Recent advances in the field have demonstrated that heterogeneity and hydro-mechanical processes strongly control gas transport in low-permeability clays, and that gas migration may involve the development of localised pathways. While high-fidelity models that explicitly represent such processes provide valuable insight into the underlying physics, they also highlight two key challenges.

First, hydro-mechanical coupling is not yet fully integrated within stochastic modelling frameworks, limiting our ability to assess the combined effects of material variability and mechanical behaviour. Second, models that explicitly represent localised features are computationally too expensive for large-scale and long-term simulations, such as those required for repository performance assessment.

The central objective of this project is therefore to develop an efficient stochastic hydro-mechanical modelling framework for gas migration in clays, capable of bridging detailed process understanding and large-scale applications. Key research directions include:

• Developing physics-based upscaled models informed by high-fidelity simulations of gas migration processes
• Exploring the use of deep learning techniques to generate realistic heterogeneous material representations and to support stochastic hydro-mechanical simulations
• Extending existing modelling approaches to include hydro-mechanical coupling in heterogeneous materials

• Incorporating uncertainty quantification (UQ) to assess the impact of spatial variability and uncertainty in material parameters on gas migration.

   

This position is part of Work Package HERMES of EURAD-2, funded by the European Commission. The project focuses on high-fidelity numerical simulations of strongly coupled processes for repository systems and design optimisation with physical models and machine learning.