Bayesian quantification and reduction of uncertainties in 3D Geomechanical‐Numerical Models
| Autor: | Moritz O. Ziegler, Oliver Heidbach |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Journal of Geophysical Research: Solid Earth |
| Popis: | The distance to failure of the upper crustal rock in the prevalent stress field is of importance to better understand fault reactivation by natural and induced processes as well as to plan and manage georeservoirs. In particular, the contemporary stress state is one of the key ingredients for this assessment. To provide a continuous description of the 3D absolute stress state geomechanical‐numerical models are used. However, stress magnitude data for model calibration are sparse and incomplete and thus, the resulting model uncertainties are large. In order to reduce the uncertainties, we incorporate additional constraints on stress magnitudes to check the plausibility of different data‐based stress states. We use formation integrity tests, borehole breakouts, drilling induced fractures, and observations of seismicity and distinct seismological quiescence. This information is weighted according to its confidence and the agreement with the different modeled stress states is assessed. The information is introduced to a Bayesian approach to estimate weights of the modeled stress states and thereby identify their plausibility. A case study in southern Germany shows the ability of the approach to identify from a wide range of stress states a small number of plausible ones and reject implausible stress states. This significantly reduces the number of stress states and thus lowers the model uncertainties. Plain Language Summary The upper crust of the Earth (upper few kilometers) is subject to a kind of pressure, referred to as stress. When the stress becomes larger than the strength of the rock, the rock breaks. Sometimes this can be measured or even felt as a seismic event. It happens naturally but may also happen due to human activity. To prevent such induced seismic event, it is important to know the stress state. But there is only few information on the magnitude of the stress so we need computer models to predict the stress state. These models are often not very precise since there is only few information on the stress magnitudes and in addition they are often contradicting. We use all stress magnitude information individually to model various stress states. Then we look at other information that is related to the stress state but does not provide stress magnitude information on its own. We compare this information with the modeled stress states to find out whether a stress state agrees with the additional information or not. This allows us to identify a few realistic stress state models out of a wide range of possible ones. This reduces the uncertainties of the stress predictions. |
| Databáze: | OpenAIRE |
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