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)
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
Dr. Patrick Bangert is the Vice President of Artificial Intelligence at Samsung SDS where he leads both the AI software development and AI consulting groups that each provide various offerings to the industry. He is the founder and Board Chair of Algorithmica Technologies, providing real-time process modeling, optimization, and predictive maintenance solutions to the process industry with a focus on chemistry and power generation. His doctorate from UCL specialized in applied mathematics, and his academic positions at NASAs Jet Propulsion Laboratory and Los Alamos National Laboratory made use of optimization and machine learning for magnetohydrodynamics and particle accelerator experiments. He has published extensively across optimization and machine learning and their relevant applications in the real world.
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