Overview of Machine Learning for Pipeline Integrity Management

Event Summary

This presentation will summarize advancements in pipeline risk modeling using machine learning processes and methods.

Machine learning is a process of learning and validating predictive models based on private and public data, domain expertise and technical standards. It is a process of continuous learning as new observations become available, inspections are performed and public data such as PHMSA reports are updated. Predictive model outputs are validated by actual observations and are fully explainable in human readable terms for the purposes of improved risk management decision-making.

Attendees will receive 1 Professional Development Hour (PDH) upon completion.

Learning Objectives

This introductory session on machine learning (ML) is tailored to senior managers of a pipeline company. The objectives will focus on high-level understanding, strategic potential, and implications for safety, efficiency, and regulatory compliance.  Topics will include:

• Non-technical overview of what machine learning is and how it differs from traditional pipeline integrity management practices.
• Introduce key concepts like supervised and unsupervised learning, predictive analytics using pipeline-related examples to make it relatable.
• Explain how machine learning can enhance risk assessment, improve predictive maintenance, optimize inspection schedules, and support regulatory compliance efforts aligning with PHMSA guidelines.
• Present case studies or scenarios showing ML applications in the oil and gas sectors (e.g., third party damage prediction, corrosion and cracking prediction).
• Inspire curiosity and confidence by emphasizing the role of ML in future-proofing operations, supporting sustainability, and enabling proactive, data-driven decision-making.

These objectives aim to create a foundation of understanding that will support informed decision-making and strategic planning related to machine learning and advanced analytics.

Who Should Attend

The webinar is tailored for senior managers who need insights into how machine learning can support their specific objectives, promote cross-functional alignment, and improve overall pipeline risk management.

• Chief Risk Officer (CRO) or Risk Management Lead: The CRO oversees risk strategies and would benefit from understanding how machine learning can enhance risk assessment models, potentially lowering risks and costs.
• Head of Operations/Operations Manager: Machine learning could optimize operational decisions, such as maintenance scheduling and failure predictions, aligning directly with an Operations Manager’s goals of safe and efficient operations.
• Chief Technology Officer (CTO) or Head of Innovation: This manager will have a vested interest in the technical and strategic benefits of adopting machine learning tools to keep the organization at the forefront of technology and innovation.
• Head of Compliance/Safety or Chief Compliance Officer (CCO): Compliance and safety officers are responsible for aligning with regulatory standards like PHMSA guidelines. Learning how machine learning can proactively address regulatory concerns would help in strategy and reporting.
• Asset Integrity Manager: This role focuses on asset performance and reliability. Learning about machine learning can help the manager improve monitoring and predictive maintenance, ultimately enhancing asset longevity.

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