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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, MA :
Gulf Professional Publishing,
2021.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
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 | ||
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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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 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 pétrole |x Informatique. | |
| 650 | 6 | |a Gaz naturel |x Informatique. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Natural gas |x Data processing |2 fast | |
| 650 | 7 | |a Petroleum engineering |x Data processing |2 fast | |
| 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 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9780128219300/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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