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Metadata Files

Data Publication

[Dataset] Global physics-based database of injection-induced seismicity

Kivi, Iman Rahimzadeh | Boyet, Auregan | Wu, Haiqing | Walter, Linus | Hanson-Hedgecock, Sara | Parisio, Francesco | Vilarrasa, Víctor

Digital.CSIC

(2022)

Descriptions

We present a comprehensive, publicly accessible database of injection-induced seismicity. The database is sourced from a comprehensive review of more than 500 published documents and contains information for 158 cases of induced earthquakes from around the world. The collected earthquakes are associated with a variety of geoenergy applications, which can be broadly categorized into geologic gas storage, geothermal energy development, shale gas fracturing, research projects and wastewater disposal. The compilation comprises more than 70 variables, including general project information, host rock properties, ‎in situ site characteristics, fault attributes, operational parameters, and recorded seismicity data (both overall seismic activities and the largest event sequence). The developed database opens up opportunities for improved understanding of the causative mechanisms of injection-induced seismicity and advancing on seismic hazard forecasting and mitigation.
The database and its accompanying files are as follows: GEoREST_database_Readme (.txt): provides an overview of the database goals and content, sharing/accessing information and methodologies used to develop the database. GEoREST_induced_seismicity_database_v20.11.2022 (.xlsx): database in a single Microsoft Excel spreadsheet. GEoREST_induced_seismicity_database_v20.11.2022 (.csv): database in ‎.csv format, considered as a standard machine-readable format for direct implementation of data in model developments. GEoREST_database_bibliography_v20.11.2022 (.docx): provides the full list of references used to collect data included in the database. GEoREST_database_dictionary_v20.11.2022 (.docx): presents unified, self-explanatory acronyms together with concise definitions for all variables included in the database.

Keywords


Originally assigned keywords
Induced Seismicity
Database
Multi-physics
Rock characteristics
Fault properties
Geoenergy resources
Energy transition

Corresponding MSL vocabulary keywords
induced seismicity

MSL enriched keywords
induced seismicity
Models
field system model
induced seismicity model
sedimentary rock
mudstone
shale
subsurface energy production
geothermal energy extraction

MSL enriched sub domains i

geo-energy test beds

Source

https://digital.csic.es/handle/10261/284662

Source publisher

Digital.CSIC


DOI

10.20350/digitalcsic/14813


Creators

Kivi, Iman Rahimzadeh

Boyet, Auregan

Wu, Haiqing

Walter, Linus

Hanson-Hedgecock, Sara

Parisio, Francesco

Vilarrasa, Víctor


Contributors

Digital.CSIC

DataManager

Digital.CSIC

HostingInstitution


Citation

Kivi, I. R., Boyet, A., Wu, H., Walter, L., Hanson-Hedgecock, S., Parisio, F., & Vilarrasa, V. (2022). [Dataset] Global physics-based database of injection-induced seismicity [Data set]. Digital.CSIC. https://doi.org/10.20350/DIGITALCSIC/14813


Dates

Accepted:

2022-12-12

Available:

2022-12-12

Issued:

2022-11


Language

en


Funding References

Funder name: This research is funded by (1) the European Research Council (ERC) under ‎the ‎‎European Union’s Horizon ‎‎2020 Research and Innovation Program through the ‎Starting Grant ‎‎GEoREST (www.georest.eu) under Grant ‎agreement No. 801809, (2) MCIN/AEI/‎‎10.13039/501100011033 and the ‎European ‎Union ‎NextGenerationEU/PRTR through the international collaboration project EASYGEOCARBON ‎‎‎‎(www.easygeocarbon.com) under Grant ‎agreement No. PCI2021-122077-2B, (3) the European Union’s Horizon 2020 Research and Innovation ‎Programme through the Marie Sklodowska-Curie Action ARMISTICE (www.armistice-energy.eu) under grant â €Žagreement No. 882733. (4) the Secretariat for Universities and ‎Research of the Ministry of Business and Knowledge of the Government of Catalonia (AGAUR) and the European ‎Social Fund (FI-2019), (5) MCIN/AEI/ ‎‎10.13039/501100011033‎ through the Excellence Servero Ochoa (IDAEA-CSIC) under Grant agreement No. ‎CEX2018-000794-S


Rights

openAccess