An open-access stress magnitude database for Germany and adjacent regions

Morawietz, Sophia; Heidbach, Oliver; Reiter, Karsten; Ziegler, Moritz; Rajabi, Mojtaba; Zimmermann, Günter; Müller, Birgit; Tingay, Mark

doi:10.1186/s40517-020-00178-5

Cited by 29 publications

(50 citation statements)

References 158 publications

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“…The new data have been integrated in the latest WSM database release (Heidbach et al, 2018). For stress magnitude data, Morawietz et al (2020) published a publicly accessible database with 568 data records including a quality assessment 110 of the data for Germany and adjacent regions. These two datasets, the SHmax orientation of the WSM with some additional data of Levi et al (2019) and the stress magnitude database are used to calibrate the geomechanical model ( Fig.…”

Section: Data Compilation Of Stress Tensor Components 100mentioning

confidence: 99%

“…In 250 a second step we vary the magnitude of these displacements on the model boundaries while keeping the ratio constant, so that a best-fit with the stress magnitude data is achieved as well. The calibration is mainly based on the Shmin magnitude due to the larger amount of data from the compilation of Morawietz et al (2020) and the fact that SHmax magnitudes are often calculated and not measured and therefore less reliable. For the best-fit model a total extension of 465 m in east-west direction and a total shortening of 325 m in north-south direction is applied.…”

Section: Displacement Boundary Conditionsmentioning

confidence: 99%

“…The modelled magnitudes of Shmin in comparison to stress magnitude data of Morawietz et al (2020) are shown in Fig. 7.…”

Section: Minimum Horizontal Stress (Shmin) Magnitudesmentioning

confidence: 99%

“…The differences are calculated as interpolated model results minus data, thus positive differences correspond to too large model values and negative ones to too low model values. We use only data from Morawietz et al (2020) with a quality of A, B and C and from depths >200 m, to avoid topographic effects. Thus, we use 74 Shmin magnitude data records from a depth of 200 to 4600 m, most of them from the upper 1000 m. As shown in Fig.…”

Section: Minimum Horizontal Stress (Shmin) Magnitudesmentioning

confidence: 99%

“…However, from the data perspective, our knowledge of the stress state in Western Central Europe is limited in particular regarding stress magnitudes information. Public stress information is provided by the World Stress Map (WSM) project 35 which supplies a global database of the orientation of maximum horizontal stress (SHmax) and the stress regime (Heidbach et al, 2018) and by a compilation of stress magnitude data for Germany and adjacent regions of Morawietz et al (2020).…”

Section: Introductionmentioning

confidence: 99%

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3D crustal stress state of Western Central Europe according to a data-calibrated geomechanical model – first results

Ahlers¹,

Henk²,

Hergert³

et al. 2020

Preprint

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Abstract. The contemporary stress state in the upper crust is of great interest for geotechnical applications and basic research likewise. However, our knowledge of the crustal stress field from the data perspective is limited. For Western Central Europe basically two datasets are available: Orientations of the maximum horizontal stress (SHmax) and the stress regime as part of the World Stress Map (WSM) database (Heidbach et al., 2018) as well as a complementary compilation of stress magnitude data of Germany and adjacent regions (Morawietz et al., 2020). However, these datasets only provide pointwise, incomplete and heterogeneous information of the 3D stress tensor. Here, we present a geomechanical-numerical model that provides a continuous description of the contemporary 3D crustal stress state on a regional scale for Western Central Europe. The model covers an area of about 1000 × 1250 km2 and extends to a depth of 100 km containing seven lithostratigraphic units, with specific material properties (density and elastic rock properties) and laterally varying thicknesses: A sedimentary unit, four different units of the upper crust, the lower crust and the lithospheric mantle. The model is calibrated by the two datasets to achieve a best-fit regarding the SHmax orientations and the minimum horizontal stress magnitudes (Shmin). The modelled orientations of SHmax are almost entirely within the uncertainties of the WSM data used and the Shmin magnitudes fit to various datasets well. Only the SHmax magnitudes show locally significant deviations, primarily indicating too low values in the lower part of the model. The model is open for further refinements regarding model geometry, e.g., additional layers with laterally varying material properties, and incorporation of future stress measurements. In addition, it can provide the initial stress state for local geomechanical models with a higher resolution.

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Section: Data Compilation Of Stress Tensor Components 100mentioning

confidence: 99%

Section: Displacement Boundary Conditionsmentioning

confidence: 99%

“…The modelled magnitudes of Shmin in comparison to stress magnitude data of Morawietz et al (2020) are shown in Fig. 7.…”

Section: Minimum Horizontal Stress (Shmin) Magnitudesmentioning

confidence: 99%

Section: Minimum Horizontal Stress (Shmin) Magnitudesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3D crustal stress state of Western Central Europe according to a data-calibrated geomechanical model – first results

Ahlers¹,

Henk²,

Hergert³

et al. 2020

Preprint

Self Cite

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Assessing the hazard of fault triggering by deep geothermal energy production in an active fault system via a 1D stress model and 3D fault mapping

Levi,

Weissl,

Decker

2024

Int J Earth Sci (Geol Rundsch)

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Borehole image data and a 1D-stress model built on open hole logs, leak-off tests (LOT) and image logs are used to evaluate the potential of seismicity caused by fault triggering during geothermal heat production in the city of Vienna. Data were derived from a 4220 m deep geothermal exploration well that investigated the geothermal potential of fractured carbonates below the Miocene fill of the Vienna Basin. The well penetrated several normal faults of the Aderklaa Fault System (AFS) that offset Pleistocene terraces at the surface and hence are regarded as active. Stress-induced borehole failures and 1D geomechanical modeling proves that the potential reservoirs are in a normal fault stress regime with Sv > SHmax > Shmin. While stress magnitudes in the upper part of the well (down to about 2000 m) are significantly below the magnitudes that would trigger the rupture of critically oriented faults including the AFS, stresses in the lower part of the drilled section in the pre-Neogene basement (below about 3300 m) are not. Data evidence a rotation of SHmax for about 45° at a fault of the AFS at 3694 m to fault-parallel below the fault suggesting that the fault is active. Critical or near-critical stressing of the fault is corroborated by the stress magnitudes calculated from the 1D geomechanical model. The safety case to exclude unintended triggering of seismic fault slip by developing geothermal reservoirs in close vicinity to one of the branch faults of the AFS may therefore be difficult or impossible to make. Graphical Abstract

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Rock Properties and Modelled Stress State Uncertainties: A Study of Variability and Dependence

Ziegler

2022

Rock Mech Rock Eng

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The safety and sustainability of subsurface applications requires a profound knowledge of the local stress state which is frequently assessed using 3D geomechanical-numerical models. Various factors lead to generally large uncertainties in these models. The variabilities in the rock properties as one of the sources of uncertainties and their influence on the modelled stress state is addressed herein. A generic 3D geomechanical-numerical model is used to investigate the influence of different distributions of variability and their effect on different stress states. The variability in rock properties clearly affects the uncertainties in the stress state in a positive correlation with differences that depend on the affected component of the stress tensor. The basic observation is that largest uncertainties are observed in the normal components of the stress tensor where the variabilities apparently are most effective. The same rock property variabilities affect the shear components uncertainties to a significantly lesser extent. Variabilities in the Young’s modulus and the Poisson’s ratio chiefly affect the uncertainties in $$\sigma _{xx}$$ σ xx and $$\sigma _{yy}$$ σ yy . The density variability, however, leads to highest uncertainties in $$\sigma _{zz}$$ σ zz . In general, variabilities in the Young’s modulus are most effective, followed by the Density and then the Poisson’s ratio. Furthermore, an influence of the tectonic stress regime on how the variability in the rock properties affects the stress state is observed. At the same time only a small effect is observed for different stress magnitudes. The eventual uncertainties in a modelled stress state depend not only on the uncertainties in the rock properties but also whether the uncertainties are found mainly in the Young’s modulus, the Poisson’s ratio or the Density. These findings indicate the importance to regard variabilities in rock properties as a source for significant uncertainties in geomechanical-numerical models. It is proposed to use the derived relations for an inexpensive quantification of uncertainties by means of a post-computation assignment of uncertainties to a stress model.

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An open-access stress magnitude database for Germany and adjacent regions

Cited by 29 publications

References 158 publications

3D crustal stress state of Western Central Europe according to a data-calibrated geomechanical model – first results

3D crustal stress state of Western Central Europe according to a data-calibrated geomechanical model – first results

Assessing the hazard of fault triggering by deep geothermal energy production in an active fault system via a 1D stress model and 3D fault mapping

Rock Properties and Modelled Stress State Uncertainties: A Study of Variability and Dependence

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