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Cameron Snow

Practical Machine Learning for Well Log Data

Cameron Snow
7:54 Hours
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Sample Lesson
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.

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.

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.)

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