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.
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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
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
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