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Mechanical behaviors of granite after thermal treatment under loading and unloading conditions
(2021)
Understanding the mechanical behaviors of granite after thermal treatment under loading and unloading conditions is of utmost relevance to deep geothermal energy recovery. In the present study, a series of loading and unloading triaxial compression tests (20, 40 and 60 MPa) on granite specimens after exposure to different temperatures (20, 200, 300, 400, 500 and 600 degrees C) was carried out to quantify the combined effects of thermal treatment and loading/unloading stress conditions on granite strength and deformation. Changes in the microstructure of granite exposed to high temperatures were revealed by optical microscopy. The experimental results indicate that both, thermal treatment and loading/unloading stress conditions, degrade the mechanical behaviors and further decrease the carrying capacity of granite. The gradual degradation of the mechanical characteristics of granite after thermal treatment is mainly associated with the evolution of thermal micro-cracks based on optical microscopy observations. The unloading stress state induces the extension of tension cracks parallel to the axial direction, and thus, the mechanical properties are degraded. Temperatures above 400 degrees C have a more significant influence on the mechanical characteristics of granite than the unloading treatment, whereby 400 degrees C can be treated as a threshold temperature for the delineation of significant deterioration. This study is expected to support feasibility and risk assessments by means of providing data for analytical calculations and numerical simulations on granite exposed to high temperatures during geothermal energy extraction.
Volcanic tremor extraction and earthquake detection using music information retrieval algorithms
(2021)
Volcanic tremor signals are usually observed before or during volcanic eruptions and must be monitored to evaluate the volcanic activity. A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contrib-ute to improving upon our understanding of the underlying physical processes. Exploiting the idea of harmonic-percussive separation in musical signal processing, we develop a method to extract the harmonic volcanic tremor signals and to detect tran-sient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time-frequency domain, we decompose the signal into two separate spec-trograms representing repeating (harmonic) and nonrepeating (transient) patterns, which correspond to volcanic tremor signals and earthquake signals, respectively. We reconstruct the harmonic tremor signal in the time domain from the complex spectrogram of the repeating pattern by only considering the phase components for the frequency range in which the tremor amplitude spectrum is significantly contribut-ing to the energy of the signal. The reconstructed signal is, therefore, clean tremor signal without transient events. Furthermore, we derive a characteristic function suitable for the detection of tran-sient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectro-gram over frequency at each time frame. Considering transient events like earthquakes, 78% of the events are detected for signal-to-noise ratio = 0.1 in our semisynthetic tests. In addition, we compared the number of detected earthquakes using our method for one month of continuous data recorded during the Holuhraun 2014-2015 eruption in Iceland with the bulletin presented in Agustsdottir et al. (2019). Our single station event detection algorithm identified 84% of the bulletin events. Moreover, we detected a total of 12,619 events, which is more than twice the number of the bulletin events.
Diagenetic trends of synthetic reservoir sandstone properties assessed by digital rock physics
(2021)
Quantifying interactions and dependencies among geometric, hydraulic and mechanical properties of reservoir sandstones is of particular importance for the exploration and utilisation of the geological subsurface and can be assessed by synthetic sandstones comprising the microstructural complexity of natural rocks. In the present study, three highly resolved samples of the Fontainebleau, Berea and Bentheim sandstones are generated by means of a process-based approach, which combines the gravity-driven deposition of irregularly shaped grains and their diagenetic cementation by three different schemes. The resulting evolution in porosity, permeability and rock stiffness is examined and compared to the respective micro-computer tomographic (micro-CT) scans. The grain contact-preferential scheme implies a progressive clogging of small throats and consequently produces considerably less connected and stiffer samples than the two other schemes. By contrast, uniform quartz overgrowth continuously alters the pore space and leads to the lowest elastic properties. The proposed stress-dependent cementation scheme combines both approaches of contact-cement and quartz overgrowth, resulting in granulometric, hydraulic and elastic properties equivalent to those of the respective micro-CT scans, where bulk moduli slightly deviate by 0.8%, 4.9% and 2.5% for the Fontainebleau, Berea and Bentheim sandstone, respectively. The synthetic samples can be further altered to examine the impact of mineral dissolution or precipitation as well as fracturing on various petrophysical correlations, which is of particular relevance for numerous aspects of a sustainable subsurface utilisation.
Knowledge of pressure-dependent static and dynamic moduli of porous reservoir rocks is of key importance for evaluating geological setting of a reservoir in geo-energy applications. We examined experimentally the evolution of static and dynamic bulk moduli for porous Bentheim sandstone with increasing confining pressure up to about 190 MPa under dry and water-saturated conditions. The static bulk moduli (K-s) were estimated from stress-volumetric strain curves while dynamic bulk moduli (K-d) were derived from the changes in ultrasonic P- and S- wave velocities (similar to 1 MHz) along different traces, which were monitored simultaneously during the entire deformation. In conjunction with published data of other porous sandstones (Berea, Navajo and Weber sandstones), our results reveal that the ratio between dynamic and static bulk moduli (K-d/K-s) reduces rapidly from about 1.5 - 2.0 at ambient pressure to about 1.1 at high pressure under dry conditions and from about 2.0 - 4.0 to about 1.5 under water-saturated conditions, respectively. We interpret such a pressure-dependent reduction by closure of narrow (compliant) cracks, highlighting thatK(d)/K(s)is positively correlated with the amount of narrow cracks. Above the crack closure pressure, where equant (stiff) pores dominate the void space,K-d/K(s)is almost constant. The enhanced difference between dynamic and static bulk moduli under water saturation compared to dry conditions is possibly caused by high pore pressure that is locally maintained if measured using high-frequency ultrasonic wave velocities. In our experiments, the pressure dependence of dynamic bulk modulus of water-saturated Bentheim sandstone at effective pressures above 5 MPa can be roughly predicted by both the effective medium theory (Mori-Tanaka scheme) and the squirt-flow model. Static bulk moduli are found to be more sensitive to narrow cracks than dynamic bulk moduli for porous sandstones under dry and water-saturated conditions.
Centroid moment tensor (CMT) parameters can be estimated from seismic waveforms. Since these data indirectly observe the deformation process, CMTs are inferred as solutions to inverse problems which are generally underdetermined and require significant assumptions, including assumptions about data noise. Broadly speaking, we consider noise to include both theory and measurement errors, where theory errors are due to assumptions in the inverse problem and measurement errors are caused by the measurement process. While data errors are routinely included in parameter estimation for full CMTs, less attention has been paid to theory errors related to velocity-model uncertainties and how these affect the resulting moment-tensor (MT) uncertainties. Therefore, rigorous uncertainty quantification for CMTs may require theory-error estimation which becomes a problem of specifying noise models. Various noise models have been proposed, and these rely on several assumptions. All approaches quantify theory errors by estimating the covariance matrix of data residuals. However, this estimation can be based on explicit modelling, empirical estimation and/or ignore or include covariances. We quantitatively compare several approaches by presenting parameter and uncertainty estimates in nonlinear full CMT estimation for several simulated data sets and regional field data of the M-1 4.4, 2015 June 13 Fox Creek, Canada, event. While our main focus is at regional distances, the tested approaches are general and implemented for arbitrary source model choice. These include known or unknown centroid locations, full MTs, deviatoric MTs and double-couple MTs. We demonstrate that velocity-model uncertainties can profoundly affect parameter estimation and that their inclusion leads to more realistic parameter uncertainty quantification. However, not all approaches perform equally well. Including theory errors by estimating non-stationary (non-Toeplitz) error covariance matrices via iterative schemes during Monte Carlo sampling performs best and is computationally most efficient. In general, including velocity-model uncertainties is most important in cases where velocity structure is poorly known.
Spectral analysis is a technique of time-series analysis that decomposes signals into linear combinations of harmonic components. Rooted in the 19th century, spectral analysis gained popularity in palaeoclimatology since the early 1980s. This was partly due to the availability of long time series of past climates, but also the development of new, partly adapted methods and the increasing spread of affordable personal computers. This paper reviews the most important methods of spectral analysis for palaeoclimate time series and discusses the prerequisites for their application as well as advantages and disadvantages. The paper also offers an overview of suitable software, as well as computer code for using the methods on synthetic examples.
Barite stands out as one of the most ubiquitous scaling agents in deep geothermal systems, responsible for irreversible efficiency loss. Due to complex parameter interplay, it is imperative to utilise numerical simulations to investigate temporal and spatial precipitation effects. A one-dimensional reactive transport model is set up with heterogeneous nucleation and crystal growth kinetics. In line with geothermal systems in the North German Basin, the following parameters are considered in a sensitivity analysis: temperature (25 to 150 degrees C), pore pressure (10 to 50 MPa), fracture aperture (10(-4) to 10(-2) m), flow velocity (10(-3) to 10(0) m s(-1)), molar volume (50.3 to 55.6 cm(3) mol(-1)), contact angle for heterogeneous nucleation (0 degrees to 180 degrees), interfacial tension (0.07 to 0.134 J m(-2)), salinity (0.1 to 1.5 mol kgw(-1) NaCl), pH (5 to 7), and supersaturation ratio (1 to 30). Nucleation and consequently crystal growth can only begin if the threshold supersaturation is exceeded, therefore contact angle and interfacial tension are the most sensitive in terms of precipitation kinetics. If nucleation has occurred, crystal growth becomes the dominant process, which is mainly controlled by fracture aperture. Results show that fracture sealing takes place within months (median 33 days) and the affected range can be on the order of tens of metres (median 10 m). The presented models suggest that barite scaling must be recognised as a serious threat if the supersaturation threshold is exceeded, in which case, large fracture apertures could help to minimise kinetic rates. The models further are of use for adjusting the fluid injection temperature.
Barite scalings are a common cause of permanent formation damage to deep geothermal reservoirs. Well injectivity can be impaired because the ooling of saline fluids reduces the solubility of barite, and the continuous re-injection of supersaturated fluids forces barite to precipitate in the host rock. Stimulated reservoirs in the Upper Rhine Graben often have multiple relevant flow paths in the porous matrix and fracture zones, sometimes spanning multiple stratigraphical units to achieve the economically necessary injectivity. While the influence of barite scaling on injectivity has been investigated for purely porous media, the role of fractures within reservoirs consisting of both fractured and porous sections is still not well understood. Here, we present hydro-chemical simulations of a dual-layer geothermal reservoir to study the long-term impact of barite scale formation on well injectivity. Our results show that, compared to purely porous reservoirs, fractured porous reservoirs have a significantly reduced scaling risk by up to 50%, depending on the flow rate ratio of fractures. Injectivity loss is doubled, however, if the amount of active fractures is increased by one order of magnitude, while the mean fracture aperture is decreased, provided the fractured aquifer dictates the injection rate. We conclude that fractured, and especially hydraulically stimulated, reservoirs are generally less affected by barite scaling and that large, but few, fractures are favourable. We present a scaling score for fractured-porous reservoirs, which is composed of easily derivable quantities such as the radial equilibrium length and precipitation potential. This score is suggested for use approximating the scaling potential and its impact on injectivity of a fractured-porous reservoir for geothermal exploitation.