EGU22-10115, updated on 28 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Environmental hazard quantification toolkit based on modular numerical simulations

Morgan Tranter1,2, Svenja Steding1,2, Christopher Otto1, Konstantina Pyrgaki3, Mansour Hedayatzadeh4, Vasilis Sarhosis4, Nikolaos Koukouzas3, Georgios Louloudis5, Christos Roumpos5, and Thomas Kempka1,2
Morgan Tranter et al.
  • 1GFZ German Research Centre for Geosciences, Fluid Systems Modelling, 14473 Potsdam, Germany
  • 2University of Potsdam, Institute of Geosciences, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
  • 3Centre for Research and Technology, Hellas (CERTH), 52 Egialias, 15125, Marousi, Greece
  • 4University of Leeds, Woodhouse Lane, LS2 9JT Leeds, United Kingdom
  • 5Public Power Corporation of Greece, 104 32 Athens, Greece

Comprehensive risk assessments for subsurface utilisation projects such as in-situ coal conversion, deep geothermal energy, geological storage, and waste disposal are implemented to a limited extent in common practice. The impacts of subsurface processes on environmental hazards (e.g., migration of groundwater-borne contaminants, induced seismicity, and subsidence) are often convoluted and therefore not trivially to predict. Furthermore, decisions on project feasibilities are commonly based on expert knowledge subject to non-standardised approaches. However, an objectively and transparently developed risk assessment is imperative for a publicly accepted, long-term economic and environmentally friendly design of future subsurface utilisation.

We propose a new environmental hazard quantification framework based on modular simulations. The aim is to create a uniform basis for both project developers and authorities to carry out risk analyses. The approach streamlines state-of-the-art numerical models [1,2], accounting for multiphase flow, geomechanics, geochemistry, and heat transport, to determine the likelihood and severity of hazards. The method uses the results of the computational expensive Monte Carlo simulations of each module to train gradient boosting machine learning algorithms. These surrogate models facilitate loose coupling within the framework and a seamless integration into a graphical user interface for demonstrating hazard probability distributions.

The approach was applied to two example study areas with complex geological settings as part of a risk assessment for in-situ coal conversion. A substantial rock volume is extracted during this operation, and a contaminant pool is potentially left behind, which may put the environment at risk. With our presented approach, the shortcoming of using conceptually simplified models are substantially reduced, since subsurface complexities are accounted for. The transparency of the assessment basis should generally increase the acceptance of geoengineering projects, which is considered one of the crucial aspects for the further development and dissemination of geological subsurface utilisation.

[1] Hedayatzadeh et al.: Ground subsidence and fault reactivation during in-situ coal conversion assessed by numerical simulations, EGU22,, 2022.
[2] Kempka et al.: Probability of contaminant migration from abandoned in-situ coal conversion reactors, EGU22,, 2022.

How to cite: Tranter, M., Steding, S., Otto, C., Pyrgaki, K., Hedayatzadeh, M., Sarhosis, V., Koukouzas, N., Louloudis, G., Roumpos, C., and Kempka, T.: Environmental hazard quantification toolkit based on modular numerical simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10115,, 2022.