Ambient groundwater flow may influence the performance and thermal recharge of deep low-enthalpy geothermal systems by altering the heat transport in the subsurface. While this effect has been studied extensively for shallow aquifer thermal energy storage systems, its effect at depths relevant for low-enthalpy geothermal production remains less understood. Additionally, at these depths the ambient groundwater flow velocities are generally unknown and difficult to quantify. This thesis investigates how ambient groundwater flow affects the technical and economic performance and the thermal recharge of deep low-enthalpy geothermal doublet systems.

Representative groundwater flow velocities for clastic sedimentary reservoirs in the Netherlands were first estimated using formation pressure measurements between depths of 1-3 km from the Southern North Sea pressure database. Hydraulic gradients between hydraulically connected wells were combined with estimates of permeability and dynamic fluid viscosity to derive average Darcy velocities. The estimated average groundwater flow velocities reached values up to approximately 0.020 m/day.

In order to assess the influence of ambient groundwater flow, these estimates were imposed in numerical reservoir simulations using the Delft Advanced Research Terra Simulator by applying lateral pressure gradients to represent background flow. Simulations were performed for one homogeneous reservoir model and five heterogeneous fluvial reservoir models, using multiple background groundwater flow directions and magnitudes. The influence of ambient groundwater flow was evaluated based on production temperature, system lifetime, thermal recharge time, reservoir temperature distributions and economic performance.

The results show that even relatively small ambient groundwater velocities can substantially influence the behaviour of geothermal systems. Ambient groundwater flow aligned with the production-induced flow, accelerates thermal breakthrough and therefore shortens system lifetime, whereas opposing flow delays thermal breakthrough and extends system lifetime. These variations in production temperature and system lifetime also affect the economic performance of geothermal systems, as scenarios with extended lifetimes generally result in higher Net Present Value and lower Levelized Cost of Heat, while shortened lifetimes reduce the economic viability of the project.

Ambient groundwater flow also strongly influences thermal recharge by displacing the cold water plume and altering the relative contribution of advective and conductive heat transport processes through which the reservoir thermally recovers. The simulations further demonstrate that geological heterogeneity strongly controls how ambient groundwater influences geothermal production and thermal recharge.

These findings highlight the importance of considering ambient groundwater flow when evaluating the technical and economic performance and thermal recharge of deep low-enthalpy geothermal systems.

This thesis develops a geological ensemble-reduction workflow for efficient uncertainty quantification (UQ) in Carbon Capture and Storage (CCS). Using early-time full-physics (FP) injection-rate data and distance-based clustering, representative models are selected to preserve ensemble percentile bounds (P10–P50-P90) within ≤5% relative RMSE. Applied to two 108-member ensembles, injection-rate uncertainty distributions were accurately reconstructed using only 6 and 9 representative models (∼7–10% of the total simulation cost). Early FP rates (≤10 days) proved strongly predictive of long-term behavior and key geological controls. Distance-based generalized sensitivity analysis (dGSA) effectively identified influential parameters governing reservoir response variability at only ~1.3% of the full simulation cost. The approach provides a transparent, low-cost framework for CCS reservoir UQ, enabling robust risk and performance assessment with order-of-magnitude computational savings.

The energy crisis and climate change, is creating an urgent need for sustainable and energy-efficient solutions. A significant portion of global energy consumption comes from households. Within this sector, the largest share is used for space heating and cooling. Therefore, efforts to reduce energy usage and cut carbon emissions should primarily target heating and cooling systems. A promising solution to this challenge lies in harvesting shallow geothermal energy through technologies such as energy piles, which have a multifunctional role to the building.

Energy piles are a specialized form of Borehole Thermal Energy Storage (BTES) systems that utilize shallow geothermal energy by taking advantage of the ground's stable temperature throughout the year. They are gaining popularity as an efficient solution for both heating and cooling, primarily because they serve a dual function within a building. On the one hand, they act as heat exchangers, enabling the transfer of thermal energy to and from the ground. On the other hand, they provide structural support, as they are typically constructed from reinforced concrete. This multifunctional role makes them a cost-effective choice and reduces the initial costs investments. These systems are commonly integrated with Ground Source Heat Pumps (GSHPs), which facilitate the exchange of thermal energy between the energy piles and the circulating fluid within the system. Their high Coefficient of Performance (COP) and environmentally friendly operation contribute positively to the transition towards more sustainable energy solutions.

This research focuses on an existing energy pile system located beneath a building on the TU Delft campus, which is responsible for covering the building's heating and cooling demands. The main objective of this research is to assess how the efficiency and reliability of this system can be improved through the implementation of advanced control strategies. To achieve this, a precise simulation model was developed to capture the underlying physical processes and monitor key performance indicators. Energy balance is a key metric and evaluation point for the system that secures the efficiency and sustainability of it. Additionally, various scenarios with different operational parameters were designed to evaluate the system's capabilities and performance.

The main findings initially indicated that the heating load is higher than the cooling load, revealing a significant imbalance. By creating various scenarios with different temperature setpoints, heating and cooling months, and durations of heating and cooling modes, an energy balance was achieved. However, the system was still unable to adequately cover the energy needs of the building during winter. Scenarios that utilized solar gains —by opening the building’s sunblinds— enabled the heating and cooling system to supply the majority of the heating load during winter. This aligns with one of the main goal of the system while maintaining energy balance throughout the year. Among the scenarios evaluated, scenario 5 demonstrated the highest heating and cooling energy delivery. According to performance evaluations, it also consumed the least electricity among the compared scenarios. Additionally, Scenario 5 provided the highest levels of visual and thermal comfort for occupants, as the sunblinds remained open during non-operational months. The system was found capable of operating under loads 50% higher than normal, although these levels push its operational limits. Moreover, it was observed that the energy piles system can store surplus energy during summer and completely cover the heating demand of a neighboring apartment.

The thermal plume interaction is investigated thoroughly and shows the rate and the magnitutude of the expansion or contraction of those. It was observed that energy is lost due to the open boundary with the ambient air temperature but varies between the scenarios. More details will be discussed in the following sections.

High-Temperature Aquifer Thermal Energy Storage (HT-ATES) for short, is a technology that can help bridge the gap between energy supply and energy demand, which is particularly interesting to use in combination with renewable energy sources. The impact of subsurface heterogeneity on energy efficiency and thermal plume extent is not yet fully understood. In this paper, subsurface heterogeneity is linked to energy efficiency and thermal plume extent. A case study at the TU-Delft is considered, in which the Pleistocene deltaic deposits of the Maassluis formation have been characterized. Insights are gained from performing well correlations with high-resolution Gamma-Ray logs, which translates to an increase in understanding the possible architecture of the deposit and which adds a level of heterogeneity to current subsurface models that depict the Maassluis formation as a homogeneous medium. Stochastic and process-based geomodels have been constructed to represent the subsurface, using sequential indicator simulation with sequential Gaussian simulation and PyBarSimPseudo3D, respectively. Due to the difference in the underlying principles of the geomodeling techniques, the created geomodels show contrasting distributions in hydraulic properties. HT-ATES simulations have been performed with these heterogeneous geomodels using open-DARTS (Delft Advanced Research Terra Simulator). The results of the simulations show that heterogeneity has a negative impact on energy efficiency. The negative impact varies between 0.4 and 9.5 percent in the final cycle of simulation. The thermal plume distortion as a result of subsurface heterogeneity and potential for buoyancy flow to develop show to be the controlling mechanisms for the long term efficiency. The impact of heterogeneity also decreases over time as the thermal plume distortion is reduced as a result of conduction. The thermal plume extend, becomes highly unpredictable when aquifers have a low net-to-gross ratios. For stratified and high net-to-gross aquifers, the predictability is higher, as the thermal plume is more circular.

Conduction-dominated geothermal systems are essential for decarbonizing the built environment, particularly in densely populated areas with high heating demand. Geothermal development in the West Netherlands Basin (WNB) has accelerated but still remains largely uncoordinated, following a "first-come, first-served" model which results in suboptimal subsurface resource utilization.

This study presents a multi-objective optimization approach for geothermal field development that simultaneously considers economic performance and reservoir longevity. The framework applies the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify Pareto-optimal configurations for well placement and operational control. Objective functions, Net Present Value (NPV) and system lifetime, are evaluated through fully coupled geothermal reservoir simulations using the Delft Advanced Research Terra Simulator (DARTS). The framework incorporates constraint-aware optimization with regulatory compliance, enabling simultaneous optimization of spatial configuration and flow rates through capacity-dependent rate allocation.

The approach is developed and validated on a small-scale synthetic model before application to a realistic corner-point geometry model of the WNB incorporating heterogeneous fluvial architecture. Multiple well configurations (10, 12, and 20 doublets) are systematically evaluated across different geological realizations.

Results demonstrate that NSGA-II effectively identifies diverse Pareto-optimal solutions spanning NPV ranges of 0.8-1.6 billion euros and system lifetimes of 35-100 years. The analysis reveals that total injection capacity directly correlates with economic performance, with higher well-count configurations achieving superior NPV through increased heat extraction capacity. The optimization consistently reveals a distinctive spatial strategy where injection wells are positioned in the thickest reservoir regions with high-permeability zones, while producers balance maximizing distance from injectors with targeting high-temperature, high-permeability areas.

This framework provides quantitative evidence that coordinated planning strategies yield superior performance compared to the current "first-come, first-served" strategies. By applying multi-objective optimization to geothermal planning, the study advocates for the move towards coordinated, regional-scale planning strategies that enable more sustainable and economically superior use of subsurface resources.

Heating and cooling accounts for nearly half of the total energy consumption in the European Union,making geothermal systems a vital component for the energy transition. However, their operational lifetime is limited. Injected cold water forms a cold thermal front that spreads outwards. When this front reaches the production well, thermal breakthrough occurs and production temperatures drop. This report investigates whether seasonally reversing the flow direction by injecting hot water during periods of low heat demand can extend the reservoir’s lifetime or allow for reduced well spacing. This is important because it can optimize the use of geothermal systems in the future.To do this, annual energy demand was studied to find when heat demand is low. The available solar power in this period was then calculated for three different scenario’s to determine what reversed flow rate can be delivered. After establishing an analytical model and baseline simulation of the Delft geothermal reservoir, the three scenario’s were tested and the new breakthrough times computed. The effects of reservoir thickness and well-spacing were then studied.It was found that with the solar power currently available on the TU Delft campus, the lifetime of the geothermal reservoir can be extended by 7 years from the original 21 years. With a limited expansion of solar power on campus, this extension could be 11 years and if the system is run at maximum capacity,21 years is possible. It was found that if the lifetime were to be kept constant, the well spacing could be reduced from the original 1100 metres down to between 934.8 and 789.2 metres depending on the scenario.It was concluded that seasonally reversing the flow direction offers several opportunities to optimize geothermal systems. Either by extending the lifetime of the geothermal system or by allowing closer wel spacing to minimize costs whilst keeping a reasonable reservoir lifetime.

The Hystream Battery is a modular, white-label product designed to produce green hydrogen using surplus photovoltaic and wind energy. The system comprises a battery, electrolyser, compressor, storage tank, and optional features such as a heat exchanger and dispensing unit, aiming to be easy to permit and install. This study investigates the technical and economic feasibility of the Hystream Battery through a make-and-adapt iterative methodology, addressing technological, economic, and regulatory aspects. Each iteration allows for further improvements to be researched.

The research shows that the Hystream Battery is technically feasible and can compete economically with conventional hydrogen production methods, such as steam methane reforming, especially when supported by subsidies like the OWE program. In terms of Levelized Cost of Hydrogen (LCOH), the Hystream Battery is also competitive with large-scale green hydrogen systems. For instance, while large-scale (>20 MW) systems have an LCOH of €13.69 (Eblé & Weeda, 2024b), the Hystream Battery, when evaluated using the same TNO model, achieves a lower LCOH of €12.36.

When evaluated under varying input conditions and grid configurations, the Hystream Battery proves to be economically viable within a defined operational range. In a stand-alone setup where the grid is used solely for standby, the system achieves a Net Present Value (NPV) above €1,000,000 and a Levelized Cost of Hydrogen (LCOH) around €11/kg when powered by a balanced input ratio of 1:1.6:1.6 (grid:PV:wind). A PV input between 1.3 and 2.5, and wind input between 1.3 and 1.7, define the viable operational window. Systems without wind input consistently return negative NPVs.

When the grid is used actively, up to a maximum input of 1500 kW, the system becomes significantly more favorable, with LCOHs dropping below €9/kg across all PV and wind input ratios. In this scenario, higher PV capacities correlate with increased NPVs, and electricity curtailment is reduced to below 5% at input ratios as low as 1:1.5:0. Additionally, system economics improve as wind input decreases. These results align closely with the energy profile of Dutch business parks, which often feature high rooftop PV potential, grid constraints, and multiple hydrogen-consuming stakeholders such as transport operators, thermal energy users, and industrial feedstock consumers.

Regulatory challenges, particularly those related to permitting, were identified as key obstacles to fast market deployment. The preparation of accurate and comprehensive documentation and studies for environmental authorities is essential to expedite the approval process, potentially reducing permitting timelines from 26 to 8 weeks. Furthermore, the inclusion of features like a heat exchange module enhances system efficiency and reduces CO2 emissions by replacing gas-based heating with recovered waste heat.

By analysing system configurations, market potential, and regulatory requirements, this thesis concludes that the Hystream Battery holds significant promise for advancing the hydrogen economy in the Netherlands. The system supports local hydrogen production, mobility applications, and renewable energy integration by reducing grid congestion, contributing to reduced energy dependence and the broader energy transition.

The increasing global demand for heating and cooling is predominantly met by fossil-based energy sources, contributing to greenhouse gas emissions and placing growing pressure on energy infrastructure. Although greater use of renewable energy is crucial for decarbonisation, its generation frequently does not coincide with demand, creating a seasonal mismatch. Seasonal thermal energy storage offers a promising solution to balance this mismatch between energy supply and demand. This thesis investigates the technical, environmental, and economic feasibility of Mine Thermal Energy Storage (MTES) by coupling a virtual MTES model with the heating and cooling system of Ruhr-University Bochum (RUB).

To achieve this, separate Python and Modelica models of the MTES and campus heating and cooling systems were developed and linked through an orchestrator algorithm. The co-simulation was conducted to evaluate system performance. Sensitivity analyses were performed to assess the influence of system design parameters, operational parameters, and input data. In addition, the long-term effects were monitored and the optimal setup was determined.

Results demonstrate that MTES can reliably store excess heat in summer and supply it during winter while maintaining stable operational temperatures. The integrated system achieved a total COP which is lower than a stand-alone heat pump, but appropriate for a combined heating-cooling configuration with seasonal storage losses. The system reduced reliance on the gas boilers and cooling tower, and it also delivered the highest CO₂ reduction among comparable storage technologies. Economic assessment shows that the levelised cost of heat is competitive with similar storage systems, even under conservative operational assumptions.

Sensitivity analyses showed that the optimal heat pump size and operational variables such as the heat pump ΔT value and MTES temperature critically influenced performance, while the MTES size had a weaker but still noticeable effect. The optimal practical and theoretical setups further demonstrated the system’s potential, showing significant increases in recovered waste heat and CO₂ reduction.

Overall, this thesis demonstrates that MTES offers a technically, economically and environmentally viable solution for seasonal thermal energy storage in the heating and cooling system of the Ruhr- University Bochum. Ultimately, MTES can represent an important step toward achieving the EU’s long-term climate targets by enabling more efficient and resilient sustainable energy systems.

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.

Heating from geothermal doublets might contribute to the adaptation from fossil energy into greener methods for a CO2-neutral future. Recently, P. van Nieuwkerk (2022) has demonstrated how a new upscaling method developed by Tang et al. (2022) produces more accurate predictions of production temperature over time when an increase in the vertical thermal conductivity parameter (kz) is implemented in simulations. The results of Van Nieuwkerk's research might be used in optimising geothermal doublet performance. This thesis explores whether a justified physical reason for increasing kz exists. Concretely, this study compares analytical solutions and modelled solutions, based on a simplified layer-cake model, of a situation of conductive heat transfer for which analytical solutions already exist. A technique called superposition has been applied to both solutions in order to match the in situ situation more accurately. Results of the most realistic cases that have been investigated show that the modelled and analytical solutions do not vary considerably in temperature predictions over time. Therefore, this research has not identified any physical grounds that justify an increase in kz. Nevertheless, since this report has only considered a simplified model that bears limited resemblance to the in situ situation, it does not mean that there is none. Based on these results, future research is necessary to make more specific evidentiary claims.

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.