Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications – eBook
- Authors: Hoss Belyadi, Alireza Haghighat
- File Size: 45 MB
- Format: PDF
- Length: 476 Pages
- Publisher: Gulf Professional Publishing; 1st edition
- Publication Date: April 9, 2021
- Language: English
- ASIN: B092M3L8Y9
- ISBN-10: 0128219297, 0128219300
- ISBN-13: 9780128219294, 9780128219300
Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications, (PDF) provides a critical training and resource tool to help engineers understand machine learning theory and practice, particularly referencing use cases in oil and gas. The reference moves from describing how Python works to step-by-step examples of utilization in several oil and gas scenarios, like well testing, shale reservoirs, and production optimization. Petroleum engineers are quickly implementing machine learning techniques to their data challenges, but there is an absence 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 describing how it works at an introductory level then linking to how to implement the algorithms into different gas and oil scenarios. While related resources are often too mathematical, this ebook balances theory with applications, including use cases that assist solve different oil and gas data challenges.
- Includes the most commonly used algorithms for both supervised and unsupervised learning
- Assists readers understand how open-source Python can be used in practical oil and gas challenges
- Offers a balanced approach of both theory and practicality while transitioning from introductory to advanced analytical techniques
NOTE: The product only includes the ebook, Machine Learning Guide for Oil and Gas Using Python in PDF. No access codes are included.