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Machine learning guide for oil and gas using Python : a step-by-step breakdown with data, algorithms, codes, and applications /

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. The refere...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Belyadi, Hoss (Autor), Haghighat, Alireza (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, MA : Gulf Professional Publishing, 2021.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Belyadi, Hoss,  |e author. 
245 1 0 |a Machine learning guide for oil and gas using Python :  |b a step-by-step breakdown with data, algorithms, codes, and applications /  |c Hoss Belyadi, Alireza Haghighat. 
264 1 |a Cambridge, MA :  |b Gulf Professional Publishing,  |c 2021. 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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504 |a Includes bibliographical references and index. 
520 |a 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. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges. 
650 0 |a Petroleum engineering  |x Data processing. 
650 0 |a Natural gas  |x Data processing. 
650 0 |a Machine learning. 
650 6 |a Technique du p�etrole  |0 (CaQQLa)201-0031336  |x Informatique.  |0 (CaQQLa)201-0380011 
650 6 |a Gaz naturel  |0 (CaQQLa)201-0008418  |x Informatique.  |0 (CaQQLa)201-0380011 
650 6 |a Apprentissage automatique.  |0 (CaQQLa)201-0131435 
650 7 |a Machine learning  |2 fast  |0 (OCoLC)fst01004795 
650 7 |a Natural gas  |x Data processing  |2 fast  |0 (OCoLC)fst01034062 
650 7 |a Petroleum engineering  |x Data processing  |2 fast  |0 (OCoLC)fst01059499 
700 1 |a Haghighat, Alireza,  |e author. 
776 0 8 |i Print version:  |a Belyadi, Hoss.  |t Machine learning guide for oil and gas using Python  |z 0128219297  |z 9780128219294  |w (OCoLC)1195448296 
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