Impact of Wettability and Relative Permeability Hysteresis in Saline Aquifers; Implication of Hydrogen Underground Storage

This paper addresses the challenge of optimizing subsurface hydrogen storage in porous media, a crucial component for advancing energy transition. The multifaceted nature of this challenge stems from the complex physics governing the process, coupled with operational limitations, and subsurface geological uncertainties. We apply a stochastic gradient-based optimization method with novel deep-learning acceleration components, tailored to maximize the efficiency of hydrogen storage by tuning well locations while honoring operational constraints. The key objective of optimization is to maximize the amount of recoverable hydrogen while maintaining operational constraints. We adopt a robust optimization approach that maximizes the mean objective function over a set of realizations representing subsurface uncertainty. The objective function, defined as the hydrogen deliverability index, is calculated using a compositional reservoir simulator with high-resolution grids to minimize numerical dispersion. Our approach leverages a deep-learning-accelerated-gradient (DLAG) method alongside these simulations. This method is applied to the Brugge field case study, which is divided into two distinct optimization scenarios. In the first case, we evaluate the effectiveness of the optimization method with only one subsurface realization, optimizing the placement of eight storage wells and comparing outcomes with and without the application of DLAG. In the second case, we extend the analysis to include five different subsurface realizations and impose specific location constraints on each of the storage wells to optimize their placement. In the first case, the application of the DLAG method showed a clear advantage over the non-DLAG approach, resulting in faster convergence. The optimization of hydrogen storage well locations in the Brugge field model yielded notable improvements in storage efficiency, demonstrating the practicality and effectiveness of our approach.

show abstract

Enhancing Hydrogen Recovery from Saline Aquifers: Quantifying Wettability and Hysteresis Influence and Minimizing Losses with a Cushion Gas

Al Homoud,

Barbosa Machado,

Daigle

et al. 2024

Hydrogen

Self Cite

View full text Add to dashboard Cite

This study aims to numerically assess the impact of wettability and relative permeability hysteresis on hydrogen losses during underground hydrogen storage (UHS) and explore strategies to minimize them by using an appropriate cushion gas. The research utilizes the Carlson model to calculate the saturation of trapped gas and the Killough model to account for water hysteresis. By incorporating the Land coefficient based on laboratory-measured data for a hydrogen/brine system, our findings demonstrate a significant influence of gas hysteresis on the hydrogen recovery factor when H2 is used as a cushion gas. The base model, which neglects the hysteresis effect, indicates a recovery factor of 78% by the fourth cycle, which can be improved. In contrast, the modified model, which considers hysteresis and results in a trapped gas saturation of approximately 17%, shows a hydrogen recovery factor of 45% by the fourth cycle. Additionally, gas hysteresis has a notable impact on water production, with an observed 12.5% increase in volume in the model that incorporates gas hysteresis. Furthermore, optimization of the recovery process was conducted by evaluating different cushion gases such as CO2, N2, and CH4, with the latter proving to be the optimal choice. These findings enhance the accuracy of estimating the H2 recovery factor, which is crucial for assessing the feasibility of storage projects.

show abstract

Impact of Wettability and Relative Permeability Hysteresis in Saline Aquifers; Implication of Hydrogen Underground Storage

Cited by 3 publications

References 39 publications

Evaluating capillary trapping in underground hydrogen storage: A pore-scale to reservoir-scale analysis

Evaluating capillary trapping in underground hydrogen storage: A pore-scale to reservoir-scale analysis

Optimizing Hydrogen Storage in the Subsurface Using a Reservoir-Simulation-Based and Deep-Learning-Accelerated Optimization Method

Enhancing Hydrogen Recovery from Saline Aquifers: Quantifying Wettability and Hysteresis Influence and Minimizing Losses with a Cushion Gas

Contact Info

Product

Resources

About