The TetonGuard Platform

Physics-based simulation engine that creates a virtual replica of your pipeline network. Continuously solves mass, momentum, and energy equations to predict hydraulic behavior under all operating conditions, including transients, thermal effects, and multi-phase flows.

Advanced leak detection system that compares real-time sensor data against RTTM predictions. Detects leaks as small as 0.5% of flow rate within minutes, with localization accuracy within 1% of pipeline length, exceeding API 1130 requirements.

Machine learning algorithms trained on historical operating data distinguish between operational changes (pump starts, batch interfaces) and actual threats. Reduces false alarm rates while maintaining 99.8% detection accuracy.

Ensemble deep learning framework that analyzes sensor data streams to forecast structural failures. Identifies corrosion hotspots, fatigue patterns, and degradation risks before they become critical, enabling predictive maintenance planning.

Advanced algorithms monitor multi-product pipeline operations, tracking batch interfaces, predicting arrival times, and maintaining product quality. Essential for custody transfer operations in refined products pipelines.

Dedicated monitoring for unauthorized withdrawals and pipeline ruptures. Identifies tapping operations for even small volume thefts and provides instant localization of major line breaks with GPS coordinates.

Continuous monitoring of pressure, flow rate, temperature, and density across all pipeline segments. Real-time hydraulic profiling enables early anomaly detection and supports operational optimization.

Advanced fiber-optic monitoring technology that captures acoustic signatures along the entire pipeline length. Detects leaks, intrusions, third-party interference, and operational activities through quantitative analysis of space-time images. Provides continuous, distributed monitoring with superior location accuracy compared to traditional point sensors.

State-of-the-art machine learning algorithms including YOLO for real-time object detection and tracking. Automatically identifies and tracks pipeline inspection gauges (PIGs), detects digging activities, and classifies acoustic signatures. Trained on minimal data sets while achieving high accuracy in complex operational environments.

Unifies fiber-optic DAS data with inspection reports, flow simulation results, and SCADA systems through automated calibration and cross-validation. Machine learning algorithms contextualize events across multiple data sources, enabling isolation of important signals at specific pipeline locations like welds, valves, and road crossings.

Seamless integration with existing SCADA systems through industry-standard protocols. Treats SCADA as foundation while adding autonomous detection capabilities where traditional systems fall short, supporting up to 1,000 samples/second.

Continuous validation against PHMSA 49 CFR 195, API RP 1130, API RP 1175, and CSA Z662 standards. Generates audit-ready reports automatically, maintaining compliance documentation and protecting regulatory assets.