Application Of Big Data In Petroleum Streams

Application Of Big Data In Petroleum Streams Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Application Of Big Data In Petroleum Streams book. This book definitely worth reading, it is an incredibly well-written.

Application of Big Data in Petroleum Streams

Jay Gohil,Manan Shah
Application of Big Data in Petroleum Streams Book PDF

Author : Jay Gohil,Manan Shah
Publisher : CRC Press
Page : 171 pages
File Size : 55,5 Mb
Release : 2022-05-09
Category : Computers
ISBN : 9781000580020

Get Book

Application of Big Data in Petroleum Streams by Jay Gohil,Manan Shah Pdf

The book aims to provide comprehensive knowledge and information pertaining to application or implementation of big data in the petroleum industry and its operations (such as exploration, production, refining and finance). The book covers intricate aspects of big data such as 6Vs, benefits, applications, implementation, research work and real-world implementation pertaining to each petroleum-associated operation in a concise manner that aids the reader to apprehend the overview of big data’s role in the industry. The book resonates with readers who wish to understand the intricate details of working with big data (along with data science, machine learning and artificial intelligence) in general and how it affects and impacts an entire industry. As the book builds various concepts of big data from scratch to industry level, readers who wish to gain big data-associated knowledge of industry level in simple language from the very fundamentals would find this a wonderful read.

Application of Big Data in Petroleum Streams

Jay Gohil,Manan Shah
Application of Big Data in Petroleum Streams Book PDF

Author : Jay Gohil,Manan Shah
Publisher : CRC Press
Page : 183 pages
File Size : 43,9 Mb
Release : 2022-05-08
Category : Computers
ISBN : 9781000580006

Get Book

Application of Big Data in Petroleum Streams by Jay Gohil,Manan Shah Pdf

- presents in-depth insights regarding fundamentals associated with big data technologies involved in petroleum streams. - builds on earlier works of researchers and inventors, which is essential source material for students in this area of study. - discusses essential processes and methodologies in petroleum streams that will direct researchers to pursue a practical approach to the field. - sheds light on challenges and problems of individual streams and inert-relation issues, while asking the reader to innovate and ideate upon those issues. - Offers an analysis of the financial aspects and business perspective on the processes to help the reader make constructive and practical decision in the field.

Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry

Kingshuk Srivastava,Thipendra P Singh,Manas Ranjan Pradhan,Vinit Kumar Gunjan
Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry Book PDF

Author : Kingshuk Srivastava,Thipendra P Singh,Manas Ranjan Pradhan,Vinit Kumar Gunjan
Publisher : CRC Press
Page : 187 pages
File Size : 55,7 Mb
Release : 2023-11-20
Category : Technology & Engineering
ISBN : 9781000995114

Get Book

Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry by Kingshuk Srivastava,Thipendra P Singh,Manas Ranjan Pradhan,Vinit Kumar Gunjan Pdf

This book covers aspects of data science and predictive analytics used in the oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital wells, value chain integration, crude basket forecasting, and so forth. It further discusses theoretical, methodological, well-established, and validated empirical work dealing with various related topics. Special focus has been given to experimental topics with various case studies. Features: Provides an understanding of the basics of IT technologies applied in the oil and gas sector Includes deep comparison between different artificial intelligence techniques Analyzes different simulators in the oil and gas sector as well as discussion of AI applications Focuses on in-depth experimental and applied topics Details different case studies for upstream and downstream This book is aimed at professionals and graduate students in petroleum engineering, upstream industry, data analytics, and digital transformation process in oil and gas.

Harness Oil and Gas Big Data with Analytics

Keith R. Holdaway
Harness Oil and Gas Big Data with Analytics Book PDF

Author : Keith R. Holdaway
Publisher : John Wiley & Sons
Page : 389 pages
File Size : 44,7 Mb
Release : 2014-05-27
Category : Business & Economics
ISBN : 9781118779316

Get Book

Harness Oil and Gas Big Data with Analytics by Keith R. Holdaway Pdf

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 rejuvenation of oil and gas assets. The book serves as a guide for fully leveraging data, statistical, and quantitative analysis, exploratory and predictive modeling, and fact-based management to drive decision making in oil and gas operations. This comprehensive resource delves into the three major issues that face the oil and gas industry during the exploration and production stages: Data management, including storing massive quantities of data in a manner conducive to analysis and effectively retrieving, backing up, and purging data Quantification of uncertainty, including a look at the statistical and data analytics methods for making predictions and determining the certainty of those predictions Risk assessment, including predictive analysis of the likelihood that known risks are realized and how to properly deal with unknown risks Covering the major issues facing the oil and gas industry in the exploration and production stages, Harness Big Data with Analytics reveals how to model big data to realize efficiencies and business benefits.

Machine Learning and Data Science in the Oil and Gas Industry

Patrick Bangert
Machine Learning and Data Science in the Oil and Gas Industry Book PDF

Author : Patrick Bangert
Publisher : Gulf Professional Publishing
Page : 290 pages
File Size : 44,7 Mb
Release : 2021-03-04
Category : Science
ISBN : 9780128209141

Get Book

Machine Learning and Data Science in the Oil and Gas Industry by Patrick Bangert Pdf

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)

Artificial Intelligence and Data Analytics for Energy Exploration and Production

Fred Aminzadeh,Cenk Temizel,Yasin Hajizadeh
Artificial Intelligence and Data Analytics for Energy Exploration and Production Book PDF

Author : Fred Aminzadeh,Cenk Temizel,Yasin Hajizadeh
Publisher : John Wiley & Sons
Page : 613 pages
File Size : 47,5 Mb
Release : 2022-09-21
Category : Science
ISBN : 9781119879695

Get Book

Artificial Intelligence and Data Analytics for Energy Exploration and Production by Fred Aminzadeh,Cenk Temizel,Yasin Hajizadeh Pdf

ARTIFICAL INTELLIGENCE AND DATA ANALYTICS FOR ENERGY EXPLORATION AND PRODUCTION This groundbreaking new book is written by some of the foremost authorities on the application of data science and artificial intelligence techniques in exploration and production in the energy industry, covering the most comprehensive and updated new processes, concepts, and practical applications in the field. The book provides an in-depth treatment of the foundations of Artificial Intelligence (AI) Machine Learning, and Data Analytics (DA). It also includes many of AI-DA applications in oil and gas reservoirs exploration, development, and production. The book covers the basic technical details on many tools used in “smart oil fields”. This includes topics such as pattern recognition, neural networks, fuzzy logic, evolutionary computing, expert systems, artificial intelligence machine learning, human-computer interface, natural language processing, data analytics and next-generation visualization. While theoretical details will be kept to the minimum, these topics are introduced from oil and gas applications viewpoints. In this volume, many case histories from the recent applications of intelligent data to a number of different oil and gas problems are highlighted. The applications cover a wide spectrum of practical problems from exploration to drilling and field development to production optimization, artificial lift, and secondary recovery. Also, the authors demonstrate the effectiveness of intelligent data analysis methods in dealing with many oil and gas problems requiring combining machine and human intelligence as well as dealing with linguistic and imprecise data and rules.

Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry

Kingshuk Srivastava,Thipendra P. Singh,Manas Ranjan Pradhan,Vinit Kumar Gunjan
Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry Book PDF

Author : Kingshuk Srivastava,Thipendra P. Singh,Manas Ranjan Pradhan,Vinit Kumar Gunjan
Publisher : Unknown
Page : 0 pages
File Size : 55,5 Mb
Release : 2024
Category : Gas industry
ISBN : 1003357873

Get Book

Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry by Kingshuk Srivastava,Thipendra P. Singh,Manas Ranjan Pradhan,Vinit Kumar Gunjan Pdf

This book covers aspects of data science and predictive analytics used in the oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital wells, value chain integration, crude basket forecasting, and so forth. It further discusses theoretical, methodological, well-established, and validated empirical work dealing with various related topics. Special focus has been given to experimental topics with various case studies. Features: Provides an understanding of the basics of IT technologies applied in the oil and gas sector Includes deep comparison between different artificial intelligence techniques Analyzes different simulators in the oil and gas sector as well as discussion of AI applications Focuses on in-depth experimental and applied topics Details different case studies for upstream and downstream This book is aimed at professionals and graduate students in petroleum engineering, upstream industry, data analytics, and digital transformation process in oil and gas.

Emerging Technologies for Sustainable and Smart Energy

Anirbid Sircar,Gautami Tripathi,Namrata Bist,Kashish Ara Shakil,Mithileysh Sathiyanarayanan
Emerging Technologies for Sustainable and Smart Energy Book PDF

Author : Anirbid Sircar,Gautami Tripathi,Namrata Bist,Kashish Ara Shakil,Mithileysh Sathiyanarayanan
Publisher : CRC Press
Page : 246 pages
File Size : 47,7 Mb
Release : 2022-08-03
Category : Technology & Engineering
ISBN : 9781000623581

Get Book

Emerging Technologies for Sustainable and Smart Energy by Anirbid Sircar,Gautami Tripathi,Namrata Bist,Kashish Ara Shakil,Mithileysh Sathiyanarayanan Pdf

Considering the alarming issue of global climate change and its drastic consequences, there is an urgent need to further develop smart and innovative solutions for the energy sector. The goal of sustainable and smart energy for present and future generations can be achieved by integrating emerging technologies into the existing energy infrastructure. This book focuses on the role and significance of emerging technologies in the energy sector and covers the various technological interventions for both conventional and unconventional energy resources and provides meaningful insights into smart and sustainable energy solutions. The book also discusses future directions for smart and sustainable developments in the energy sector.

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

Keith R. Holdaway,Duncan H. B. Irving
Enhance Oil and Gas Exploration with Data Driven Geophysical and Petrophysical Models Book PDF

Author : Keith R. Holdaway,Duncan H. B. Irving
Publisher : John Wiley & Sons
Page : 368 pages
File Size : 47,9 Mb
Release : 2017-10-09
Category : Business & Economics
ISBN : 9781119215103

Get Book

Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models by Keith R. Holdaway,Duncan H. B. Irving Pdf

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 alternative analytical workflow, and in-depth discussion addresses the many Big Data issues in geophysics and petrophysics. From data collection and context through real-world everyday applications, this book provides an essential resource for anyone involved in oil and gas exploration. Recent and continual advances in machine learning are driving a rapid increase in empirical modeling capabilities. This book shows you how these new tools and methodologies can enhance geophysical and petrophysical data analysis, increasing the value of your exploration data. Apply data-driven modeling concepts in a geophysical and petrophysical context Learn how to get more information out of models and simulations Add value to everyday tasks with the appropriate Big Data application Adjust methodology to suit diverse geophysical and petrophysical contexts Data-driven modeling focuses on analyzing the total data within a system, with the goal of uncovering connections between input and output without definitive knowledge of the system's physical behavior. This multi-faceted approach pushes the boundaries of conventional modeling, and brings diverse fields of study together to apply new information and technology in new and more valuable ways. Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models takes you beyond traditional deterministic interpretation to the future of exploration data analysis.

Data Intensive Computing Applications for Big Data

M. Mittal,V.E. Balas,D.J. Hemanth
Data Intensive Computing Applications for Big Data Book PDF

Author : M. Mittal,V.E. Balas,D.J. Hemanth
Publisher : IOS Press
Page : 618 pages
File Size : 50,7 Mb
Release : 2018-01-31
Category : Computers
ISBN : 9781614998143

Get Book

Data Intensive Computing Applications for Big Data by M. Mittal,V.E. Balas,D.J. Hemanth Pdf

The book ‘Data Intensive Computing Applications for Big Data’ discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing paradigms. It brings together researchers to report their latest results or progress in the development of the above mentioned areas. Since there are few books on this specific subject, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings. The book is intended as a reference work for advanced undergraduates and graduate students, as well as multidisciplinary, interdisciplinary and transdisciplinary research workers and scientists on the subjects of big data and cloud/parallel and distributed computing, and explains didactically many of the core concepts of these approaches for practical applications. It is organized into 24 chapters providing a comprehensive overview of big data analysis using parallel computing and addresses the complete data science workflow in the cloud, as well as dealing with privacy issues and the challenges faced in a data-intensive cloud computing environment. The book explores both fundamental and high-level concepts, and will serve as a manual for those in the industry, while also helping beginners to understand the basic and advanced aspects of big data and cloud computing.

Data Analytics in Reservoir Engineering

Sathish Sankaran,Sebastien Matringe,Mohamed Sidahmed
Data Analytics in Reservoir Engineering Book PDF

Author : Sathish Sankaran,Sebastien Matringe,Mohamed Sidahmed
Publisher : Unknown
Page : 108 pages
File Size : 41,5 Mb
Release : 2020-10-29
Category : Electronic
ISBN : 1613998201

Get Book

Data Analytics in Reservoir Engineering by Sathish Sankaran,Sebastien Matringe,Mohamed Sidahmed Pdf

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

Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry

Manan Shah,Ameya Kshirsagar,Jainam Panchal
Applications of Artificial Intelligence AI and Machine Learning ML in the Petroleum Industry Book PDF

Author : Manan Shah,Ameya Kshirsagar,Jainam Panchal
Publisher : CRC Press
Page : 162 pages
File Size : 41,5 Mb
Release : 2022-09-02
Category : Technology & Engineering
ISBN : 9781000629552

Get Book

Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry by Manan Shah,Ameya Kshirsagar,Jainam Panchal Pdf

Today, raw data on any industry is widely available. With the help of artificial intelligence (AI) and machine learning (ML), this data can be used to gain meaningful insights. In addition, as data is the new raw material for today’s world, AI and ML will be applied in every industrial sector. Industry 4.0 mainly focuses on the automation of things. From that perspective, the oil and gas industry is one of the largest industries in terms of economy and energy. Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry analyzes the use of AI and ML in the oil and gas industry across all three sectors, namely upstream, midstream, and downstream. It covers every aspect of the petroleum industry as related to the application of AI and ML, ranging from exploration, data management, extraction, processing, real-time data analysis, monitoring, cloud-based connectivity system, and conditions analysis, to the final delivery of the product to the end customer, while taking into account the incorporation of the safety measures for a better operation and the efficient and effective execution of operations. This book explores the variety of applications that can be integrated to support the existing petroleum and adjacent sectors to solve industry problems. It will serve as a useful guide for professionals working in the petroleum industry, industrial engineers, AI and ML experts and researchers, as well as students.

Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration

Kang Li,Yusheng Xue,Shumei Cui,Qun Niu,Zhile Yang,Patrick Luk
Advanced Computational Methods in Energy Power Electric Vehicles and Their Integration Book PDF

Author : Kang Li,Yusheng Xue,Shumei Cui,Qun Niu,Zhile Yang,Patrick Luk
Publisher : Springer
Page : 824 pages
File Size : 49,9 Mb
Release : 2017-09-01
Category : Computers
ISBN : 9789811063640

Get Book

Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration by Kang Li,Yusheng Xue,Shumei Cui,Qun Niu,Zhile Yang,Patrick Luk Pdf

The three-volume set CCIS 761, CCIS 762, and CCIS 763 constitutes the thoroughly refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2017, and of the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, held in Nanjing, China, in September 2017. The 208 revised full papers presented were carefully reviewed and selected from over 625 submissions. The papers of this volume are organized in topical sections on: Biomedical Signal Processing; Computational Methods in Organism Modeling; Medical Apparatus and Clinical Applications; Bionics Control Methods, Algorithms and Apparatus; Modeling and Simulation of Life Systems; Data Driven Analysis; Image and Video Processing; Advanced Fuzzy and Neural Network Theory and Algorithms; Advanced Evolutionary Methods and Applications; Advanced Machine Learning Methods and Applications; Intelligent Modeling, Monitoring, and Control of Complex Nonlinear Systems; Advanced Methods for Networked Systems; Control and Analysis of Transportation Systems; Advanced Sliding Mode Control and Applications; Advanced Analysis of New Materials and Devices; Computational Intelligence in Utilization of Clean and Renewable Energy Resources; Intelligent Methods for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Computing and Control in Power Systems; Modeling, Simulation and Control in Smart Grid and Microgrid; Optimization Methods; Computational Methods for Sustainable Environment.

Data Analytics for Drilling Engineering

Qilong Xue
Data Analytics for Drilling Engineering Book PDF

Author : Qilong Xue
Publisher : Springer Nature
Page : 312 pages
File Size : 52,7 Mb
Release : 2019-12-30
Category : Science
ISBN : 9783030340353

Get Book

Data Analytics for Drilling Engineering by Qilong Xue Pdf

This book presents the signal processing and data mining challenges encountered in drilling engineering, and describes the methods used to overcome them. In drilling engineering, many signal processing technologies are required to solve practical problems, such as downhole information transmission, spatial attitude of drillstring, drillstring dynamics, seismic activity while drilling, among others. This title attempts to bridge the gap between the signal processing and data mining and oil and gas drilling engineering communities. There is an urgent need to summarize signal processing and data mining issues in drilling engineering so that practitioners in these fields can understand each other in order to enhance oil and gas drilling functions. In summary, this book shows the importance of signal processing and data mining to researchers and professional drilling engineers and open up a new area of application for signal processing and data mining scientists.

Big Data 2.0 Processing Systems

Sherif Sakr
Big Data 2 0 Processing Systems Book PDF

Author : Sherif Sakr
Publisher : Springer Nature
Page : 145 pages
File Size : 40,6 Mb
Release : 2020-07-09
Category : Computers
ISBN : 9783030441876

Get Book

Big Data 2.0 Processing Systems by Sherif Sakr Pdf

This book provides readers the “big picture” and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data). The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems. After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while the main focus of Chapter 5 is on several systems that have been designed to provide scalable solutions for processing big data streams, and on other sets of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems. Next, Chapter 6 focuses on covering the emerging frameworks and systems in the domain of scalable machine learning and deep learning processing. Lastly, Chapter 7 shares conclusions and an outlook on future research challenges. This new and considerably enlarged second edition not only contains the completely new chapter 6, but also offers a refreshed content for the state-of-the-art in all domains of big data processing over the last years. Overall, the book offers a valuable reference guide for professional, students, and researchers in the domain of big data processing systems. Further, its comprehensive content will hopefully encourage readers to pursue further research on the subject.