Sustainable Vertiports for Bringing Autonomous Drone Swarm Inspection to Oil and Gas Industry Community
Lead PI:
Sihua Shao

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.

Sihua Shao
Dr. Sihua Shao currently serves as an Assistant Professor with the Department of Electrical Engineering at New Mexico Tech. He obtains the Ph.D. degree in electrical engineering and the Hashimoto Prize for best doctoral dissertation from New Jersey Institute of Technology in 2018. He is honored to receive the NSF CRII Award and NM EPSCoR Mentor Award in 2023. He is a Senior Member of IEEE. His area of expertise is wireless communication and networking, mainly focusing on reconfigurable intelligent surfaces, integrated sensing and communication, optical wireless communication, backscatter communication, machine learning in communications and networking, wireless communication and networking in harsh environments, UAV assisted wireless networks, heterogeneous radio-optical wireless networks, positioning, navigation and timing.
Performance Period: 10/01/2023 - 09/30/2024
Institution: New Mexico Institute of Mining and Technology
Award Number: 2323050