A multi-stage 3D stress field modelling approach exemplified in the Bavarian Molasse Basin

Moritz O. Ziegler, Oliver Heidbach, John Reinecker, Anna M. Przybycin, Magdalena Scheck-Wenderoth
2016 Solid Earth Discussions  
The knowledge of the contemporary in-situ stress state is a key issue for a safe and sustainable subsurface engineering. However, information on the orientation and magnitudes of the stress state are few and often not available in the areas of interest. Therefore 3D geomechanical numerical modelling is used to estimate the in-situ stress state and the distance of faults from failure for application in subsurface engineering. The main challenge in this approach is to bridge the gap in scales
more » ... een the widely scattered data used for calibration of the model and the high resolution in the target area required for the application. We present a multi-stage 3D geomechanical numerical approach which provides a state of the art model of the stress field for a reservoir scale area from widely scattered data records. Therefore we first use a large scale regional model which is calibrated by available stress data and provides the full 3D stress tensor at discrete points in the entire model volume. The modelled stress state is used subsequently for the calibration of a smaller scale model located within the large scale model in an area without any observed stress data records. We exemplify this approach with two-stages for the area around Munich in the German Molasse Basin. We estimate the scalar values for slip tendency and fracture potential as measures for the criticality of fault reactivation in the reservoir scale model. Furthermore, the modelling results show that variations due to uncertainties in the input data are mainly introduced by the uncertain material properties and missing S<sub>Hmax</sub> magnitude data. This leads to the conclusion that at this stage the model's reliability depends only on the amount and quality of available input data rather than on the modelling technique itself. Any improvements of modelling and increases in model reliability can only be achieved by more high-quality data for calibration.
doi:10.5194/se-2016-92 fatcat:y47g3mybtbca5ofilxxo745yia