Event Summary
Are you confident your pipeline data is telling the full story?
For integrity teams tasked with protecting critical infrastructure, the volume of data available today is both a resource and a risk. Without the right tools and context, even experienced engineers can miss key signals that threaten safety, reliability, and compliance.
This course introduces the practical application of machine learning in pipeline integrity management, helping you shift from reactive to predictive decision-making. You’ll explore how to apply data science techniques to improve anomaly detection, assess ILI data, and support defensible integrity decisions—without needing a Ph.D. in AI.
Attendees will receive 16 Professional Development Hours (PDH) upon completion.
Course Objectives
- Demystify machine learning for pipeline integrity professionals
- Provide practical methods to integrate ML into your current workflows
- Show how ML tools can improve anomaly detection, risk ranking, and repair planning
- Help you assess ML outputs with confidence and communicate their value to leadership
Learning Objectives
By the end of this course, participants will be able to:
- Understand the fundamentals of machine learning in the context of pipeline integrity
- Identify how ML can enhance traditional integrity management practices
- Recognize common pitfalls and limitations when applying ML to ILI and other asset data
- Evaluate ML model outputs and interpret results for use in decision-making
- Explore real-world case studies that illustrate how ML has improved integrity outcomes
What to Expect
- Day 1: Get grounded in the fundamentals—learning types, models, validation, and data preparation—all in the context of pipeline integrity.
- Day 2: Apply those fundamentals to real-world use cases, from predicting rare threats to integrating machine learning into your risk management workflow.
Through a secure web-based tool, you’ll experiment with models, validate results, and experience how machine learning reveals patterns and risks you may be missing today.
Who Should Attend
- Pipeline Integrity Managers – Managers who are responsible for integrity management programs will gain a basic understanding of machine learning methods and how the model performance indicators of the model can be used to support allocation of resources and budget.
- Pipeline Integrity Engineers – Engineers who are responsible for identifying potential risk will understand how to develop machine learned models with a specific target of interest (e.g. external corrosion predictions).
- Data Science Analysts – Data specialists will learn Exploratory Data Analytics (EDA) fundamentals and the best practices for collecting the data required for machine learning models.
Course Materials
Will be shared with you digitally the week prior to the workshop.
Location
Pipeline Research Council International – Conference Room 1
6410-J Langfield Road
Houston, TX 77092
Close parking is available in front and on the side of the building. An elevator is available for wheelchair access.
Meals
Continental breakfast & lunch will be provided daily.
Meet Your Instructors
Mike Gloven
Managing Partner, Pipeline-Risk (PLR)
Mike Gloven is Managing Partner of Pipeline-Risk, a provider of machine learning based integrity management and risk solutions for the oil, gas and water industries. He’s a risk and asset management practitioner with more than 30 years of experience working as an asset and integrity manager, technical consultant, software developer, business owner and energy company executive. Mike is a frequent speaker on machine learning based risk & integrity management and has led the development of numerous technology-based solutions currently in use in the energy industry. Mike is professional engineer and holds bachelor’s degree in mechanical engineering from Louisianna State University.
Andy Florence
Principal, Pipeline-Risk (PLR)
Andy Florence is currently a Principal at Pipeline-Risk. Andy has been delivering innovative technology solutions to Fortune 500 companies across the globe for over 35 years. For the last seventeen years, Andy’s focus has been primarily on the Oil and Gas midstream industry, more specifically pipeline integrity management and risk assessment solutions. Andy has delivered multiple industry presentations to the midstream sector over the years, including topics on AC and DC Stray Current Threats and Pipeline Risk Assessment Best Practices. Andy holds an undergraduate degree in Business Analysis and Research from Texas A&M University and a Master of Business from the University of Denver
About Pipeline-Risk (PLR)
Pipeline-Risk (PLR) is an engineering and technology company serving the oil, gas, and water pipeline industries. The company has completed risk projects across hundreds of thousands of miles of pipeline in North and South America using its ML.ai machine learning platform. The objective of ML.ai is to improve the identification, prediction and mitigation of risks for the purposes of improved safety, reliability and cost effectiveness of critical infrastructure.
