Evolutionary Digital Twin for Oil and Gas Pipelines: A Cognitive Multi-Agent Framework with Continuous Feedback...

Area of Science:

• Engineering
• Artificial Intelligence
• Data Science

Background:

• Pipeline integrity management faces challenges from data heterogeneity, complex degradation, and dynamic environments.
• Traditional AI models struggle with cross-domain knowledge fusion and historical context retention.
• Existing methods lack robust solutions for privacy-preserving, dynamic risk assessment over long infrastructure lifecycles.

Purpose of the Study:

• To propose an evolutionary digital twin framework for enhanced pipeline integrity and risk management.
• To address limitations of monolithic AI models in handling diverse data and long-term operational contexts.
• To develop a privacy-compliant methodology for dynamic, interpretable pipeline management.

Main Methods:

• Developed an evolutionary digital twin framework with collaborative small specialized models and a large general model.
• Integrated physics-informed models as domain expert agents for edge computation and localized data handling.
• Employed a privacy-preserving large language model as a central cognitive hub for risk synthesis and strategy formulation.
• Implemented a continuous feedback learning mechanism with parameter stabilization to mitigate catastrophic forgetting.

Main Results:

• The framework effectively synthesizes localized risks using a privacy-preserving large language model.
• Physics-informed agents ensure rigorous numerical computation at the edge, localizing sensitive data.
• The system demonstrates enhanced interpretability and predictive foresight in pipeline integrity management.
• Continuous learning mechanism captures tacit knowledge and updates the knowledge base dynamically.

Conclusions:

• The proposed framework offers a reliable and privacy-compliant methodology for long-distance pipeline integrity.
• It overcomes the limitations of traditional AI in cross-domain knowledge fusion and historical context forgetting.
• This approach significantly enhances the interpretability and predictive foresight of pipeline risk management systems.