Xue Q. Data Analytics for Drilling Engineering: Theory, Algorithms, Experiments, Software

Springer, 2020. — 324 p. — (Information Fusion and Data Science). — ISBN: 978-3-030-34034-6.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.
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.Application of Data Processing in Drilling Engineering: A Review
Signal Detection and Processing of Downhole Information Transmission
Dynamic Measurement of Spatial Attitude at the Bottom Rotating Drillstring
Analysis of Vibration Noise on the Fiber-Optic Gyroscope
Measurement and Analysis of Drillstring Dynamics
Data Processing and Mining in Seismic While Drilling
Signal Processing in Logging While Drilling
Prospect of Big Data Application in Drilling Engineering