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The oil and gas industry are seriously in the grip of this digital transformation with bettering asset management through reliable and cost-effective solutions. The most critical challenge identified for the oil industry is related to early leakage detection in remote pipelines for prevention of environmental hazards and economic losses. This article reports an IoT-based leak-detection system for pipelines using the low-power LoRaWAN communication protocol. A flow-sensor-based pipeline leakage-detection prototype system employing NodeMCU microcontrollers and a LoRaWAN gateway was deployed and tested on a water-filled pipeline under controlled conditions. At a level of 90.83% leak-detection accuracy, the system permitted visual real-time monitoring and alert notifications through ThingSpeak. These results establish the suitability of this design in offering scalable low-cost and energy-efficient monitoring of extensive networks of pipelines in rural and inaccessible areas. The results of the studies above highlight the feasibility of LoRaWAN-enabled systems in industrial infrastructure, thereby enhancing safety and operational efficiency.

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A Low-Power LoRaWAN-Based Solution for Early Leak Detection of Remote Located Oil Pipelines

  • 会议论文

  • 发表日期：

    2025年

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  • Vrinda Parkhi,
  • Kimaya Joshi,
  • Rohan Khandare,
  • Anushree Laddha,
  • Om Khandare,
  • Shalvi Kshirsagar

摘要

The oil and gas industry are seriously in the grip of this digital transformation with bettering asset management through reliable and cost-effective solutions. The most critical challenge identified for the oil industry is related to early leakage detection in remote pipelines for prevention of environmental hazards and economic losses. This article reports an IoT-based leak-detection system for pipelines using the low-power LoRaWAN communication protocol. A flow-sensor-based pipeline leakage-detection prototype system employing NodeMCU microcontrollers and a LoRaWAN gateway was deployed and tested on a water-filled pipeline under controlled conditions. At a level of 90.83% leak-detection accuracy, the system permitted visual real-time monitoring and alert notifications through ThingSpeak. These results establish the suitability of this design in offering scalable low-cost and energy-efficient monitoring of extensive networks of pipelines in rural and inaccessible areas. The results of the studies above highlight the feasibility of LoRaWAN-enabled systems in industrial infrastructure, thereby enhancing safety and operational efficiency.