Practical Machine Learning for Well Log Data Course
This professional training course, Practical Machine Learning for Well Log Data, empowers petrophysicists, reservoir engineers, and geologists to harness machine learning techniques for interpreting well log data. Covering essential methods such as clustering, classification, and regression, the course emphasizes understanding when and why to apply each technique effectively. Participants gain hands-on experience using Python-based tools to process, model, and visualize well log data in geoscience and geological software environments. The course highlights practical applications, focusing on generating consistent, faster, and potentially more accurate interpretations that create business value. This course bridges petrophysics expertise with modern AI workflows to enhance productivity and data-driven decision-making in the oil and gas industry.Read more...
Who Should Take This Course
• Petrophysicists working with well log data
• Geologists interpreting well log measurements
• Professionals with basic petrophysics knowledge
• Reservoir engineers analyzing subsurface formations
What You Will Learn
• Train and test machine learning models
• Apply machine learning to well log data
• Evaluate model performance and consistency
• Use clustering, classification, and regression methods
Why This Course Works
• Increase productivity through automation
• Obtain faster data interpretation results
• Create valuable business outcomes from data
• Generate more consistent and accurate answers
Course contents
Chapter 1 - Course Introduction
Chapter 2 - Introduction to Well Log Data and Petrophysics
- 02-01 - Introduction to Well Log Data and Petrophysics (14 min.) Quiz
Chapter 3 - Understand The Fundamentals Of Petrophysics
- 03-01 - Introduction to Machine Learning (18 min.)
- 03-02 - Workflow of an ML Project (14 min.)
- 03-03 - Coding with AI (4 min.) Quiz
Chapter 4 - Training vs. Test Data In Petrophysics
- 04-01 - Training vs. Test Data in Petrophysics (17 min.) Quiz
Chapter 5 - Python And Petrophysical Data
- 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.) Quiz
Chapter 6 - Clustering Methods For Petrophysical Data
- 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.) Quiz
Chapter 7 - Classification Methods For Petrophysical Data
- 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.) Quiz
Chapter 8 - Regression Methods For Petrophysical Data
- 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.) Quiz
Chapter 9 - Model Deployment
- 09-01 - Model Deployment (14 min.) Quiz
Chapter 10 - Course Wrap-Up
- 10-01 - Course Wrap-Up (14 min.) Quiz
- 10-02 - Bonus Lesson on LLMs (10 min.)