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Digital Twin-Based Real-Time Monitoring and Intelligent Maintenance System for Oil and Gas Pipelines

Yihan Wang

Yihan Wang

College of Petroleum and Natural Gas Engineering, Southwest Petroleum UniversitySichuan, China
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Wang, Yihan
  
11 apr 2025
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Pubblicato online: 11 apr 2025
Ricevuto: 05 nov 2024
Accettato: 05 mar 2025
DOI: https://doi.org/10.2478/amns-2025-0849
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© 2025 Yihan Wang, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Overall workflow of the digital twin-based monitoring and maintenance system. Data from pipeline sensors are incorporated into the digital twin via the EnKF, and the updated states are analyzed by a machine learning module. Anomalies trigger maintenance actions, and the entire process adapts over time through continual retraining and model refinement.
Overall workflow of the digital twin-based monitoring and maintenance system. Data from pipeline sensors are incorporated into the digital twin via the EnKF, and the updated states are analyzed by a machine learning module. Anomalies trigger maintenance actions, and the entire process adapts over time through continual retraining and model refinement.

Figure 2.

Experiment 1 (Baseline Condition): Average residual magnitude remains low and stable, indicating accurate alignment between sensor data and model forecasts.
Experiment 1 (Baseline Condition): Average residual magnitude remains low and stable, indicating accurate alignment between sensor data and model forecasts.

Figure 3.

Experiment 2 (Localized Corrosion): Wall thickness loss over time with the red dashed line indicating the point when the machine learning module issued a high-confidence anomaly alert.
Experiment 2 (Localized Corrosion): Wall thickness loss over time with the red dashed line indicating the point when the machine learning module issued a high-confidence anomaly alert.

Figure 4.

Experiment 3 (Combined Leak and Blockage): Timeline showing the onset and detection of two simultaneous anomalies. Dashed lines mark the true start of each anomaly, and dots indicate when the system raised alarms.
Experiment 3 (Combined Leak and Blockage): Timeline showing the onset and detection of two simultaneous anomalies. Dashed lines mark the true start of each anomaly, and dots indicate when the system raised alarms.
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