Fiber Optic PIG Georeferencing for Accurate Pipeline Tracking
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Solution Overview
Problem
Current methods for pipeline tracking using fiber optics are inefficient and lack accuracy, particularly in oil and gas pipelines, as they rely on manual field activities and do not provide precise georeferencing.
Innovation Solution
A real-time pipeline inspection gauge (PIG) tracking algorithm utilizing machine learning and pattern recognition, which includes training a model with PIG data, calibrating optical fiber distances to pipeline distances, and contextualizing fiber optic events with inspection reports and SCADA systems for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual field activities are used for fiber route geo-referencing, then the system is simpler to implement, but the accuracy and efficiency of pipeline tracking deteriorates
Solution Approach 1:
The patent replaces manual mechanical field activities with an automated optical sensing system. Fiber optic cables act as distributed sensors that automatically detect PIG positions through optical backscatter signals, eliminating the need for manual measurement and recording while significantly improving tracking accuracy along the pipeline route.
Solution Approach 2:
The patent introduces fiber optic cables as an intermediary element between the PIG and the monitoring system. These cables serve as both the communication medium and the sensing element, translating mechanical PIG movement into optical signal variations that can be automatically detected and processed for precise georeferencing.
2Productivity
If manual field activities are used for fiber route geo-referencing, then the implementation is simpler, but the productivity and efficiency deteriorates
Solution Approach 1:
The patent implements continuous real-time monitoring of PIG positions along the pipeline using fiber optic sensors. The system continuously detects optical backscatter signals and processes them to track PIG movement without interruption, providing uninterrupted productivity improvement compared to intermittent manual surveying activities.
Solution Approach 2:
The fiber optic sensing system is self-monitoring and automatically detects PIG positions without requiring external manual intervention. The system processes its own optical signals, performs automatic georeferencing calculations, and generates tracking data independently, maximizing productivity while the initial system setup handles the complexity.
3Measurement precision
If automated calibration is implemented, then the measurement precision improves, but the device complexity and processing requirements worsen
Solution Approach 1:
The patent performs preliminary calibration by establishing the relationship between fiber optic distance measurements and actual pipeline distances before operational use. Known pipeline features serve as reference points for initial calibration, and this preliminary setup enables highly accurate automated tracking throughout the system's operational life without requiring complex real-time calibration mechanisms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The algorithm achieves high-accuracy PIG tracking and event detection, enabling automated calibration and cross-validation of inspection reports, providing operators with precise pipeline integrity insights.
Implementation Method 1
an optical fiber to detect fiber optic events
Implementation Method 2
optical fiber to detect fiber optic events
Data Source
AI summary
A method may include receiving first data collected as a first pipeline inspection gauge (PIG) passes through a first pipeline and training a machine learning model using the first data. The method may further include receiving second data collected via an optical fiber as a second PIG passes through a second pipeline, wherein the second data is representative of one or more fiber optic events and applying the trained machine learning model to the second data. Additionally, the method may include identifying a position of the second PIG and generating a graphical user interface (GUI) to display the position of the second PIG.