Geophysical Process Simulations
_Copyright: © CGRE
In order to achieve the sustainable developments goals of the UN, we require predictions of the processes in the earth’s subsurface. Unfortunately, we do not have direct access to the subsurface. Hence, we require numerical simulations based on physical models to obtain these predictions, nonetheless. To account for complex model geometries and the correct in-situ conditions, as well as area wide information we can neither rely on analytical nor laboratory analyses. Therefore, we require numerical simulations to characterize the complex coupled physical processes of the earth’s subsurface. The processes include, for instance, fluid and heat transport, chemical species transport, and mechanical processes.
In our research group, we mainly focus on thermal and hydraulic simulation and on electrical resistivity tomography applications (link to ERT site of Rhea). Determining the subsurface properties is usually an inverse process. Geoscientific simulations are objected to several issues:
- We need to analyze complex coupled processes over large spatial and temporal domains
- The highly heterogenous nature of the subsurface further increases the dimensionality of the problem yielding the “curse of dimensionality”
- Due to limited data access the retrieval of information from the subsurface is a non-trivial process
- Measurements are associated to high uncertainties
- The introduced simplifications, and the uncertainties of the model geometry are another error source
Because of all these issues we need probabilistic approaches to account for the arising uncertainties. However, these approaches require quickly thousands to millions of forward simulations. This makes them prohibitive for standard state-of-the-art finite element simulations. This is the reason why, we explore the potential of model order reduction techniques (link to MOR site) for geophysical process simulations.