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Improved Microseismicity Detection During Newberry EGS Stimulations

Metadata Updated: June 25, 2021

Effective enhanced geothermal systems (EGS) require optimal fracture networks for efficient heat transfer between hot rock and fluid. Microseismic mapping is a key tool used to infer the subsurface fracture geometry. Traditional earthquake detection and location techniques are often employed to identify microearthquakes in geothermal regions. However, most commonly used algorithms may miss events if the seismic signal of an earthquake is small relative to the background noise level or if a microearthquake occurs within the coda of a larger event. Consequently, we have developed a set of algorithms that provide improved microearthquake detection. Our objective is to investigate the microseismicity at the DOE Newberry EGS site to better image the active regions of the underground fracture network during and immediately after the EGS stimulation. Detection of more microearthquakes during EGS stimulations will allow for better seismic delineation of the active regions of the underground fracture system. This improved knowledge of the reservoir network will improve our understanding of subsurface conditions, and allow improvement of the stimulation strategy that will optimize heat extraction and maximize economic return.

Access & Use Information

Public: This dataset is intended for public access and use. License: Creative Commons Attribution

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Dates

Metadata Created Date June 24, 2021
Metadata Updated Date June 25, 2021

Metadata Source

Harvested from OpenEI data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date June 24, 2021
Metadata Updated Date June 25, 2021
Publisher Lawrence Livermore National Laboratory
Maintainer
Doi 10.15121/1148783
Identifier https://data.openei.org/submissions/3124
Data First Published 2013-11-01T06:00:00Z
Data Last Modified 2019-11-14T22:45:25Z
Public Access Level public
Bureau Code 019:20
Metadata Context https://openei.org/data.json
Metadata Catalog ID https://openei.org/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Data Quality True
Harvest Object Id fc32052c-e08c-40ce-9b83-4b5529eeb446
Harvest Source Id 7cbf9085-0290-4e9f-bec1-91653baeddfd
Harvest Source Title OpenEI data.json
Homepage URL https://gdr.openei.org/submissions/281
License https://creativecommons.org/licenses/by/4.0/
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Program Code 019:006
Projectlead Lauren Boyd
Projectnumber FY13 AOP 25728
Source Datajson Identifier True
Source Hash 69ab05c0964643e0b0b89ffdcc22be97b2510d23
Source Schema Version 1.1
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