Data Analytics for Drilling Engineering: Theory, Algorithms, Experiments, Software

Xue, Qilong

ISBN 10: 3030340376 / ISBN 13: 9783030340377

Edité par Springer Nature, 2021

A propos de cet article

328 pages. 9.25x6.10x0.91 inches. In Stock.

N° de réf. du vendeur x-3030340376

Détails bibliographiques

• Titre: Data Analytics for Drilling Engineering: Theory, Algorithms, Experiments, Software
• Éditeur: Springer Nature
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 3030340376
• ISBN à 13 chiffres: 9783030340377
• Reliure: Paperback
• État de l'article: Brand New

A propos de ce titre

Synopsis

This book presents the signal processing and data mining challenges encountered in drilling engineering, and describes the methods used to overcome them. In drilling engineering, many signal processing technologies are required to solve practical problems, such as downhole information transmission, spatial attitude of drillstring, drillstring dynamics, seismic activity while drilling, among others. This title attempts to bridge the gap between the signal processing and data mining and oil and gas drilling engineering communities. There is an urgent need to summarize signal processing and data mining issues in drilling engineering so that practitioners in these fields can understand each other in order to enhance oil and gas drilling functions. In summary, this book shows the importance of signal processing and data mining to researchers and professional drilling engineers and open up a new area of application for signal processing and data mining scientists.