Design and Analysis of Refined Inspection of Field Conditions of Oilfield Pumping Wells Based on Rotorcraft UAV Technology

• Published in: Electronics (Basel) Vol. 8; no. 12; p. 1504
• Main Authors: Zhou, Yu, Wu, Chunxue, Wu, Qunhui, Eli, Zelda Makati, Xiong, Naixue, Zhang, Sheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2019
• Subjects: Aerial photography; Algorithms; Computer vision; Deep learning; Fault diagnosis; Ground stations; Indicator diagrams; Inspection; Manpower; Multiple target tracking; Neural networks; Neurons; Oil field equipment; Oil fields; Pumping; Real time; Rotary wing aircraft; Semantics; Sensors; Sorting algorithms; Target detection; Unmanned aerial vehicles; Working conditions
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics8121504

The traditional oil well monitoring method relies on manual acquisition and various high-precision sensors. Using the indicator diagram to judge the working condition of the well is not only difficult to establish but also consumes huge manpower and financial resources. This paper proposes the use of computer vision in the detection of working conditions in oil extraction. Combined with the advantages of an unmanned aerial vehicle (UAV), UAV aerial photography images are used to realize real-time detection of on-site working conditions by real-time tracking of the working status of the head working and other related parts of the pumping unit. Considering the real-time performance of working condition detection, this paper proposes a framework that combines You only look once version 3 (YOLOv3) and a sort algorithm to complete multi-target tracking in the form of tracking by detection. The quality of the target detection in the framework is the key factor affecting the tracking effect. The experimental results show that a good detector makes the tracking speed achieve the real-time effect and provides help for the real-time detection of the working condition, which has a strong practical application.