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Utah FORGE 6-3712: Report on a Data Foundation for Real-Time Identification of Microseismic Events
L o a d i n g
Organization
National Renewable Energy Laboratory (NREL) - view all
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Last updated5 days ago
Format
Overview

This submission is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of extracting events from the borehole seismic sensors. To be effective once deployed, the process must be done in real-time. A summary of the methodology is as follows: bandpass filter, shift (via cross-correlation) and stack signals, envelope function, peak detection, transfer function from amplitude to magnitude, creation of magnitude-frequency distribution, and finally, extract MFD "a" and "b" parameters. The datasets used in this work are linked below and include the raw waveform data and the seismic event catalog used for magnitude calibration, also hosted on the GDR.

AIEGSMLUtah FORGEartificial intelligenceborehole seismicdata processingenergyevent catalogevent detectiongeophysicsgeothermalinduced seismicitymachine learningmagnitude-frequency distributionmicroseismicphysics informedreal-timerecurrent neural networksseismic datatechnical report
Additional Information
KeyValue
Dcat Issued2025-01-21T07:00:00Z
Dcat Modified2025-01-22T21:04:55Z
Dcat Publisher NameGlobal Technology Connection, Inc.
Guidhttps://data.openei.org/submissions/8318
Harvest Object Id5f3fa4ff-a911-431c-820e-fa154ab65bd0
Harvest Source Id4eb7107f-a2b1-40e3-b36a-8161aa98a56e
Harvest Source TitleOpenEI Data Portal
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Files
  • Technical Report.pdf

  • 2022 Well Stimulation Seismicity Data

  • Seismic Event Catalogue from the April 2022 Stimulation