Multiple studies have shown the potential for CO2 plume geothermal (CPG) to be a sustainable, reliable energy source that can be utilized in numerous regions worldwide. Compared to conventional
brine-based systems, a significant benefit is that CO2 allows for direct electricity generation at lower temperatures than brine. It could serve as both a continuous source of energy generation and a dispatchable source when energy demand is high. In addition, it could serve as a pre-carbon capture and sequestration (CCS) phase. Where it could verify the integrity of the reservoir and acquire information to characterize the reservoir and understand its behaviour under CO2 injection. Before a proof-ofconcept site can be chosen, candidate fields should be evaluated to find the optimal environment for CPG. In this work, we investigate which systems, aquifer or gasfield, injection-production scheme and what kind of environment provide the best performance for CPG. We use the Open Delft Advanced Research Terra Simulator (Open-DARTS) to simulate this on a reservoir scale. Open-DARTS uses the Operator-Based Linearization (OBL) approach to model all non-linear physics involved. To get an estimate of the electricity and heat generated by the system, we extend the open-DARTs framework to include a simple wellbore model and surface infrastructure. In our results, we look at the performance of two types of reservoirs: aquifers and gas fields. Where we consider the amount of electricity generated energy and other performance metrics. We find major differences between CPG performance in aquifers and gas fields. The results show that maintaining steady electrical energy generation through CO2 production in an aquifer appears to be much easier than it is for gas fields. With Aquifers consistently having a higher water cut. Furthermore, the inclusion of a plume establishment (PE) phase does boost performance once the CPG stage starts for both the aquifer and gas field types.

In this multidisciplinary project several aspects of geosciences are combined. The regional geology background was summarized and linked to the borehole data.
Multiple tests were conducted on the well to answer several questions. The slug test indicated that the fracture is still open and essentially confirmed that it is a shear fracture, however it is unclear to what extent that the fracture is open. The fracture seems to be hydraulically connected to a permeable unit or shallow aquifer. Unfortunately, the length of the fracture could not be determined with the data collected from the test.
Electrical resistivity tomography (ERT) and seismics were both applied to a location near the borehole to acquire lateral information of the subsurface. The ERT results showed that the layers were horizontally continuous and indicated layers with different compositions based on resistive properties.
Seismic refraction tomography conducted along a part of the same profile showed similar results as the ERT for that part of the profile. P-wave velocities indicate a horizontally layered subsurface in the upper 40m. Additionally surface wave analysis of the same setup utilizing active and passive measurements resulted in a vertical s-wave velocity profile that can be used for future implementation of the planned Borehole Thermal Energy Storage (BTES) system.
The last geophysical method was using gravity data on the region around the site. A map was made by using available data on changes in gravity in the region and plotting the results. On this map the location of remnants of volcanos and the Litoměřice deep fault can be recognised.
Thermal properties of cores were analyzed using a Hot Disk and an optical scanner. Unfortunately the drilling of a new well from which the cores were to be analyzed was delayed, and cores from an uranium mine were used. This way the advantages and disadvantages of both measuring devices could be argued and used for future research.
Past analysis of geothermal regions have shown that exploration of geothermal energy causes surface displacement. It can also be observed during the drilling phase. Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) are valuable tools to monitor land surface changes. Measurement of surface deformation being one of its many applications. For this study, the above tools have been used to measure surface displacement in the region of Litoměřice.

In this thesis we have analysed the behaviour of a physics informed neural network and it’s competence in predicting a wave in a non-homogeneous medium. During this project we have used a fully connected network with labelled input data of a 2D acoustic wave. On top of this we used a special loss function that calculated whether the output of the network satisfies the wave equation. Our experiment consisted of the tuning of the hyper parameters, analysing the optimal choice of activation function and the optimisation of the input data and improving the loss function. During this project the unpredictable nature of machine learning has become very clear. We have experimented with several activation functions and have found that the optimal choice of activation function depends on how long you are willing to train the network, as the development of the loss function differs immensely between activation functions. When we looked at the optimal scaling of the input values we find that a non-trivial scaling seems to work better than for example, normalisation of these values. Furthermore we have tried to improve the sampling of the points we use to calculate whether the prediction of the neural network satisfies the wave equation and got interesting results. When we implement all op- timisation techniques, we find that the neural network is extremely capable of predicting the wave’s behaviour in a high contrast media within the time frames of the input data. Prediction outside of this time frame does work but the results do deteriorate especially in the positive time direction. Predicting in the negative time direction yield slightly better results.