Project Full Name：Intelligent inspection system forshale oil production area cluster

Company：Petro China National Petroleum Corporation Changqing Oilfield Branch Kunlun digital Technology Co., Ltd

Awarded： Digital & Intelligent Techology（China)

Standard:  International  Advanced Level

Project Number：ECF-2024-SEP-1003

Principal Accomplishers： 

Key Innovation Points：

1: Build a drone management platform to achieve cluster control of drones, and combine artificial intelligence technology with GIS to replace traditional manual task allocation mode, achieving intelligent optimization of drone task allocation and route planning.

  2: For the first time, a redundant airport deployment method was adopted, combined with the distribution of production areas, to select and deploy airports to ensure that when a certain unmanned hangar or drone fails, other unmanned hangars and drones can immediately take over its tasks, ensuring the continuity and reliability of operations. 

3: Using big data analysis and artificial intelligence technology to analyze and diagnose the collected environmental and equipment data, identify potential risks and abnormal situations in real time, and issue alerts and push notifications for risk anomalies. Establish a new closed-loop management model for identifying, diagnosing, and alerting hidden dangers.

4: Build an open drone platform ecosystem to meet the integration needs of third- party devices and software, enhance system compatibility and scalability.

Main Application and Technical Principal： 

It took the lead in adopting artificial intelligence technology, GIS and Internet of Things. It was the first time to build a production area cluster UAV intelligent inspection system with 11 functional modules including patrol task planning, flight situation monitoring and AI identification detection, creating a new means for shale oil production area patrol operations, achieving all-day, all-weather automatic inspections in production areas. The priority is to adopt a redundant airport deployment method, ensuring that multiple drone libraries/drones can access the same production area. This ensures that when one drone library/drone fails, other drone libraries and drones can immediately take over its tasks, guaranteeing operational continuity and reliability. For abnormal hidden data of personnel, vehicles and pipelines in the production area, a new algorithm for accurate identification is developed through big data analysis and artificial intelligence technology using YOLO v7 algorithm model. At the same time, it combines with UAV intelligent inspection system.Create a new system of "identification-diagnosis-alarm" for abnormal hidden dangers. Create an open drone platform ecosystem, support the integration of third-party devices and software, successfully connect with systems such as shale oil branch tanker positioning and instant messaging, improve system compatibility and scalability.

Technology Application： 

Since it was formally completed and used at the end of April 2024, the UAV Intelligent Inspection System for Shale Oil Production Area has been comprehensively 

applied to the five central stations in the production area of the shale oil branch, with 230 inspection routes planned, 150 inspection tasks created, a total of

 128,000 flights flown automatically, about 140,000 pictures taken, and more than 80,000 minutes of video recorded, realizing full-coverage monitoring and control

 of well sites, pipelines, high-consequence zones and other production units in the production area of the shale oil branch of Changqing Oilfield. It has realized

 full-coverage monitoring of production units such as well sites, pipelines and high-consequence zones in the region.

Especially, it has made outstanding contributions in daytime pipeline inspection and nighttime oil theft prevention. In daytime pipeline inspection, clustered UAV

 intelligent inspection system realizes unmanned regular inspection of field stations, well fields and pipelines through remote group control of UAV airports,

 and the comprehensive efficiency is 6 times higher than the efficiency of traditional manual inspection, and the unmanned intelligent inspection finds out the

 hidden danger in a more timely, comprehensive and accurate manner, so as to avoid economic losses caused by the hidden danger of safety. In terms of

 nighttime oil theft prevention, the drone can arrive at the scene at the first time, and through infrared thermal imaging and other technologies to find suspicious 

targets, and monitoring and tracking, convenient for management personnel to command decision-making.