Machine Learning and Data Science in the Oil and Gas Industry

preview-18
  • Machine Learning and Data Science in the Oil and Gas Industry Book Detail

  • Author : Patrick Bangert
  • Release Date : 2021-03-04
  • Publisher : Gulf Professional Publishing
  • Genre : Science
  • Pages : 290
  • ISBN 13 : 0128209143
  • File Size : 47,47 MB

Machine Learning and Data Science in the Oil and Gas Industry by Patrick Bangert PDF Summary

Book Description: 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 stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful Gain practical understanding of machine learning used in oil and gas operations through contributed case studies Learn change management skills that will help gain confidence in pursuing the technology Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

Disclaimer: www.yourbookbest.com does not own Machine Learning and Data Science in the Oil and Gas Industry books pdf, neither created or scanned. We just provide the link that is already available on the internet, public domain and in Google Drive. If any way it violates the law or has any issues, then kindly mail us via contact us page to request the removal of the link.