Machine Learning and Data Science in the Oil and Gas Industry: Best Practices, Tools, and Case Studies - Tapa blanda

Acerca del autor

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.

De la contraportada

Today’s more complex oil and gas fields rely on quality of data, new software, and upcoming technology, but engineers are trained in the proven workflows and mechanisms of using large data sets in more sophisticated technology, such as machine learning.

Machine Learning and Data Science in the Oil and Gas Industry explains when the critical facets around machine learning specifically tailored to oil and gas 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 approach, the reference provides a chapter devoted to the early career engineer that is just starting in the industry and then builds up a full-scale project, supported by real-world case studies from various industry and academic contributors. Lessons learned and technology drivers are discussed, carving a path for future engineers to apply. Rounding out with a glossary, Machine Learning and Data Science in the Oil and Gas Industry delivers a reference to cut through the hype and help petroleum engineers today understand machine learning and where it will benefit their operations.