Boosting Oil Field Efficiency with IoT Acoustic Sensors: A Case Study

1. CASE STUDY Transforming Oil Field Operations with IoT Acoustic Sensors CLIENT BACKGROUND The client is a leading oil and gas company with operations across upstream, midstream, and downstream sectors. Focused on innovation and safety, they aim to enhance operational efficiency through advanced technologies. Their digital transformation strategy includes IoT, smart sensors, and predictive maintenance solutions. PROBLEM STATEMENT Despite advanced equipment, the client faced operational challenges that impacted efficiency and increased costs Pipeline leaks often went undetected, risking safety and violations. Unexpected equipment failures led to costly unplanned downtime. Manual inspections in remote areas exposed personnel to risks. No real-time asset visibility made proactive issue resolution hard. Rising maintenance costs due to reactive repairs and replacements.

2. IOT SOLUTIONS IMPLEMENTED To address these challenges, PetroCore deployed a network of IoT-enabled smart acoustic sensors across its field operations. Key implementation highlights: Installed on pipelines, compressors, and drilling equipment Integrated with a centralized cloud-based monitoring system Leveraged AI-powered analytics to detect abnormal acoustic patterns Enabled automated alerts and predictive maintenance workflows TECHNOLOGY BEHIND IOT ACOUSTIC SENSORS IoT acoustic sensors detect sound and vibrations to monitor equipment and pipelines for early signs of issues like leaks and wear. Real-Time Data: IoT sensors transmit continuous data, enabling immediate feedback for quick monitoring and faster decision-making. Cloud Connectivity: Data from sensors is stored on the cloud, providing remote access for centralized monitoring and analysis. Remote Monitoring: IoT sensors reduce manual inspections in hazardous areas, improving safety and operational efficiency. AI/ML Role: Predictive Maintenance: AI and ML analyze sensor data to predict equipment failures, enabling proactive maintenance and minimizing unplanned downtime. Anomaly Detection: AI algorithms detect small deviations in sensor data, identifying potential issues before they escalate. Optimization: AI-driven insights optimize asset performance, improve resource allocation, and reduce operational costs. info@rejigdigital.com rejigdigital.com +1 (510) 956 6572

3. IMPLEMENTATION TIMELINE Phase Duration Activities Field survey, risk analysis, and pilot planning Assessment 2 weeks Sensor installation, IoT platform configuration 6 weeks Deployment Data sync with existing SCADA and maintenance systems 3 weeks Integration ML model training for enhanced anomaly detection Optimization Ongoing With IoT acoustic sensors integrated, we now have complete visibility into the health of our field assets, allowing us to address potential issues before they impact operations. Rejig’s solution paid for itself in under 12 months. PLANT MANAGER, A MID-SIZED GRINDING MACHINE COMPANY info@rejigdigital.com rejigdigital.com +1 (510) 956 6572