Machine Learning Guide for Oil and Gas Using Python

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. 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 learning Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques

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  • Author : Hoss Belyadi
  • Publisher : Gulf Professional Publishing
  • Pages : 476 pages
  • ISBN : 0128219300
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKMachine Learning Guide for Oil and Gas Using Python

Machine Learning Guide for Oil and Gas Using Python

Machine Learning Guide for Oil and Gas Using Python
  • Author : Hoss Belyadi,Alireza Haghighat
  • Publisher : Gulf Professional Publishing
  • Release : 09 April 2021
GET THIS BOOKMachine Learning Guide for Oil and Gas Using Python

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

Machine Learning in the Oil and Gas Industry

Machine Learning in the Oil and Gas Industry
  • Author : Yogendra Narayan Pandey,Ayush Rastogi,Sribharath Kainkaryam,Srimoyee Bhattacharya,Luigi Saputelli
  • Publisher : Apress
  • Release : 03 November 2020
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Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the different stages of industry operations. This leads to a survey of some interesting problems, which are good candidates for applying machine and deep learning approaches. The initial chapters provide a primer on the Python programming language used for

Machine Learning and Data Science in the Oil and Gas Industry

Machine Learning and Data Science in the Oil and Gas Industry
  • Author : Patrick Bangert
  • Publisher : Gulf Professional Publishing
  • Release : 04 March 2021
GET THIS BOOKMachine Learning and Data Science in the Oil and Gas Industry

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often

Methods for Petroleum Well Optimization

Methods for Petroleum Well Optimization
  • Author : Rasool Khosravanian,Bernt S. Aadnoy
  • Publisher : Gulf Professional Publishing
  • Release : 22 September 2021
GET THIS BOOKMethods for Petroleum Well Optimization

Drilling and production wells are becoming more digitalized as oil and gas companies continue to implement machine learning and big data solutions to save money on projects while reducing energy and emissions. Up to now there has not been one cohesive resource that bridges the gap between theory and application, showing how to go from computer modeling to practical use. Methods for Petroleum Well Optimization: Automation and Data Solutions gives today’s engineers and researchers real-time data solutions specific to

Artificial Intelligence with Python

Artificial Intelligence with Python
  • Author : Prateek Joshi
  • Publisher : Packt Publishing Ltd
  • Release : 27 January 2017
GET THIS BOOKArtificial Intelligence with Python

Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence

Transactional Machine Learning with Data Streams and AutoML

Transactional Machine Learning with Data Streams and AutoML
  • Author : Sebastian Maurice
  • Publisher : Apress
  • Release : 20 May 2021
GET THIS BOOKTransactional Machine Learning with Data Streams and AutoML

Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights). This book will strengthen your knowledge of the inner workings of TML solutions using data streams with auto machine learning integrated with Apache Kafka. Transactional Machine Learning with Data Streams

Applications of Artificial Intelligence Techniques in the Petroleum Industry

Applications of Artificial Intelligence Techniques in the Petroleum Industry
  • Author : Abdolhossein Hemmati Sarapardeh,Aydin Larestani,Nait Amar Menad,Sassan Hajirezaie
  • Publisher : Gulf Professional Publishing
  • Release : 26 August 2020
GET THIS BOOKApplications of Artificial Intelligence Techniques in the Petroleum Industry

Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. The reference begins with fundamentals, covering preprocessing of data, types of intelligent models, and training and optimization algorithms. The book moves on to methodically address artificial intelligence technology and applications by the upstream sector, covering exploration, drilling, reservoir and production engineering. Final sections cover current gaps and future challenges. Teaches how to apply

Practical Machine Learning with Python

Practical Machine Learning with Python
  • Author : Dipanjan Sarkar,Raghav Bali,Tushar Sharma
  • Publisher : Apress
  • Release : 20 December 2017
GET THIS BOOKPractical Machine Learning with Python

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python

Hydraulic Fracturing in Unconventional Reservoirs

Hydraulic Fracturing in Unconventional Reservoirs
  • Author : Hoss Belyadi,Ebrahim Fathi,Fatemeh Belyadi
  • Publisher : Gulf Professional Publishing
  • Release : 18 June 2019
GET THIS BOOKHydraulic Fracturing in Unconventional Reservoirs

Hydraulic Fracturing in Unconventional Reservoirs: Theories, Operations, and Economic Analysis, Second Edition, presents the latest operations and applications in all facets of fracturing. Enhanced to include today’s newest technologies, such as machine learning and the monitoring of field performance using pressure and rate transient analysis, this reference gives engineers the full spectrum of information needed to run unconventional field developments. Covering key aspects, including fracture clean-up, expanded material on refracturing, and a discussion on economic analysis in unconventional reservoirs,

Shale Analytics

Shale Analytics
  • Author : Shahab D. Mohaghegh
  • Publisher : Springer
  • Release : 09 February 2017
GET THIS BOOKShale Analytics

This book describes the application of modern information technology to reservoir modeling and well management in shale. While covering Shale Analytics, it focuses on reservoir modeling and production management of shale plays, since conventional reservoir and production modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the presence of massive multi-cluster, multi-stage hydraulic fractures. Given the fact that the physics of storage and fluid flow

Math for Programmers

Math for Programmers
  • Author : Paul Orland
  • Publisher : Manning Publications
  • Release : 12 January 2021
GET THIS BOOKMath for Programmers

In Math for Programmers you’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you’ll master the key Python libraries used to turn them into real-world software applications. Summary To score a job in data science, machine learning, computer graphics, and cryptography,

Artificial Intelligence and Machine Learning for Business for Non-Engineers

Artificial Intelligence and Machine Learning for Business for Non-Engineers
  • Author : Stephan S. Jones,Frank M. Groom
  • Publisher : CRC Press
  • Release : 18 December 2019
GET THIS BOOKArtificial Intelligence and Machine Learning for Business for Non-Engineers

The next big area within the information and communication technology field is Artificial Intelligence (AI). The industry is moving to automate networks, cloud-based systems (e.g., Salesforce), databases (e.g., Oracle), AWS machine learning (e.g., Amazon Lex), and creating infrastructure that has the ability to adapt in real-time to changes and learn what to anticipate in the future. It is an area of technology that is coming faster and penetrating more areas of business than any other in our

Mastering Machine Learning with Python in Six Steps

Mastering Machine Learning with Python in Six Steps
  • Author : Manohar Swamynathan
  • Publisher : Apress
  • Release : 01 October 2019
GET THIS BOOKMastering Machine Learning with Python in Six Steps

Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages. You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key

Burn Out

Burn Out
  • Author : Dieter Helm
  • Publisher : Yale University Press
  • Release : 01 January 2017
GET THIS BOOKBurn Out

Introduction -- The end of the commodity super-cycle -- Binding carbon constraints -- An electric future -- The US: the lucky country -- The Middle East: more trouble to come -- Russia: blighted by the resource curse -- China: the end of the transition -- Europe: not as bad as it seems -- The gradual end of big oil -- Energy utilities: a broken model -- The new energy markets and the economics of the Internet -- Conclusion