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Machine Learning and Data Science in the Oil and Gas Industry: Best Practices, Tools, and Case Studies explains critical facets around machine learning that are specifically tailored to oil and gas. Practical in its approach, this reference provides a chapter devoted to the early career engineer that is just starting in the industry. It then builds to a full-scale project that is supported by real-world case studies from various industry and academic contributors. Lessons learned and technology drivers are also discussed, creating a path for future engineers to apply.
Rounding out with a glossary, this book delivers a reference that cuts through the hype to help today's petroleum engineers understand machine learning and where it can benefit their operations.
- Helps readers gain a practical understanding of machine learning used in oil and gas operations
- Presents change management skills that will help readers gain confidence in pursuing new technology
- Provides the workflow of a full scale project and where machine learning is and isn't impactful
Oil and gas industry expert and practitioner working either in exploration, drilling, completions, engineering, production, maintenance or management
- Data Science, Statistics and Time-Series
2. Machine Learning
3. Introduction to Machine Learning in O&G
4. Data Management from the DCS to the Historian
5. Designing the Business Case
6. Project Management for an ML Project
7. Choosing the Right Methods and Tools (KPI on how to compare them)
8. Integration of ML into Plant Architecture
9. Quantification of Added-Value (benefit and limitations)
10. Case Studies
- No. of pages:
- © Gulf Professional Publishing 2021
- 1st March 2021
- Gulf Professional Publishing
- Paperback ISBN:
Patrick Bangert is founder and CEO 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. He is also CTO of APO Offshore Inc, a leading data analytics company responsible for developing predictive maintenance techniques in the O&G industry. His doctorate from UCL specialized in Applied Mathematics, and his academic positions at NASA 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 relevant applications in the real world.
CEO Algorithmica Technologies, Cupertino, California, USA
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