Machine Learning in the Oil and Gas Industry

Machine Learning in the Oil and Gas Industry

  • Author: Ayush Rastogi, Luigi Saputelli, Sribharath Kainkaryam, Srimoyee Bhattacharya, Yogendra Narayan Pandey
  • ISBN: 1484260937
  • Year: 2020
  • Pages: 300
  • Language: English
  • File size: MB
  • File format: PDF, ePub
  • Category: Computers & Technology

Book Description:

Employ machine and deep learning to solve a few of the challenges in the oil and gas market. The book begins with a concise discussion of the gas and oil exploration and production life cycle in the context of data flow through the different phases of sector operations. This leads to a survey of several interesting problems, which can be good candidates for implementing deep and machine learning approaches. The initial chapters offer a primer about the Python programming language used for implementing the calculations; this can be followed by an overview of supervised and unsupervised machine learning theories. The writers provide industry examples using open-source data sets along with practical explanations of these algorithms, without diving too deep into the theoretical characteristics of the algorithms employed. Machine Learning in the Gas and Oil Industry covers problems surrounding diverse industry issues, including geophysics (seismic interpretation), geological modeling, reservoir engineering, and manufacturing engineering.

Through the novel, the emphasis will be on providing a pragmatic approach with step-by-step definitions and code examples of implementing equipment and deep learning algorithms for solving real-life problems in the oil and gas industry.

What You Will Learn

  • Understanding the end-to-end industry life cycle and flow of data in the industrial operations of the petroleum and gas sector.
  • Get the basic concepts of computer programming and equipment and deep learning required for implementing the algorithms utilized.
  • Research intriguing industry problems that are good candidates for being solved by a system and deep learning.
  • Discover the practical concerns and challenges for implementing machine and profound learning projects in the oil and gas sector.
  • Professionals in the oil and gas sector who can benefit from a practical comprehension of the machine and deep learning method of solving real-life issues.

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