Oil and Gas Technical training > Predictive Maintenance: Using Machine Learning Technology in Oil & Gas Maintenance Operations - Virtual Instructor Led Training (VILT)

About this Virtual Instructor Led Training (VILT) 

Technologies involving IOT, Cloud Computing, Robotics and AI & Big (Extreme) Data demand new skills and capabilities. These new technologies are transforming the way we work and operate, as it increases efficiency and reduces redundancy. At the forefront of these technologies is Data Science & Analytics, a core skill required to operate in this new era. This Virtual Instructor Led Training (VILT) course goes through options of using sensors in Oil & Gas operations to optimise production, reduce failures and issue early warnings for trips, shutdowns and other potential health & safety risk issues. This VILT will also introduce participants to C3.ai platform.

  • Learn and apply python programming for data science and analytics problems
  • Be able to identify the right tools and techniques to approach either a regression or classification problem in a predictive maintenance project
  • Understand time series algorithms that form sensor data and interdependency between instances
  • Distinguish different machine learning algorithms like anomaly detection, trees, and linear models to apply in any predictive analytics problems
  • Deploy a predictive analytics project to either cloud or on – premise server.

This Virtual Instructor Led Training (VILT) course is specifically designed for engineers and technical professionals in operations, maintenance and data management, who are interested to learn predictive analytics using machine learning for maintenance of machines and equipment.

The VILT will be delivered online in 5 half-day sessions comprising 4 hours per day, with 2 breaks of 10 minutes per day.

Your course leader’s experience begins from being a Manufacturing Research Engineer at Schlumberger REMS, adjunct professor at IPE Heriot Watt University to a Senior Data Scientist at Shell. He has built and delivered various analytics solutions in identifying root causes via clustering algorithms, digitalising data collection and monitoring system, forecasting system failures before major mishaps, identifying lithofacies and many other natural language processing solutions to summarise text data and perform sentiment analysis to name a few. Recently, he was also a part of Shell’s digital coaches to transform the workforce into being digital and agile savvy, especially in the area of data and analytics.  Prem holds a Master of Science Degree in Petroleum Engineering from the Institute of Petroleum Engineering, Heriot-Watt University, Edinburgh. He also qualified with a Bachelor of Engineering in Electrical and Electronics Engineering (Honours) majoring in Artificial Intelligence and Robotics.