IoT and AI-Powered Oil & Gas Facility Management System

Optimizing Upstream Operations: IoT and AI-Powered Oil & Gas Facility Management System

Project Summary

The Oil & Gas Upstream Facility Management System is an IoT-enabled platform designed for a leading energy company to manage upstream facilities, integrating real-time monitoring, AI-driven analytics, and a Flutter-based mobile app for operational control. Deployed across 50 upstream facilities with thousands of assets, it incorporates IoT sensors for equipment monitoring, AI for predictive maintenance and AFE automation, and asset tracking with RFID and GPS. Built with Python for AI processing, Flutter for cross-platform mobile access, and AWS with Docker and Kubernetes for scalability, the system enhances facility efficiency, ensures regulatory compliance, and integrates with broader smart city energy frameworks.

Key Challenges Addressed

  • Real-Time Facility and Asset Monitoring: Managing data from distributed IoT sensors across remote upstream facilities to monitor equipment health and asset status in real time.
  • Automated AFE Processing and Compliance: Streamlining complex AFE workflows while ensuring adherence to regulatory standards in a high-volume operational environment.
  • Predictive Maintenance and Asset Tracking: Forecasting equipment failures and tracking assets accurately amidst harsh environmental conditions and dynamic operational demands.

Solutions Implemented

To address real-time facility and asset monitoring, we deployed IoT sensor nodes using STM32 microcontrollers with LoRa for long-range, low-power communication, paired with RFID and GPS trackers for precise asset tracking across upstream facilities. These nodes, equipped with edge computing, transmitted data to AWS-hosted gateways, ensuring low-latency integration with facility management systems. For automated AFE processing and compliance, we developed AI-driven workflows using Python and NLP to parse and route AFE documents, automating notifications and approvals with 100% compliance to regulatory standards, integrated with AWS for scalable processing. To enable predictive maintenance and asset tracking, we built AI models with TensorFlow and computer vision to analyze sensor data and camera feeds, achieving 90% accuracy in predicting equipment failures, while RFID and GPS provided real-time asset visibility. The Flutter-based mobile app for iOS and Android delivered live equipment status, AFE tracking, and AI-driven alerts, supported by Dockerized microservices and Kubernetes for seamless scalability.

Technology Stack

  • AI & Data Processing: Python, TensorFlow, NLP, Computer Vision
  • Mobile: Flutter, iOS, Android
  • IoT: LoRa, STM32 microcontrollers, RFID, GPS
  • Cloud & Deployment: AWS, Docker, Kubernetes
  • APIs: RESTful APIs
  • Tools: Visual Studio Code, Git

Client Benefits

  • Enhanced Operational Efficiency: Reduced equipment downtime by 30% through AI-driven predictive maintenance and real-time IoT monitoring.
  • Streamlined AFE Processes: Automated AFE workflows cut processing time by 40%, ensuring 100% regulatory compliance.
  • Improved Asset Visibility: Achieved 95% asset tracking accuracy with RFID and GPS, optimizing resource allocation and reducing losses.

Approach

  • IoT-Driven Monitoring
    Deployed STM32-based sensor nodes with LoRa, RFID, and GPS for real-time equipment and asset tracking, integrated with AWS for secure data processing.
  • AI-Powered Analytics and AFE Automation
    Built TensorFlow and NLP-based models for predictive maintenance and automated AFE workflows, optimizing operational decisions and compliance.
  • Mobile Accessibility
    Developed a Flutter-based app for iOS and Android, offering live facility updates, asset tracking, and AFE management, deployed with Docker and Kubernetes.
arrow Talk to us

Crafting digital strategies that work