ERE3.7 | Upscaling subsurface fluid and energy storage: from multiphysics insights to ‎forecasting tools
EDI
Upscaling subsurface fluid and energy storage: from multiphysics insights to ‎forecasting tools
Convener: Atefeh VafaieECSECS | Co-conveners: Iman Rahimzadeh Kivi, Niklas Heinemann, Victor Vilarrasa, Sam Krevor

Large-scale deployment of underground fluid and energy storage technologies, ranging ‎from CO2 and hydrogen to geothermal energy storage, and deep waste containment, is ‎crucial for a sustainable and climate-resilient future. Achieving safe, efficient, and ‎cost-effective operations at scale requires advancing both our process understanding ‎and our ability to forecast subsurface system behavior under diverse geological and ‎operational conditions. This session welcomes studies that explore knowledge, ‎workflows, and tools to extend pilot or early-stage commercial projects to regional ‎deployment, acknowledging cross-disciplinary approaches that integrate physico-‎chemical insights, engineering design, monitoring strategies, and predictive models. ‎We especially encourage contributions that combine multiscale experimentation under ‎heavily monitored conditions, from core-scale to underground rock laboratories and ‎demonstration projects, with computationally efficient, physics-informed, and/or data-‎driven models. Emphasis is placed on strategies that enable real-time decision making ‎and long-term performance evaluations, paving the way for storage at scale. The ‎session aims to highlight advances that translate fundamental understanding into ‎practical, scalable geostorage solutions by addressing key challenges related to storage ‎capacity, integrity, and sustainability. By discussing multiple storage applications, this ‎session seeks to identify transferable methodologies, best-practice guidelines, and a ‎path toward accelerating the safe and effective use of the subsurface for the energy ‎transition and long-term environmental protection.‎

Solicited authors:
Hadi Hajibeygi
Please check your login data.