SCC-PG: Sustainable Vertiports for Bringing Autonomous Drone Swarm Inspection to Oil and Gas Industry Community

这笔美国国家科学基金会智能互联社区(S和CC)规划拨款将为探索一种可持续的垂直港口系统奠定基础，该系统能够部署自主无人机群来测量孤立油井的甲烷排放。规划拨款还将审查监管机构和运营商对潜在技术变化的反应。这些废弃的油井或气井通常是化石燃料开采行业在运营成本超过生产率时留下的，它们极大地增加了温室气体排放。鉴于封堵、修复和恢复这些油井的成本很高，需要强有力的数据驱动的证据来证明州和联邦资金的分配并确定其优先顺序。传统的甲烷排放测量涉及在每个开放井口安装通量室，具有高昂的资本和运营成本，而且在难以到达的地区部署具有挑战性。虽然主要针对石油和天然气行业，但研究成果可以为野生动物监测、反偷猎倡议、基础设施和飞机检查、建筑工地监测和水污染监测等不同领域提供有价值的见解。该研究旨在为一个集成的、可持续的垂直港口创建一个新的跨领域框架，以帮助自主无人机群检查系统。该项目围绕三个技术目标：1)开发低成本、便携和可持续的垂直机场，以便于多架无人机的精确着陆和住房、保护和充电；2)构建安全的联邦深度强化学习算法，使无人机能够在恶劣环境中着陆和起飞；3)研究三维无人机群路径规划，以实现高效的甲烷烟羽定位和排放量化。同时，该项目将追求两个社会科学目标：1)对分配给清理孤井的联邦资金的潜在效率和公平改善进行量化评估；2)通过加强二氧化碳封存税收优惠的公平性，鼓励运营商采用这一具有成本效益的监测系统。研究成果将彻底改变测量和监测技术，使石油和天然气行业能够找到经济上可行和可持续的解决方案来减少温室气体排放，同时提供适用于高影响领域的宝贵见解和工具，如机载无线边缘计算和自主无人机群防御。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This NSF Smart and Connected Community (S&CC) planning grant will set the groundwork for exploring a sustainable vertiport system capable of deploying autonomous drone swarms for methane emission measurements over orphaned wells. The planning grant will also scrutinize the responses of regulators and operators to the potential technological changes. These abandoned oil or gas wells, typically left behind by the fossil fuel extraction industry when operating expenses outstrip production rates, contribute significantly to greenhouse gas emissions. Given the high costs associated with the plugging, remediation, and restoration of these wells, robust, data-driven evidence is required to justify and prioritize the allocation of state and federal funds. Traditional methane emission measurements, involving flux chamber installation at each open wellhead, carry high capital and operational costs and are challenging to deploy in hard-to-reach areas. While primarily targeting the oil and gas industry community, the research outcomes could offer valuable insights applicable to diverse areas such as wildlife monitoring, anti-poaching initiatives, infrastructure and aircraft inspections, construction site surveillance, and water pollution monitoring.The research aims to create a new cross-domain framework for an integrated, sustainable vertiport that aids an autonomous drone swarm inspection system. The project revolves around three technical objectives: 1) Developing a low-cost, portable, and sustainable vertiport to facilitate precision landing and housing, protection, and recharging of multiple drones; 2) Constructing a safe federated deep reinforcement learning algorithm to enable drone swarm landing and takeoff in harsh environments; 3) Examining three-dimensional drone swarm path planning for efficient methane plume localization and emission quantification. Simultaneously, the project will pursue two social science objectives: 1) Quantitative assessment of potential efficiency and equity improvements in federal funds allocated for cleaning up orphaned wells; 2) Encouraging operators to adopt this cost-effective monitoring system by enhancing equity in carbon dioxide sequestration tax incentives. The research outcomes will revolutionize measurement and monitoring technologies, enabling the oil and gas industry to identify economically viable and sustainable solutions to reduce greenhouse gas emissions, while providing valuable insights and tools applicable to high-impact areas such as airborne wireless edge computing and autonomous drone swarm defense.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.