M. Yudhy, Muhammad Pratama, R. Wibawa, Ardiyansyah Lubis, P. Pujihatma
{"title":"无人机与计算机视觉在复杂油田提高输配电可靠性试点的成功案例与经验教训","authors":"M. Yudhy, Muhammad Pratama, R. Wibawa, Ardiyansyah Lubis, P. Pujihatma","doi":"10.2523/iptc-22811-ea","DOIUrl":null,"url":null,"abstract":"\n Electrical power supply reliability is a key enabler in supporting massive and aggressive exploration and exploitation campaigns in the upstream sector. For a certain complex oilfield asset, a total of more than 3,000 km of power transmission and distribution lines are operated and maintained to support its massive operation. All these power lines shall be monitored and inspected regularly to ensure it is free of operational threat that could reduce their reliability. From historical data, there are two main threats in power line operations, vegetation risk (trees and animals) and broken insulators. The current method of manual inspection and monitoring through Operator Routine Duties Check (ORDC) is not very effective since it took a considerably long period to complete and can only cover a limited area for each inspection activity session whereas the area to be inspected is vast. As a result, the inspection and monitoring program was sub-optimal to detecting the operational threat earlier.\n The advances in digital technology, particularly computer vision, cloud computing, and artificial intelligence, enable every device with a camera and internet connection to become additional \"eyes\" that monitor, inspect and analyze everything in sight. One of the optical devices with high potential for utilization in power system inspection is the Unmanned Aerial Vehicle (UAV) or best known as the drone. Drone enhanced with computer vision will have the optimal capability for inspection and surveillance of vegetation risk and broken insulators for larger areas in each inspection round. Hence, we could automate the inspection and surveillance activities and even improve their effectiveness and efficiency compared with the manual method.\n In this paper, we will discuss the successful pilot implementation of drones, computer vision, and artificial intelligence technology in power system operations that have improved the effectiveness of the surveillance program at a lower cost. The pilot implementation has been proven to reduce the number of power outages caused by vegetation risk and broken insulators by 50% and bring verified financial benefit of USD 7,619 per month from avoiding loss of production opportunities due to power outages related to vegetation risk and broken insulators. The financial benefit can pay off the implementation cost in 4 months of continuous operation. As a path forward, the pilot implementation will be expanded further to several other areas with a high risk of vegetation threat and broken insulators to assess its applicability in other locations before full-scale implementation.","PeriodicalId":283978,"journal":{"name":"Day 1 Wed, March 01, 2023","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Success Stories and Lessons Learned from Pilot Implementation of Unmanned Air Vehicle and Computer Vision to Improve Transmission & Distribution Reliability in Complex Oilfield\",\"authors\":\"M. Yudhy, Muhammad Pratama, R. Wibawa, Ardiyansyah Lubis, P. Pujihatma\",\"doi\":\"10.2523/iptc-22811-ea\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Electrical power supply reliability is a key enabler in supporting massive and aggressive exploration and exploitation campaigns in the upstream sector. For a certain complex oilfield asset, a total of more than 3,000 km of power transmission and distribution lines are operated and maintained to support its massive operation. All these power lines shall be monitored and inspected regularly to ensure it is free of operational threat that could reduce their reliability. From historical data, there are two main threats in power line operations, vegetation risk (trees and animals) and broken insulators. The current method of manual inspection and monitoring through Operator Routine Duties Check (ORDC) is not very effective since it took a considerably long period to complete and can only cover a limited area for each inspection activity session whereas the area to be inspected is vast. As a result, the inspection and monitoring program was sub-optimal to detecting the operational threat earlier.\\n The advances in digital technology, particularly computer vision, cloud computing, and artificial intelligence, enable every device with a camera and internet connection to become additional \\\"eyes\\\" that monitor, inspect and analyze everything in sight. One of the optical devices with high potential for utilization in power system inspection is the Unmanned Aerial Vehicle (UAV) or best known as the drone. Drone enhanced with computer vision will have the optimal capability for inspection and surveillance of vegetation risk and broken insulators for larger areas in each inspection round. Hence, we could automate the inspection and surveillance activities and even improve their effectiveness and efficiency compared with the manual method.\\n In this paper, we will discuss the successful pilot implementation of drones, computer vision, and artificial intelligence technology in power system operations that have improved the effectiveness of the surveillance program at a lower cost. The pilot implementation has been proven to reduce the number of power outages caused by vegetation risk and broken insulators by 50% and bring verified financial benefit of USD 7,619 per month from avoiding loss of production opportunities due to power outages related to vegetation risk and broken insulators. The financial benefit can pay off the implementation cost in 4 months of continuous operation. As a path forward, the pilot implementation will be expanded further to several other areas with a high risk of vegetation threat and broken insulators to assess its applicability in other locations before full-scale implementation.\",\"PeriodicalId\":283978,\"journal\":{\"name\":\"Day 1 Wed, March 01, 2023\",\"volume\":\"94 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Day 1 Wed, March 01, 2023\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2523/iptc-22811-ea\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 1 Wed, March 01, 2023","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2523/iptc-22811-ea","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Success Stories and Lessons Learned from Pilot Implementation of Unmanned Air Vehicle and Computer Vision to Improve Transmission & Distribution Reliability in Complex Oilfield
Electrical power supply reliability is a key enabler in supporting massive and aggressive exploration and exploitation campaigns in the upstream sector. For a certain complex oilfield asset, a total of more than 3,000 km of power transmission and distribution lines are operated and maintained to support its massive operation. All these power lines shall be monitored and inspected regularly to ensure it is free of operational threat that could reduce their reliability. From historical data, there are two main threats in power line operations, vegetation risk (trees and animals) and broken insulators. The current method of manual inspection and monitoring through Operator Routine Duties Check (ORDC) is not very effective since it took a considerably long period to complete and can only cover a limited area for each inspection activity session whereas the area to be inspected is vast. As a result, the inspection and monitoring program was sub-optimal to detecting the operational threat earlier.
The advances in digital technology, particularly computer vision, cloud computing, and artificial intelligence, enable every device with a camera and internet connection to become additional "eyes" that monitor, inspect and analyze everything in sight. One of the optical devices with high potential for utilization in power system inspection is the Unmanned Aerial Vehicle (UAV) or best known as the drone. Drone enhanced with computer vision will have the optimal capability for inspection and surveillance of vegetation risk and broken insulators for larger areas in each inspection round. Hence, we could automate the inspection and surveillance activities and even improve their effectiveness and efficiency compared with the manual method.
In this paper, we will discuss the successful pilot implementation of drones, computer vision, and artificial intelligence technology in power system operations that have improved the effectiveness of the surveillance program at a lower cost. The pilot implementation has been proven to reduce the number of power outages caused by vegetation risk and broken insulators by 50% and bring verified financial benefit of USD 7,619 per month from avoiding loss of production opportunities due to power outages related to vegetation risk and broken insulators. The financial benefit can pay off the implementation cost in 4 months of continuous operation. As a path forward, the pilot implementation will be expanded further to several other areas with a high risk of vegetation threat and broken insulators to assess its applicability in other locations before full-scale implementation.