Utah FORGE LBNL 3-2535 Preliminary Report on Development of a Reservoir Seismic Velocity Model

Description

This report describes the development of a preliminary 3D seismic velocity model at the Utah FORGE site and first results from estimating seismic resolution in the generated fracture volume during Stage 3 of the April 2022 stimulation. A preliminary 3D velocity model for the larger FORGE area was developed using RMS velocities of the seismic reflection survey and seismic velocity logs from borehole measurements as an input model. To improve the accuracy of the model in the shallow subsurface, travel times phase arrivals of the direct propagating P-waves were determined from the seismic reflection data, using PhaseNet, a deep-neural-network-based seismic arrival time picking method. The travel times were subsequently inverted using the input velocity model. The results showed that the input velocity model needs improvement as the resulting model appears too fast in the easter region of the FORGE area. During the next phase of this work, we will update the input velocity model and generate P-wave arrival times for additional seismic source locations, to improve the horizontal resolution in the sedimentary layer and to obtain a model that better matches the sedimentary layer and the travel time observations.

Resources

Name Format Description Link
33 This milestone report report describes the development of a 3D seismic velocity model at the Utah FORGE site and first results from estimating seismic resolution in the generated fracture volume during Stage 3 of the April 2022 stimulation. https://gdr.openei.org/files/1470/LBNL_3-2535_Task_3_Milestone_Report_Task_3.1.pdf

Tags

  • phasenet
  • report
  • geophysics
  • forge
  • 3d-seismic-velocity-model
  • egs
  • deep-learning
  • utah-forge
  • milford
  • neural-networking
  • reservoir
  • seismic-resolution
  • model
  • characterization
  • seismic
  • preliminary
  • velocity
  • machine-learning
  • geothermal
  • energy

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