Near-borehole coupled thermo-hydro-mechanical (THM) processes are critical in defining the success or failure of geothermal projects. Injectivity decline due to clogging or the increased viscosity of cold water reduces the safe operational window of geothermal projects. However, near-borehole fracturing, a result of near-borehole coupled thermo-hydro-mechanical (THM) processes, is thought to possibly be able to contribute to maintaining or improving the re-injection performance. Understanding and being able to simulate the fracturing processes is therefore of great significance for both analysing re-injection and designing stimulation operations. This thesis presents a geo-mechanical tool, based on the finite element method and cohesive zone model, to simulate the fracture initiation and propagation under THM loadings. The geo-mechanical tool is used to study several stimulation scenarios, including monotonic, stepwise, cyclic, and stepwise combined with cyclic stimulation. To monitor such stimulation processes, this thesis proposes a dual-cable distributed acoustic sensing (DAS) system in a single vertical well and investigates its feasibility to localise and understand near-borehole cracking events.

In the numerical method, possible discontinuities are represented by zero-thickness triple-nodded interface elements, which allow solid elements to separate with mechanical damage and the simulation of longitudinal and transversal fluid/heat flow in the discontinuity. The cubic law is used to simulate the fracture transmissivity changes, while an elasto-damage law is used to characterise the mechanical response of the discontinuity. To simulate the fracture initiation and propagation from high-permeability intact rock, interface elements are inserted in-between all the solid elements, with high stiffness and transversal hydraulic coefficient assigned to reduce artificial compliance. An artificial heat conductivity is introduced to stabilise the numerical solution, in which high Peclet numbers lead to numerical divergence. Substantial verifications and validation are implemented to demonstrate the performance of the developed method.

A new elasto-damage law is developed by incorporating a fatigue damage variable into the tensile branch, in order to account for the fatigue effects during the simulation of cyclic (thermal) stimulation to geothermal reservoirs. The fatigue damage variable is calibrated using the number of loading cycles and fatigue life at different load intensities, with Palmgren-Miner’s rule used to account for varying-amplitude cyclic loading. The proposed model is validated against extensive laboratory tests, including cyclic Brazilian test, cyclic hydraulic fracturing test and cyclic thermo-hydraulic fracturing test. The validation results show good agreement with the experimental data, demonstrating that the proposed model is capable of handling fatigue damage under cyclic and coupled THM loadings.

The developed tool is then used to study stimulation to a synthetic sedimentary reservoir, which, according to regional experience, is assumed to be clogged in the near-borehole region. THM simulations of various stimulation strategies - monotonic, stepwise, cyclic, and stepwise combined with cyclic - demonstrate that the stepwise stimulation yields the most favourable outcomes. Specifically, it enables a significantly lower peak injection pressure with more near-borehole damage. This performance is not achievable using either monotonic or cyclic strategies (assuming same Qinj and Tinj). Conversely, cyclic-injection-rate stimulation slightly underperforms (under high injection rate) or slightly outperforms (under low injection rate) the monotonic stimulation. A combined approach incorporating both cyclic and stepwise strategies may lead to slightly better stimulation performance, showing lower peak pressure, compared to corresponding monotnic stimulation, but is inferior to the stepwise stimulation alone.

The feasibility of using a single-well dual-cable DAS to fully localise and understand the near-borehole micro-seismic events is investigated based on synthetic signals, assuming homogenous and isotropic media. A localisation method is introduced to determine the source depth, epicentral distance and azimuth. Sensitivity analysis shows that the localisation accuracy is not sensitive to source with frequency varying from 50 Hz to 200 Hz. But a low signal-to-noise ratio and/or source-to-receiver azimuth close to 0◦ can lead to decreasing accuracy. Moreover, resolvability analysis suggest that non double-couple moment tensor components Mxx,Myy and Mzz can be reliably resolved with an epicentral distance within 20 meters, showing improvement on the case of only one cable in a well. A discussion based on the geo-mechanical simulation demonstrates that the single-well dual-cable DAS can be used to understand near-borehole tensile fractures induced during thermal stimulation, with a limited epicentral distance, which implies it is well suited to monitoring stimulation operations.

This thesis contributes to the energy transition by developing a geo-mechanical model to simulate cyclic and coupled THM processes, including the development of fractures, around the near field of the wellbore which can allow the design of novel cyclic thermal stimulation and by proposing a single-well dual-cable DAS configuration that is demonstrated to be feasible to localise and understand near-borehole micro-seismic events to monitor thermal stimulation operations.
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Achieving net zero in greenhouse gases emissions attributed to human activities depends on the transition to renewable energy resources. Low-enthalpy geothermal systems, characterized by their widespread geographical distribution and suitability for direct heating applications, represent a promising alternative to fossil fuels. However, maintaining the long-term efficiency and economic viability of such geothermal reservoirs requires the development of new methods to monitor subtle variations in their properties, particularly those induced by temperature changes during energy extraction and reinjection.
This thesis evaluates the feasibility and advances the methodology of two key geophysical approaches for reservoir monitoring: the controlled-source electromagnetic (CSEM) method and the full waveform inversion (FWI) of seismic data. The research is grounded in two study areas: the Delft campus geothermal project in the Netherlands and the Munich geothermal project in Germany.
A feasibility study of CSEM monitoring was carried out on the Delft site to assess its sensitivity to subtle resistivity variations corresponding to temperature changes in the reservoir. Surface-to-borehole CSEM survey configuration was modeled to optimize source frequency and offset, with results demonstrating the detectability of a 4 Ω・m resistivity increase calculated for a 25 ◦C temperature drop in the Delft Sandstone reservoir. The study systematically analyzed the impacts of environmental disturbances—random noise, repeatability errors, seasonal near-surface temperature fluctuations, and the presence of steel-cased wells—on the performance of CSEM monitoring data. It was shown that a careful survey design and adequate source parameters allow CSEM monitoring, which is robust against most undesired effects, although steel casings require careful consideration due to their strong field attenuation within a radius of 100 m for a frequency of 1 Hz.
For high-resolution seismic characterization, the thesis develops and validates a novel sequential FWI approach for reconstructing high-resolution models of P-wave velocity and impedance from vertical seismic profiling (VSP) data. The method incorporates traveltime tomography for starting models and introduces a temporal phase resemblance step to improve convergence and mitigate phase error propagation in impedance inversion. Inversion experiments of synthetic data demonstrate that this approach enables the detection of impedance variations greater than 2 %, directly linked to temperature-driven reservoir changes. Field application to baseline VSP data at the Munich geothermal site confirms the robustness of the approach. A comparative analysis of distributed acoustic sensing (DAS) and conventional geophone-based FWI of P-wave velocity further elucidates the operational benefits and challenges of fiber-optic deployments inside the casing for characterization of geothermal reservoirs.
The results presented in this thesis establish CSEM and advanced seismic FWI as promising and complementary tools for noninvasive monitoring of low-enthalpy geothermal reservoirs. The work concludes with a discussion of current limitations, practical considerations for field deployment, and recommendations for future research.
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Seismic data reconstruction addresses the challenge of accurately restoring incomplete or damaged seismic datasets, which is crucial for subsurface imaging and exploration. The missing data is often due to equipment failure, signal loss, environmental noise, or poor geological conditions. It may also result from limitations in data acquisition, such as sparse sampling and noise interference. This thesis proposes a Clean Convergent alternating Projections onto Convex Sets (CCP) method for data reconstruction. By incorporating an initial value tweaking step into the alternating projections process in the Convergent alternating Projections onto Convex Sets (CP) method, this method aims to reduce ringing noise in the reconstruction results by the CP method. Initially, the traditional CP method and its application in seismic data reconstruction are introduced, highlighting its shortcomings in addressing ringing noise. To overcome this issue, a CCP method based on a non-local means algorithm for initial value tweaking is proposed and applied within the outer loop of the CP method. The theoretical foundation and implementation steps of the CCP method are discussed in detail, and its effectiveness is validated through a series of experiments. The experimental results demonstrate that, compared to the traditional CP method, the CCP method significantly reduces ringing noise and improves the quality of the reconstructed data. Various datasets, including images, 2D seismic section data from the SEAM II Arid model, and 3D seismic model cubes, are used to showcase the reconstruction capabilities of the CCP method in different scenarios. Finally, the thesis provides an in-depth discussion of the CCP method, including intermediate results in the outer loop, the necessity of data preconditioning, and parameter testing, offering a practical set of control parameter settings. The study shows that the CCP method has broad application prospects in data reconstruction, providing valuable references for future research. ... Read more

In 3D seismic data acquisition, sufficiently dense spatial sampling is most often not possible, because of physical, financial, and temporal constraints. The resulting aliased data is a major obstacle for accurate subsurface images. Therefore, methods are needed to reconstruct these sparse data to an adequate sampling. We develop a novel kinematic wavefront-based seismic data reconstruction method that uses the existing nonlinear beamforming (NLBF) framework, which allows for building a more detailed sampling from a sparse input. We present the theory and methodology of our NLBF reconstruction algorithm and test it on a synthetic Society of Exploration Geophysicists Advanced Modeling (SEAM) Arid dataset and a field dataset. We attempt to answer the following question: can the NLBF framework be used for seismic data reconstruction, and how do these results compare to some conventional reconstruction methods? Control parameter tests are performed to find the optimal NLBF reconstruction, and results are compared to those from several control methods including the convergent alternating projection onto convex sets (POCS) and bootstrap POCS methods, which were also developed for this study. Our NLBF reconstruction method successfully reconstructs high-quality data on both datasets. We find that the optimal NLBF control parameters are ultimately dataset-dependent as the concrete data acquisition geometry plays a central role. Specifically for our datasets, optimal parameters include a time window of 12∆ts, an operator aperture of 600 m x 600 m, and a parameter trace interval of ∆x = ∆y = 60 m. The synthetic SEAM Arid NLBF reconstruction results show high trace fidelity to the ground truth. The field data NLBF results show better-reconstructed gaps, and wavefield variations closer to those of physical propagating waves in comparison to our control methods. These results show the potential of the NLBF reconstruction method to become a common data reconstruction tool used in the seismic industry. ... Read more

The analysis of microseismic measurements acquired during borehole acquisition surveys is essential for a thorough understanding of the source mechanisms in hydraulic fracturing operations. Due to the injection of high-pressure fluids, induced fractures can produce seismic events of low magnitude that can be recorded and subsequently analyzed to infer the stress field at the location of the event. The seismic moment tensor has been widely used to describe general seismic sources as it can provide information about the type of motion and the distribution of forces. Estimating such quantities from the recorded data can significantly improve the real-time microseismic monitoring operations and help to make assumptions about the structure of the reservoir. However, several challenges have to be faced when working with microseismic borehole measurements. They are characterized by a low signal-to-noise ratio and a limited angle coverage, which may ultimately affect the predictions about the location and the fracturing behavior inside the reservoir. In this thesis the inversion of microseismic measurements for retrieving the moment tensor has been tackled by using a deep feedforward neural network. The seismograms used to train the network were generated through the discrete-wavenumber method. The neural network was used to predict the six independent moment-tensor components and the fault angles from seismic sources at different positions than those used during the training. The predictive capabilities of the network were tested for realistic borehole acquisition geometries and wave propagation models. The moment-tensor components were retrieved with good accuracy when using noisy seismograms and, non-double-couple source mechanisms. As the generation of synthetic data can be expensive in terms of memory consumption, the training and prediction have been also implemented in the frequency domain using only a limited portion of the Fourier transform of the seismograms. The inversion results indicated that using narrow bands can still yield satisfactory results when predicting the moment-tensor components. ... Read more

A key practice in the development of unconventional hydrocarbon resources is to monitor hydraulic fracturing operations and analyze in detail the induced microseismicity, for understanding the extent of the volume affected by the fractures. Even though microseismic data are one of the few geophysical measurements that can be used for this purpose they are not always used at their full potential: the common analyses are limited to the interpretation of the locations of the induced seismic events in terms of fractured volume, while the propagation and the dynamic evolution of the microseismic events that are directly related to the source process are not studied. The source moment tensor can be used to describe the deformation mechanism occurring at the seismic source and the corresponding geomechanical parameters, which are relevant aspects for the monitoring of hydraulic fracturing operations. Its value can be estimated through an inversion process using the P-wave and S-wave waveforms from the observations at the seismic receivers. However, the limited aperture of the receiver arrays in borehole acquisition is inadequate to solve the inverse problem. The goal of this thesis project is study the feasibility of estimating the source moment tensor with borehole microseismic data making use of machine learning algorithms. The task is done by implementing a forward model to compute waveforms generated by known values of the source moment tensor in a borehole acquisition geometry (forward problem). Forward modeling was carried out using a modied version of the discrete wavenumber method. Afterwards, this data is used to train a neural network to estimate the moment tensor at the source given the microseismic data (inverse problem). The neural network was designed, adapting an existing architecture used in previous studies to solve similar inverse problems. The implementation was done entirely in the Python programming language, using the Tensorflow library for the machine learning parts. The network was trained with the dataset generated in the forward model until reaching an optimal mean squared error minimum. Finally, the capability of the network to invert microseismic measurements and retrieve acceptable values of the source moment-tensor components was tested using synthetic data, resulting in an average prediction accuracy of 0.997. Our results indicate that for this case, a neural network is able to satisfactory invert for the full moment-tensor components. Additionally, the neural network was also trained to handle the presence of Gaussian noise, achieving an average prediction accuracy of 0.990 for tested synthetic data with 10% Gaussian noise. ... Read more

Forensic investigations focused on determining clandestine buried weapons, narcotics or even homicide evidence, can be expensive, inefficient and depend greatly on prior information and the tools available. Geophysical tools have potential to improve these investigations, on the ground that they can detect shallow buried objects in a non-invasive way. This report studies the use of electrical resistivity tomography (ERT) in combination with ground penetrating radar (GPR) to detect buried objects at two different sites, both with conditions found here in The Netherlands. The first is a test site at the Technical University of Delft, The Netherlands. Here, two plastic barrels are buried, one is empty and the other is filled with metal rods. The second site is at the ARISTA Facility in Amsterdam, The Netherlands. Here, human cadavers are buried for forensic research. The ERT was used in two ways, the first was to produce 2 dimensional (2D) surveys, which were combined to create a 3 dimensional (3D) model. The second method made measurements that created a 3D model directly. For each the dipole-dipole array was used. At the TU Delft site the results for the 2D method showed clear resistivity anomalies at the locations of the barrels. These anomalies corresponded to clear reflections in the GPR radargrams. The results at the ARISTA facility are inconclusive due to damage in the instrument used. The grid designs made for both ERT methods could however be used in a continuation of this study, and future research should be done within this topic to improve forensic investigations. ... Read more

Since its inception in 1975, Genetics Algorithms (GAs) have been successfully used as a tool for global optimization on non-convex problems in a wide range of real world applications. Its creation was inspired by natural adaptation and selection mechanisms that evolve from one population of chromosomes to a fitter population by means of an artificial natural selection dictated by the operators of elitism, crossover and mutation. An advanced Genetic Algorithm (aGA) was proposed by Sun et al. [2017], and this algorithm seeks the global maximum of n-th dimensional non-convex functions. However, convergence speed is a key factor for the success of a global optimization algorithm when it comes to scalability; in production even a slight efficiency improvement matters as it can easily takes weeks or even months to process a huge data set. Therefore, the goal of this project is to improve the convergence speed of the currently available aGA by simultaneously enhancing both its global and its local search capabilities. To this end, two solutions were proposed. The first is a modified version of the well known Island model GAs and the second was named Self Adaptive Differential Evolution (SADE) fine tuning scheme.
After a successful demonstration of its improved performance on several multi-modal test functions, the enhanced Genetic Algorithm (eGA) is used to tackle two common non-linear geophysical problems: static correction and Common Reflection Surface (CRS) stacking. In the former, the near-surface related time-shifts are estimated without resorting to an explicit velocity-depth model; instead, the events of interest are aligned in a data-driven fashion by maximizing the stacking power. The latter is a novel alternative to the traditional Common Midpoint (CMP) stacking that has proven to yield higher quality images, specially when applied to low Signal to Noise Ratio (SNR) data or data with challenging structures like the anomalies encountered in the subsurface. This improvement in the quality of the stacked image is attributed to a non-local mean mentality, which enables using traces from different CMP gathers to the CMP gather being resolved. Owing to the high non-linearity of both problems, they are ideal test beds for global optimization algorithms. The effectiveness of the eGA is demonstrated on synthetic data sets with promising results in these problems. ... Read more

The Jakobshavn glacier was responsible for approximately 1 mm eustatic sea level rise in the period of 2000 to 2010 [Howat et al. 2011]. As such, the Jakobshavn glacier became one of the largest outlet glaciers in Greenland [Joughin et al. 2004]. Ice flow velocities within the same period reached over 10 km/yr with strong seasonal variation [Howat et al. 2011, Joughin et al. 2012]. More recently from 2011 until 2013, even higher ice flow velocities of at least 15 km/yr were observed [Lemos et al. 2018]. Due to the relatively high ice flow velocities, the ice discharge plays the largest role in the mass balance (MB) of the Jakobshavn glacier. Quantification of the ice discharge from ice flow velocities is however, not a common procedure. Yet the evolution of the ice discharge of single glaciers not only improves understanding of the climate-cryosphere system, but also aids quantification of sea level contribution on a drainage basin scale. To that end, this study embodies an indirect ice discharge estimation of the Jakobshavn glacier over the period of November 2010 until March 2016 using altimetry (CryoSat-2 Level 1b (L1b)) and gravimetry (GRACE Level 2 (L2)) data in combination with a regional climate model (RACMO 2.3p2). By subtraction of the altimetric and gravimetric mass balance estimates from the atmospheric component (i.e. the surface mass balance (SMB)), two ice discharge estimates are obtained. This approach does not suffer from the drawbacks involved when estimating ice discharge from velocity fields directly, which are based on offset tracking. Data gaps for long polar nights and clouds in the visible spectrum and decorrelation in general, when the duration between subsequent images over the same location is long, are thus avoided. This is because offset tracking algorithms require recognisable characteristics in subsequent satellite recordings to determine the velocity, i.e. satellite recordings need to be sufficiently correlated. To derive the mass balance from altimetry data, adequate spatial sampling is desired. To that end, this study applies swath processing to CryoSat-2 L1b data with an adapted waveform sample selection criterion to obtain an unprecedented spatial sampling with about 2 order of magnitude more height observations compared to conventional retracking techniques. As a consequence, elevation changes can be derived at a relatively high spatial resolution (250 m).
The elevations are converted to elevation changes, volume change and mass change using weighted least squares estimations (WLSE), hypsometric averaging and density models, respectively. The GRACE-based mass change estimate is acquired using a point-mass assumption at the location of the Jakobshavn glacier. The known, simulated point mass is then scaled to the observed mass by GRACE. In addition, data weighting of GRACE Stokes' coefficients is attempted using the full noise covariance matrix. Subsequently, a LSE is used to infer the mass balance from the two time series (with and without weighting of the Stokes' coefficients). ... Read more

For reservoir characterization, the subsurface heterogeneity needs to be qualified in which the distribution of lithologies is an essential part since it determines the location and migration paths of hydrocarbons. Preliminary analysis of well-log data could help to identify various lithologies in a one-dimensional direction (depth), while the lateral information is missing because of the sparse locations. On the other hand, a larger areal coverage of the target reservoir could be provided by seismic data, and from the inversion thereof, inferences of lithologies could be made. However, just like other geophysical inversions, translation of seismic inversion results to these categorical variables (lithologies) is a non-unique problem, which means that different lithologies could produce the same, or similar, property responses. In order to mitigate this problem, geological prior information should be introduced in the sense of Bayes’ theorem. Thus, the main motivation for this thesis is to investigate the usage of geological prior information in the classification of reservoir lithologies from properties obtained from seismic inversion. Different methods have been tried in this process in order to fully understand their performances and to make comparisons. ... Read more