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. Chapter 1 : Introduction - p. Page xi 2. Chapter 2 : Data Science, Statistics, and Time-Series - p. Page 11 3. Chapter 3 : Machine Learning - p. Page 36 4. Chapter 4 : Introduction to Machine Learning in the Oil and Gas Industry - p. Page 66 5. Chapter 5 : Data Management from the DCS to the Historian - p. Page 76 6. Chapter 6 : Getting the Most Across the Value Chain - p. Page 109 7. Chapter 7 : Project Management for a Machine Learning Project - p. Page 128 8. Chapter 8 : The Business of AI Adoption - p. Page 150 9. Chapter 9 : Global Practice of AI and Big Data in Oil and Gas Industry - p. Page 179 10. Chapter 10 : Soft Sensors for NOx Emissions - p. Page 210 11. Chapter 11 : Detecting Electric Submersible Pump Failures - p. Page 221 12. Chapter 12 : Predictive and Diagnostic Maintenance for Rod Pumps - p. Page 235 13. Chapter 13 : Forecasting Slugging in Gas Lift Wells - p. Page 250 14. Index - p. Page 266
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. Chapter 1 : Introduction - p. Page xi 2. Chapter 2 : Data Science, Statistics, and Time-Series - p. Page 11 3. Chapter 3 : Machine Learning - p. Page 36 4. Chapter 4 : Introduction to Machine Learning in the Oil and Gas Industry - p. Page 66 5. Chapter 5 : Data Management from the DCS to the Historian - p. Page 76 6. Chapter 6 : Getting the Most Across the Value Chain - p. Page 109 7. Chapter 7 : Project Management for a Machine Learning Project - p. Page 128 8. Chapter 8 : The Business of AI Adoption - p. Page 150 9. Chapter 9 : Global Practice of AI and Big Data in Oil and Gas Industry - p. Page 179 10. Chapter 10 : Soft Sensors for NOx Emissions - p. Page 210 11. Chapter 11 : Detecting Electric Submersible Pump Failures - p. Page 221 12. Chapter 12 : Predictive and Diagnostic Maintenance for Rod Pumps - p. Page 235 13. Chapter 13 : Forecasting Slugging in Gas Lift Wells - p. Page 250 14. Index - p. Page 266
