Oil and Gas Pipeline Monitoring during COVID-19 Pandemic via Unmanned Aerial Vehicle

TL;DR

This paper explores the use of UAVs equipped with AI and deep learning for pipeline monitoring during COVID-19, enabling safer, faster inspections of hard-to-reach areas to detect hazards early.

Contribution

It introduces a UAV-based inspection system with AI-driven image recognition for oil and gas pipeline surveillance, addressing pandemic-related staffing challenges.

Findings

UAVs can perform real-time data transfer during inspections.

The system can identify potential hazards like corrosion and insulation damage.

Expert surveys define functional and non-functional system requirements.

Abstract

The vast network of oil and gas transmission pipelines requires periodic monitoring for maintenance and hazard inspection to avoid equipment failure and potential accidents. The severe COVID-19 pandemic situation forced the companies to shrink the size of their teams. One risk which is faced on-site is represented by the uncontrolled release of flammable oil and gas. Among many inspection methods, the unmanned aerial vehicle system contains flexibility and stability. Unmanned aerial vehicles can transfer data in real-time, while they are doing their monitoring tasks. The current article focuses on unmanned aerial vehicles equipped with optical sensing and artificial intelligence, especially image recognition with deep learning techniques for pipeline surveillance. Unmanned aerial vehicles can be used for regular patrolling duties to identify and capture images and videos of the area of interest. Places that are hard to reach will be accessed faster, cheaper and with less risk. The current paper is based on the idea of capturing video and images of drone-based inspections, which can discover several potential hazardous problems before they become dangerous. Damage can emerge as a weakening of the cladding on the external pipe insulation. There can also be the case when the thickness of piping through external corrosion can occur. The paper describes a survey completed by experts from the oil and gas industry done for finding the functional and non-functional requirements of the proposed system.