Machine Learning and Data Science in the Oil and Gas Industry
Best Practices, Tools, and Case Studies

Helps readers understand the terminology, technology and skills needed to apply machine learning to oil and gas operations

Patrick Bangert (Edited by)

9780128207147, Elsevier Science

Paperback, published 8 March 2021

306 pages
22.9 x 15.1 x 2 cm, 0.57 kg

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value.

1. Introduction
2. Data Science, Statistics, and Time-Series
3. Machine Learning
4. Introduction to Machine Learning in the Oil and Gas Industry
5. Data Management from the DCS to the Historian
6. Getting the Most Across the Value Chain
7. Getting the Most Across the Value Chain
8. The Business of AI Adoption
9. Global Practice of AI and Big Data in Oil and Gas Industry
10. Soft Sensors for NOx Emissions
11. Detecting Electric Submersible Pump Failures
12. Predictive and Diagnostic Maintenance for Rod Pumps
13. Forecasting Slugging in Gas Lift Wells

Subject Areas: Machine learning [UYQM], Petroleum technology [THFP], Gas technology [THFG], Fossil fuel technologies [THF]