Development of the microBayesloc Location Method Email Page
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Performer: LLNL - Lawrence Livermore National Laboratory
Microseismic event locations with their 95 percent probability volumes.
Microseismic event locations with their 95 percent probability volumes.
Website: Lawrence Livermore National Laboratory
Award Number: FWP-FEW0193
Project Duration: 10/01/2014 – 09/30/2017
Total Award Value: $1,000,000
DOE Share: $1,000,000
Performer Share: $0
Technology Area: Geologic Storage
Key Technology: GS: Geomechanical Impacts
Location: Livermore, California

Project Description

The locations of microseismic events are used to assess the evolving underground state of stress during CO2 storage operations. Uncertainty in subsurface structure contributes significantly to error in estimated microseismic event locations. This project will adapt Lawrence Livermore National Laboratory’s Bayes location (Bayesloc) algorithm to more accurately predict the microseismic event location needed to inform studies about rock strain and fracture that may occur during CO2 injection. At the core of the Bayesloc is a statistical model that links observed data to unobserved parameters. The statistical model consists of three main components: (1) a prior probability model for the source parameters in 3D location and time, (2) a statistical model for the correction to the underground model (i.e., the travel-time corrections), and (3) a statistical model for the error in the observed data (e.g., the spread of the arrival-time residuals). In the field, seismic sensors, indicated by the blue circles in the figure, are used to measure microseismic events and infer location. The microBayesloc algorithm produces an estimate of location uncertainty, an essential feature to help distinguish real from phantom faults. MicroBayesloc determined microseismic event location uncertainty estimates are indicated by the red ellipsoid clouds.

Project Benefits

This project will develop a fundamentally better approach to geological site characterization and early hazard detection. It combines innovative techniques for analyzing microseismic data with a physics-based inversion model to forecast microseismic event locations. This project will devise fast-running methodologies that will allow field operators to respond quickly to changing subsurface conditions.

Contact Information

Federal Project Manager Andrea McNemar:
Technology Manager Traci Rodosta:
Principal Investigator Susan Carroll:


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