Leveraging Deep-Learning Methods for Operational Analysis at Enhanced Geothermal Systems

In recent decades, geothermal systems have gained increasing importance and attention. They have the potential to greatly contribute to the transition toward green energy and the establishment of a climate-neutral economy. Enhanced Geothermal Systems (EGS) represent a significant advancement in energy production methodologies. EGS utilize hydraulic stimulation techniques to inject and extract fluids, thereby enabling the harnessing of geothermal energy, which is crucial for electricity generation.

In addition to the existing natural seismicity, this production loop of hot and cold fluids may generate induced seismic events, specifically those caused by pressure changes that affect active faults or lead to stress variations within the rock volume. For these reasons, EGS could potentially induce medium to severe earthquakes that might impact nearby communities if not properly monitored and managed, or if strict monitoring methods are not followed to mitigate risks at EGS sites, particularly during operational stages.

Various physical and mechanical properties are recorded in real-time during operational stages. With the continual advancement of deep learning methods, these time series data can be analyzed individually and collectively for short-term forecasting of expected seismic magnitudes from future earthquakes.

Specifically, this work presents an experimental technique that leverages the spatio-temporal capabilities of graph neural networks by connecting these time series within a dynamic graph structure for short-term predictions of the maximum expected magnitude. This method is effective in identifying relationships that traditional approaches can sometimes overlook. Our preliminary results indicate that our algorithm can indeed assist in risk mitigation at EGS sites, potentially serving as a valuable complement to the current state-of-the-art frameworks (i.e., Traffic Light Systems, TLS) used globally.