Göttingen is not within the type of area traditionally considered to have high geothermal potential. Moreover, there is currently little information about the geology of the target rocks at 3000 to 5000 m depths, which consist of complexly folded and thrusted metamorphic rocks. Such a geothermal target has not previously been exploited for geothermal energy. There is therefore need for a new concept and design for the implementation of an “Enhanced geothermal system” (EGS). This design must use parameters derived from economic analyses undertaken by MEET of the heat consumption infrastructure.
The recently published study on different scenarios of EGS development for the Göttingen demonstration site (https://www.mdpi.com/2076-3263/11/8/349) focuses on finding favorable conditions for a potentially profitable project. The scenarios include such parameters as wellhead temperature, mass flow rate, distance between the field and the campus, government subsidies, drilling costs, operational expenditures, discount rate, carbon tax, temperature drawdown, and injection temperature. On average, the considered single EGS well doublet would cover about 20% of the heat demand and 6% of the cooling demand of the campus. The results show that the EGS heat output should be at least 11 MWth (with the brine flow rate being 40 l/s and wellhead temperature being 140°C). Sensitivity analysis did, however, present some conditions that yield better results. For example, government subsidies or close distance between the field and the campus can lead to the required heat output being 7.2 MWth (30 l/s and 125°C). If realized, the EGS project in Göttingen might save up to 18,100 t CO2 (34%) annually.
In order to find out how the required EGS heat output for the Göttingen demonstration site can be achieved, geologists and engineers of the MEET project are focusing on geological characterization of the target rocks at analogue sites, simple modelling of stresses and fluid pressures to understand how fractures develop in such rocks, and setting up reservoir models to learn about the productivity and lifespan of the reservoir.