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 reference moves from explaining how Python works to step-by-step examples of is utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different data challenges. Helps readers understand how open source Python can be utilized in practical oil and gas challenges Covers the most commonly used algorithms for both supervised and unsupervised learningPresents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques INDICE: Introduction to Machine Learning and Python Data Import and Visualization Machine Learning Workflows and Types Unsupervised Machine Learning: Clustering Algorithms Supervised Learning Neural Networks Model Evaluation Fuzzy Logic Evolutionary Optimization

• ISBN: 978-0-12-821929-4
• Editorial: Gulf Professional Publishing
• Encuadernacion: Rústica
• Páginas: 300
• Fecha Publicación: 01/04/2021
• Nº Volúmenes: 1
• Idioma: Inglés