Greenhouse gas seepage into the oceans is a major environmental challenge. As ocean temperatures continue to rise, significant amounts of methane (CH4) currently trapped in permafrost are expected to be released into the oceans. Further, carbon capture and storage (CCS) is a rapidly emerging technology aimed at reducing CO2 emissions and reaching international climate goals by injecting and permanently storing large amounts of CO2 in geological reservoirs below the seabed. Finally, there are numerous legacy oil and gas wells offshore potentially acting as conduits of CH4 from shallow geological layers to the marine environment. Monitoring and documenting these processes using current technology is challenging and, in many cases, economically prohibitive. Autonomous underwater vehicles (AUVs) have excellent marine monitoring capabilities, but currently lack the ability to use sensor input in real-time for optimized mapping of emissions. SmartAUVs will dramatically improve the monitoring capabilities of AUVs, by applying artificial intelligence (AI) in concert with specialized signal processing techniques to enable AUV decision autonomy, i.e., the ability to take intelligent action in real time based on sensor input. This new level of autonomy will allow an AUV to map a large area sparsely, but to recognize features of interest and adapt its travel path and data acquisition scheme to ensure a sufficiently detailed mapping and characterization of features of interest before leaving the location. Algorithms for automatic leak detection and adaptive sampling will be fully implemented in the HUGIN AUV processing unit, and a basic level of decision autonomy will be implemented in the Eelume as a proof of concept. These AUVs represent two fundamentally different AUV technologies with complementary marine monitoring capabilities. Dedicated field trials will be carried out in the Oslo Fjord near Horten to test, verify and adjust algorithms developed during the project.
Monitoring the marine environment is a vital part of ensuring safe and sustainable marine operations and understanding the dynamics of the global carbon cycle. AUVs play a key role in marine monitoring because of their ability to cover large areas and use sensors tailored to the monitoring needs. Currently these vehicles have limited decision autonomy and therefore follow a pre-defined travel path. SmartAUVs will dramatically improve the monitoring capabilities of AUVs, by applying artificial intelligence (AI) in concert with specialized signal processing techniques to enable AUV decision autonomy, i.e., the ability to take intelligent action in real time based on sensor input.
The aim of WPs 1 and 2 is to understand critical CO2/CH4 emission scenarios and simulate these to provide detailed knowledge about plume properties. These insights determine the desired AUV behaviour including travel path and sensor usage. In WP3 we develop intelligent algorithms for automatic leak detection; converting raw sensor data to information based on which the AUV can act (e.g., sonar data converted to information about seepage). In WP4 we develop AUV autonomy including situational awareness and optimized travel path. Algorithms for leak detection (WP3) and adaptive behaviour (WP4) will be implemented in the HUGIN AUV processing unit, and full autonomy demonstrated during field trials (WP5). The Eelume AUV will also be used during the field trials, and a basic level of decision autonomy developed and demonstrated. The HUGIN and Eelume AUVs have fundamentally different properties and complementary monitoring capabilities.
SmartAUVs will contribute to improved monitoring of the oceans, which in turn will enable sound management of marine activities, strengthened confidence in safe CO2 storage, and insights into the amount of CH4 entering the oceans through natural- and industry related processes. Active industry engagement will enable efficient benefits realization.