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The energy transition to meet net-zero emissions by 2050 has created demand for underground caverns needed to safely store CO2, hydrocarbon, hydrogen, and wastewater. Salt domes are ideal for underground storage needs because of their low permeability and affordable costs, which makes them the preferred choice for large-scale storage projects like the US Strategic Petroleum Reserves. However, the uneven upward movement of salt spines can create drilling problems and breach cavern integrity, releasing harmful gases into overlying aquifers and endangering nearby communities. Here, we present a novel application of data-driven geophysical methods combined with machine learning that improves salt dome characterization during feasibility studies for site selection and potentially advances the effectiveness of current early-warning systems. We utilize long-term, non-invasive seismic monitoring to investigate deformation processes at the Sorrento salt dome in Louisiana. We developed a hybrid autoencoder model and applied it to an 8-month dataset from a nodal array deployed in 2020, to produce a high-fidelity microearthquake catalog. Our hybrid model outperformed traditional event detection techniques and other neural network detectors. Seismic signals from storms, rock bursts, trains, aircraft, and other anthropogenic sources were identified. Clusters of microearthquakes were observed along two N-S trends referred to as Boundary Shear Zones (BSZ), along which we infer that salt spines are moving differentially. Time-lapse sonar surveys were used to confirm variations in propagation rates within salt spines and assess deformation within individual caverns. Seismicity along one BSZ is linked with a well failure incident that created a 30-ft wide crater at the surface in 2021. This study introduces a novel method for mapping spatial and temporal variations in salt shear zones and provides insights into the subsurface processes that can compromise the safety and lifetime of underground storage sites.
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Monitoring Salt Domes Used for Energy Storage With Microseismicity: Insights for a Carbon‐Neutral Future
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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- Award ID(s):
- 2438339
- PAR ID:
- 10555094
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geochemistry, Geophysics, Geosystems
- Volume:
- 25
- Issue:
- 11
- ISSN:
- 1525-2027
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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