Induced micro-seismicity, artificial intelligence, and thermal-hydro-mechanical modelling: 2 new PhDs in partnership with INERIS and GFZ Potsdam

23 mars 2020 par LabEx GP
The LabEx welcomed two new PhD candidates this year - Kamel Drif and Qinglin Deng - who will be using numerical modelling and big-data approaches to characterise the evolution of deep geothermal reservoirs in Alsace.

The first thesis – carried out by Kamel Drif, a graduate of EOST in 2018 – aims to develop tools for monitoring EGS deep geothermal reservoirs using high resolution monitoring of induced micro-seismicity and artificial intelligence. The thesis is co-funded by EOST and Ineris, the National Institute for the Industrial Environment and Risks and is co-supervised by Olivier Lengliné and Jean Schmittbuhl from EOST and Jannes Kinscher and Emmanuelle Klein from Ineris.

Combining seismological analysis, artificial intelligence tools, and geological and geomechanical modelling, the objective of this project is to develop methods to understand the evolution of seismicity generated during the stimulation and exploitation of a deep geothermal reservoir. These new approaches will be tested and applied to data from the Soultz-sous-Forêts geothermal reservoir ( in an effort to infer the likely evolution of the structures identified within that reservoir. Ultimately, the goal is to develop a predictive tool that uses innovative artificial intelligence approaches to analyse induced microseismic activity in real time.

The second PhD project – carried out by Qinglin Deng – will study the multi-scale hydromechanical behaviour of natural fractures during stimulation of EGS geothermal reservoirs. This project is funded by a grant from the Chinese government and co-supervised by Jean Schmittbuhl (EOST) and Guido Blöcher (GFZ Potsdam).

The goal of this project is to produce numerical models of the hydro-mechanical behaviour of natural fractures, and to extend this approach to the reservoir scale with the ultimate goal of predicting reservoir response to stimulation. These models will be  correlated with field data to better characterise reservoir properties. The study will be based in particular on the data collected at Soultz-sous-Forêts and hosted by the Deep Geothermal Data Center (