Towards intelligent forecasting of injection-induced seismicity: A global sensitivity analysis guided approach

Abstract

In subsurface engineering operations such as hydraulic fracturing, wastewater disposal, and enhanced geothermal systems, large volumes of fluids are injected to change reservoir conditions and stress states. These operations are controlled by engineering parameters, such as injection rate, and subsurface hydraulic properties, which directly regulate subsurface stress conditions and associated seismic risk. Subsurface fluid injection can induce earthquakes by changing pore pressure and effective stress within fault zones. However, the governing dynamics of these processes are highly nonlinear, and the influence of uncertain input parameters on model predictions is difficult to quantify, introducing substantial uncertainty in risk assessment. In this study, we employ a poroelastic spring-slider model combined with the Homma-Saltelli (H-S) global sensitivity analysis to systematically evaluate the relative importance of key parameters. Three representative outputs including pore pressure, slip velocity and the state variable, are analyzed to capture hydrological response, kinematic slip behavior, and frictional state evolution. The results demonstrate that pore pressure is most sensitive to the fluid injection rate and hydraulic diffusivity, with higher injection and lower diffusivity leading to stronger pore-pressure buildup. Slip velocity is governed primarily by the far-field loading velocity, whereas the state variable is controlled by both the loading velocity and the frictional evolution parameter b, with negative correlations observed in both cases. These findings highlight that zones of low hydraulic diffusivity combined with high injection rates can act as nucleation hotspots, significantly elevating the risk of injection-induced seismicity. Beyond mechanistic insights, we propose a smart, data-driven framework in which sensitivity rankings inform the design of monitoring strategies and the construction of efficient surrogate models. This provides a pathway towards intelligent, real-time forecasting and risk management of injection-induced earthquakes.

Keywords

Fluid injection / Induced earthquake / Model parameters / Global sensitivity analysis / Smart management

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