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

这项NSF智能互联社区（S CC）规划拨款将为探索可持续的垂直起降系统奠定基础，该系统能够部署自主无人机群，用于测量孤儿威尔斯井的甲烷排放量。规划补助金还将审查监管机构和运营商对潜在技术变革的反应。这些废弃的石油或天然气威尔斯井，通常是化石燃料开采业在运营费用超过生产率时留下的，是温室气体排放的重要来源。鉴于这些威尔斯油井的封堵、补救和恢复成本很高，因此需要强有力的数据驱动证据来证明州和联邦资金的分配是合理的，并对其进行优先排序。传统的甲烷排放测量涉及在每个开放式井口安装通量室，具有很高的资本和运营成本，并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，但研究成果可以提供适用于不同领域的有价值的见解，如野生动物监测，反偷猎举措，基础设施和飞机检查，建筑工地监控和水污染监测。该研究旨在为集成的，可持续的垂直机场创建一个新的跨领域框架，以辅助自主无人机群检查系统。该项目围绕三个技术目标：1）开发一个低成本、便携式和可持续的垂直起降平台，以促进多架无人机的精确着陆和收容、保护和充电; 2）构建一个安全的联合深度强化学习算法，使无人机能够在恶劣环境下群着陆和起飞; 3）检查三维无人机群路径规划，以实现有效的甲烷羽流定位和排放量化。同时，该项目将追求两个社会科学目标：1）定量评估分配用于清理孤威尔斯井的联邦资金的潜在效率和公平改进; 2）通过加强二氧化碳封存税收激励措施的公平性，鼓励运营商采用这种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确定经济上可行和可持续的解决方案，以减少温室气体排放，同时提供有价值的见解和工具，适用于高该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响评审标准。

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.