The aim of the chapter is twofold: First, it is to show that there are many applications that are realistic and have been carried out on real-world assets, that is, machine learning is not a dream. Second, the
This chapter will attempt to provide an overview over some of the practical applications that machine learning has found in oil and gas. The aim of the chapter is twofold: First, it is to show that there are many applications that are realistic and have been carried out on real-world asset...
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 surv...
1 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 th...
Machine learning in oil & gas market research report presents an overview of adoption of machine learning technologies in the oil and gas industry. It analyses the value chain, the challenges faced by the oil and gas industry, and how machine learning is enabling the industry to tackle these ...
The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers...
In 2013, Birdzell saw an opportunity for AI and machine learning to deliver unprecedented value to the oil and gas industry. He formed OAG Analytics to create an AI platform that enables oil and gas companies to use more of their data to help solve critical problems like well spacing. ...
This work examines the application of machine learning (ML) algorithms to evaluate dissolved gas analysis (DGA) data to quickly identify incipient faults in oil-immersed transformers (OITs). Transformers are pivotal equipment in the transmission and distribution of electrical power. The failure of a ...
The lubricant industry is heading towards automation through condition monitoring and useful life prediction. This shift is not only happening on the experimental side to choose a lubricant; rather, it starts with the extraction of the crude oil from the oil bore [58]. The geological data and ...
0 machine learning guide for oil and gas using python: a step-by-step breakdown with data, algorithms, codes, and applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. ...