ModelAge

The Upper Jurassic aquifer within the Bavarian Molasse Basin (BaySGMB) represents significant potential for sustainable geothermal energy utilization. Accurate and robust numerical modeling is essential for predicting the efficiency of geothermal operations and ensuring their long-term sustainability. While existing geological and hydrogeological models are primarily based on hydraulic potential and geothermal temperature distributions they face limitations due to the high heterogeneity of the basin. The identification of groundwater recharge zones, flow regimes, and water origins remains uncertain, leaving fundamental research questions unresolved.

The ModelAge project will address these challenges by integrating groundwater age distribution analysis with advanced numerical simulations. Building upon prior research outcomes from the IsoChem and IsoMol projects, which expanded the understanding of aquifers dynamics through hydrochemical and isotopic data. ModelAge aims to enhance reservoir management strategies through inverse groundwater age modeling. This project is strongly associated with the project BEM-TG and GeoChaNce.

The overall goal is to develop a localized, three-dimensional numerical model capable of simulating the apparent groundwater ages of the deep geothermal groundwater systems and to investigate the complex evolution of flow patterns over time in the BaySGMB. 

The key objectives are:

• Development and implementation of a FEFLOW-based modeling framework to simulate groundwater age distributions in deep aquifer systems, incorporating radiocarbon (¹⁴C) and Krypton-81 (⁸¹Kr) tracers

• Estimation of flow velocities and water fluxes derived from simulated groundwater age distributions

• Determination of the locations and conditions governing deep groundwater formation in the BaySGMB (recharge zones)

• Evaluation of volumetric flow significance to assess their impact on the sustainability and long-term performance of geothermal reservoirs

• Detection of BTEX and PAK contaminant sources in the deep hydrothermal aquifer, along with the evaluation of their concentrations and distribution patterns as critical factors for site characterization and analysis.

The outcomes of this research will directly contribute to the development of decision-support systems for reservoir management and provide insights into the sustainable utilization of groundwater resources in deep geothermal energy systems. These findings will also enhance the predictive capabilities of existing models, offering valuable tools for optimizing geothermal energy development in the BaySGMB.