Description
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.- Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful- Gain practical understanding of machine learning used in oil and gas operations through contributed case studies- Learn change management skills that will help gain confidence in pursuing the technology- Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)
Table of Contents
1. Introduction2. Data Science, Statistics, and Time-Series3. Machine Learning4. Introduction to Machine Learning in the Oil and Gas Industry5. Data Management from the DCS to the Historian6. Getting the Most Across the Value Chain7. Getting the Most Across the Value Chain8. The Business of AI Adoption9. Global Practice of AI and Big Data in Oil and Gas Industry10. Soft Sensors for NOx Emissions11. Detecting Electric Submersible Pump Failures12. Predictive and Diagnostic Maintenance for Rod Pumps13. Forecasting Slugging in Gas Lift Wells
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