Research topic:

The focus of our research is the processing and interpretation of hydrochemical data of geothermal fluid.
Mineral solubilities can be calculated from the chemical composition of a hot fluid. These saturation indices of dissolved mineral phases can be used to draw conclusions about the dissolution and precipitation reactions in the thermal water cycle. This is the base for estimating the potential of a thermal spring or a geothermal reservoir.

In addition, by using the initial chemistry of a geothermal fluid numerical modelling is created that determine the changes in fluid properties. This allows to calculate and evaluate geochemical processes within the reservoir and the power plant. This enables the optimal usage of the fluid and minimisation of uncontrolled outgassing or scaling.

Research activities

Geochemical exploration with solute geothermometry

In solute geothermometry, the hydrogeochemical composition of a geothermal fluid is used to predict the reservoir temperature in the subsurface.

Development and programming of the multicomponent geothermometer "MulT_predict"
"MulT_predict" is a numerically optimised geothermometer, wich calculates and optimises the saturation indices of multiple dissolved mineral phases in order to predict the reservoir temperature.

Development and programming of the artificial neural network geothermometer "AnnRG" 
"AnnRG" is a multi-layer perceptron, which has been trained with geochemical parameters and validated using in-situ measured reservoir temperatures.

Hydrogeochemical modelling

Based on PHREEQC, hyrdogeochemical reactions in geothermal fluids are modeled. Our focus is on the following topics:

• Improvement of thermodynamic databases
• Solubility reactions
• Precipitation reactions
• Degassing

Projects:

• VESTA  (Very-High-Temperature Heat Aquifer Storage)

The "VESTA" project is investigating the utilisation of a former crude oil reservoir as a seasonal heat storage facility. As part of the project, we are investigating the release and mobilisation of residual crude oil by injecting hot fluids into the reservoir.

• MALEG (Machine Learning for Enhancing Geothermal Energy Production)

The "MALEG" project is investigating how to increase the efficiency of geothermal power plants. For this purpose, power plant processes are quantified both physically, making experiments at a demonstrator (hardware twin), and deterministically, using hydrogeochemical modeling (digital twin). These results provide the database for an artificial intelligence that reflects the thermodynamic relationships within the power plant and shows potential for improvement.

Responsibilities: 

• Project work
• Supervision of student work
• Teaching
• IT officer

CV:

Profile links:

• Researchgate
• Google Scholar
• ORCID