A major pipeline operator managing thousands of miles of midstream assets faced increasing pressure to detect potential leaks and abnormal line behavior faster. Existing monitoring processes relied heavily on manual SCADA reviews, periodic inspections, and legacy detection algorithms that frequently produced false positives—or missed subtle early indicators altogether. Leadership needed a more accurate, real-time analytics solution to improve safety, reduce operational risk, and strengthen regulatory compliance.
We helped implement a new leak detection analytics platform that significantly improved response time, accuracy, and operational reliability across the pipeline network:
35% faster detection of potential leak events
Reduction in false positives, improving controller confidence and reducing operational noise
Improved incident triage, enabling quicker response and mitigation
Greater situational awareness across high-risk pipeline segments
Regulatory alignment with enhanced reporting, traceability, and audit readiness
THE SITUATION: Operational data was dispersed across SCADA systems, historians, flow meters, and pressure telemetry, making it difficult for controllers to form a complete picture of pipeline conditions. High-frequency signals were not fully utilized, anomaly classification varied across controllers, and manual root-cause analysis slowed emergency response times. Excessive false alarms contributed to alert fatigue, creating both safety risks and operational inefficiencies.
THE PROBLEM: The operators faced significant data, technology, and process challenges that limited leak detection accuracy and response time, including:
SCADA and historian data stored in disparate systems
Legacy leak detection tools unable to process high-frequency telemetry
Excessive false alarms leading to alert fatigue
Inconsistent anomaly classification across controllers
Limited real-time visibility into pressure, flow variance, and imbalance signals
Built a modern leak detection analytics platform that unified operational data, introduced machine learning and physics-informed analytics, and delivered real-time visibility for pipeline controllers and integrity teams.
Integrated SCADA, historian, meter, and pressure/flow telemetry into a unified lakehouse environment
Developed ML-based anomaly detection models to identify pressure drops, imbalance patterns, and flow irregularities
Built physics-informed analytics blending hydraulic modeling with machine learning
Deployed real-time monitoring dashboards and early-warning alerting
Implemented a risk scoring system to help controllers prioritize events
