This website uses cookies

This website uses cookies to improve user experience. By using our website you consent to all cookies in accordance with our Cookie Policy.

Falls Sie Probleme mit einer wiederauftauchenden Cookie-Meldung haben sollten, können Ihnen diese Anweisungen weiterhelfen.

Essential cookies enable basic functions and are necessary for the website to function properly.
Statistics cookies collect information anonymously. This information helps us to understand how our visitors use our website.
Mitglied werden Sponsor werden

11:30-11:50 am - Forum 4

Revising Northern Upper Rhine Graben Models using Uncertainty Analysis

van der Vaart, Jeroen; Frey, Matthis; Bär, Kristian; Sass, Ingo

TU-Darmstadt, Germany

The Upper Rhine Graben, with its geothermal anomalies and high geothermal gradients, has been an area of interest for geothermal energy extraction for many decades. This interest has resulted in a number of investigations on the geothermal potential and the need of further geothermal exploration in the Upper Rhine Graben. As a result, several regional models of different areas of the Upper Rhine Graben were produced and published, as for instance, as outcome of the projects Hessen 3D (1.0 and 2.0) and GeORG.
In the scope of the EU-NW-Interreg project DGE-Rollout (NWE 892), we make use of these models and take them a step further. Using uncertainty analysis and new data from the recent years, we combine and update the current models to a new and complete Upper Rhine Graben model.

Presenting the work done in the northern Upper Rhine Graben, we have an option to use a multitude of different stochastic methods for geological modelling and geothermal model parametrization. Each method can create numerous equally probable models to describe the subsurface. Yet, some algorithms and methods are more applicable for some areas or model units than others. This depends on different factors, which include geological history, geological knowledge, data availability, scale, etc. With the application of uncertainty modelling, through the use of Monte Carlo simulations, we derive a quantitative rather than qualitative description of the subsurface. Based on the simulation outcomes, we can estimate a validity of modelling algorithms in their respective areas and select the best approach for geological areas on both quantitative and qualitative basis. On top of this methodological assessment, uncertainty analysis provides us insight in the accuracy of the most viable modelling method, and therefore we can estimate the reliability of the models and maps we produce. With the estimation of this geological risk we can estimate its implications on the prospective risk which is key for decision making, e.g. if further exploration measures are required before drilling to unlock the potential of geothermal energy.

Unsere Medienpartner