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Free keywords:
geomechanical-numerical model, stress, in-situ stress, model calibration, stress tensor calibration, modelling tool, model quality assessment
Abstract:
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.
