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.

与加拿大北大西洋和北冰洋近海石油和天然气以及航运作业相关的石油泄漏风险不断增加，需要先进的地下数据收集技术。该项目提出了自主水下航行器（AUV）技术，以帮助了解油羽行为并提高加拿大漏油响应的效率。事实证明，与其他传统勘测方法相比，AUV 提供的高分辨率地下数据更适合搜索和描绘不规则的油羽流。该项目旨在通过考虑水中油的实际行为和灾难响应的时间紧迫性来弥补当前 AUV 技术的差距。该项目将基于自适应采样方法，为 AUV 开发创新的水下数据收集策略。使用这种方法，AUV 根据机载传感器检测到的羽流特征实时自主修改其任务，从而形成一条集中在羽流附近或内部以及传感器识别的信息丰富区域的路径。迄今为止，针对水下溢油特征的AUV和油类传感技术仍在开发中。只有少数实验是在真实的漏油情况下进行的；大多数是在实验室和中尺度实验或石油代理中，这极大地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点是通过在巴芬岛附近自然存在石油的水中进行现场试验，在真实的水包油海洋环境中测试我们的石油检测方法。我们的项目与加拿大 AUV 运营商和制造商合作，将在使用 AUV 描绘地下油羽流方面向前迈出实质性一步，从而显着改善加拿大的溢油响应和水下人工智能能力，并增强海洋知识石油污染物和监测。