Event Summary
Machine learning is emerging as a fundamental practice in support of pipeline integrity and risk management. As the amount and complexity of data is ever expanding, machine learning is making it possible to efficiently identify risks and manage what is important to integrity objectives. This course presents the fundamentals of machine learning through interactive discussion and presentation of real-world use cases.
Attendees will receive 16 Professional Development Hours (PDH) upon completion.
Course Objectives
This course is a 2-day highly interactive hands-on workshop using provided example or attendee data to take the mystery out of machine learning and associated analytics in the context of pipeline integrity. Through a secure web-based application, the attendee will work through key fundamentals and use cases to personally experience how models are learned and validated, complex patterns revealed, outliers identified, and data quality assessed and mitigated.
Day 1 of the course focuses on the machine learning process and fundamentals including learning types, learning models, model validation, data quality and mitigation, and data preparation in support of learning.
Day 2 focuses on high-value use cases including learning threat models, predicting mitigation costs, predicting missing data, assessing rare threats, threat model validation and using machine learning to support risk analysis. Special emphasis will be placed on how machine learning practices can support new and existing PHMSA based requirements.
The expectation is that the attendee will leave with a sound understanding of the process, the ability to converse in the context of machine learning, and access to resources to continue building their expertise.
Learning Objectives
This course will present the basics of machine learning and how the process is used to support integrity and risk management. You will learn:
- Data sampling, preparation & quality assurance methods
• Feature analysis & engineering
• Classification & cluster learning methods
• Regression learning methods
• Basics of inferential statistics & sampling
• Outlier detection
• Model validation
• Machine learning based risk & QRA
• Where to go to learn & perform your own machine learning
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.
