Oil & Gas Journal Data Book PDF Download
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Author:
Publisher:
ISBN:
Category : Gas industry
Languages : en
Pages : 378
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Book Description
Author:
Publisher:
ISBN:
Category : Gas industry
Languages : en
Pages : 378
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Book Description
Author: Patrick Bangert
Publisher: Gulf Professional Publishing
ISBN: 0128209143
Category : Science
Languages : en
Pages : 290
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Book Description
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)
Author:
Publisher:
ISBN:
Category : Gas industry
Languages : en
Pages : 356
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Book Description
Author: A. M. El-Sherik
Publisher: Woodhead Publishing
ISBN: 0081012195
Category : Science
Languages : en
Pages : 928
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Book Description
Trends in Oil and Gas Corrosion Research and Technologies: Production and Transmission delivers the most up-to-date and highly multidisciplinary reference available to identify emerging developments, fundamental mechanisms and the technologies necessary in one unified source. Starting with a brief explanation on corrosion management that also addresses today's most challenging issues for oil and gas production and transmission operations, the book dives into the latest advances in microbiology-influenced corrosion and other corrosion threats, such as stress corrosion cracking and hydrogen damage just to name a few. In addition, it covers testing and monitoring techniques, such as molecular microbiology and online monitoring for surface and subsurface facilities, mitigation tools, including coatings, nano-packaged biocides, modeling and prediction, cathodic protection and new steels and non-metallics. Rounding out with an extensive glossary and list of abbreviations, the book equips upstream and midstream corrosion professionals in the oil and gas industry with the most advanced collection of topics and solutions to responsibly help solve today's oil and gas corrosion challenges. - Covers the latest in corrosion mitigation techniques, such as corrosion inhibitors, biocides, non-metallics, coatings, and modeling and prediction - Solves knowledge gaps with the most current technology and discoveries on specific corrosion mechanisms, highlighting where future research and industry efforts should be concentrated - Achieves practical and balanced understanding with a full spectrum of subjects presented from multiple academic and world-renowned contributors in the industry
Author: Saeid Mokhatab
Publisher: Gulf Professional Publishing
ISBN: 0123869145
Category : Technology & Engineering
Languages : en
Pages : 830
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Book Description
A unique, well-documented, and forward-thinking work, the second edition of Handbook of Natural Gas Transmission and Processing continues to present a thoroughly updated, authoritative, and comprehensive description of all major aspects of natural gas transmission and processing. It provides an ideal platform for engineers, technologists, and operations personnel working in the natural gas industry to get a better understanding of any special requirements for optimal design and operations of natural gas transmission pipelines and processing plants. First book of its kind that covers all aspects of natural gas transmission and processing Provides pivotal updates on the latest technologies, which have not been addressed in-depth in any existing books Offers practical advice for design and operation based on sound engineering principles and established techniques Examines ways to select the best processing route for optimal design of gas-processing plants Contains new discussions on process modeling, control, and optimization in gas processing industry
Author: G.D. Crain (Jr., Pub)
Publisher:
ISBN:
Category : Advertising
Languages : en
Pages : 492
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Book Description
Author:
Publisher:
ISBN:
Category : Advertising
Languages : en
Pages : 456
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Book Description
Author:
Publisher:
ISBN:
Category : Advertising
Languages : en
Pages : 492
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Book Description
Author: Yogendra Narayan Pandey
Publisher: Apress
ISBN: 9781484260937
Category : Computers
Languages : en
Pages : 300
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Book Description
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 implementing the algorithms; this is followed by an overview of supervised and unsupervised machine learning concepts. The authors provide industry examples using open source data sets along with practical explanations of the algorithms, without diving too deep into the theoretical aspects of the algorithms employed. Machine Learning in the Oil and Gas Industry covers problems encompassing diverse industry topics, including geophysics (seismic interpretation), geological modeling, reservoir engineering, and production engineering. Throughout the book, the emphasis is on providing a practical approach with step-by-step explanations and code examples for implementing machine and deep learning algorithms for solving real-life problems in the oil and gas industry. What You Will Learn Understanding the end-to-end industry life cycle and flow of data in the industrial operations of the oil and gas industry Get the basic concepts of computer programming and machine and deep learning required for implementing the algorithms used Study interesting industry problems that are good candidates for being solved by machine and deep learning Discover the practical considerations and challenges for executing machine and deep learning projects in the oil and gas industry Who This Book Is For Professionals in the oil and gas industry who can benefit from a practical understanding of the machine and deep learning approach to solving real-life problems.
Author: Eric Monteiro
Publisher: MIT Press
ISBN: 0262372290
Category : Technology & Engineering
Languages : en
Pages : 229
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Book Description
How is digitalization of the offshore oil industry fundamentally changing how we understand work and ways of knowing? Digitalization sits at the forefront of public and academic conversation today, calling into question how we work and how we know. In Digital Oil, Eric Monteiro uses the Norwegian offshore oil and gas industry as a lens to investigate the effects of digitalization on embodied labor, and in doing so shows how our use of new digital technology transforms work and knowing. For years, roughnecks have performed the dangerous and unwieldy work of extracting the oil that lies three miles below the seabed along the Norwegian Continental Shelf. Today, the Norwegian oil industry is largely digital, operated by sensors and driven by data. Digital representations of physical processes inform work practices and decision-making with remotely operated, unmanned deep-sea facilities. Drawing on two decades of in-depth interviews, observations, news clips, and studies of this industry, Eric Monteiro dismantles the divide between the virtual and the physical in Digital Oil. What is gained or lost when objects and processes become algorithmic phenomena with the digital inferred from the physical? How can data-driven work practices and operational decision-making approximate qualitative interpretation, professional judgement, and evaluation? How are emergent digital platforms and infrastructures, as machineries of knowing, enabling digitalization? In answering these questions Monteiro offers a novel analysis of digitalization as an effort to press the limits of quantification of the qualitative.
