Machine Learning and Data Science in the Oil and Gas Industry/Машинное обучение и анализ данных в нефтегазовой отрасли

Книга на английском языке
This book will have served its purpose if an oil and gas company uses the advice given here in facilitating a machine learning project or toolset to drive value. It is meant partially as an instruction manual and partially as an inspiration for oil company managers who want to use machine learning and artificial intelligence to improve the industry and its efficiency.
As such, this book is deliberately designed to be a passionate discussion starter for what I consider to be one of the digital world’s most important debates. I believe the authors were successful in their mission to open up and stimulate further discussions. In a rapid transition of energy systems, such debates are essential and can only be achieved by fundamental research, free from economic and political constraints, and by people free to rethink our current challenging business environment.

Contents
Dedication, Front matter, Copyright, Contributors, Foreword
Chapter 1 - Introduction
Chapter 2 - Data Science, Statistics, and Time-Series
Chapter 3 - Machine Learning
Chapter 4 - Introduction to Machine Learning in the Oil and Gas Industry
Chapter 5 - Data Management from the DCS to the Historian
Chapter 6 - Getting the Most Across the Value Chain
Chapter 7 - Project Management for a Machine Learning Project
Chapter 8 - The Business of Al Adoption
Chapter 9 - Global Practice of Al and Big Data in Oil and Gas Industry
Chapter 10 - Soft Sensors for NOx Emissions
Chapter 11 - Detecting Electric Submersible Pump Failures
Chapter 12 - Predictive and Diagnostic Maintenance for Rod Pumps
Chapter 13 - Forecasting Slugging in Gas Lift Wells
Index