Utah FORGE 3-2417: Simulations for Distributed Acoustic Sensing Strain Signatures as an Indicator of Fracture Connectivity
This dataset encompasses simulations of strain signatures from both hydraulically connected and "near-miss" fractures in enhanced geothermal systems (EGS). The files and results are presented from the perspective of digital acoustic sensing's (DAS) potential to differentiate the two fracture types. This dataset was acquired by the FOGMORE R&D project (Fiber Optic Geophysical MOnitoring of Reservoir Evolution), under Utah FORGE R&D Project 3-2417. Included are simulation and results via MatLab and COMSOL files, as well as a thesis and paper summarizing the results.
Some stimulated fractures may be incomplete, approaching but not intersecting the production well. These "near-miss" fractures can be addressed in future stimulation stages or re-stimulated to complete the connection. We propose the use of fiber optic distributed acoustic sensing (DAS) as a method by which near-miss stimulated fractures may be identified and distinguished from hydraulically connected fractures. The low-frequency sub-nanostrain signatures of both complete and near-miss fractures in DAS data are simulated in this study using a hydrogeomechanical discrete fracture network model. The spatial distribution of strain was found to be an accurate indicator. However, this indicator must be evaluated in the context of DAS gauge length and spatial sampling. These simulations are a precursor to tests conducted at FORGE in 2023.
Citation Formats
TY - DATA
AB - This dataset encompasses simulations of strain signatures from both hydraulically connected and "near-miss" fractures in enhanced geothermal systems (EGS). The files and results are presented from the perspective of digital acoustic sensing's (DAS) potential to differentiate the two fracture types. This dataset was acquired by the FOGMORE R&D project (Fiber Optic Geophysical MOnitoring of Reservoir Evolution), under Utah FORGE R&D Project 3-2417. Included are simulation and results via MatLab and COMSOL files, as well as a thesis and paper summarizing the results.
Some stimulated fractures may be incomplete, approaching but not intersecting the production well. These "near-miss" fractures can be addressed in future stimulation stages or re-stimulated to complete the connection. We propose the use of fiber optic distributed acoustic sensing (DAS) as a method by which near-miss stimulated fractures may be identified and distinguished from hydraulically connected fractures. The low-frequency sub-nanostrain signatures of both complete and near-miss fractures in DAS data are simulated in this study using a hydrogeomechanical discrete fracture network model. The spatial distribution of strain was found to be an accurate indicator. However, this indicator must be evaluated in the context of DAS gauge length and spatial sampling. These simulations are a precursor to tests conducted at FORGE in 2023.
AU - Ward-Baranyay, Megan
A2 - Ajo-Franklin, Jonathan
A3 - Ghassemi, Ahmad
DB - Geothermal Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO - 10.15121/2369582
KW - geothermal
KW - energy
KW - FORGE
KW - Utah FORGE
KW - EGS
KW - Milford
KW - Utah
KW - COMSOL
KW - DFN
KW - MatLab
KW - simulation
KW - near-miss fracture
KW - DAS
KW - distributed acoustic sensing
KW - modeling
KW - strain
KW - FOGMORE
KW - geophysics
KW - hydrogeomechanics
KW - sub-nanostrain
KW - code
KW - stimulation
LA - English
DA - 2023/01/01
PY - 2023
PB - Rice University
T1 - Utah FORGE 3-2417: Simulations for Distributed Acoustic Sensing Strain Signatures as an Indicator of Fracture Connectivity
UR - https://doi.org/10.15121/2369582
ER -
Ward-Baranyay, Megan, et al. Utah FORGE 3-2417: Simulations for Distributed Acoustic Sensing Strain Signatures as an Indicator of Fracture Connectivity. Rice University, 1 January, 2023, Geothermal Data Repository. https://doi.org/10.15121/2369582.
Ward-Baranyay, M., Ajo-Franklin, J., & Ghassemi, A. (2023). Utah FORGE 3-2417: Simulations for Distributed Acoustic Sensing Strain Signatures as an Indicator of Fracture Connectivity. [Data set]. Geothermal Data Repository. Rice University. https://doi.org/10.15121/2369582
Ward-Baranyay, Megan, Jonathan Ajo-Franklin, and Ahmad Ghassemi. Utah FORGE 3-2417: Simulations for Distributed Acoustic Sensing Strain Signatures as an Indicator of Fracture Connectivity. Rice University, January, 1, 2023. Distributed by Geothermal Data Repository. https://doi.org/10.15121/2369582
@misc{GDR_Dataset_1582,
title = {Utah FORGE 3-2417: Simulations for Distributed Acoustic Sensing Strain Signatures as an Indicator of Fracture Connectivity},
author = {Ward-Baranyay, Megan and Ajo-Franklin, Jonathan and Ghassemi, Ahmad},
abstractNote = {This dataset encompasses simulations of strain signatures from both hydraulically connected and "near-miss" fractures in enhanced geothermal systems (EGS). The files and results are presented from the perspective of digital acoustic sensing's (DAS) potential to differentiate the two fracture types. This dataset was acquired by the FOGMORE R&D project (Fiber Optic Geophysical MOnitoring of Reservoir Evolution), under Utah FORGE R&D Project 3-2417. Included are simulation and results via MatLab and COMSOL files, as well as a thesis and paper summarizing the results.
Some stimulated fractures may be incomplete, approaching but not intersecting the production well. These "near-miss" fractures can be addressed in future stimulation stages or re-stimulated to complete the connection. We propose the use of fiber optic distributed acoustic sensing (DAS) as a method by which near-miss stimulated fractures may be identified and distinguished from hydraulically connected fractures. The low-frequency sub-nanostrain signatures of both complete and near-miss fractures in DAS data are simulated in this study using a hydrogeomechanical discrete fracture network model. The spatial distribution of strain was found to be an accurate indicator. However, this indicator must be evaluated in the context of DAS gauge length and spatial sampling. These simulations are a precursor to tests conducted at FORGE in 2023.},
url = {https://gdr.openei.org/submissions/1582},
year = {2023},
howpublished = {Geothermal Data Repository, Rice University, https://doi.org/10.15121/2369582},
note = {Accessed: 2025-04-24},
doi = {10.15121/2369582}
}
https://dx.doi.org/10.15121/2369582
Details
Data from Jan 1, 2023
Last updated Aug 22, 2024
Submitted May 10, 2024
Organization
Rice University
Contact
Matthew W Becker
562.985.8983
Authors
Keywords
geothermal, energy, FORGE, Utah FORGE, EGS, Milford, Utah, COMSOL, DFN, MatLab, simulation, near-miss fracture, DAS, distributed acoustic sensing, modeling, strain, FOGMORE, geophysics, hydrogeomechanics, sub-nanostrain, code, stimulationDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080