Towards a digital twin for modelling geothermal reservoirs in channelised fluvial systems

Geothermal energy plays a key role in energy transition by offering a clean baseload alternative to fossil fuels for space heating. Long-term geothermal production is subject to inherent uncertainty due to the heterogeneity of geological formations that host the geothermal resource, and the limited data available to characterize and quantify these heterogeneities. It is insufficient to explore and quantify such uncertainty based on a single concept or interpretational scenario. The TU Delft campus geothermal project has been initiated to provide a dedicated research environment with the vision to scale-up the deployment of geothermal energy as well as providing and storing heat for the TU Delft campus. Inspired by the reservoir that hosts the geothermal resource at TU Delft - a channelised fluvial system - we are presenting a framework of an open-source digital twin for geothermal reservoirs that aims to integrate geological scenario modelling, production simulation, uncertainty analysis, and data assimilation to mitigate operational risks, reduce maintenance costs, extend reservoir longevity, and enhance the overall sustainability for geothermal production.

We propose a scenario-based geological modelling approach using Rapid Reservoir Modelling (RRM), in which channelised fluvial layer templates are stacked and constrained by facies information along well trajectories. Multiple geological scenarios with distinct channel distributions are generated. Heterogeneous petrophysical properties are then assigned to different facies in the reservoir models. Uncertainties in both, reservoir architecture and petrophysical properties, are captured. The flow and thermal simulations are performed with the open-source Delft Advanced Research Terra Simulator (open-DARTS), and production uncertainty is quantified by evaluating the impact of reservoir architectures and petrophysical heterogeneities. The Ensemble Smoother with Multiple Data Assimilation (ESMDA) is then applied across these scenarios to constrain production and reservoir forecasts using well temperature and pressure observations, tracer tests, and related monitoring data. Scenarios that fail to reproduce the observations after data assimilation are falsified, while data-worth analysis is conducted on the remaining plausible scenarios to provide a reliable evaluation of data acquisition strategies and identify the most cost-effective options for reliable assessment of geothermal production.

Our digital-twin framework enables us to explore a broader range of geological uncertainties and constrains production uncertainties, thereby enabling a more reliable assessment of geothermal reservoir performance and production forecasts, both of which are essential for optimizing operational strategies and supporting informed decision-making for geothermal systems.