Induced seismicity poses a significant challenge to the safe and sustainable development of Enhanced Geothermal Systems (EGS). This study explores the application of machine learning (ML) for forecasting cumulative seismic moment (CSM) of induced seismic events to evaluate reservoir stability in response to fluid injections. Using data from the Cooper Basin (Australia), the St1 Helsinki geothermal project (Finland), and a controlled laboratory injection experiment, we evaluate ML models that integrate catalogue and operational features with various frameworks. Results indicate that feature-rich models outperform simpler ones in complex seismic environments like the Cooper Basin and laboratory cases, where seismicity is promoted by earthquake interaction and fault reactivation. However, in scenarios like St1 Helsinki, with minimal event clustering, additional features offer limited predictive benefits. While ML models are promising, several challenges impede reliable forecasting, including data scarcity from operational wells, the extrapolation demands of cumulative output (i.e. CSM) and the difficulty of predicting abrupt CSM increases for large seismic events. Enhancing model robustness requires synthetic data augmentation and improved feature selection capable of capturing diverse reservoir dynamics. These advancements may enable more accurate near real-time forecasts of problematic induced seismic events, informing operational decisions to mitigate seismic risks while maximizing energy extraction, and hence offering a pathway for broader adoption of ML in renewable energy development and management.
