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 manag...
This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventi...
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 ...
Manufacturing is taking a leap forward, using machine learning in robotics for predictive maintenance, and for making factories leaner and more agile.
diagnosis spatiotemporal pattern network (STPN) with convolutional neural network (CNN) have been used as a hybrid model. This deep learning methods has been used for the analysis of bearing fault data set as a case study. The performance of STPN-CNN has been evaluated based on accuracy ...
Machine learning Artificial intelligence Bottom hole pressure Artificial neural network Random forest K-nearest neighbors 1. Introduction Flowing bottom-hole pressure (FBHP) is one of the critical parameters in the evaluation of performance of oil and gas wells. The value of FBHP varies during the ...
These machine-learning algorithms are configured to predict Poisson's ratio (ϑ) and maximum horizontal stress (σH) from available well-log input data. A large dataset from three wellbores drilled though the Gachsaran Formation in the Marun oil field, the second largest in Iran, is used to...