Geothermal heat flow is an important indicator for estimating the potential of geothermal energy production and is of relevance for various subsurface storage applications. However, heat flow datasets often have a low data density and quality. This is the case for temperature data from deep wells or for thermal conductivity data in general. Furthermore, heat flow is influenced by an interplay of many additional and difficult to control factors such as topography, conductive fluid flow and recent geodynamic processes.
In Switzerland, the most up-to-date nation-wide heat flow map is already 30 years old (Medici & Rybach 1995). We therefore launched a project together with the Swiss Geological Survey (swisstopo), during which we are updating this map applying state-of-the-art methods for data selection, harmonisation and interpolation. Instead of relying solely on data-based interpolation algorithms, we considered additional factors like tectonic structures, geodynamic processes (e.g., recent exhumation), and known hydrothermal systems. We aim at a consistent, more detailed new version of the Swiss heat flow map that accounts for the processed heat flow data and tectonic and hydrogeological setting.
With our data processing approach, we are reproducably documenting the data selection, filtering and uncertainty parametrisation. A first draft of the new heat flow map is planned to be ready by the end of 2024.
To continue, we will test different approaches to improve resulting heat flow models in the upcoming project phases. (1) Compiling thermal conductivity datasets that represent conditions for vertical heat conduction through layered sedimentary sequences best, (2) improving correction algorithms for influencing factors such as topography, (3) using 3D heat flow modelling including tectonic data, and (4) applying probabilistic modelling to account for a 3D heat flow domain and for data uncertainties. With this new integrated structural, thermal conductivity and heat flow model for Switzerland, we will be able to reduce uncertainties and quantify them in 3D. This will allow for deriving new insights on potentially economic heat resources and the results can directly be used to estimate reservoir potentials.