Data Analytics for Drilling Engineering

Theory, Algorithms, Experiments, Software
1. Auflage 2020
ISBN: 978-3-030-34034-6
Verlag: Springer International Publishing

Buch, Englisch, 312 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 658 g

Reihe: Information Fusion and Data Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Xue, Qilong

Fachgebiete

• Technische Wissenschaften Bauingenieurwesen Boden- und Felsmechanik, Geotechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Statik, Dynamik, Kinetik, Kinematik
• Geowissenschaften Geologie Geotechnik

Inhaltsverzeichnis

Introduction to the Application of Drilling Data Processing.- Signal Processing of Downhole Information Transmission.- Dynamic Measurement of Spatial Attitude at the Bottom Rotating DrillString.- Measurement and Analysis of Drillstring Dynamics.- Signal processing in logging while drilling.- Data Mining in Seismic While Drilling.- Applications of Big Data in Drilling Engineering.- Summary and Outlook.