Pipeline Linepack Delay Prediction for Compressor Pressure Control
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Solution Overview
Problem
Physical delivery systems, such as gas pipeline networks, face challenges in accurately predicting linepack delays due to compressible fluids, which can lead to inefficiencies in pressure maintenance and demand management, especially in non-monitored or SCADA-unequipped sections.
Innovation Solution
A computer-implemented method and system that utilize temporal sensor measurements to generate a causality graph and topological network, predicting linepack delays by identifying temporal dependencies between stations and estimating relative distances, enabling compressor stations to maintain predetermined pressures through data-driven models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temporal sensor measurements are used to generate causality graphs and topological networks for predicting linepack delays, then pressure management accuracy and demand fulfillment improve, but system complexity and computational requirements increase
Solution Approach 1:
The pipeline network is divided into discrete topological segments or zones based on sensor locations and causality relationships. Each segment can be independently analyzed for linepack delays, allowing the complex overall system to be broken down into manageable units that can be processed separately and then integrated.
Solution Approach 2:
A computational intermediary layer (the causality graph and topological network model) is introduced between the raw sensor measurements and the pressure management decisions. This intermediary processes temporal relationships and propagates delay predictions through the network, reducing the complexity at any single point while maintaining overall system accuracy.
2Productivity
If compressor stations pump gas ahead of actual demand occurrence to meet expected demand, then demand fulfillment improves, but energy consumption and operational inefficiency increase
Solution Approach 1:
The system performs preliminary calculations of linepack delays using historical sensor data and topological models to predict future gas arrival times at delivery points. This allows compressor stations to time their pumping actions more precisely, pumping gas slightly ahead of actual demand only when necessary, rather than continuously pumping to meet expected demand.
Solution Approach 2:
The pipeline system uses its own historical operational data and sensor measurements to automatically predict linepack delays and optimize compressor timing without requiring external intervention or conservative over-pumping. The system self-adjusts based on learned temporal patterns in gas flow and demand.
3Loss of information
If SCADA systems are installed in all pipeline sections for real-time monitoring, then measurement accuracy and control precision improve, but system cost and infrastructure complexity increase
Solution Approach 1:
Instead of installing physical sensors in every pipeline section, the system creates virtual copies of sensor data through the topological network model. The causality graph propagates measurements from actual sensor locations through modeled pipeline segments, generating virtual measurements for unmonitored sections based on temporal relationships and flow dynamics.
Solution Approach 2:
The topological network model serves multiple functions simultaneously: it predicts linepack delays, propagates sensor measurements through unmonitored sections, identifies leak locations, and optimizes compressor timing. This multi-functionality reduces the need for dedicated infrastructure for each function, particularly reducing sensor requirements through the measurement propagation capability.
Data Source
AI summary
Technical solutions are described for predicting linepack delays. An example method includes receiving temporal sensor measurements of a first fluid-delivery pipeline network and generating a causality graph of the first fluid-delivery pipeline network. The method also includes determining a topological network of the stations based on the causality graph, where the topological network identifies a temporal delay between a pair of stations. The method also includes generating a temporal delay prediction model based on the topological network and predicting the linepack delays of a second fluid-delivery pipeline network based on the temporal delay prediction model, where a compressor station of the second fluid-delivery pipeline network compresses fluid based on the predicted linepack delays to maintain a predetermined pressure.