Part IV: Machine Learning & AI
Chapter 16
Machine Learning Fundamentals for Petroleum Engineers
schedule15 min readfitness_center4 exercises
infoWhat You'll Learn
- Understand the ML workflow (data prep, training, evaluation, deployment)
- Distinguish between supervised, unsupervised, and reinforcement learning
- Master the bias-variance tradeoff and cross-validation
- Build your first predictive model with scikit-learn
lightbulbDatasets Used in This Chapter
well_log_training.csv
What Is Machine Learning? (For Engineers)
main.py
The ML Pipeline
main.py
Feature Engineering for Petroleum Data
main.py
Model Evaluation and Cross-Validation
main.py
Overfitting, Underfitting, and Regularization
main.py
Your First Model — Predicting Porosity from Logs
main.py
Exercises
fitness_center
Exercise 16.1Practice
Exercise 16.1
...
arrow_forward
codePythonSolve Nowarrow_forward
fitness_center
Exercise 16.2Practice
Exercise 16.2
...
arrow_forward
codePythonSolve Nowarrow_forward
fitness_center
Exercise 16.3Practice
Exercise 16.3
...
arrow_forward
codePythonSolve Nowarrow_forward
fitness_center
Exercise 16.4Practice
Exercise 16.4
...
arrow_forward
codePythonSolve Nowarrow_forward