Characterization and delineation of oil-in-water at the Scott Inlet seeps through robotic autonomous underwater vehicle technology

通过机器人自主水下航行器技术对斯科特湾的水包油进行表征和描绘

• 批准号: 561516-2020
• 负责人: Bose, Neil
• 金额: $ 12.31万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=722027
• 关键词: Characterization; delineation; oil; water; Scott

Increased risks of oil spills associated with the offshore oil and gas as well as shipping operations in the northern Atlantic and Arctic oceans of Canada call for advanced technologies for subsurface data collection. Autonomous underwater vehicle (AUV) technology is proposed in this project to help understand oil plume behavior and improve the efficiency of Canada's oil spill response. High-resolution subsurface data provided by AUVs has proven to be more suitable for searching and delineating patchy oil plumes compared with other traditional survey methods. This project aims to address gaps in current AUV technology by considering the realistic behaviour of oil in water and the time-critical nature of disaster response. The project will develop an innovative underwater data collection strategy for AUVs, based on an adaptive sampling approach. Using this approach, an AUV autonomously modifies its mission in real-time based on features of the plumes detected by on-board sensors, resulting in a path concentrated nearby or within the plume and in information-rich areas identified by the sensors. To date, AUV and oil sensing technologies specific to the characteristics of underwater oil spills are still under development. Only a few experiments were done in real oil spill conditions; most in laboratory and mesoscale experiments or with oil proxies, which significantly limits the ability to understand and capture the complexity found in the field. Hence, another key feature of our project is to test our oil detection methods in real oil-in-water ocean environments through field trials near Baffin Island where oil is naturally present in the water. Our project, in partnership with Canadian AUV operators and manufacturers, will result in a substantial step forward in delineation of subsurface oil plumes using AUVs, providing a significant improvement in Canada's oil spill response and underwater artificial intelligence capacity, as well as enhanced knowledge of marine oil contaminants and monitoring.

与海上石油和天然气以及北大西洋和加拿大北极海洋相关的漏油风险增加，加拿大北极海洋要求采用高级技术来收集地下数据。在该项目中提出了自动驾驶水下汽车（AUV）技术，以帮助了解石油羽流的行为并提高加拿大溢油响应的效率。与其他传统的调查方法相比，AUVS提供的高分辨率地下数据更适合于搜索和描述斑点油羽。该项目的目的是通过考虑石油在水中的现实行为以及灾难反应的时间关键性质来解决当前AUV技术的差距。该项目将基于自适应抽样方法为AUV制定创新的水下数据收集策略。使用这种方法，AUV根据板载传感器检测到的羽毛的特征自动修改其任务，从而导致集中在附近或羽流中以及传感器识别的信息丰富的区域中的路径。迄今为止，AUV和油感应技术针对水下油的特征仍在开发中。在实际的漏油条件下，只进行了几次实验。大多数在实验室和中尺度实验或石油代理中，这显着限制了理解和捕获现场发现的复杂性的能力。因此，我们项目的另一个关键特征是通过巴芬岛附近的现场试验测试我们的石油检测方法，那里的石油自然存在于水中。我们的项目与加拿大AUV运营商和制造商合作，将在使用AUV的地下油李子上迈出一大步，从而为加拿大的溢油响应和水下人工智能能力提供了重大改善，并增强了海洋油污染物和监测的知识。