Practical Machine Learning for Well Log Data Course
This course will guide students through the application of fundamental machine learning techniques to well log data. Techniques covered include clustering, classification, and regression via multiple methodologies. The emphasis of
each module is to understand when different techniques should be applied to the data and why. Students will run code to read well log data, train and test machine learning models, and to write log data back for visualization in their G&G software; This course is not designed to teach students how to code in Python, but rather how to apply machine learning to create value for their company.Read more...
Target Audience: Petrophysicists, reservoir engineers, and geologists working with well
log data.
Prerequisites: Basic understanding of petrophysics and well log data is required.
Familiarity with Python programming is helpful but not mandatory.
Software: Python, Pandas, NumPy, Scikit-learn, XG Boost, Matplotlib, and Lasio will be
used for the coding and machine learning exercises.