Digital Twins (DTs) are dynamic virtual representations of physical processes, already applied in engineering and industry. Their main strength lies in the continuous assimilation and visualisation of large, spatially distributed datasets, integrating different sources and types of data with numerical simulation models. This enables replication of system behaviour, provides an up-to-date status of ongoing physical processes, and supports informed decision-making. DTs represent powerful frameworks that bridge physics-based models, observational data, and AI to improve our understanding, forecasting and management of the subsurface. While a digital twin is often designed to address a specific question or topic, there is still no standardised workflow or consensus on the methodology to be used. Given the growing number of emerging projects, the complexity of workflows, and the wide range of disciplines involved, this remains an important topic for discussion.
This session invites contributions on methodologies, (semi)automated workflows, and applications of digital twins for the subsurface, with a special focus on uncertainty quantification, data assimilation, multi-source data streams, automated data cleaning, and decision support. We particularly welcome studies addressing subsurface workflows from multi-type data to decision-making, including advanced optimisation methods, Bayesian approaches, machine learning, hybrid modeling, as well as economic, social components and policy considerations. Case studies from groundwater, geothermal energy, energy storage, hydrogen, carbon storage, geomodelling, natural risks and other subsurface-related systems are also encouraged. The session aims to foster dialogue on methods across disciplines and highlight both challenges and opportunities in building reliable subsurface digital twins.