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 ...
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 has offered novel ways to increase automation, efficiency, and productivity in many sectors - including the oil and gas industry. Its ability to find patterns in huge volumes of data has made machine learning into a highly effective tool, thanks to the amount of informati...
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...
This article aims to provide a comprehensive overview of the current use of machine learning and digital technologies for risk assessment and management in the oil and gas industry. A comprehensive methodology was used, including a literature review, data collection from academic journals, industry ...
AI and machine learning play a crucial role in the oil and gas industry. Learn more here about AI applications, solutions, and use case in oil and gas.
Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applicationsdelivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The ref...
A typical Recurrent Neural Networks include Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Simple RNN has been developed to predict oil and gas production. The results have shown that machine learning gained good results in the early stage of the production phase and 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...