Understanding the contemporary stress state in rock volumes is crucial for applications such as reservoir management, geothermal energy, and underground storage. Geomechanical-numerical modelling, which predicts the 3D stress state based on geological structures, density distributions, and elastic properties, requires calibration using stress magnitude data records acquired in-situ. However, these data records can include outliers—stress measurements significantly deviating from expected values due to errors or localized geological anomalies. These outliers can skew model calibrations, leading to inaccurate predictions of boundary conditions and stress magnitudes, particularly in sets with limited numbers of data records. A systematic approach to identifying and handling outliers is essential to mitigate inaccuracies. The Python-based script DOuGLAS (Detection of Outliers in Geomechanics using Linear-elastic Assumption and Statistics) was developed to address this challenge. The software is part of the FAST (Fast Automatic Stress Tensor) suite of programs. Its function is to identify outliers in sets of stress magnitude data records by assessing the respective impact of individual data records on boundary condition predictions, using iterative combinations of data records. Results are analysed through dimensionality reduction and statistical scoring, providing visual and quantitative tools for outlier detection. The script aids users in improving model reliability by identifying and addressing anomalous data. It supports sets of different numbers of stress magnitude data records and integrates seamlessly with tools such as Tecplot 360 EX and GeoStress. This manual provides a comprehensive guide for using DOuGLAS, interpreting its outputs, and understanding its application in geomechanical modeling.
Understanding the contemporary stress state in rock volumes is crucial for applications such as reservoir management, geothermal energy, and underground storage. Geomechanical-numerical modelling, which predicts the 3D stress state based on geological structures, density distributions, and elastic properties, requires calibration using stress magnitude data records acquired in-situ. However, these data records can include outliers—stress measurements significantly deviating from expected values due to errors or localized geological anomalies. These outliers can skew model calibrations, leading to inaccurate predictions of boundary conditions and stress magnitudes, particularly in sets with limited numbers of data records. A systematic approach to identifying and handling outliers is essential to mitigate inaccuracies. The Python-based script DOuGLAS (Detection of Outliers in Geomechanics using Linear-elastic Assumption and Statistics) was developed to address this challenge. The software is part of the FAST (Fast Automatic Stress Tensor) suite of programs. Its function is to identify outliers in sets of stress magnitude data records by assessing the respective impact of individual data records on boundary condition predictions, using iterative combinations of data records. Results are analysed through dimensionality reduction and statistical scoring, providing visual and quantitative tools for outlier detection. The script aids users in improving model reliability by identifying and addressing anomalous data. It supports sets of different numbers of stress magnitude data records and integrates seamlessly with tools such as Tecplot 360 EX and GeoStress. This manual provides a comprehensive guide for using DOuGLAS, interpreting its outputs, and understanding its application in geomechanical modeling.
The 3D geomechanical-numerical modelling aims at a continuous description of the stress state in a subsurface volume. The model is fitted to the model-independent stress data records by adaptation of the displacement boundary conditions. This process is herein referred to as model calibration. Depending on the amount of available stress data records and the complexity of the model the calibration can be a lengthy process of trial-and-error to estimate the best-fit boundary conditions. The tool FAST Calibration (Fast Automatic Stress Tensor Calibration) is a Matlab script that facilitates and speeds up this calibration process. By using a linear regression it requires only three test model scenarios with different displacement boundary conditions to calibrate a geomechanical-numerical model on available stress data records. The differences between the modelled and observed stresses are used for the linear regression that allows to compute the displacement boundary conditions required for the best-fit estimation. The influence of observed stress data records on the best-fit displacement boundary conditions can be weighted. Furthermore, FAST Calibration provides a cross checking of the best-fit estimate against indirect stress information that cannot be used for the calibration process, such as the observation of borehole breakouts or drilling induced fractures. In order to bridge the scale gap between a regional stress model and a local reservoir model, the multistage calibration procedure is applied where a local model is calibrated solely on the stress state provided by a regional model. FAST Calibration provides the necessary tools and guidelines.
The classical way to model the stress state in a rock volume is to estimate displacement boundary conditions that minimize the deviation of the modelled stress state with respect to model-independent stress information such as stress magnitude data. However, these data records are usually subject to significant uncertainties and measurement errors. Hence, it has to be expected that not all stress magnitude data records are representative and can be used in a model. In order to identify unreliable stress data records, the stress state that is based on individual data records is solved and compared with observations at a few discrete locations. While this method works, it is not efficient in that most of the solved model scenarios will be discarded. The solving of the entire model consumes immense amount of computation time for a high-resolution model. Yet, the stress state is required at only a very limited number of locations. For linear geomechanical models it is sufficient to estimate the stress state from three model scenarios with arbitrary, but different displacement boundary conditions. These three results can be used to estimate analytically using a linear regression at discrete points stress states based on user-defined boundary conditions. The tool Fast Automatic Stress Tensor Estimation (FAST Estimation) is a Python function that automatizes this approach. FAST Estimation provides very efficiently the stress states at pre-defined locations for all possible boundary conditions. It does not provide the continuous stress field as provided by a solved geomechanical model. Instead, it is a cost-efficient solution for the rapid assessment of stress states at a limited number of discrete locations based on pre-defined boundary conditions.
The 3D geomechanical-numerical modelling aims at a continuous description of the stress state in a subsurface volume. The model is fitted to the model-independent stress data records by adaptation of the displacement boundary conditions. This process is herein referred to as model calibration. Depending on the amount of available stress data records and the complexity of the model the calibration can be a lengthy process of trial-and-error to estimate the best-fit boundary conditions. The tool FAST Calibration (Fast Automatic Stress Tensor Calibration) is a Matlab script that facilitates and speeds up this calibration process. By using a linear regression it requires only three test model scenarios with different displacement boundary conditions to calibrate a geomechanical-numerical model on available stress data records. The differences between the modelled and observed stresses are used for the linear regression that allows to compute the displacement boundary conditions required for the best-fit estimation. The influence of observed stress data records on the best-fit displacement boundary conditions can be weighted. Furthermore, FAST Calibration provides a cross checking of the best-fit estimate against indirect stress information that cannot be used for the calibration process, such as the observation of borehole breakouts or drilling induced fractures.
The 3D geomechanical-numerical modelling of the in-situ stress state aims at a continuous description of the stress state in a subsurface volume. It requires observed stress information within the model volume that are used as a reference. Once the modelled stress state is in agreement with the observed reference stress data the model is assumed to provide the continuous stress state in its entire volume. The modelled stress state is fitted to the reference stress data records by adaptation of the displacement boundary conditions. This process is herein referred to as calibration. Depending on the amount of available stress data records and the complexity of the model the manual calibration is a lengthy process of trial-and-error modelling and analysis until best-fit boundary conditions are found. The Fast Automatic Stress Tensor Calibration (FAST Calibration) is a Python function that facilitates and speeds up this calibration process. By using a linear regression it requires only three model scenarios with different boundary conditions. The stress states from the three model scenarios at the locations of the reference stress data records are extracted. The differences between the modelled and observed stress states are used for a linear regression that allows to compute the displacement boundary conditions required for the best-fit modelled stress state. If more than one reference stress state is provided, the influence of the individual observed stress data records on the best-fit boundary conditions can be weighted.
The 3D geomechanical-numerical modelling of the in-situ stress state requires observed stress information at reference locations within the model area to be compared to the modelled stress state. This comparison of stress states and the ensuing adaptation of the displacement boundary conditions provide a best fit stress state in the entire model region that is based on the available stress information. This process is also referred to as calibration. Depending on the amount of available information and the complexity of the model the calibration is a lengthy process of trial-and-error modelling and analysis.The Fast Automatic Stress Tensor Calibration (FAST Calibration) is a method and a Matlab script that facilitates and speeds up the calibration process that has been developed in the framework of the World Stress Map (WSM, Heidbach et al., 2010; 2016). The method requires only three model scenarios with different boundary conditions. The modelled stress states at the locations of the observed stress state are extracted. Then they are used to compute the displacement boundary conditions that are required in order to achieve the best fit of the modelled to the observed stress state. Furthermore, the influence of the individual observed stress information on the resulting stress state can be weighted.The FAST-Calibration (Fast Automatic Stress Tensor Calibration) is a Matlab tool that controls the statistical calibration of a 3D geomechanical-numerical model of the stress state following the approach described by Reiter and Heidbach (2014), Hergert et al. (2015), and Ziegler et al. (2016). It is mainly designed to support the multi-stage modelling procedure presented by Ziegler et al. (2016). However, it can also be used for the calibration of a single-stage model. The tools run in Matlab 2017a and higher and are meant to work with the visualization software Tecplot 360 EX 2015 R2 and higher (https://www.tecplot.com/products/tecplot-360/) in conjunction with the Tecplot 360 Add-on GeoStress (Stromeyer and Heidbach, 2017). The user should be familiar with 3D geomechanical-numerical modelling, Matlab, Tecplot 360 EX, including a basic knowledge of Tecplot 360 EX macro functions, and the Tecplot 360 EX Add-on GeoStress. This FAST Calibration manual provides an overview of the scripts and is designed to help the user to adapt the scripts for their own needs.
Proposed research: This research programme proposes to analyze the predictability of the hydrologic behaviour of Alpine ecosystems at the spatio-temporal scales relevant for water management, i.e. at spatial scales of between 200 km2 (e.g. a hydropower production catchment) and around 5000 km2 (e.g. flood management of the Swiss Rhone catchment) and at temporal scales ranging from hours to seasons. Research context: Quantitative stream flow predictions are essential for the sustainable management of our natural and man-made environment and for the prevention of natural hazards. Despite of ever better insights into the involved physical processes at the point scale, many existing catchment scale runoff prediction models still show a lack of reliability for stream flow prediction. The present research programme addresses this foremost issue in Alpine environments, which are the source of many major European rivers and play a dominant role for hydropower production and flood protection. Stream flow prediction in such environments is particularly challenging due to the high spatial variability of the meteorological driving forces opposed to notorious data scarcity in remote and high elevation areas. Project context: The present proposal is a follow-up proposal of the Ambizione project Hydrologic Prediction in Alpine Environments. During the main phase of the project (3 years), certain essential research objectives could not be reached, due namely to the maternity leave of the principal investigator (PI), but also due to additional research questions that emerged at the very beginning of this research. The present follow-up project proposes to complete the research programme during a complementary project phase (2 years). Objectives: The main objective of this research programme is to assess under which conditions simple hydrological models can give reliable stream flow predictions in Alpine environments. This objective will be reached based on an analysis of the variability of natural flow generation processes and of the variability of corresponding state-of-the-art hydrological model outputs. During the main phase of the project, the research was concentrated on the analysis of flow generation processes related to snowmelt, which in Alpine areas dominate the hydrological response over a large part of the year. The achieved results include a new hourly snowmelt model combined to a spatially-explicit precipitation-runoff model, an improved snowfall-limit prediction method for hydrological models and a weather generator that produces coupled temperature and prediction scenarios to analyze how these two meteorological variables integrate to the snow-hydrological response.(...)
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