Titel: Bias evaluated structural and probabilistic subsurface modelling: a case study of the Münsterland Basin, NW Germany
Marius Pischke1,2, Alexander Magnus Jüstel1,2, Frank Strozyk1, Peter Kukla1,3, Florian Wellmann2
1Fraunhofer IEG, Fraunhofer Research Institution for Energy Infrastructures and Geothermal Systems, Am Hochschulcampus 1, 44801 Bochum, Germany; 2RWTH Aachen University, Computational Geoscience and Reservoir Engineering, Wüllnerstraße 2, 52062 Aachen, Germany; 3RWTH Aachen University, Geological Institute, Wüllnerstraße 2, 52062 Aachen, Germany
Veranstaltung: GeoKarlsruhe 2021
The analysis of uncertainties in the description of the subsurface is an important aspect for resource exploration and material storage. Because of the complexity of the subsurface and an often inhomogeneous data situation, models exhibit several aspects of uncertainties. These may be caused by the interpolation of locally sparse data and must be considered when constraining a structural geological model. Further, these interpolations may be subject to errors caused by psychological biases, which need to be identified to avoid error propagation during the model building.
The aim of this study is to develop structural geological models of the Cretaceous units of the Münsterland Basin on the basis of stratigraphic boundaries and orientation measurements derived from maps, boreholes and literature as a framework for future geothermal exploration. In the model construction phase, it is attempted to separate processed input data in a first model setup from additional geological assumptions required to obtain geologically meaningful representations. Potential sources for bias are evaluated during the data processing, and standard deviations of input data points are accounted for during a subsequent uncertainty analysis using probabilistic geomodelling approaches.
The resulting structural models reveal the effects and limitations of purely input data-driven models versus models with additional integrated data and the uncertainties derived from different input data types. The integration of results of the planned seismic investigation in 2021/2022 by the Geological Survey NRW and the results of seismic campaigns acquired in the 1970s and 1980s may help to close these knowledge gaps in future work.
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