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

Machine Learning and Data Science in the Oil and Gas Industry is a guide book that explains how machine learning can be tailored to oil and gas use cases, how it is already used in operations, and how to manage the data stream. It includes real-life case studies and practical advice for practitioners.

Format: Paperback / softback
Length: 306 pages
Publication date: 08 March 2021
Publisher: Elsevier Science & Technology

Machine Learning and Data Science in the Oil and Gas Industry delves into the transformative power of machine learning in the realm of oil and gas operations. This comprehensive guide provides petroleum engineers with valuable insights on when to employ machine learning, exploring its existing applications in the industry, and equipping them with the skills to navigate the future of data management. With a practical approach, the book covers every aspect of a data science or machine learning project, including the often overlooked managerial components that can lead to project failures.

To enhance the learning experience, real-life case studies are included, showcasing practical applications in areas such as predictive maintenance, soft sensing, and forecasting. Viewed as a guiding manual, this resource empowers practitioners to embark on successful data science projects within the oil and gas industry, avoiding common pitfalls and effectively communicating the business value derived from these initiatives.

By leveraging machine learning and data science, the oil and gas industry can unlock new opportunities for efficiency, optimization, and profitability. This book serves as a valuable tool for professionals seeking to stay at the forefront of this rapidly evolving field, enabling them to make informed decisions and drive innovation in the industry.

Weight: 476g
Dimension: 229 x 152 x 19 (mm)
ISBN-13: 9780128207147