By the end of this training, participants will learn to:

• Apply Machine learning concepts in oil and gas industry
• Learn to identify the impact of data quality and data management on success of oil and gas enterprise
• Acquire the knowledge about data management framework
• Identify the machine learning algorithms applied within the oil and gas industry
• Apply Machine Learning Algorithm to predict problems in the oil and gas fields operations.

This training course is suitable to a wide range of professionals but will greatly benefit:

• Petroleum Data Analysts
• Petroleum Engineers
• Systems Analysts
• Programmers
• Data Analysts
• Database Administrators
• Project Leaders
• Managers
• Software Engineers

This interactive Training will be highly interactive, with opportunities to advance your opinions and ideas and will include:

• Lectures
• Workshop & Work Presentation
• Case Studies and Practical Exercise
• Videos and General Discussions

Day 1

• What is artificial intelligence?
• Applications of artificial intelligence in oil and gas industry.
• Case studies and examples of the application of Artificial Intelligence in Oil and gas Industry.

Day 2

• Introduction to Machine Learning
• History of machine learning in oil and gas industry
• Machine Learning Algorithms.
• Artificial neural network (ANN).
• Data Management from the DCS to the Historian

Day 3

• Project Management for a Machine Learning Project
• Big Data in Oil and Gas Industry
• Detecting Electric Submersible Pump Failures

Day 4

• Predictive and Diagnostic Maintenance for Rod Pumps
• Forecasting Slugging in Gas Lift Wells
• Machine Learning application to optimize production in heavy oil reservoirs.

Day 5

• Rate of Penetration Prediction in Drilling Operation in Oil and Gas Wells by K-Nearest Neighbors and Multilayer Perceptron Algorithms.
• Prediction of fold-of-increase in productivity index post limited entry fracturing using artificial neural network

A RECTUS attendance certificate will be awarded to all participants who complete at least 80% of the total course duration.