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Machine learning in the oil and gas ...

Pandey, Yogendra Narayan.

Machine learning in the oil and gas industryincluding geosciences, reservoir engineering, and production engineering with Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning in the oil and gas industryby Yogendra Narayan Pandey ... [et al.].
Reminder of title:	including geosciences, reservoir engineering, and production engineering with Python /
other author:	Pandey, Yogendra Narayan.
Published:	Berkeley, CA :Apress :2020.
Description:	xx, 300 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Petroleum engineeringData processing.
Online resource:	https://doi.org/10.1007/978-1-4842-6094-4
ISBN:	9781484260944$q(electronic bk.)

Machine learning in the oil and gas industryincluding geosciences, reservoir engineering, and production engineering with Python /
Machine learning in the oil and gas industryincluding geosciences, reservoir engineering, and production engineering with Python /[electronic resource] :by Yogendra Narayan Pandey ... [et al.]. - Berkeley, CA :Apress :2020. - xx, 300 p. :ill., digital ;24 cm.

Chapter 1: Towards Oil and Gas 4.0 -- Chapter 2: Python Programming Primer -- Chapter 3: Overview of Machine and Deep Learning Concepts -- Chapter 4: Geophysics and Seismic Data Processing -- Chapter 5: Geomodeling -- Chapter 6: Reservoir Engineering -- Chapter 7: Production Engineering -- Chapter 8: Opportunities, Challenges, and Expected Future Trends.

Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the different stages of industry operations. This leads to a survey of some interesting problems, which are good candidates for applying machine and deep learning approaches. The initial chapters provide a primer on the Python programming language used for implementing the algorithms; this is followed by an overview of supervised and unsupervised machine learning concepts. The authors provide industry examples using open source data sets along with practical explanations of the algorithms, without diving too deep into the theoretical aspects of the algorithms employed. Machine Learning in the Oil and Gas Industrycovers problems encompassing diverse industry topics, including geophysics (seismic interpretation), geological modeling, reservoir engineering, and production engineering. Throughout the book, the emphasis is on providing a practical approach with step-by-step explanations and code examples for implementing machine and deep learning algorithms for solving real-life problems in the oil and gas industry. You will: Understanding the end-to-end industry life cycle and flow of data in the industrial operations of the oil and gas industry Get the basic concepts of computer programming and machine and deep learning required for implementing the algorithms used Study interesting industry problems that are good candidates for being solved by machine and deep learning Discover the practical considerations and challenges for executing machine and deep learning projects in the oil and gas industry.

ISBN: 9781484260944$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-6094-4doiSubjects--Topical Terms:

664904
Petroleum engineering
--Data processing.

LC Class. No.: TN871

Dewey Class. No.: 622.3380285

Machine learning in the oil and gas industryincluding geosciences, reservoir engineering, and production engineering with Python /
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505 0 $a Chapter 1: Towards Oil and Gas 4.0 -- Chapter 2: Python Programming Primer -- Chapter 3: Overview of Machine and Deep Learning Concepts -- Chapter 4: Geophysics and Seismic Data Processing -- Chapter 5: Geomodeling -- Chapter 6: Reservoir Engineering -- Chapter 7: Production Engineering -- Chapter 8: Opportunities, Challenges, and Expected Future Trends.
520 $a Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the different stages of industry operations. This leads to a survey of some interesting problems, which are good candidates for applying machine and deep learning approaches. The initial chapters provide a primer on the Python programming language used for implementing the algorithms; this is followed by an overview of supervised and unsupervised machine learning concepts. The authors provide industry examples using open source data sets along with practical explanations of the algorithms, without diving too deep into the theoretical aspects of the algorithms employed. Machine Learning in the Oil and Gas Industrycovers problems encompassing diverse industry topics, including geophysics (seismic interpretation), geological modeling, reservoir engineering, and production engineering. Throughout the book, the emphasis is on providing a practical approach with step-by-step explanations and code examples for implementing machine and deep learning algorithms for solving real-life problems in the oil and gas industry. You will: Understanding the end-to-end industry life cycle and flow of data in the industrial operations of the oil and gas industry Get the basic concepts of computer programming and machine and deep learning required for implementing the algorithms used Study interesting industry problems that are good candidates for being solved by machine and deep learning Discover the practical considerations and challenges for executing machine and deep learning projects in the oil and gas industry.
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