The North German basin holds high potential for geothermal lithium exploration. Yet, so far, the assessment of reservoirs concentrates on locations in the Upper Rhine Graben. This thesis gives an example for the evaluation of a potential geothermal lithium reservoir of the Middle Buntsandstein at the location Horstberg in Lower Saxony. The targeted layers are part of the Solling and the Detfurth formation.

To determine the porosities and volumes of the two potential reservoir layers, including their uncertainties, well log data and a structural model are evaluated. The model is created using \textit{Gempy}, an open-source library to create 3-D models in Python. Based on the estimated parameters and data from the literature, the lithium and heat quantities stored in place are estimated. Furthermore, calculations are carried out that target the design and production volume of a geothermal doublet with a theoretical production rate of 24\,L/s.

For the Solling, the resulting mean quantities are 13,836\,t of lithium and a stored heat of 2.59 - 3.04\,GJ\,m$^{-2}$. The values of the Detfurth are much lower. The amount of stored lithium amounts to 3,773\,t and the heat-in-place is determined to be 1.85 - 2.19\,GJ\,m$^{-2}$. Regarding the calculations of the doublet, depending on the input parameters, the mean required distance between injection and production well is 1,037 - 2,021\,m. Based on these results, 8-28 doublets would fit in the study area. One doublet system could produce 540.6 - 660.5\,t of LCE per year.

Overall, the conditions for geothermal lithium exploration at Horstberg are rather poor. The reservoir is mainly limited by its low thickness and low production rate. Nevertheless, this study gave a good example of how to assess a potential geothermal lithium reservoir in the North German Basin.

The recent discovery of the Zama field in offshore Sureste Basin in the southern Gulf of Mexico has piqued interest in the Mexican petroleum industry regarding the hydrocarbon potential within the region. In this study, a deposition-only thermal model was developed in an area of interest (AOI) in this region. Using structural input obtained from seismic interpretation conducted in Petrel©, the model was constructed and simulated in PetroMod© to acquire initial estimates of the maturity of the Tithonian J100 source rock in the AOI in terms of the maturity parameters Vitrinite Reflectance (%Ro) and Transformation Ratio (TR). A one-at-a-time (OAT) sensitivity analysis approach was also undertaken to assess the impact of uncertainties in some inputs in isolation on the output maturity in order to identify key uncertain input parameters. Based on the modeling results, at the C-1 well location of interest within the AOI, the source rock was simulated to be within the wet gas generation window based on simulated %Ro values in the present, having emerged into oil window between 41 - 27 Ma (late Eocene-Oligocene) and into the wet gas window between 6 - 0.5Ma (late Miocene-Pleistocene). Across the wider AOI, present-day %Ro values indicate the predominant presence of late oil to wet gas generation windows, with most of the source rock across the AOI having emerged into the oil generation window between 23 Ma and 11.6 Ma (early to middle Miocene). The highest uncertainties in simulated %Ro values were associated with uncertainties in the Bajocian (J60) autochtonous salt sequence thickness distribution and with the maturity models used for model simulation. At the C-1 well location, TR values of 95% to near 100% in the present indicate that most of the kerogen (source rock organic matter) has already been converted into hydrocarbons, with kerogen conversion initiated between 55 - 48 Ma at the location. Across the wider AOI, average present-day TR values in the range of 81 - 97% were simulated. The highest uncertainties in simulated TR values were also associated with uncertainties in the Bajocian (J60) autochtonous salt sequence thickness distribution, and with the source rock maturation kinetics input used for model simulation. The timing of maturation of the J100 source rock at the C-1 well location - assumed to have been constrained by the OAT sensitivity analyses results for simplicity - in relation to the timing of other petroleum system elements and processes indicated by literature, suggests that hydrocarbon accumulations charged from the Tithonian source rock in the reservoir intervals in the well vicinity are possible i.e. presence of hydrocarbon accumulations cannot be ruled out based only on maturation timing considerations.

Backward erosion piping is a dike failure mechanism. It is the internal erosion process by which sand is eroded away from underneath a dike or levee by seepage flow. This erosion process progresses in the direction opposite to the direction of seepage flow and forms a small pipe directly beneath the dike. As erosion continues, this process can lead to dike failure. During this erosion process, the groundwater flow pattern is subject to continuous change, due to the growth of the pipe. Self-potential (SP) monitoring is sensitive to changes in the groundwater flow pattern, because of the electrokinetic coupling between fluid flow and the streaming potential. The SP field due to flow underneath a test dike was modeled with a FreeCAD -> Gmsh -> pyGIMLi workflow. This workflow can also be used to effectively resolve a wide range of standard and customizable geophysical modeling and inversion tasks. After modeling, field experiments were conducted, on the same test dike, to further assess the possibilities and limitations of SP monitoring to track the progress of backward erosion piping. Given that it is essential to have an accurate resistivity model in order to find the location of the SP source, an integrated electrical resistivity tomography (ERT) and SP monitoring system was designed. The electrodes used in this monitoring system were polarizable stainless steel stakes. The reliability of polarizable electrodes was greatly overestimated, as they turned out to give unstable SP measurements. The reason for the inferior reliability of polarizable compared to non-polarizable electrodes, was found through extensive literature research. The reason being that the largest potential in any electrode originates from the contact between the metal and the electrolytes in solution. The metal of polarizable electrodes is in direct contact with the electrolytes in the soil, which have variable concentrations. Therefore, the potential measured fluctuates together with the concentration of soil electrolytes in contact with the metal. The metal of non-polarizable electrodes, on the other hand, is in contact with a solution of its own salt, which has a constant concentration. Finally, piping is not expected to be measurable with SP monitoring, before a large sand boil is visible in the field. Once a positive SP anomaly develops at the sand boil, changes in the SP field due to the growth of the pipe are expected to be too small relative to the SP anomaly associated with water flow through the sand boil. Even though an integrated ERT and SP monitoring system is known to provide useful information about the hydrology of a dike, such a system is not sensitive enough to be able to monitor the development of a backward erosion pipe.

The automatic interpretation of well logs has been the focus of research, especially in oil and gas industry, for more than 50 years and, aside from that, benefits from the fast developments of machine learning algorithms during the recent decades. Moreover, Bayesian inference is increasingly utilized to model geological data, enabling the consideration of all available information and a quantification of uncertainties. In order to combine unsupervised segmentation of well data with 3D geological modeling, a fully automated approach to directly create threedimensional structural models from raw well data is intended and, further, tested on synthetic data with different standard deviations.
For this purpose, unsupervised segmentation, which considers the statistical nature as well as the spatial correlation of the data, is combined with a zonation method that extracts interface information from clustered data by maximizing probabilities within continuous zones. This data is then screened to automatically obtain geological information and, furthermore, is inserted into the structural modeling algorithm, which is based on implicit potential-field interpolation while at the same time honoring the geological spatial continuity.
It is shown that unsupervised segmentation is capable of segmenting raw well logs and that the zonation appropriately determines boundaries between stratigraphic units. Model reconstruction demonstrates that the fully automated process is proffcient at recovering several common subsurface structures. Moreover, the implementation of a three-dimensional model in the segmentation process, filling the empty space between boreholes, reduces uncertainties in the geological modeling routine. The combination of unsupervised segmentation and 3D geological modeling, resulting in a fully automated process, taking all available information into consideration, is found to be a suitable method in order to build structural geological modeling directly from raw well logs.

The inversion process of data sets acquired with the electrical resistivity tomography can be a computational expensive calculation, because it requires a lot of forward simulations. Since computational resources are limited, the inversion for large field surveys can require immense computing times. One approach for reducing the computing times of forward simulations is the reduced basis method. It splits the problem into a parameter-dependent and parameter-independent part, which allows a precomputation of the parameter-independent part to speed up the calculation. Since the method of reduced basis has not been applied for the problem of electrical resistivity tomography so far, the question arises if and how it is applicable for this problem. Therefore, I performed forward simulations of the electrical potential distribution using the finite elements method and simulations using the reduced basis method for comparing the results. The methods give similar results, but unfortunately deviations above 1*10^4 occurred, while the error tolerance of the reduced basis method is set to 1*10^5. Since the reduced basis method has been successfully applied on other applications, I assume that the problem is in my reduced basis simulation and can be solved by more fine tuning. By comparing the computing times of the simulations, a speed-up of 109.512 is achieved by using the reduced basis method. Furthermore, the pay-off is reached after 8.803 simulations. During the project, I noticed that only considered the resistivities of the subsurface as a varying parameter in the reduced basis is not sufficient. The source position needs to be a variable parameter in simulations of the electrical resistivity tomography, as well. This is necessary to tap the full potential of the reduced basis method in the application of the electrical resistivity tomography. The reduced basis method gives significant speed-ups and therefore, it has the potential to reduce the computing times of electrical resistivity tomography inversions.

Current Port of Rotterdam procedure is to define the nautical depth based on the density of a fluid-mud layer, that settles at the bottom of the port. Rheological parameters could be an improved indicator for nautical depth and decrease the frequency and cost of dredging. I investigate the use of reflected seismic waves to derive in situ rheological parameters. I model a simplified port profile to investigate the effects of o_set on the types of seismic reflections acquired. Four models of how the density of the fluid-mud layer varies with depth are proposed. I undertake simple laboratory experiments to measure the effects of consolidation time, density and frequency has on the seismic velocities of the fluid-mud layer. From these laboratory experiments I calculate the shear modulus, bulk modulus, Young's modulus and Poisson's ratio for the fluid-mud layer of varying densities. Reflected seismic-wave velocities prove to be promising in deriving in situ rheological parameters, elastic constants and, potentially, viscosity.