Practical Machine Learning for Well Log Data Course

Cameron Snow

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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. Read more...

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Course contents

    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.)