Pipeline Leak Forecasting via Pressure Deviation Thresholds
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Solution Overview
Problem
Current pipeline network monitoring systems face challenges in accurately determining topological connectivity and relative distances between stations, and in forecasting leaks and linepack delays, especially in compressible fluid delivery systems like natural gas, due to delays and unpredictable flow patterns.
Innovation Solution
A computer-implemented method and system that utilizes historical temporal sensor measurements to generate prediction models for pressure measurements, identifies deviations indicative of leaks, and estimates linepack delays by analyzing causality and temporal lags between stations, facilitating automated topological network mapping and leak detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring systems are used to track pipeline pressure, then basic pressure data can be obtained, but accurate leak forecasting and topological connectivity determination cannot be achieved
Solution Approach 1:
The system performs preliminary actions by collecting historical temporal sensor measurements from multiple stations before actual leak detection is needed. These historical data are used to train prediction models that can forecast pressure measurements and identify deviations indicating leaks, enabling proactive rather than reactive monitoring.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing predicted pressure measurements from the prediction model with actual sensor measurements. When deviations exceed threshold values, the system generates leak alerts and can trigger automated responses, creating a closed-loop monitoring system that continuously improves through feedback.
2Extent of automation
If manual methods are used to determine topological connectivity and relative distances, then basic network mapping can be achieved, but automated real-time leak detection cannot be implemented
Solution Approach 1:
The system enables self-service by automatically determining topological connectivity and relative distances between stations using sensor measurements from the pipeline network. The prediction models are automatically trained on historical data and continuously updated, eliminating the need for manual network mapping and enabling real-time automated leak detection without human intervention.
3Measurement precision
If comprehensive historical data from all stations is collected for leak detection, then accurate predictions can be made, but system complexity and computational requirements increase
Solution Approach 1:
The system applies segmentation by dividing the pipeline network into multiple stations, each with its own prediction model. This allows the complex problem of network-wide leak detection to be broken down into smaller, manageable sub-problems, where each station's model processes local sensor data independently, reducing overall computational complexity while maintaining accuracy.
Data Source
AI summary
Technical solutions are described for forecasting leaks in a pipeline network. An example method includes identifying a subsystem in the pipeline network that includes a first station. The method also includes accessing historical temporal sensor measurements of the stations. The method also includes generating a prediction model for the first station that predicts a pressure measurement at the first station based on the historical temporal sensor measurements at each station in the subsystem. The method also includes predicting a series of pressure measurements at the first station based on the historical temporal sensor measurements. The method also includes determining a series of deviations between the series of pressure measurements and historical pressure measurements of the first station and identifying a threshold value from the series of deviations, where a pressure measurement at the first station above or below the threshold value is indicative of a leak in the subsystem.