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.

The United Nations’ Paris Agreement forces nations and industries to transition from conventional hydrocarbon-based energy sources to more sustainable and less-emitting alternatives such as geothermal energy. Geothermal doublets are used to provide a source of green heat to greenhouses and residential buildings. In industry, reservoir simulators are used to make predictions about the energy production and thermal lifetime of such a geothermal project; however they can be computational expensive. Upscaling approximates the expensive fine-grid simulation and reduces the necessary computational power, but is by definition not mathematically exact. This thesis tests a newly derived thermal dispersion-based upscaling method, referred to as Taylor-based upscaling, and compares it to conventional arithmetic upscaling and a reference fine-grid simulation. The upscaling methods are applied on reservoir descriptions from the Margretheholm-1A and the HON-GT-01 reservoirs, and simulations are done using Delft Advanced Research Terra Simulator (DARTS). The reservoirs are modelled as 2D layer-cake models in the form of a geothermal doublet with a fine longitudinal resolution, 1000 grid blocks between injector and producer, to minimize numerical dispersion. The results are presented as 2D-temperature profiles and breakthrough-curves. The deviations are quantified in the form of L2-norm calculations and by comparing the amount of days it takes for each upscaling method to reach a 1◦C or 5◦C drop in production temperature. The results show that arithmetic upscaling underestimates the spreading of the cold-temperature front leading to later breakthrough, whereas Taylor-based upscaling overestimates this spreading. For the Margretheholm-1A reservoir Taylor-based upscaling mimics the fine-grid reference simulation better compared to arithmetic upscaling, whereas arithmetic averaging performs better for the HON-GT-01 reservoir. Increasing the vertical thermal conductivity (kz ) leads to Taylor-based upscaling coming closer to the fine-grid reference simulation; however different reservoir descriptions require a different kz adjustment. In conclusion, the Taylor-based upscaling method does not outperform conventional arithmetic upscaling for all reservoir descriptions. Also, no universal rule was found for increasing the kz -value to improve the Taylor-based upscaling method.

This research describes how thermal fractures impact the near-wellbore (NWB) region of a depleted gasfield in a carbon sequestration project. As CO2 is usually injected in its supercritical phase, the injection fluid is injected on high pressures and low temperatures. This is in high contrast with the depleted gasfields, which have a low reservoir pressure. The increase in pressure and decrease in temperature causes a thermoporoelastic response, resulting in a reduction of stress inside the reservoir. Once the fracture initiation stress, the so-called fracture stress, is reached, thermal fractures form.

Thermal fractures form only due to extensive cooling of the reservoir. The fractures impact the NWB region; due to opening of fractures there is a drop in pressure in the bottomhole pressure (BHP). This increases the reservoir's injectivity. This research uses CMG GEM to model this. The simulation uses a homogeneous box dual permeability model with the model being initialized as a generalized depleted gas reservoir in the North Sea. To model the fractures, the Barton Bandis model is used. This model changes the permeability in a fracture cell once fracture conditions are met.

From this model, the moment of fracturing (fracture time), the fracture halflength and the injectivity of the reservoir is researched by performing a sensitivity analysis on key parameters. It is found that the thermal fractures propagate conform to the propagation of the coldest part of the thermal front. The thermal front propagates further once the fracture conditions are met sooner due to fluid highways or when the pressure build up in the reservoir is slower.

The sensitivity on the geomechanical parameters showed that only the stress conditions in the reservoir changed, causing the injection constant to change and thus a different fracture time. The way the reservoir reacted to the initiation of fractures was the same; the injectivity was improved similarly for each parameter.

The effective permeability (thickness and permeability) determines, together with the injection rate, the way the pressure builds up in the reservoir changes the increase of injectivity due to fracturing slightly. Increasing the reservoir volume causes a slower pressure build-up inside of the reservoir, allowing the thermal front to propagate further and thus longer fracture lengths.

Lastly, the sensitivity on the thermal effects showed that a higher difference between the reservoir and injection temperature causes the fracture to be less dependent on the increase of pressure to fracture, resulting in earlier fracturing and longer fracture halflength. The pressure build-up is not changed, so the injectivity remains similar to the basecase scenario.

All in all, this thesis gives an insight on how key parameters impact thermal fracture behavior. It also shows what range of parameters can be expected. Combining these two gives an insight on what parameters the focus should be on to better describe the behavior of thermal fractures, to economize the operation by leaving out or including extensive data collection on key parameters. This helps to improve the injection strategy with CO2 injection projects in depleted gasfields.

Mineral Resource Modeling (MRM) is used to predict the properties of an orebody, however it does not come without uncertainties. Multiple approaches can be used to reduce the latter. Due to limited knowledge about the subsurface, predictions are difficult to be made. In this thesis the application of Bayesian Evidential Learning (BEL), in order to reduce uncertainties on MRM, will be researched. The uncertainties present in MRM will be linked to the knowledge obtained from case studies in different geological domains where BEL has been applied successfully and reduced certain parameter uncertainties. The gap in todays industry is the knowledge and proof that using BEL for MRM will reduce uncertainties and risks, works effectively and will consume less money. The aim of this study is to show that BEL is a useful approach to reduce uncertainty of the predictions in MRM. The success of a project is supported by the accuracy of the model utilized and the geological interpretation. BEL is a framework based on statistical relationships between data and prediction variables. It will predict the posterior distribution of the prediction variable. The various case studies that have been discussed are (Hermans et. al, 2019), (Hermans et. al, 2018), (Thibaut et. al, 2021) and (Tadjer and Bratvold, 2021). BEL has successfully been able to reduce uncertainties related to geological problems of the following:
• The temperature in an alluvial aquifer
• The efficiency of the thermal energy storage capacity in an alluvial aquifer
• The wellhead protection areas surrounding the pumping well using tracing experiments as predictors
• The prediction of leakages of CO2 and the storage of CO2
Thus, BEL can be seen as a potential approach to also reduce uncertainties in a mineral resource domain; the research of this thesis. In attempt to prove this, a descriptive case study on Tropicana Gold Mine has been executed. The aim is to show that the use of BEL will, based on the geological and geochemical properties such as lithology, grade and mineral type, reduce the uncertainty in the prediction of a geometallurgy property, namely the hardness of a rock. The six steps of BEL’s framework will be followed consisting of Monte Carlo simulations, Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) in order to obtain relations between the data and predictor variables. Where the data variables are from exploration drillhole data and prediction variable the hardness of the rock. It shows that BEL is able to reduce the uncertainty of a geometallurgy property by using geological and geochemical properties. Meaning BEL can be applied to MRM to reduce uncertainties.

The main objective of this thesis is to assess the combined influences of specified reservoir conditions and operational parameters on the profitability of a geothermal project and on the potential for fault reactivation. The aim is to propose potential development strategies to maximize the profitability and minimize the potential for slip and reactivation of pre-existing critically stressed faults. The reservoir conditions of interest in this study include the fault permeability, the fault throw and the friction coefficient of the sandstone. The operational parameters of interest are the flowrate, the injection temperature of the re-injected water and the distance between the wells and the fault. As a case study a simplified homogeneous 3D box-shaped reservoir model is simulated based on the Delft Sandstone Member in the West Netherlands Basin, using the Delft Advanced Research Terra Simulator (DARTS). Reservoir production data and local pore pressure data are generated with DARTS and serve as the input data for the fault stability model and the economic model, which are both built in Python. The fault stability model is built based on the method of Mohr circles, regional stress values in the Delft area and the failure criterion of sandstone and allows to assess the fault slip tendency of a pre-existing fault. The economic model is based on the Dutch fiscal system and policies and includes the costs of the phases of a geothermal project and required energy calculations. The outputs of the model allow to assess the profitability of a geothermal project based on the Net Present Value (NPV). Outcomes of this study have shown that the profitability and the fault stability depend highly on the joint influences of specific reservoir conditions and operational options. Sealing faults generally have a negative influence on both the NPV outcomes and the fault stability as the presence leads to decreasing heat production, higher pumping costs and higher pressure build up near the fault. This influence is strengthened when the wells are placed close to the fault, while it is reduced by placing the wells far from the fault. The flowrate and the used injection temperature are found to be the most important operational options, regardless of the reservoir conditions. Combining the highest possible flowrates and the lowest possible injection temperature maximizes the NPV outcomes. As it is found that NPV outcomes increase by a factor 6 when increasing the flowrate 2.5 times and decreasing the injection temperature by 5 K may increase the NPV values up to 19\% to 43\% depending on the flowrate. With respect to the reservoir conditions the study has shown that the fault permeability and the sandstone friction coefficient are the most important influencing reservoir conditions, compared to the fault throw. The risk for fault instability increases with decreasing value of the friction coefficient and of the fault permeability. With respect to the operational options the potential for fault slip is minimized when the lowest possible flowrate is combined with the highest possible injection temperature. However, the use of a 5 K higher injection temperature allows the use of a 600 m3/day higher flowrate. Placing the wells minimally 200 m from a fault in the homogeneous reservoir and using a minimum flowrate of 7200 m3/day maximizes the NPV outcomes and fault reactivation is reduced as much as possible. The assessment of the fault stability and the profitability is however very sensitive to the reservoir conditions, which is explicitly found from the results comparing a homogeneous and a heterogeneous reservoir. This makes the potential development strategies extremely prone to heterogeneity effects and subsurface conditions which makes them highly dependent on locations specific properties. Though, the general influences of the reservoir conditions and operational options on a heterogeneous reservoir are similar to those found for the homogeneous reservoir.

In a future scenario, the successful development of geothermal industry will result in the large-scale deployment of new deep geothermal projects. In highly populated areas, such as the Netherlands, such a development will lead to a dense grid of neighbouring licenses. In such a situation a new project requires careful design and planning as the available subsurface space can become scarce, leading to the potential thermal interference with the neighbouring licenses and competing usage of the resources. Interference can cause the reduction of lifetime, produced energy, and the profitability of neighbouring projects. Therefore, it becomes important to consider the thermal interference while designing these systems in such dense areas to be able to obtain maximum energy and profit without hindering neighbours. This research project assesses the question of thermal interferences in neighbouring geothermal systems using a numerical simulation approach regarding what parameters are ideal for optimisation in neighbouring systems and what parameters should be monitored. To generate a numerical model capable of describing and predicting the effect of thermal interference the software package COMSOL Multiphysics 5.4 was used. The physics of fluid flow and heat transfer in porous media is applied to the reservoir model, and the finite element method is used to approximate the solution of these equations. Sensitivity analyses are performed on all input parameters as a post-processed simulation to control their influence on the output performance. The input parameters are divided into two categories; first, operational-controlled parameters: start time, injection temperature, injection/production flow rate, well spacing, well distance to the license border, and second, natural-controlled parameters including permeability and its anisotropy as kx/ky ratio. At the end of the parametric sensitivity analysis, an economic model is used as an instrument to evaluate how these input parameters can affect the long-term project feasibility. In this study, we present the results of global comparisons between each parameter while incorporating all measurement control (lifetime, cumulative energy and NPV). The results from the 3D qualification, the correlation between measurement controls and the reference base case study are used as background for this comparison. Our results show that the injection temperature and well spacing are ideal designed parameters to optimise profitability because the negative effect on the neighbour is only 1% for every 10°C reduction injection temperature and increasing 300 m of well spacing. On the other hand, injection / production flow rate is the most influential parameter that must be monitored in neighbouring production areas.

Transition to cleaner energy while sustaining the heat demand necessity has always been an emphasized topic in the recent years. With the slowly declining utilization and production of natural gas in Netherlands, geothermal energy shows a promising future in providing sustainable power for heating and various applications. For majority of economically feasible geothermal project, either natural fractures or hydraulic fractures are present. These fractures served as a preferential fluid pathway between geothermal well couplets, which determines crucial parameters such as breakthrough time and project life. In this study, the main focus is the impact of stochastic distribution of fracture aperture with or without pressure dependence and gravity effect on geothermal energy production. The simulation conditions as well as reservoir physical model are collected from analogous enhanced geothermal systems (EGS)that are classified as deep geothermal energy projects. The test cases include base case, case with no pressure or stress dependence, case with half well doublet distance, and case with gravity that has well positioned in the dip or strike direction in relative to the fault orientation. The ensemble simulation of 100 realization is run for each scenario considered. The relevant parameters analyzed are pressure difference between well couplet, production temperature, NPV, and most importantly, net cumulative energy production. It is found out that the geothermal energy production greatly relies on the fracture aperture distribution with or without pressure dependence. For base case, the range of energy production is from 1.50E13 to 3.75E13 KJ with temperature variation of 35 K between the minimum and maximum value. Without pressure dependence, only pressure difference between injector and producer slightly changes, and production temperature as well net energy pro-duction remains nearly identical. For the case with half of original well-spacing, the spread of energy production distribution reduces from 1.50E13 to 3.25E13 KJ. The diminishing energy production results from less total enthalpy in the flow path with shorter distance, yet the outcome of energy production still demonstrates a considerable range. By taking account of gravity effect and populate the reservoir with non-uniform pressure and temperature distribution with corresponding gradient, the net energy production drops to approximately two times less than the base case. However, the ratio of maximum energy production to the minimum over100 realization is similar to that of the base case. Last but not least, two types of well orientation, along the strike and along the dip of fracture plane, are considered to observe the impact of gravity-assisted flow. It is concluded that with the current model setting, the low temperature gradient of injector in the dip configuration case overweighs the effect of gravity assistance, resulting in a smaller net energy production than the strike case. This project concludes that the fracture aperture heterogeneity is an important factor in predicting the outcome of deep geothermal production. The gravity is another crucial component to portray accurate flow behavior between the wells, but it does not result in additional increase in the range of energy production outcome in comparison to the base come with the simulation conditions applied.

This research identifies and provides a relative ranking of the parameters that control the thermal breakthrough time of a geothermal doublet. The ranking is based on simulations modelled by a three-dimensional model build in COMSOL Multiphysics with a simulation duration of 50 years. The model is a nonisothermal, isotropic, cuboid consisting of a sandstone aquifer surrounded by identical impermeable layers. The ranking is derived based on a solution space covering a minimum, maximum and mean value for each of the simulation parameters. The investigated simulation parameters are the density, porosity, permeability, thermal conductivity and specific heat capacity of both the surrounding rock formations and the aquifer, the depth and thickness of the aquifer, the flow rate, injection temperature and the well spacing. The mean values of the solution space form the base model, from which the parameters will divert separately to monitor the model’s behaviour on the varying parameters.

In this research, the breakthrough time is defined as the time at which the production temperature is decreased by 1% to 99% of its initial value. The ranking is based on comparing the proportional change of each parameter from the base model to the change in breakthrough time. The results show that the thickness, flow rate and the well spacing are the most crucial parameters influencing the thermal breakthrough time of a reservoir. Overall, the flow rate has the greatest impact, with a decrease of 16.7 years between a flow rate of 150 m3/hour and 250 m3/hour.

In addition, the surrounding rock parameters have a notably smaller impact on the thermal breakthrough time compared to the reservoir rock and process parameters. The surrounding rock parameter with the relatively largest impact on the breakthrough time is the specific heat capacity, which is only a change of 0.2 years between a specific heat capacity of 1150 and 1250 Jkg-1K-1.