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.
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Table of Contents
1. Introduction to Machine Learning and Python2. Data Import and Visualization3. Machine Learning Workflows and Types4. Unsupervised Machine Learning: Clustering Algorithms5. Supervised Learning6. Neural Networks7. Model Evaluation8. Fuzzy Logic9. Evolutionary Optimization
