In an effort to tackle climate change geothermal energy serves as a good alternative to fossil fuels. It is on this backdrop that the THC-Prognos project was designed. In this study, we focus on two main objectives: First is the collection and validation of available data. Second is to simplify and speed up access to quality-checked fluid data relevant for the utilization of geothermal energy in Southern Germany via GeotIS.

The sustainable use of geothermal energy requires the comprehensive understanding of the subsurface geology, temperature and fluids. It is therefore planned to map the fluid composition and other fluid parameters of Southern Germany as part of this project. This will be based on 3D geological models, the 3D temperature distribution of the subsurface and the collected hydrochemistry data sets. These maps will be created using geostatistical methods.

One of the main tasks of this project is to collect and validate hydrochemical data from existing databases, published literature, past and present projects and from partners involved in this project related to the Southern part of Germany. All these data will be used for mapping and to expand GeotIS.

The anticipated outcome of this project is to visualize the spatial variability of fluid properties in the Southern Germany and to establish GeotIS as the primary portal for geothermal fluid data in Germany. The project results will stimulate further geoscientific research and help in the planning and operation of geothermal reservoirs.

In an effort to tackle climate change geothermal energy serves as a good alternative to fossil fuels. It is on this backdrop that the THC-Prognos project was designed. In this study, we focus on two main objectives: First is the collection and validation of available data. Second is to simplify and speed up access to quality-checked fluid data relevant for the utilization of geothermal energy in Southern Germany via GeotIS.

The sustainable use of geothermal energy requires the comprehensive understanding of the subsurface geology, temperature and fluids. It is therefore planned to map the fluid composition and other fluid parameters of Southern Germany as part of this project. This will be based on 3D geological models, the 3D temperature distribution of the subsurface and the collected hydrochemistry data sets. These maps will be created using geostatistical methods.

One of the main tasks of this project is to collect and validate hydrochemical data from existing databases, published literature, past and present projects and from partners involved in this project related to the Southern part of Germany. All these data will be used for mapping and to expand GeotIS.

The anticipated outcome of this project is to visualize the spatial variability of fluid properties in the Southern Germany and to establish GeotIS as the primary portal for geothermal fluid data in Germany. The project results will stimulate further geoscientific research and help in the planning and operation of geothermal reservoirs.

New isotopic and hydrochemical investigation methods, including geothermometry, together with structural geological data were applied on the thermal fluids of the Baden-Baden area, to get detailed information on their origin and development. We used the test site, to evaluate our methods for application in deep granitic geothermal reservoirs in the Upper Rhine Graben. Changing flowrates and total dissolved solids of the thermal waters with time indicate a rather dynamic geothermal fluid system. Although the thermal waters (springs, boreholes) emerge from different lithologies (granites, schists, arkosic sandstones), major and trace element concentrations are very similar implying no significant impact of these lithologies. Application of a newly developed Na/K-geothermometer result in a reservoir temperature of c. 200°C. The thermal waters are i.a. supersaturated with respect to aragonite, quartz, and calcite, which is well in agreement with a 2000 years old sinter cone. The ratio of Cl- and Li concentrations correspond to those of deep thermal waters in the crystalline basement and Permotriassic siliciclastic rocks of the deep URG. Stable water isotope data indicate that meteoric water has interacted in the subsurface with granitic rocks, particularly supported by Sr-isotopic composition and by S- and O-isotopes indicating that SO₄ in the thermal waters can only be derived by oxidation of disseminated sulfides in basement rocks. Stress data indicate a general (N)NW-(S)SE trending S_Hmax, which may be the preferred direction of fluid transport in the crystalline basement, whereas the NE-trending structures rather act as hydraulic barriers forcing the thermal fluids to emerge to the surface.

Accurate estimation of reservoir temperature is a key factor in geothermal exploration studies. Advances in predictive algorithms can significantly improve the efficiency of geothermal energy exploration. The use of machine learning (ML) to predict reservoir temperatures has, therefore, attracted a great deal of interest. To investigate its practicality, northern Morocco was chosen as the research area, 99 water samples were taken in situ from springs and wells for research purposes, and five machine learning algorithms were applied. The results showed that our ML models outperformed traditional methods. XGBoost model demonstrated the best predictive accuracy with an R² of 0.9967. In addition, Shapley's additive explanation (SHAP) was used as an explanation technique to evaluate the predictive decisions of XGBoost by interpreting that SiO2 solute concentration is the most important variable for predicting reservoir temperature. This underlines the potential of ML for accurate prediction of reservoir temperature, offering advances in the understanding of geothermal resources.

Assessment of intergranular pressure solution (IPS) creep has substantial safety and economic importance in reservoirs for hydrocarbon production, geothermal operations, underground CO₂ sequestration, and hydrogen storage processes. IPS creep is a temperature-dependent, stress-driven deformation mechanism that alters mineral grain shapes by dissolution, precipitation, and diffusion in a chemically closed system. The mechanical compaction and chemical reactions of minerals lead to dissolution or precipitation related to alterations in porosity and permeability that impact the flow and, ultimately, the lifetime of the reservoir. IPS creep can be examined with experiments and some thermodynamic analytical solutions. Several IPS creep equations for uniaxial compaction and assumed linear kinetic relations between chemical dissolution and precipitation rates. According to the theory, the mineral grains have spherical shapes arranged in a cubic-packed form. Similar models also estimate the compaction occurred at slightly greater porosities. These models frequently overestimate compaction and strain rates by up to many orders of magnitude when the porosity is below 0.2. The reason is that the reaction rate parameters are estimated based on empirical equations in which the saturation indices of minerals are assumed constant. Moreover, the rate of change of grain diameters is set constant. A better approximation can be achieved using the thermodynamic databases and iterative time-dependent chemical equilibrium mass balance calculations that can be carried out in a geochemical computation program such as PHREEQC. The proposed algorithm combines the conventional IPS equation with geochemcal computation, is helpful for better inspection purposes, and provides good agreement with experimental results.