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Development of geophysical methods to characterize methane hydrate reservoirs on a laboratory scale
(2015)
Gas hydrates are crystalline solids composed of water and gas molecules. They are stable at elevated pressure and low temperatures. Therefore, natural gas hydrate deposits occur at continental margins, permafrost areas, deep lakes, and deep inland seas. During hydrate formation, the water molecules rearrange to form cavities which host gas molecules. Due to the high pressure during hydrate formation, significant amounts of gas can be stored in hydrate structures. The water-gas ratio hereby can reach up to 1:172 at 0°C and atmospheric pressure. Natural gas hydrates predominantly contain methane. Because methane constitutes both a fuel and a greenhouse gas, gas hydrates are a potential energy resource as well as a potential source for greenhouse gas.
This study investigates the physical properties of methane hydrate bearing sediments on a laboratory scale. To do so, an electrical resistivity tomography (ERT) array was developed and mounted in a large reservoir simulator (LARS). For the first time, the ERT array was applied to hydrate saturated sediment samples under controlled temperature, pressure, and hydrate saturation conditions on a laboratory scale. Typically, the pore space of (marine) sediments is filled with electrically well conductive brine. Because hydrates constitute an electrical isolator, significant contrasts regarding the electrical properties of the pore space emerge during hydrate formation and dissociation. Frequent measurements during hydrate formation experiments permit the recordings of the spatial resistivity distribution inside LARS. Those data sets are used as input for a new data processing routine which transfers the spatial resistivity distribution into the spatial distribution of hydrate saturation. Thus, the changes of local hydrate saturation can be monitored with respect to space and time.
This study shows that the developed tomography yielded good data quality and resolved even small amounts of hydrate saturation inside the sediment sample. The conversion algorithm transforming the spatial resistivity distribution into local hydrate saturation values yielded the best results using the Archie-var-phi relation. This approach considers the increasing hydrate phase as part of the sediment frame, metaphorically reducing the sample’s porosity. In addition, the tomographical measurements showed that fast lab based hydrate formation processes cause small crystallites to form which tend to recrystallize.
Furthermore, hydrate dissociation experiments via depressurization were conducted in order to mimic the 2007/2008 Mallik field trial. It was observed that some patterns in gas and water flow could be reproduced, even though some setup related limitations arose.
In two additional long-term experiments the feasibility and performance of CO2-CH4 hydrate exchange reactions were studied in LARS. The tomographical system was used to monitor the spatial hydrate distribution during the hydrate formation stage. During the subsequent CO2 injection, the tomographical array allowed to follow the CO2 migration front inside the sediment sample and helped to identify the CO2 breakthrough.
Die genauen Einsatzzeiten seismischer P-Phasen von Erdbeben werden in SeisComP3 und anderen Auswerteprogrammen standardmäßig und in Echtzeit automatisch bestimmt. S-Phasen stellen dagegen eine weit größere Herausforderung dar. Nur mit genauen Picks der P- bzw. S-Phasen können die Erdbebenlokationen korrekt und stabil bestimmt werden. Darum besteht erhebliches Interesse, diese mit hoher Genauigkeit zu bestimmen. Das Ziel der vorliegenden Bachelorarbeit war es, vier verschiedene, bereits vorhandene S-Phasenpicker auf ausgewählte Parameter optimal zu konfigurieren, auf Testdaten anzuwenden und deren Leistungsfähigkeit objektiv zu bewerten. Dazu wurden ein S-Picker (S-L2) aus dem OpenSource SeisComp3-Programmpaket, zwei S-Picker (S-AIC, S-AIC-V) als kommerzielles Modul der Firma gempa GmbH für SeisComP3 und ein S-Picker (Frequenzband) aus dem OpenSource PhasePaPy-Paket ausgewählt. Die Bewertung erfolgte durch Vergleich automatischer Picks mit manuell bestimmten Einsatzzeiten. Alle vier Picker wurden separat konfiguriert und auf drei verschiedene Datensätze von Erdbeben in N-Chile und im Vogtland, Deutschland, angewandt. Dazu wurden regional bzw. lokal typische Erdbeben zufällig ausgewählt und die P- und S-Phasen manuell bestimmt. Mit den zu testenden S-Pickeralgorithmen wurden dieselben Daten durchsucht und die Picks automatisch bestimmt. Die Konfigurationen der Picker wurden gleichzeitig automatisch und objektiv durch iterative Anpassung optimiert. Ein neu erstelltes Bewertungssystem vergleicht die manuellen und die automatisch gefundenen S-Picks anhand von definierten Qualitätsfaktoren. Die Qualitätsfaktoren sind: der Mittelwert und die Standardabweichung der zeitlichen Differenzen zwischen den S-Picks, die Anzahl an übereinstimmenden S-Picks, die Prozentangaben über mögliche S-Picks und die benötigt Rechenzeit. Die objektive Bewertung erfolgte anhand eines Scores. Der Scorewert ergibt sich aus der gewichteten Summe folgender normierter Qualitätsfaktoren: Standardabweichung (20%), Mittelwert (20%) und Prozentangabe über mögliche S-Picks (60%). Konfigurationen mit hohem Score werden bevorzugt. Die bevorzugten Konfigurationen der verschiedenen Picker wurden miteinander verglichen, um den am besten geeigneten S-Pickeralgorithmus zu bestimmen. Allgemein zeigt sich, dass der S-AIC Picker für jeden der drei Datensätze die höchsten Scores und damit die besten Ergebnisse liefert. Dabei wurde für jeden Datensatz ein andere Konfiguration der Parameter des S-AIC Pickers als die am besten geeignete bezeichnet. Daher ist für jede Erdbebenregion eine andere Konfigurationen erforderlich, um optimale Ergebnisse mit diesem S-Picker zu bekommen.
Thermokarst lakes are prevalent in Arctic coastal lowland regions and sublake permafrost degradation and talik development contributes to greenhouse gas emissions by tapping the large permafrost carbon pool. Whereas lateral thermokarst lake expansion is readily apparent through remote sensing and shoreline measurements, sublake thawed sediment conditions and talik growth are difficult to measure. Here we combine transient electromagnetic surveys with thermal modeling, backed up by measured permafrost properties and radiocarbon ages, to reveal closed-talik geometry associated with a thermokarst lake in continuous permafrost. To improve access to talik geometry data, we conducted surveys along three transient electromagnetic transects perpendicular to lakeshores with different decadal-scale expansion rates of 0.16, 0.38, and 0.58m/year. We modeled thermal development of the talik using boundary conditions based on field data from the lake, surrounding permafrost and a borehole, independent of the transient electromagnetics. A talik depth of 91m was determined from analysis of the transient electromagnetic surveys. Using a lake initiation age of 1400years before present and available subsurface properties the results from thermal modeling of the lake center arrived at a best estimate talk depth of 80m, which is on the same order of magnitude as the results from the transient electromagnetic survey. Our approach has provided a noninvasive estimate of talik geometry suitable for comparable settings throughout circum-Arctic coastal lowland regions.
Modern mobile devices (i.e. smartphones and tablet computers) are widespread, everyday tools, which are equipped with a variety of sensors including three-axis magnetometers. Here, we investigate the feasibility and the potential of using such mobile devices to mimic geophysical experiments in the classroom in a table-top setup. We focus on magnetic surveying and present a basic setup of a table-top experiment for collecting three-component magnetic data across well-defined source bodies and structures. Our results demonstrate that the quality of the recorded data is sufficient to address a number of important basic concepts in the magnetic method. The shown examples cover the analysis of magnetic data recorded across different kinds of dipole sources, thus illustrating the complexity of magnetic anomalies. In addition, we analyze the horizontal resolution capabilities using a pair of dipole sources placed at different horizontal distances to each other. Furthermore, we demonstrate that magnetic data recorded with a mobile device can even be used to introduce filtering, transformation, and inversion approaches as they are typically used when processing magnetic data sets recorded for real-world field applications. Thus, we conclude that such table-top experiments represent an easy-to-implement experimental procedure (as student exercise or classroom demonstration) and can provide first hands-on experience in the basic principles of magnetic surveying including the fundamentals of data acquisition, analysis and processing, as well as data evaluation and interpretation.
The Andean Plateau (Altiplano-Puna Plateau) of the southern Central Andes is the second-highest orogenic plateau on our planet after Tibet. The Andean Plateau and its foreland exhibit a pronounced segmentation from north to south regarding the style and magnitude of deformation. In the Altiplano (northern segment), more than 300 km of tectonic shortening has been recorded, which started during the Eocene. A well-developed thin-skinned thrust wedge located at the eastern flank of the plateau (Subandes) indicates a simple-shear shortening mode. In contrast, the Puna (southern segment) records approximately half of the shortening of the Altiplano - and the shortening started later. The tectonic style in the Puna foreland switches to a thick-skinned mode, which is related to pure-shear shortening. In this study, carried out in the framework of the StRATEGy project, high-resolution 2D thermomechanical models were developed to systematically investigate controls of deformation patterns in the orogen-foreland pair. The 2D and 3D models were subsequently applied to study the evolution of foreland deformation and surface topography in the Altiplano-Puna Plateau. The models demonstrate that three principal factors control the foreland-deformation patterns: (i) strength differences in the upper lithosphere between the orogen and its foreland, rather than a strength difference in the entire lithosphere; (ii) gravitational potential energy of the orogen (GPE) controlled by crustal and lithospheric thicknesses, and (iii) the strength and thickness of foreland-basin sediments. The high-resolution 2D models are constrained by observations and successfully reproduce deformation structures and surface topography of different segments of the Altiplano-Puna plateau and its foreland. The developed 3D models confirm these results and suggest that a relatively high shortening rate in the Altiplano foreland (Subandean foreland fold-and-thrust belt) is due to simple-shear shortening facilitated by thick and mechanically weak sediments, a process which requires a much lower driving force than the pure-shear shortening deformation mode in the adjacent broken foreland of the Puna, where these thick sedimentary basin fills are absent. Lower shortening rate in the Puna foreland is likely accommodated in the forearc by the slab retreat.
Data assimilation algorithms are used to estimate the states of a dynamical system using partial and noisy observations. The ensemble Kalman filter has become a popular data assimilation scheme due to its simplicity and robustness for a wide range of application areas. Nevertheless, this filter also has limitations due to its inherent assumptions of Gaussianity and linearity, which can manifest themselves in the form of dynamically inconsistent state estimates. This issue is investigated here for balanced, slowly evolving solutions to highly oscillatory Hamiltonian systems which are prototypical for applications in numerical weather prediction. It is demonstrated that the standard ensemble Kalman filter can lead to state estimates that do not satisfy the pertinent balance relations and ultimately lead to filter divergence. Two remedies are proposed, one in terms of blended asymptotically consistent time-stepping schemes, and one in terms of minimization-based postprocessing methods. The effects of these modifications to the standard ensemble Kalman filter are discussed and demonstrated numerically for balanced motions of two prototypical Hamiltonian reference systems.
The shallow Earth’s layers are at the interplay of many physical processes: some being driven by atmospheric forcing (precipitation, temperature...) whereas others take their origins at depth, for instance ground shaking due to seismic activity. These forcings cause the subsurface to continuously change its mechanical properties, therefore modulating the strength of the surface geomaterials and hydrological fluxes. Because our societies settle and rely on the layers hosting these time-dependent properties, constraining the hydro-mechanical dynamics of the shallow subsurface is crucial for our future geographical development. One way to investigate the ever-changing physical changes occurring under our feet is through the inference of seismic velocity changes from ambient noise, a technique called seismic interferometry. In this dissertation, I use this method to monitor the evolution of groundwater storage and damage induced by earthquakes. Two research lines are investigated that comprise the key controls of groundwater recharge in steep landscapes and the predictability and duration of the transient physical properties due to earthquake ground shaking. These two types of dynamics modulate each other and influence the velocity changes in ways that are challenging to disentangle. A part of my doctoral research also addresses this interaction. Seismic data from a range of field settings spanning several climatic conditions (wet to arid climate) in various seismic-prone areas are considered. I constrain the obtained seismic velocity time-series using simple physical models, independent dataset, geophysical tools and nonlinear analysis. Additionally, a methodological development is proposed to improve the time-resolution of passive seismic monitoring.
Rapidly growing seismic and macroseismic databases and simplified access to advanced machine learning methods have in recent years opened up vast opportunities to address challenges in engineering and strong motion seismology from novel, datacentric perspectives. In this thesis, I explore the opportunities of such perspectives for the tasks of ground motion modeling and rapid earthquake impact assessment, tasks with major implications for long-term earthquake disaster mitigation.
In my first study, I utilize the rich strong motion database from the Kanto basin, Japan, and apply the U-Net artificial neural network architecture to develop a deep learning based ground motion model. The operational prototype provides statistical estimates of expected ground shaking, given descriptions of a specific earthquake source, wave propagation paths, and geophysical site conditions. The U-Net interprets ground motion data in its spatial context, potentially taking into account, for example, the geological properties in the vicinity of observation sites. Predictions of ground motion intensity are thereby calibrated to individual observation sites and earthquake locations.
The second study addresses the explicit incorporation of rupture forward directivity into ground motion modeling. Incorporation of this phenomenon, causing strong, pulse like ground shaking in the vicinity of earthquake sources, is usually associated with an intolerable increase in computational demand during probabilistic seismic hazard analysis (PSHA) calculations. I suggest an approach in which I utilize an artificial neural network to efficiently approximate the average, directivity-related adjustment to ground motion predictions for earthquake ruptures from the 2022 New Zealand National Seismic Hazard Model. The practical implementation in an actual PSHA calculation demonstrates the efficiency and operational readiness of my model. In a follow-up study, I present a proof of concept for an alternative strategy in which I target the generalizing applicability to ruptures other than those from the New Zealand National Seismic Hazard Model.
In the third study, I address the usability of pseudo-intensity reports obtained from macroseismic observations by non-expert citizens for rapid impact assessment. I demonstrate that the statistical properties of pseudo-intensity collections describing the intensity of shaking are correlated with the societal impact of earthquakes. In a second step, I develop a probabilistic model that, within minutes of an event, quantifies the probability of an earthquake to cause considerable societal impact. Under certain conditions, such a quick and preliminary method might be useful to support decision makers in their efforts to organize auxiliary measures for earthquake disaster response while results from more elaborate impact assessment frameworks are not yet available.
The application of machine learning methods to datasets that only partially reveal characteristics of Big Data, qualify the majority of results obtained in this thesis as explorative insights rather than ready-to-use solutions to real world problems. The practical usefulness of this work will be better assessed in the future by applying the approaches developed to growing and increasingly complex data sets.
Water stored in the unsaturated soil as soil moisture is a key component of the hydrological cycle influencing numerous hydrological processes including hydrometeorological extremes. Soil moisture influences flood generation processes and during droughts when precipitation is absent, it provides plant with transpirable water, thereby sustaining plant growth and survival in agriculture and natural ecosystems.
Soil moisture stored in deeper soil layers e.g. below 100 cm is of particular importance for providing plant transpirable water during dry periods. Not being directly connected to the atmosphere and located outside soil layers with the highest root densities, water in these layers is less susceptible to be rapidly evaporated and transpired. Instead, it provides longer-term soil water storage increasing the drought tolerance of plants and ecosystems.
Given the importance of soil moisture in the context of hydro-meteorological extremes in a warming climate, its monitoring is part of official national adaption strategies to a changing climate. Yet, soil moisture is highly variable in time and space which challenges its monitoring on spatio-temporal scales relevant for flood and drought risk modelling and forecasting.
Introduced over a decade ago, Cosmic-Ray Neutron Sensing (CRNS) is a noninvasive geophysical method that allows for the estimation of soil moisture at relevant spatio-temporal scales of several hectares at a high, subdaily temporal resolution. CRNS relies on the detection of secondary neutrons above the soil surface which are produced from high-energy cosmic-ray particles in the atmosphere and the ground. Neutrons in a specific epithermal energy range are sensitive to the amount of hydrogen present in the surroundings of the CRNS neutron detector. Due to same mass as the hydrogen nucleus, neutrons lose kinetic energy upon collision and are subsequently absorbed when reaching low, thermal energies. A higher amount of hydrogen therefore leads to fewer neutrons being detected per unit time. Assuming that the largest amount of hydrogen is stored in most terrestrial ecosystems as soil moisture, changes of soil moisture can be estimated through an inverse relationship with observed neutron intensities.
Although important scientific advancements have been made to improve the methodological framework of CRNS, several open challenges remain, of which some are addressed in the scope of this thesis. These include the influence of atmospheric variables such as air pressure and absolute air humidity, as well as, the impact of variations in incoming primary cosmic-ray intensity on observed epithermal and thermal neutron signals and their correction. Recently introduced advanced neutron-to-soil moisture transfer functions are expected to improve CRNS-derived soil moisture estimates, but potential improvements need to be investigated at study sites with differing environmental conditions. Sites with strongly heterogeneous, patchy soil moisture distributions challenge existing transfer functions and further research is required to assess the impact of, and correction of derived soil moisture estimates under heterogeneous site conditions. Despite its capability of measuring representative averages of soil moisture at the field scale, CRNS lacks an integration depth below the first few decimetres of the soil. Given the importance of soil moisture also in deeper soil layers, increasing the observational window of CRNS through modelling approaches or in situ measurements is of high importance for hydrological monitoring applications.
By addressing these challenges, this thesis aids to closing knowledge gaps and finding answers to some of the open questions in CRNS research. Influences of different environmental variables are quantified, correction approaches are being tested and developed. Neutron-to-soil moisture transfer functions are evaluated and approaches to reduce effects of heterogeneous soil moisture distributions are presented. Lastly, soil moisture estimates from larger soil depths are derived from CRNS through modified, simple modelling approaches and in situ estimates by using CRNS as a downhole technique. Thereby, this thesis does not only illustrate the potential of new, yet undiscovered applications of CRNS in future but also opens a new field of CRNS research. Consequently, this thesis advances the methodological framework of CRNS for above-ground and downhole applications. Although the necessity of further research in order to fully exploit the potential of CRNS needs to be emphasised, this thesis contributes to current hydrological research and not least to advancing hydrological monitoring approaches being of utmost importance in context of intensifying hydro-meteorological extremes in a changing climate.
Today, near-surface investigations are frequently conducted using non-destructive or minimally invasive methods of applied geophysics, particularly in the fields of civil engineering, archaeology, geology, and hydrology. One field that plays an increasingly central role in research and engineering is the examination of sedimentary environments, for example, for characterizing near-surface groundwater systems. A commonly employed method in this context is ground-penetrating radar (GPR). In this technique, short electromagnetic pulses are emitted into the subsurface by an antenna, which are then reflected, refracted, or scattered at contrasts in electromagnetic properties (such as the water table). A receiving antenna records these signals in terms of their amplitudes and travel times. Analysis of the recorded signals allows for inferences about the subsurface, such as the depth of the groundwater table or the composition and characteristics of near-surface sediment layers. Due to the high resolution of the GPR method and continuous technological advancements, GPR data acquisition is increasingly performed in three-dimensional (3D) fashion today.
Despite the considerable temporal and technical efforts involved in data acquisition and processing, the resulting 3D data sets (providing high-resolution images of the subsurface) are typically interpreted manually. This is generally an extremely time-consuming analysis step. Therefore, representative 2D sections highlighting distinctive reflection structures are often selected from the 3D data set. Regions showing similar structures are then grouped into so-called radar facies. The results obtained from 2D sections are considered representative of the entire investigated area. Interpretations conducted in this manner are often incomplete and highly dependent on the expertise of the interpreters, making them generally non-reproducible.
A promising alternative or complement to manual interpretation is the use of GPR attributes. Instead of using the recorded data directly, derived quantities characterizing distinctive reflection structures in 3D are applied for interpretation. Using various field and synthetic data sets, this thesis investigates which attributes are particularly suitable for this purpose. Additionally, the study demonstrates how selected attributes can be utilized through specific processing and classification methods to create 3D facies models. The ability to generate attribute-based 3D GPR facies models allows for partially automated and more efficient interpretations in the future. Furthermore, the results obtained in this manner describe the subsurface in a reproducible and more comprehensive manner than what has typically been achievable through manual interpretation methods.