Machine Learning and Data Science in the Oil and Gas Industry

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  • Publisher : Gulf Professional Publishing
  • Release : 04 March 2021
  • ISBN : 9780128209141
  • Page : 306 pages
  • Rating : 4.5/5 from 103 voters

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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 the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful Gain practical understanding of machine learning used in oil and gas operations through contributed case studies Learn change management skills that will help gain confidence in pursuing the technology Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

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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

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 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

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

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

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.

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

Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models

Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models
  • Author : Keith R. Holdaway,Duncan H. B. Irving
  • Publisher : John Wiley & Sons
  • Release Date : 2017-10-09
  • ISBN : 9781119215103
GET THIS BOOKEnhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models

Leverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this

Harness Oil and Gas Big Data with Analytics

Harness Oil and Gas Big Data with Analytics
  • Author : Keith R. Holdaway
  • Publisher : John Wiley & Sons
  • Release Date : 2014-05-27
  • ISBN : 9781118779316
GET THIS BOOKHarness Oil and Gas Big Data with Analytics

Use big data analytics to efficiently drive oil and gas exploration and production Harness Oil and Gas Big Data with Analytics provides a complete view of big data and analytics techniques as they are applied to the oil and gas industry. Including a compendium of specific case studies, the book underscores the acute need for optimization in the oil and gas exploration and production stages and shows how data analytics can provide such optimization. This spans exploration, development, production and

Bits, Bytes, and Barrels

Bits, Bytes, and Barrels
  • Author : Geoffrey Cann,Rachael Goydan
  • Publisher : Madcann Press
  • Release Date : 2019-01-08
  • ISBN : 1999514904
GET THIS BOOKBits, Bytes, and Barrels

The oil and gas industry is at a crossroads. Recent low prices, rapidly growing alternative fuels like renewables, the permanent swing from peak oil to super abundance, shifting consumer preferences, and global pressures to decarbonize suggest a challenged industry for the foreseeable future. Digital advances offer ways to lower costs of production, improve productivity, reduce carbon emissions, and regain public confidence. A wait-and-see attitude to digital innovation has failed many industries already, and the leaders of oil and gas urgently

Data-Driven Analytics for the Geological Storage of CO2

Data-Driven Analytics for the Geological Storage of CO2
  • Author : Shahab Mohaghegh
  • Publisher : CRC Press
  • Release Date : 2018-05-20
  • ISBN : 9781315280790
GET THIS BOOKData-Driven Analytics for the Geological Storage of CO2

Data driven analytics is enjoying unprecedented popularity among oil and gas professionals. Many reservoir engineering problems associated with geological storage of CO2 require the development of numerical reservoir simulation models. This book is the first to examine the contribution of Artificial Intelligence and Machine Learning in data driven analytics of fluid flow in porous environments, including saline aquifers and depleted gas and oil reservoirs. Drawing from actual case studies, this book demonstrates how smart proxy models can be developed for

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

Intelligent Digital Oil and Gas Fields

Intelligent Digital Oil and Gas Fields
  • Author : Gustavo Carvajal,Marko Maucec,Stan Cullick
  • Publisher : Gulf Professional Publishing
  • Release Date : 2017-12-14
  • ISBN : 9780128047477
GET THIS BOOKIntelligent Digital Oil and Gas Fields

Intelligent Digital Oil and Gas Fields: Concepts, Collaboration, and Right-time Decisions delivers to the reader a roadmap through the fast-paced changes in the digital oil field landscape of technology in the form of new sensors, well mechanics such as downhole valves, data analytics and models for dealing with a barrage of data, and changes in the way professionals collaborate on decisions. The book introduces the new age of digital oil and gas technology and process components and provides a backdrop

Advances in Subsurface Data Analytics

Advances in Subsurface Data Analytics
  • Author : Shuvajit Bhattacharya,Haibin Di
  • Publisher : Elsevier
  • Release Date : 2022-05-27
  • ISBN : 9780128223086
GET THIS BOOKAdvances in Subsurface Data Analytics

Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also

Applied Statistical Modeling and Data Analytics

Applied Statistical Modeling and Data Analytics
  • Author : Srikanta Mishra,Akhil Datta-Gupta
  • Publisher : Elsevier
  • Release Date : 2017-10-27
  • ISBN : 9780128032800
GET THIS BOOKApplied Statistical Modeling and Data Analytics

Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences provides a practical guide to many of the classical and modern statistical techniques that have become established for oil and gas professionals in recent years. It serves as a "how to" reference volume for the practicing petroleum engineer or geoscientist interested in applying statistical methods in formation evaluation, reservoir characterization, reservoir modeling and management, and uncertainty quantification. Beginning with a foundational discussion of exploratory data analysis, probability