Saga Wisdom

Introduction to Machine Learning for Upstream Oil & Gas

Machine Learning is gaining popularity in upstream petroleum engineering. Similar to classical engineering techniques, machine learning is a toolset that can be misused, misapplied and misunderstood. Integrating domain knowledge with machine learning is critical for obtaining results that are meaningful, reliable and communicable. In this course, you will learn the fundamentals of machine learning as it applies to upstream data. You will learn how to: apply these techniques for field development optimization, appropriately interpret the results, and communicate findings to stakeholders.

Chapter 1 - Machine Learning Introduction

1.01 Introduction Sample Lesson Quiz

1.02 Regression Quiz

1.03 Algorithms Quiz

1.04 Why Use Machine Learning? Quiz

1.05 Predicting Latitude & Longitude from a UWI

1.06 Measuring Performance Quiz

1.07 Cross-Validation Quiz

1.08 Noise, Bias and Missing Information Quiz

1.09 Sample Size Quiz

Chapter 2 - Building and Interpreting Models

2.01 Feature Engineering Quiz

2.02 Feature Selection Quiz

2.03 Model Interpretation

2.04 Liquids Rich Montney Case Study

Chapter 3 - Upstream Oil and Gas Example Workflow

3.01 Exploratory Data Analysis