Machine Learning for Petroleum Engineers and Geoscientists
This hands-on course is designed for participants seeking to incorporate machine learning tools in their E&P workflows. It is expected that participants be familiar with the mathematical and statistical foundations of machine learning algorithms as well as Python and its common libraries. In this course, participants will make the transition from classroom instruction to practical problem solving with several use cases from the industry. This course enables participants to understand the characteristics of different algorithms, their impact on model performance, and to select appropriate machine learning workflows to solve specific E&P-related problems.
The course begins with a comprehensive discussion of use cases of machine learning in the E&P industry. This is followed by practical, hands-on examples dealing with several, real-world problems in petroleum engineering and geosciences. The hands-on examples are preceded by a short lecture to introduce participants to the problem and the expected outcomes.