Field validation shows effective anomaly detection in subsea oil and gas monitoring, suggesting enhanced risk management measures.
Hydrocarbon and CO₂ leakage from subsea infrastructure presents a significant risk to offshore safety and environmental integrity. This paper describes the development, deployment, and field validation of a wide-area acoustic monitoring system for subsea leak detection and integrity assurance. Originally developed by Metas AS and now owned by Imenco Future Technologies AS, the system employs broadband sonar and machine learning algorithms to detect and classify acoustic anomalies such as gas plumes and oil droplets. The modular platform supports both standalone operation and integration with offshore Safety and Automation Systems (SAS) in accordance with API 17F. Field deployments, including multi-year monitoring at an Equinor production site, demonstrated detection ranges exceeding 1,000 meters for gas-phase releases and four hundred meters for denser fluids, with low false alarm rates and stable classification under varying environmental conditions. These capabilities enable persistent leak monitoring and early anomaly detection. The system architecture allows operators to tailor coverage and integrate alerting functions into operational systems. This field-proven acoustic monitoring solution supports proactive risk management and complements surface monitoring tools such as satellite-based SAR, especially in detecting and localizing subsea leak sources.
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Smith et al. (2025) studied this question.
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