models to appropriately describe the behavior of sandstone, shale rock and salt rock. These constitutive models are studied and benchmarked against both numerical and experimental results in the literature. An implicit time-integration scheme is developed to preserve the stability of the simulation. In order to ensure its scalability, the numerical strategy adopts a multiscale finite element formulation, in which coarse scale systems with locally-computed basis functions are constructed and solved. Further, the effect of heterogeneity on the results and estimation of deformation is analyzed. Lastly, the Bergermeer test case—an active Dutch natural gas storage field—is studied to investigate the influence of inelastic deformation on the uplift caused by cyclic injection and production of gas. The present study shows acceptable subsidence predictions in this field-scale test, once the properties of the finite element representative elementary volumes are tuned with the experimental data.