Microseismic Techniques for Detecting Induced Seismicity Hazard

Abstract:

Induced seismicity is inherently associated with underground fluid injections and poses a risk for geologic carbon sequestration efforts, enhanced geothermal systems and shale gas development. If fluids are injected in proximity to a pre-existing fault or fracture system, the resulting elevated pressures can trigger dynamic earthquake slip, which could damage surface structures and create new migration pathways.

The goal of this research is to develop a fundamentally better approach to geological site characterization and early hazard detection. We combine innovative techniques for analyzing microseismic data with a physics-based inversion model to forecast microseismic cloud evolution. The key challenge is that faults at risk of slipping are often too small to detect during the site characterization phase.

A natural response to fluid injection is the creation of microseismicity. Often thousands of microquakes are associated with an injection well. These microquakes are not of concern, as they are too small to be felt at the surface. However, they effectively illuminate the subsurface, allowing us to monitor plume growth and identify previously hidden faults. Precise seismic measurements on these microquakes is key.

Using ambient noise correlation we create sharp images of the subsurface. These images together with Bayesian techniques dramatically improve the precision with which microquakes are located. Matched field processing increases the range of magnitudes that can be identified. The virtual seismometer method lights up the seismically active region, allowing us to monitor the evolution of the seismicity, measure changes in the style of faulting, sort microseisms by location and magnitude, and to identify previously un-observed fault zones. Finally, using a hydromechanical inversion, we create a model of the pressure field that is consistent with the microseismic data. We use timing information about the microseismic cloud evolution to estimate major flow paths in the reservoir. The model is initially simple, becoming more sophisticated as new data are added.

This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was funded by the LDRD Program at LLNL under project tracking code 14-ER-051