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.

Content duration: (4:50 hours)

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    Chapter 1 - Machine Learning Introduction

    1.01 Introduction (20 min.)  Sample Lesson Quiz

    1.02 Regression (14 min.) Quiz

    1.03 Algorithms (14 min.) Quiz

    1.04 Why Use Machine Learning? (18 min.) Quiz

    1.05 Predicting Latitude & Longitude from a UWI (9 min.)

    1.06 Measuring Performance (24 min.) Quiz

    1.07 Cross-Validation (12 min.) Quiz

    1.08 Noise, Bias and Missing Information (18 min.) Quiz

    1.09 Sample Size (10 min.) Quiz

    Chapter 2 - Building and Interpreting Models

    2.01 Feature Engineering (12 min.) Quiz

    2.02 Feature Selection (10 min.) Quiz

    2.03 Model Interpretation (21 min.)

    2.04 Liquids Rich Montney Case Study (23 min.)

    Chapter 3 - Upstream Oil and Gas Example Workflow

    3.01 Exploratory Data Analysis (27 min.)