As climate change accelerates, the transition to renewable energy systems, such as geothermal energy, underground hydrogen storage, and carbon capture and storage (CCS), is essential. These technologies introduce new challenges in the subsurface, including limited data availability, highly heterogeneous reservoirs, and complex thermal and multiphase fluid-flow behavior.

Geo-modelling and geophysical inversion are powerful tools for addressing these challenges, enabling the integration of geological, geophysical, and petrophysical data into consistent three-dimensional subsurface representations. Recent advances in modelling algorithms, inversion techniques, and computational approaches, including machine learning and AI, allow for improved accuracy, uncertainty quantification, and interpretability across multiple spatial and temporal scales, even in data-scarce environments.

This session highlights recent developments in 3D geological modelling, reservoir and fluid-flow simulation, and model-based inversion, with a focus on applications supporting the energy transition and sustainable subsurface use. Both methodological contributions and case studies are welcome.

Topics of interest include, but are not limited to:
• 3D geological and structural modelling approaches
• Geo-modelling for geothermal energy, CCS, and hydrogen storage
• Fluid-flow and reservoir modelling (single- and multiphase)
• Model-based geophysical inversion and data integration
• Multi-scale modelling, upscaling, and representative elementary volumes (REVs)
• Uncertainty quantification and computational efficiency
• Machine learning and AI in geological modelling and inversion