Utah FORGE: 2023 Large Upscaled Discrete Fracture Network Models

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This dataset includes the data and a report on the large upscaled discrete fracture network modeling done for the Utah FORGE project in 2023. The FORGE modeling team is making five discrete fracture network (DFN) realizations of a large reservoir model available to researchers. These models have been upscaled to a continuum mesh or grid at resolutions of 10 meters and 20 meters providing reservoir properties for fracture porosity, permeability, and compressibility. The models are available in both the reference global coordinate frame and a local coordinate frame aligned with principal stress directions.

Citation Formats

TY - DATA AB - This dataset includes the data and a report on the large upscaled discrete fracture network modeling done for the Utah FORGE project in 2023. The FORGE modeling team is making five discrete fracture network (DFN) realizations of a large reservoir model available to researchers. These models have been upscaled to a continuum mesh or grid at resolutions of 10 meters and 20 meters providing reservoir properties for fracture porosity, permeability, and compressibility. The models are available in both the reference global coordinate frame and a local coordinate frame aligned with principal stress directions. AU - Finnila, Aleta DB - Geothermal Data Repository DP - Open EI | National Renewable Energy Laboratory DO - 10.15121/2007506 KW - geothermal KW - energy KW - DFN KW - fractures KW - Utah FORGE KW - EGS fracture model KW - WSP KW - FORGE KW - EGS KW - large upscaled DFN KW - Roosevelt Hot Springs KW - well 16A78-32 KW - reservoir engineering KW - FracMan KW - engineered geothermal systems KW - stimulation KW - modelling KW - permeability KW - porosity KW - reservoir modeling KW - discrete fracture network KW - reservoir properties KW - fracture modeling LA - English DA - 2023/10/02 PY - 2023 PB - Energy and Geoscience Institute at the University of Utah T1 - Utah FORGE: 2023 Large Upscaled Discrete Fracture Network Models UR - https://doi.org/10.15121/2007506 ER -
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Finnila, Aleta. Utah FORGE: 2023 Large Upscaled Discrete Fracture Network Models. Energy and Geoscience Institute at the University of Utah, 2 October, 2023, Geothermal Data Repository. https://doi.org/10.15121/2007506.
Finnila, A. (2023). Utah FORGE: 2023 Large Upscaled Discrete Fracture Network Models. [Data set]. Geothermal Data Repository. Energy and Geoscience Institute at the University of Utah. https://doi.org/10.15121/2007506
Finnila, Aleta. Utah FORGE: 2023 Large Upscaled Discrete Fracture Network Models. Energy and Geoscience Institute at the University of Utah, October, 2, 2023. Distributed by Geothermal Data Repository. https://doi.org/10.15121/2007506
@misc{GDR_Dataset_1554, title = {Utah FORGE: 2023 Large Upscaled Discrete Fracture Network Models}, author = {Finnila, Aleta}, abstractNote = {This dataset includes the data and a report on the large upscaled discrete fracture network modeling done for the Utah FORGE project in 2023. The FORGE modeling team is making five discrete fracture network (DFN) realizations of a large reservoir model available to researchers. These models have been upscaled to a continuum mesh or grid at resolutions of 10 meters and 20 meters providing reservoir properties for fracture porosity, permeability, and compressibility. The models are available in both the reference global coordinate frame and a local coordinate frame aligned with principal stress directions.}, url = {https://gdr.openei.org/submissions/1554}, year = {2023}, howpublished = {Geothermal Data Repository, Energy and Geoscience Institute at the University of Utah, https://doi.org/10.15121/2007506}, note = {Accessed: 2025-04-24}, doi = {10.15121/2007506} }
https://dx.doi.org/10.15121/2007506

Details

Data from Oct 2, 2023

Last updated Oct 5, 2023

Submitted Oct 4, 2023

Organization

Energy and Geoscience Institute at the University of Utah

Contact

Aleta Finnila

206.316.5647

Authors

Aleta Finnila

WSP Golder

DOE Project Details

Project Name Utah FORGE

Project Lead Lauren Boyd

Project Number EE0007080

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