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Abstract Underground storage in geologic formations will play a key role in the energy transition by providing low‐cost storage of renewable fuels such as hydrogen. The sealing qualities of caverns leached in salt and availability of domal salt bodies make them ideal for energy storage. However, unstable boundary shear zones of anomalous friable salt can enhance internal shearing and pose a structural hazard to storage operations. Considering the indistinct nature of internal salt heterogeneities when imaged with conventional techniques such as reflection seismic surveys, we develop a method to map shear zones using seismicity patterns in the US Gulf Coast, the region with the world's largest underground crude oil emergency supply. We developed and finetuned a machine learning algorithm using tectonic and local microearthquakes. The finetuned model was applied to detect microearthquakes in a 12‐month long nodal seismic dataset from the Sorrento salt dome. Clustered microearthquake locations reveal the three‐dimensional geometry of two anomalous salt shear zones and their orientations were determined using probabilistic hypocenter imaging. The seismicity pattern, combined with borehole pressure measurements, and cavern sonar surveys, shows the spatiotemporal evolution of cavern shapes within the salt dome. We describe how shear zone seismicity contributed to a cavern well failure and gas release incident that occurred during monitoring. Our findings show that caverns placed close to shear zones are more susceptible to structural damage. We propose a non‐invasive technique for mapping hazards related to internal salt dome deformation that can be employed in high‐noise industrial settings to characterize storage facilities.
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Monitoring Subsurface Changes in Salt Dome Caverns using Fine-scale Microseismicity Variations
The US Gulf Coast has several massive underground caverns within salt domes. These caverns can store vast amounts of hydrocarbons, including the US Strategic Petroleum Reserve, used to increase energy supplies during emergency shortages. Unstable caverns can collapse, leading to sinkhole formation and the release of gas. Previous studies have identified elevated seismicity and surface deformation as precursors to salt cavern collapse and sinkhole formation. However, identifying sporadic seismicity can be complicated, requiring complex methods for robust detection and characterization of events, especially in high-noise settings. We investigate deformation of the Sorrento salt dome in Louisiana using 8 months of data from seismic arrays first deployed in February 2020. Our arrays are comprised of 12 to 17 SmartSolo 3C seismic nodes, spaced 0.2-1.9 km and installed around the dome. We recorded more than 1.2 Tb of data, sampled at 500 Hz. Waveforms of identified events range from <1 s to over 30 s in length, rendering power detection methods like the STA/LTA inefficient. Building on recent studies that use machine learning methods to identify small magnitude (Mw -2.0 to 2.0) earthquakes, we developed a custom-trained convolutional neural network and applied it over sliding windows of the waveforms to detect earthquakes, pick P-wave arrivals, and reduce false positives. We correlated waveforms across all stations and identified events when they were observed on at least 60% of the array stations. We used spectrograms to infer fluid content around sources and to eliminate anthropogenic signals, including but not limited to, helicopters, trains, and boats from the catalog. Event locations were used to identify microearthquake swarms within the dome. Our preliminary results show elevated seismicity in the days preceding a well failure, suggesting our method can be used to monitor underground caverns and similar settings, such as mines, dams, and geothermal sites.
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- Award ID(s):
- 2045983
- PAR ID:
- 10323700
- Date Published:
- Journal Name:
- American Geophysical Union Fall Meeting
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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