Utah FORGE 3-2417: Simulations for Distributed Acoustic Sensing Strain Signatures as an Indicator of Fracture Connectivity

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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 -
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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

Megan Ward-Baranyay

Rice University

Jonathan Ajo-Franklin

Rice University

Ahmad Ghassemi

University of Oklahoma

DOE Project Details

Project Name Utah FORGE

Project Lead Lauren Boyd

Project Number EE0007080

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