The main objective of this course is to provide Exploration & Development professionals with the opportunity to develop hands-on experience of various Machine Learning techniques. It will cover all essential approaches from fundamental theoretical background to high-level real work information/techniques.

This course will cover the following:

·      What is machine learning and how does it apply to seismic exploration and unconventional resource development?

·      What is the difference between supervised and unsupervised machine learning?

·      When is an analysis statistical and when is it machine learning?

·      What is attribute space and what is the mathematical foundation of this technology?

·      How do you know if the results are any good?

·      What are some case histories that illustrate machine learning principles?

·      What are some practical tips?

Target Audience

This course is designed for all Oil industry Technical Professionals, which will cover from fundamental theoretical background to high-level real work information/ techniques.

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

• Seismic Interpreters
• Geophysicists
• Geologists
• Geo-Modelers
• Petrophysicists
• Drilling Engineers
• Reservoir Engineers
• Technical Support Personnel
• Team Leaders
• Managers

Course Agenda

o   What Interpreters Should Know about Machine Learning?

o   Introduction to Machine Learning.

o   Why use Machine Learning?

o   Machine learning in the Geoscience.

o   What human interpreter sees vs. what computer sees?

o   Machine Learning Definition.

o   Categories of Data Science.

o   Artificial Intelligence.

o   Machine Learning.

o   Deep Learning.

o   Shallow Learning vs. Deep Learning.

o   Types of Machine Learning.

o   Supervised Machine Learning.

o   Un-Supervised Machine Learning.

o   Semi-Supervised Machine Learning.

o   Reinforcement Machine Learning.

o   Non-Neural Network Machine Learning.

o   Artificial Neural Network.

o   Deep Neural Network.

o   Neural Network for Seismic Facies.

o   Machine Learning foundations.

o   Attribute Selection list.

o   Principal Component Analysis (PCA).

o   Self-Organizing Maps (SOM).

o   Depositional facies classification.

o   Fault Detection.

o   Channel Extraction.

o   Machine Learning Predictions.

o   Machine Learning and computer Power.

o   Offshore Gulf of Mexico Case Study.