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.
In geosciences 3D geomechanical-numerical models are used to estimate the in-situ stress state. In such a model each geological unit is populated with the rock properties Young’s module, Poisson ratio, and density. Usually, each unit is assigned a single set of homogene-ous properties. However, variable rock properties are observed and expected within the same geological unit. Even within small volumes large variabilities may occur. The Python script HIPSTER (Homogeneous to Inhomogeneous rock Properties for Stress TEnsor Research) provides an algorithm to include inhomogeneities in geomechanical-numerical models that use the solver Abaqus® or the MOOSE Framework. The user specifies the mean values for the rock properties Young's module, Poisson ratio and density, and their variability for each geological unit. The variability of the material properties is indi-vidually defined for each of the three rock properties in each geological layer. For each unit or unit subset HIPSTER generates a normal or uniform distribution for each rock property. From these distributions for each single element or subset of elements HIPSTER draws indi-vidual rock properties and writes them to a separate material file. This file defines differ-ent material properties for each element. The file is included in the geomechanical-numerical analysis solver deck and the numerical model is solved as usual. HIPSTER is fully documented in the associated data report (Ziegler, 2021, https://doi.org/10.48440/wsm.2021.001) and can also be accessed at Github (http://github.com/MorZieg/hipster).
In geosciences the discretization of complex 3D model volumes into finite elements can be a time-consuming task and often needs experience with a professional software. In particular, low angle outcropping or out-pinching geological units, i.e. geological layers that are represented in the model volume, pose serious challenges. Another example are changes in the geometry of a model, which can occur at one point of a project, when re-meshing is not an option anymore or would involve a significant amount of additional time to invest. In order to speed up and automate the process of discretization, Apple PY (Automatic Portioning Preventing Lengthy manual Element assignment for PYthon) separates the process of mesh-generation and unit assignment. It requires an existing mesh together with separate information on the depths of the interfaces between geological units (herein called horizons). These two pieces of information are combined and used to assign the individual elements to different units. The uniform mesh is created with a standard meshing software and has to be available as an Abaqus input file. The information on the horizons depths and lateral variations in the depths is provided in a text file. Apple PY compares the element location and depth with that of the horizons in order to assign each element to a corresponding geological unit below or above a certain horizon.
In geosciences the discretization of complex 3D model volumes into finite elements can be a time-consuming task and often needs experience with a professional software. Especially outcropping or out-pinching geological units, i.e. geological layers that are represented in the model volume, pose serious challenges. Changes in the geometry of a model may occur well into a project at a point, when re-meshing is not an option anymore or would involve a significant amount of additional time to invest.In order to speed up and automate the process of discretization, Apple PY (Automatic Portioning Preventing Lengthy manual Element assignment for PYthon) separates the process of mesh-generation and unit assignment. It requires an existing uniform mesh together with separate information on the depths of the interfaces between geological units (herein called horizons). These two pieces of information are combined and used to assign the individual elements to different units. The uniform mesh is created with a standard meshing software and contains no or only very few and simple structures. The mesh has to be available as an Abaqus input file. The information on the horizons depths and lateral variations in the depths is provided in a text file. Apple PY compares the element location and depth with that of the horizons in order to assign each element to a corresponding geological unit below or above a certain horizon.Version History:Version 1.01 (29 August 2019) : Bug fixes - no change in functionality Manual for Version 1.0 remains valid- elems_exclude works now as designed and described in the manual.- commenting out elems_exclude does not crash the script anymore.- create_horizon_file does not create two instances of the uppermost horizon.
In geosciences 3D geomechanical-numerical models are used to estimate the in-situ stress state. In such a model each geological unit is populated with the rock properties Young’s module, Poisson ratio, and density. Usually, each unit is assigned a single set of homogeneous properties. However, variable rock properties are observed and expected within the same geological unit. Even in small volumes large variabilities may. The Python script HIPSTER (Homogeneous to Inhomogeneous rock Properties for Stress TEnsor Research) provides an algorithm to include inhomogeneities in geomechanical-numerical models that use the solver Abaqus®. The user specifies the mean values for the rock properties Young's module, Poisson ratio and density, and their variability for each geological unit. The variability of the material properties is individually defined for each of the three rock properties in each geological layer. For each unit HIPSTER generates a normal or uniform distribution for each rock property. From these distri-butions for each single element HIPSTER draws individual rock properties and writes them to a separate material file. This file defines different material properties for each element. The file is included in the geomechanical-numerical analysis solver deck and the numerical model is solved as usual.HIPSTER is fully documented in the associated data report (Ziegler, 2019, http://doi.org/10.2312/WSM.2019.003) and can also be accessed at Github (http://github.com/MorZieg/hipster)
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