Shale-Gas-Related Induced Seismicity: Geospatial Patterns, Forecasting Approaches and Risk Governance

As global energy systems pursue lower-carbon transition pathways, the rapid expansion of shale-gas development has increased subsurface fluid injection, intensifying concerns about induced seismicity and associated seismic risk. Although regional studies have documented specific induced-seismicity sequences, global patterns, regional variability, forecasting approaches, and governance practices have rarely been examined together within an integrated framework. This review synthesizes shale-gas-related induced seismicity across major production regions, including North America, Europe, and China, with particular emphasis on common patterns and contrasting seismic responses to hydraulic fracturing and wastewater disposal. Across diverse geological and operational settings, the reviewed evidence indicates that wastewater disposal is generally associated with larger-magnitude and more spatially widespread seismicity, whereas hydraulic fracturing typically produces localized seismicity but can also be associated with significant events where critically stressed faults are hydraulically connected to the stimulated zone. Building on this regional synthesis, we compare statistical, physics-based, hybrid, and machine-learning forecasting methods in terms of their operational applicability, predictive capabilities, data requirements, and limitations. However, translating these methodological advances into effective risk reduction requires their integration with robust monitoring and adaptive governance practices. Current governance measures can reduce risk, but their effectiveness depends on sufficiently sensitive monitoring, timely data exchange, regionally calibrated and adaptive operational thresholds, and continued post-injection surveillance. Future risk management should therefore move toward dynamic, physics-constrained geospatial frameworks that integrate seismic observations, injection histories, fault architecture, and regional stress conditions to support adaptive operational decision-making.