01-01 – Course Introduction (20 min.)
02-01 – Introduction to Well Log Data and Petrophysics (14 min.)
03-01 – Introduction to Machine Learning (18 min.)
03-02 – Workflow of an ML Project (14 min.)
03-03 – Coding with AI (4 min.)
04-01 – Training vs. Test Data in Petrophysics (17 min.)
05-01 – Python and Petrophysical Data (16 min.)
05-02 – Checking The Python Setup (4 min.)
05-03 – Reading LAS Files (13 min.)
05-04 – Some Basic Examples (16 min.)
05-05 – Data Display With Commercial Software (7 min.)
05-06 – Pre-Processing Data (8 min.)
05-07 – Pre-Processing Examples (20 min.)
06-01 – Clustering Methods for Petrophysical Data (13 min.)
06-02 – K-Means Clustering Exercise (17 min.)
06-03 – K-Means Clustering Exercise – Visualization (16 min.)
06-04 – Key Takeaways (6 min.)
07-01 – Classification Methods for Petrophysical Data (9 min.)
07-02 – Ensemble Methods (14 min.)
07-03 – Random Forest – Net Pay Exercise (16 min.)
07-04 – Net Pay Exercise (Continued) (12 min.)
07-05 – Lithology Classification Exercise (13 min.)
07-06 – Lithology Classification Exercise (continued) (10 min.)
07-07 – Exercise Overview & Key Takeaways (8 min.)
08-01 – Regression Methods for Petrophysical Data (6 min.)
08-02 – Common Regression Methods (8 min.)
08-03 – Regression Exercises – Overview (3 min.)
08-04 – Regression Exercise 1 (22 min.)
08-05 – Regression Exercise 1 – Key Takeaways (5 min.)
08-06 – TOC Prediction – Exercise 2 (4 min.)
08-07 – TOC Prediction – Key Takeaways (11 min.)
08-08 – Regression Summary (3 min.)
09-01 – Model Deployment (14 min.)
10-01 – Course Wrap-Up (14 min.)
10-02 – Bonus Lesson on LLMs (10 min.)