Data Analytics for Drilling Engineering

This book PDF is perfect for those who love Science genre, written by Qilong Xue and published by Springer Nature which was released on 30 December 2019 with total hardcover pages 312. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Data Analytics for Drilling Engineering books below.

Data Analytics for Drilling Engineering
Author : Qilong Xue
File Size : 45,7 Mb
Publisher : Springer Nature
Language : English
Release Date : 30 December 2019
ISBN : 9783030340353
Pages : 312 pages
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Data Analytics for Drilling Engineering by Qilong Xue Book PDF Summary

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.

Data Analytics for Drilling Engineering

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,

Get Book
Data Analytics in Reservoir Engineering

Data Analytics in Reservoir Engineering describes the relevance of data analytics for the oil and gas industry, with particular emphasis on reservoir engineering.

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Shale Analytics

This book describes the application of modern information technology to reservoir modeling and well management in shale. While covering Shale Analytics, it focuses on reservoir modeling and production management of shale plays, since conventional reservoir and production modeling techniques do not perform well in this environment. Topics covered include tools

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Methods for Petroleum Well Optimization

Drilling and production wells are becoming more digitalized as oil and gas companies continue to implement machine learning andbig data solutions to save money on projects while reducing energy and emissions. Up to now there has not been one cohesiveresource that bridges the gap between theory and application, showing how

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Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry

This book covers aspects of data science and predictive analytics used in the oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital wells, value chain integration, crude basket forecasting, and so forth. It further

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Machine Learning and Data Science in the Oil and Gas Industry

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use 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

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Applied Drilling Engineering

Applied Drilling Engineering presents engineering science fundamentals as well as examples of engineering applications involving those fundamentals.

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Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry

This book covers aspects of data science and predictive analytics used in the oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital wells, value chain integration, crude basket forecasting, and so forth. It further

Get Book