Geological 3D modeling in crystalline bedrock for a cavern thermal energy storage site in S Finland - defining properties and parameters for deformation zones

Heating water using waste heat and excess renewable energy in Cavern Thermal Energy Storage (CTES) systems provides a sustainable solution for large-scale thermal energy storage. In addition to challenging economics of CTES, unexpectedly high thermal losses have been a major concern in many cases in the past. In crystalline bedrock, fractured brittle deformation zones can act as fluid pathways, potentially causing thermal losses and compromising cavern stability. Therefore, detailed modelling of these zones is essential for the safe and efficient operation of CTES facilities.

This study presents a workflow for geological characterization and deformation zone modeling at the planned site of the world’s largest CTES facility, VARANTO, in Vantaa, southern Finland. The dataset includes drill core samples totaling over 4 km, acoustic and optical borehole imaging, outcrop observations, and a photogrammetric model. We delineated deformation zone intersections from core samples and classified them into core and damage zones, defining zone dimensions for altered and fractured bedrock. We clustered orientation data from borehole imaging and core logs to determine mean fracture orientations, which, together with zone dimensions, were integrated into a 3D geological model to construct a volumetric representation of deformation zones. Additionally, we parameterized these zones based on properties such as core fracturing, fracture infill, and alteration to characterize and evaluate their structural significance in terms of stability and potential hydraulic conductivity.

The resulting 3D model improves understanding of potential fracture zones and thus pathways for groundwater flow and their impact for thermal and mechanical behaviour, supporting system simulations, monitoring, and maintenance. Representing deformation zones as volumes rather than surfaces enhances integration with groundwater flow models, reduces uncertainty, and enables more accurate prediction of hydraulic connectivity and thermal losses, thereby optimizing system performance. This workflow also provides a transferable methodology for other underground energy storage projects, facilitating risk assessment and design optimization in crystalline bedrock environments.