Description du sujet de thèse

Storage of hydrogen in aquifers or geological reservoirs is receiving increasing attention. Hydrogen storage in sedimentary reservoirs has not yet been performed at an industrial scale. Despite the vast experience and know-how regarding the storage of compressed air, natural gas, and town-gas in porous formations, storing hydrogen poses additional technical challenges. The migration processes and the fat of hydrogen in the reservoirs is still poorly understood. Numerical simulations through physics-based models are irreplaceable tool to address this issue. However, modeling migration of hydrogen in porous domains is a complex problem because it involves multi-physical processes acting at different temporal scales. Migration of hydrogen in porous domains involves two-phase flow, precipitation and dissolution and thermodynamics processes. These processes may not be very different from those of natural gas. However, the thermodynamic properties of hydrogen and its biochemical affinity to interact with the host rock formation and fluids raise additional technical challenges and operational costs. For instance, the mass density of hydrogen is approximately one order of magnitude lower than that of methane under similar temperature and pressure conditions. As a result, the storage capacity of hydrogen by mass in an underground container will be ten times lower than that of methane under similar conditions. Moreover, the low viscosity of hydrogen and its potential reactivity with the mineral formations, pore fluids, biosystem, and facility material require mitigation strategies that are not required for the UGS of natural gas. Hydrogen gas exhibits specific biochemical characteristics that may impact the storage mechanisms in terms of relevance and significance (Hoteit and Afifi, 2024). The migration of hydrogen is also impacted by thermal processes.
Numerical modeling of these coupled and nonlinear bio-chemo-hydro-thermal processes is computationally challenging (Wendling et al. 2019). The challenges are related to convergence, accuracy, robustness, and efficiency of the numerical schemes that could be used for solving the governing equations. Traditional methods such as finite element or finite difference introduce numerical artifacts that cannot be avoided without using unusual computational meshes. Furthermore, as dissolution, biochemical, thermal and flow processes occur at different time scales, time integration is a critical issue for performing efficient simulation while maintaining high accuracy. The Klinkenberg slippage effect, the dissolution processes, and the Langmuir adsorption of the hydrogen in pore surfaces, render the equations more nonlinear than the common cases of two-phase flow. The compressibility of the hydrogen introduces complex thermodynamics processes that are described using complex and nonlinear equation of state (Younes et al., 2024). Thus, in view of these specific challenges, understanding hydrogen migration in porous domains requires specific techniques that could outperform standard and existing numerical techniques. This is the main objective of this project that aims at suggesting and advanced numerical model for the simulation of hydrogen migration in porous domains by coupling advanced numerical schemes for spatial discretization and time integration with machine learning.

Contexte de travail

The PhD will be carried out at the Earth and Environmental Sciences Institute in Strasbourg. The laboratory is located on the main campus, which has an on-site university dining facility. The site is easily accessible by public transportation.
The candidate will be officially enrolled at the University of Strasbourg, under the Doctoral School of Earth and Environmental Sciences (ED413). The thesis is co-supervised by Brahim Amaziane from the university of Pau.
Required skills: numerical analysis, discretization methods, and scientific computing, with additional experience in machine learning. Knowledge of flow and transport in porous media is considered an asset.
Please provide a CV, a cover letter and reference letters.

Contraintes et risques

This work does not involve any particular constraints or significant risks.