Fiber-optic Acoustic Sensing (FOAS) has been rapidly adopted by the petroleum industry, where fibers encased in cables are a low cost option suited to make downhole measurements that were previously difficult or impossible. In particular, fiber measurements have been useful in characterizing microseismicity and fractures produced by hydraulic stimulation in unconventional hydrocarbon production (Karrenbach et al. 2018). Applications also exist in areas relevant to national security such as explosion monitoring research.
Our study enhanced interpretation of signals measured by distributed FOAS with primary emphasis on applications in subsurface energy extraction. Our project consisted of two major elements: 1) computational-based simulation of subsurface signals, with emphasis on hydraulic stimulation used in hydrocarbon extraction and enhanced geothermal systems, as observed by FOAS sensors, and 2) development of a custom interrogator to conduct lab-based measurements of custom fibers fabricated on a fiber draw tower. For the first project element, simulations of hydraulic fracturing as measured by FOAS were modeled using a computational rock mechanics code. Simulations were also conducted of seismic wave propagation recorded by FOAS. Work on the second project element included design, construction, and testing of a distributed fiber optic interrogator and development of a test bed to evaluate custom fibers. As a result, we improved capabilities in modeling subsurface fractures and seismic events and developed a largely lab-based interrogator for testing fibers and validating numerical models.
Modeling subsurface events is key to improving subsurface measurements of fossil energy extraction, subsurface carbon sequestration, and geothermal energy, all of which play key roles Lawrence Livermore National Laboratory's energy security mission area. The work leveraged Livermore's core capabilities in high-performance computing, simulation, and data science and advanced the Laboratory's expertise in fiber fabrication to create a new fiber testing capability unique across the Department of Energy national laboratory complex. The capability is also relevant to explosion monitoring research and development, which supports the advancement of science, technology, and engineering competencies advancing the NNSA mission.
Karrenbach, M., et al. 2018. "Fiber-Optic Distributed Acoustic Sensing of Microseismicity, Strain and Temperature During Hydraulic Fracturing." Geophysics 84 (1): D11-D23. doi.org/10.1190/geo2017-0396.1.
Mellores, R., et al. 2017. "Modeling Borehole Microseismic and Strain Signals Measured by a Distributed Fiber-Optic Sensor." American Geophysical Union Fall Meeting, New Orleans, LA, December 2017. LLNL-POST-743107.
——— . 2018. "Simulating Distributed Fiber Optic Sensing in the Subsurface." SEG Summer Research Workshop: Recent Advances and Applications in Borehole Geophysics, Galveston, TX, August 2018. LLNL-PRES-757268.
——— . 2018. "Understanding Distributed Fiber-Optic Sensing Response for Modeling of Signals." SEG Technical Program Expanded Abstracts: 4679-4682. doi: 10.1190/segam2018-2997008.1. LLNL-PRES-760060.
——— . 2019, "Potential Use of Distributed Acoustic Sensors to Monitor Fractures and Microseismicity at the FORGE EGS site." 44th Workshop on Geothermal Reservoir Engineering Stanford University, February 2019. LLNL-CONF-767223.
——— . 2019. "Modeling Potential EGS Signals from a Distributed Fiber Optic Sensor Deployed in a Borehole."43rd Workshop on Geothermal Reservoir Engineering Stanford University, February 2019. LLNL-PRES-748366.
——— . 2019. "Fiber Optic Sensing of Local and Regional Earthquakes." Seismological Society of America Annual Meeting, Seattle, WA, April 2019. LLNL-PRES-775059.
Sherman, C., et al. 2017. "Simulating Fracture-Induced Strain Signals measured by a Distributed Fiber-Optic Sensor." SEG Technical Program Expanded Abstracts: 4113-4117. doi: 10.1190/segam2017-17678887.1. LLNL-ABS-751094.
——— . 2019. "Geomechanical Modeling of Distributed Fiber-Optic Sensor Measurements." Interpretation 7(1): SA21—SA27. doi: 10.1190/INT-2018-0063.1. LLNL-JRNL-746313.
——— . 2019. "Subsurface Monitoring via Physics-Informed Deep Neural Network Analysis of DAS." 53rd U.S. Rock Mechanics and Geomechanics Symposium, New York City, NY, ARMA-2019-0433, June 2019. LLNL-PROC-745469.
——— . 2019. "Modeling Distributed Fiber Optic Sensor Signals Using Computational Rock Mechanics." Society of Economic Geophysicists Proceedings of the 6th Unconventional Resources Technology Conference Meeting Abstracts: 2135-2140. doi: 10.15530/urtec-2018-2900760. LLNL-CONF-750564.
——— . 2019. "Building a Synthetic Training Dataset for Distributed Acoustic Sensor Measurements through Geomechanical Modeling." American Geophysical Union Fall Meeting, Washington, D.C., December 2019. LLNL-PRES-751645.
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