Machine Learning and Data Science in the Oil and Gas Industry: Best Practices, Tools, and Case Studies
Author:   Patrick Bangert (Vice President of Artificial Intelligence at Samsung SDSA, San Jose, CA, United States, and Founder and Board Chair of Algorithmica Technologies GmbH, Bad Nauheim, Germany)
Publisher:   Elsevier Science & Technology
ISBN:  

9780128207147


Pages:   306
Publication Date:   08 March 2021
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Machine Learning and Data Science in the Oil and Gas Industry: Best Practices, Tools, and Case Studies


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Full Product Details

Author:   Patrick Bangert (Vice President of Artificial Intelligence at Samsung SDSA, San Jose, CA, United States, and Founder and Board Chair of Algorithmica Technologies GmbH, Bad Nauheim, Germany)
Publisher:   Elsevier Science & Technology
Imprint:   Gulf Professional Publishing
Weight:   0.570kg
ISBN:  

9780128207147


ISBN 10:   0128207140
Pages:   306
Publication Date:   08 March 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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

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Author Information

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 NASA’s 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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