Machine Learning Guide for Oil and Gas Using Python

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  • Publisher : Gulf Professional Publishing
  • Release : 09 April 2021
  • ISBN : 9780128219300
  • Page : 476 pages
  • Rating : 4.5/5 from 103 voters

Download Machine Learning Guide for Oil and Gas Using Python in PDF, Epub and Kindle

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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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 Date : 2021-04-09
  • ISBN : 9780128219300
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 Date : 2020-11-03
  • ISBN : 1484260937
GET THIS BOOKMachine Learning in the Oil and Gas Industry

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 Date : 2021-03-04
  • ISBN : 9780128209141
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 Date : 2021-09-22
  • ISBN : 9780323902328
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

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 Date : 2020-08-26
  • ISBN : 9780128223857
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

Applications of Machine Learning

Applications of Machine Learning
  • Author : Prashant Johri,Jitendra Kumar Verma,Sudip Paul
  • Publisher : Springer Nature
  • Release Date : 2020-05-04
  • ISBN : 9789811533570
GET THIS BOOKApplications of Machine Learning

This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics.

Hydraulic Fracturing in Unconventional Reservoirs

Hydraulic Fracturing in Unconventional Reservoirs
  • Author : Hoss Belyadi,Ebrahim Fathi,Fatemeh Belyadi
  • Publisher : Gulf Professional Publishing
  • Release Date : 2019-06-18
  • ISBN : 9780128176665
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,

Machine Learning for Subsurface Characterization

Machine Learning for Subsurface Characterization
  • Author : Siddharth Misra,Hao Li,Jiabo He
  • Publisher : Gulf Professional Publishing
  • Release Date : 2019-10-12
  • ISBN : 9780128177372
GET THIS BOOKMachine Learning for Subsurface Characterization

Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods

Practical Machine Learning with Python

Practical Machine Learning with Python
  • Author : Dipanjan Sarkar,Raghav Bali,Tushar Sharma
  • Publisher : Apress
  • Release Date : 2017-12-20
  • ISBN : 9781484232071
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

Shale Analytics

Shale Analytics
  • Author : Shahab D. Mohaghegh
  • Publisher : Springer
  • Release Date : 2017-02-09
  • ISBN : 9783319487533
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

Mastering Machine Learning with Python in Six Steps

Mastering Machine Learning with Python in Six Steps
  • Author : Manohar Swamynathan
  • Publisher : Apress
  • Release Date : 2019-10-01
  • ISBN : 9781484249475
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

Transactional Machine Learning with Data Streams and AutoML

Transactional Machine Learning with Data Streams and AutoML
  • Author : Sebastian Maurice
  • Publisher : Apress
  • Release Date : 2021-05-20
  • ISBN : 1484270223
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

Data Analytics in Reservoir Engineering

Data Analytics in Reservoir Engineering
  • Author : Sathish Sankaran,Sebastien Matringe,Mohamed Sidahmed
  • Publisher : Unknown
  • Release Date : 2020-10-29
  • ISBN : 1613998201
GET THIS BOOKData Analytics in Reservoir Engineering

Data Analytics in Reservoir Engineering describes the relevance of data analytics for the oil and gas industry, with particular emphasis on reservoir engineering.

Math for Programmers

Math for Programmers
  • Author : Paul Orland
  • Publisher : Manning Publications
  • Release Date : 2021-01-12
  • ISBN : 9781617295355
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,

Machine Learning and Deep Learning Using Python and TensorFlow

Machine Learning and Deep Learning Using Python and TensorFlow
  • Author : Venkata Reddy Konasani,Shailendra Kadre
  • Publisher : McGraw Hill Professional
  • Release Date : 2021-04-29
  • ISBN : 9781260462302
GET THIS BOOKMachine Learning and Deep Learning Using Python and TensorFlow

Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in