About this Virtual Instructor Led Training (VILT)
In the current Industrial revolution, technologies ranging from IOT, Cloud Computing, Robotics, AI & Big (Extreme) Data, and Cyber Security are demanding a huge number of new skills and capabilities. New technologies are transforming the way we work, play and do our daily task; and also transforming the energy industries at a bigger scale, mainly to increase efficiency and reduce redundancy. At the forefront of these technologies is Data Science and Analytics, a core skill required to survive in this era. This course goes through options of using sensors from oil & gas operations to optimise production and reduce failures and issue early warnings for trips, shut downs and potential HSE issues. This course will also introduce participants to C3.ai platform.
- Apply python programming for data science and analytics problems
- 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
- Able to distinguish different machine learning algorithms like anomaly detection, trees, and linear models to apply in any predictive analytics problems
- Able to deploy a predictive analytics project to either cloud or on – premise server
This training is specifically designed for executives, technical personnel, academicians and practitioners in the oil and gas operations interested to learn predictive analytics using machine learning for maintenance of machines and
equipment in upstream.
The VILT will be delivered online in 5 sessions comprising 4 hours per day, with 2 breaks of 10 minutes per day.
Your expert course leader has an extensive experience as a practitioner and trainer in the area of Data and Analytics, and Engineering with over 10 years of experience in the Corporate and Training industry. His experience lies in planning, executing and deploying predictive models for maintenance, anomaly and fraud detection, petrophysics, reservoir simulation and econometrics by utilizing the latest technologies focusing on Data Science and AI.
His solid background ranges from being a Manufacturing Research Engineer at Schlumberger REMS, being a consultant to top Corporates and Startups in the region, adjunct professor at IPE Heriot Watt University, to a Senior Data Scientist at Shell. He had built and delivered various analytics solutions in identifying root causes via clustering algorithms, digitalizing data collection and monitoring system, forecasting system failures before major mishaps, identifying lithofacies and many other natural language processing solutions to summarize 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.
He holds a Master of Science Degree in Petroleum Engineering from the Institute of Petroleum Engineering, HeriotWatt University, Edinburgh, 2014. He obtained his Bachelor of Engineering in Electrical and Electronics Engineering (Honours) majoring in Artificial Intelligence and Robotics from Universiti Tenaga Nasional (UNITEN), Malaysia in 2010. He is also a Microsoft Certified Trainer and holds various other qualifying certificates in both training and technical aspects.
He is an expert and trains in a broad range of technical development including:
- Python Programming
- Data Analysis
- Data Science and AI
- Cyber Security using Python
- Machine Learning and Artificial Intelligence
- Petroleum Economics
- Petroleum Reservoir Rock Evaluation
- Basics and Advanced Calculus and Statistics
- Application of Data Science and Analytics in Oil and Gas Production,
Operations and Maintenance.