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)

• Cover 2
• Dedicated 4
• Title page 5
• Copyright 6
• Contents 7
• Contributors 13
• Foreword 15
• Chapter 1 - Introduction 19
  • 1.1 - Who this book is for 19
  • 1.2 - Preview of the content 20
  • 1.3 - Oil and gas industry overview 22
  • 1.4 - Brief history of oil exploration 24
  • 1.5 Oil and gas as limited resources 25
  • 1.6 - Challenges of oil and gas 26
  • References 29