# Machine Learning and Data Science in the Oil and Gas Industry

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## Machine Learning and Data Science in the Oil and Gas Industry

Author | : Patrick Bangert |

Publsiher | : Gulf Professional Publishing |

Total Pages | : 306 |

Release | : 2021-03-04 |

ISBN 10 | : 0128209143 |

ISBN 13 | : 9780128209141 |

Language | : EN, FR, DE, ES & NL |

**Machine Learning and Data Science in the Oil and Gas Industry Book Review:**

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)

## Machine Learning Guide for Oil and Gas Using Python

Author | : Hoss Belyadi,Alireza Haghighat |

Publsiher | : Gulf Professional Publishing |

Total Pages | : 476 |

Release | : 2021-04-09 |

ISBN 10 | : 0128219300 |

ISBN 13 | : 9780128219300 |

Language | : EN, FR, DE, ES & NL |

**Machine Learning Guide for Oil and Gas Using Python Book Review:**

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

## Machine Learning in the Oil and Gas Industry

Author | : Yogendra Narayan Pandey,Ayush Rastogi,Sribharath Kainkaryam,Srimoyee Bhattacharya,Luigi Saputelli |

Publsiher | : Apress |

Total Pages | : 300 |

Release | : 2020-11-03 |

ISBN 10 | : 9781484260937 |

ISBN 13 | : 1484260937 |

Language | : EN, FR, DE, ES & NL |

**Machine Learning in the Oil and Gas Industry Book Review:**

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.

## Machine Learning Guide for Oil and Gas Using Python

Author | : Hoss Belyadi,Alireza Haghighat |

Publsiher | : Elsevier |

Total Pages | : 476 |

Release | : 2021-04-27 |

ISBN 10 | : 0128219297 |

ISBN 13 | : 9780128219294 |

Language | : EN, FR, DE, ES & NL |

**Machine Learning Guide for Oil and Gas Using Python Book Review:**

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

## Applications of Artificial Intelligence Techniques in the Petroleum Industry

Author | : Abdolhossein Hemmati Sarapardeh,Aydin Larestani,Nait Amar Menad,Sassan Hajirezaie |

Publsiher | : Gulf Professional Publishing |

Total Pages | : 322 |

Release | : 2020-08-26 |

ISBN 10 | : 0128223855 |

ISBN 13 | : 9780128223857 |

Language | : EN, FR, DE, ES & NL |

**Applications of Artificial Intelligence Techniques in the Petroleum Industry Book Review:**

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 machine learning algorithms that work best in exploration, drilling, reservoir or production engineering Helps readers increase their existing knowledge on intelligent data modeling, machine learning and artificial intelligence, with foundational chapters covering the preprocessing of data and training on algorithms Provides tactics on how to cover complex projects such as shale gas, tight oils, and other types of unconventional reservoirs with more advanced model input

## Machine Learning and Data Science in the Power Generation Industry

Author | : Patrick Bangert |

Publsiher | : Elsevier |

Total Pages | : 274 |

Release | : 2021-01-14 |

ISBN 10 | : 0128226005 |

ISBN 13 | : 9780128226001 |

Language | : EN, FR, DE, ES & NL |

**Machine Learning and Data Science in the Power Generation Industry Book Review:**

Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls

## Harness Oil and Gas Big Data with Analytics

Author | : Keith R. Holdaway |

Publsiher | : John Wiley & Sons |

Total Pages | : 384 |

Release | : 2014-05-05 |

ISBN 10 | : 1118910893 |

ISBN 13 | : 9781118910894 |

Language | : EN, FR, DE, ES & NL |

**Harness Oil and Gas Big Data with Analytics Book Review:**

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.

## Shale Analytics

Author | : Shahab D. Mohaghegh |

Publsiher | : Springer |

Total Pages | : 287 |

Release | : 2017-02-09 |

ISBN 10 | : 3319487531 |

ISBN 13 | : 9783319487533 |

Language | : EN, FR, DE, ES & NL |

**Shale Analytics Book Review:**

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 in shale are not well-understood and well-defined, Shale Analytics avoids making simplifying assumptions and concentrates on facts (Hard Data - Field Measurements) to reach conclusions. Also discussed are important insights into understanding completion practices and re-frac candidate selection and design. The flexibility and power of the technique is demonstrated in numerous real-world situations.

## Bits Bytes and Barrels

Author | : Geoffrey Cann,Rachael Goydan |

Publsiher | : Madcann Press |

Total Pages | : 290 |

Release | : 2019-01-08 |

ISBN 10 | : 9781999514907 |

ISBN 13 | : 1999514904 |

Language | : EN, FR, DE, ES & NL |

**Bits Bytes and Barrels Book Review:**

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 need guidance on how digital both disrupts and enhances their industry. Written by the world's leading experts on the intersection of digital technologies and the oil and gas industry, Bits, Bytes, and Barrels sets out the reasons why adoption is slow, describes the size and scale of both the opportunity and the threat from digital, identifies the key digital technologies and the role that they play in a digital future, and recommends a set of actions for leaders to take to accelerate the adoption of digital in the business. Providing an independent and expert perspective, Bits, Bytes, and Barrels addresses the impacts of digital across the breadth of the industry--from onshore to offshore, from upstream to midstream to integrated--and outlines a roadmap to help the decision-makers at all levels of the industry take meaningful action toward promising and rewarding digital adoption.

## Artificial Intelligence Machine Learning and Data Science Technologies

Author | : Neeraj Mohan,Ruchi Singla,Priyanka Kaushal,Seifedine Kadry |

Publsiher | : CRC Press |

Total Pages | : 310 |

Release | : 2021-10-12 |

ISBN 10 | : 1000460541 |

ISBN 13 | : 9781000460544 |

Language | : EN, FR, DE, ES & NL |

**Artificial Intelligence Machine Learning and Data Science Technologies Book Review:**

This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact. The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science.

## Data Analytics in Reservoir Engineering

Author | : Sathish Sankaran,Sebastien Matringe,Mohamed Sidahmed |

Publsiher | : Unknown |

Total Pages | : 108 |

Release | : 2020-10-29 |

ISBN 10 | : 9781613998205 |

ISBN 13 | : 1613998201 |

Language | : EN, FR, DE, ES & NL |

**Data Analytics in Reservoir Engineering Book Review:**

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

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

Author | : Keith R. Holdaway,Duncan H. B. Irving |

Publsiher | : John Wiley & Sons |

Total Pages | : 368 |

Release | : 2017-10-04 |

ISBN 10 | : 1119302587 |

ISBN 13 | : 9781119302582 |

Language | : EN, FR, DE, ES & NL |

**Enhance Oil and Gas Exploration with Data Driven Geophysical and Petrophysical Models Book Review:**

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 Analytics Applied to the Mining Industry

Author | : Ali Soofastaei |

Publsiher | : CRC Press |

Total Pages | : 254 |

Release | : 2020-11-13 |

ISBN 10 | : 0429781768 |

ISBN 13 | : 9780429781766 |

Language | : EN, FR, DE, ES & NL |

**Data Analytics Applied to the Mining Industry Book Review:**

Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes critical opportunity areas for mining optimization Brings experience and learning in digital transformation from adjacent sectors

## Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Author | : Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian |

Publsiher | : IGI Global |

Total Pages | : 355 |

Release | : 2019-11-29 |

ISBN 10 | : 1799811948 |

ISBN 13 | : 9781799811947 |

Language | : EN, FR, DE, ES & NL |

**Deep Learning Techniques and Optimization Strategies in Big Data Analytics Book Review:**

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

## Data Driven Analytics for the Geological Storage of CO2

Author | : Shahab Mohaghegh |

Publsiher | : CRC Press |

Total Pages | : 282 |

Release | : 2018-05-20 |

ISBN 10 | : 1315280809 |

ISBN 13 | : 9781315280806 |

Language | : EN, FR, DE, ES & NL |

**Data Driven Analytics for the Geological Storage of CO2 Book Review:**

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 complex numerical reservoir simulation models. Smart proxy incorporates pattern recognition capabilities of artificial intelligence and machine learning to build smart models that learn the intricacies of physical, mechanical and chemical interactions using precise numerical simulations. This ground breaking technology makes it possible and practical to use high fidelity, complex numerical reservoir simulation models in the design, analysis and optimization of carbon storage in geological formations projects.

## Math for Programmers

Author | : Paul Orland |

Publsiher | : Manning Publications |

Total Pages | : 688 |

Release | : 2021-01-12 |

ISBN 10 | : 1617295353 |

ISBN 13 | : 9781617295355 |

Language | : EN, FR, DE, ES & NL |

**Math for Programmers Book Review:**

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, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest programming fields. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Skip the mathematical jargon: This one-of-a-kind book uses Python to teach the math you need to build games, simulations, 3D graphics, and machine learning algorithms. Discover how algebra and calculus come alive when you see them in code! About the book 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. What's inside Vector geometry for computer graphics Matrices and linear transformations Core concepts from calculus Simulation and optimization Image and audio processing Machine learning algorithms for regression and classification About the reader For programmers with basic skills in algebra. About the author Paul Orland is a programmer, software entrepreneur, and math enthusiast. He is co-founder of Tachyus, a start-up building predictive analytics software for the energy industry. You can find him online at www.paulor.land. Table of Contents 1 Learning math with code PART I - VECTORS AND GRAPHICS 2 Drawing with 2D vectors 3 Ascending to the 3D world 4 Transforming vectors and graphics 5 Computing transformations with matrices 6 Generalizing to higher dimensions 7 Solving systems of linear equations PART 2 - CALCULUS AND PHYSICAL SIMULATION 8 Understanding rates of change 9 Simulating moving objects 10 Working with symbolic expressions 11 Simulating force fields 12 Optimizing a physical system 13 Analyzing sound waves with a Fourier series PART 3 - MACHINE LEARNING APPLICATIONS 14 Fitting functions to data 15 Classifying data with logistic regression 16 Training neural networks

## Industry 4 0 AI and Data Science

Author | : Vikram Bali,Kakoli Banerjee,Narendra Kumar,Sanjay Gour,Sunil Kumar Chawla |

Publsiher | : CRC Press |

Total Pages | : 282 |

Release | : 2021-07-21 |

ISBN 10 | : 1000413454 |

ISBN 13 | : 9781000413458 |

Language | : EN, FR, DE, ES & NL |

**Industry 4 0 AI and Data Science Book Review:**

The aim of this book is to provide insight into Data Science and Artificial Learning Techniques based on Industry 4.0, conveys how Machine Learning & Data Science are becoming an essential part of industrial and academic research. Varying from healthcare to social networking and everywhere hybrid models for Data Science, Al, and Machine Learning are being used. The book describes different theoretical and practical aspects and highlights how new systems are being developed. Along with focusing on the research trends, challenges and future of AI in Data Science, the book explores the potential for integration of advanced AI algorithms, addresses the challenges of Data Science for Industry 4.0, covers different security issues, includes qualitative and quantitative research, and offers case studies with working models. This book also provides an overview of AI and Data Science algorithms for readers who do not have a strong mathematical background. Undergraduates, postgraduates, academicians, researchers, and industry professionals will benefit from this book and use it as a guide.

## Applied Statistical Modeling and Data Analytics

Author | : Srikanta Mishra,Akhil Datta-Gupta |

Publsiher | : Elsevier |

Total Pages | : 250 |

Release | : 2017-10-27 |

ISBN 10 | : 0128032804 |

ISBN 13 | : 9780128032800 |

Language | : EN, FR, DE, ES & NL |

**Applied Statistical Modeling and Data Analytics Book Review:**

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 distributions and linear regression modeling, the book focuses on fundamentals and practical examples of such key topics as multivariate analysis, uncertainty quantification, data-driven modeling, and experimental design and response surface analysis. Data sets from the petroleum geosciences are extensively used to demonstrate the applicability of these techniques. The book will also be useful for professionals dealing with subsurface flow problems in hydrogeology, geologic carbon sequestration, and nuclear waste disposal. Authored by internationally renowned experts in developing and applying statistical methods for oil & gas and other subsurface problem domains Written by practitioners for practitioners Presents an easy to follow narrative which progresses from simple concepts to more challenging ones Includes online resources with software applications and practical examples for the most relevant and popular statistical methods, using data sets from the petroleum geosciences Addresses the theory and practice of statistical modeling and data analytics from the perspective of petroleum geoscience applications

## Methods for Petroleum Well Optimization

Author | : Rasool Khosravanian,Bernt S. Aadnoy |

Publsiher | : Gulf Professional Publishing |

Total Pages | : 552 |

Release | : 2021-09-22 |

ISBN 10 | : 0323902324 |

ISBN 13 | : 9780323902328 |

Language | : EN, FR, DE, ES & NL |

**Methods for Petroleum Well Optimization Book Review:**

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 drilling and production assets. Structured for training, this reference covers key concepts and detailed approaches from mathematical to real-time data solutions through technological advances. Topics include digital well planning and construction, moving teams into Onshore Collaboration Centers, operations with the best machine learning (ML) and metaheuristic algorithms, complex trajectories for wellbore stability, real-time predictive analytics by data mining, optimum decision-making, and case-based reasoning. Supported by practical case studies, and with references including links to open-source code and fit-for-use MATLAB, R, Julia, Python and other standard programming languages, Methods for Petroleum Well Optimization delivers a critical training guide for researchers and oil and gas engineers to take scientifically based approaches to solving real field problems. Bridges the gap between theory and practice (from models to code) with content from the latest research developments supported by practical case study examples and questions at the end of each chapter Enables understanding of real-time data solutions and automation methods available specific to drilling and production wells, such as digital well planning and construction through to automatic systems Promotes the use of open-source code which will help companies, engineers, and researchers develop their prediction and analysis software more quickly; this is especially appropriate in the application of multivariate techniques to the real-world problems of petroleum well optimization

## Internet of Things and Data Analytics Handbook

Author | : Hwaiyu Geng |

Publsiher | : John Wiley & Sons |

Total Pages | : 800 |

Release | : 2017-01-10 |

ISBN 10 | : 1119173647 |

ISBN 13 | : 9781119173649 |

Language | : EN, FR, DE, ES & NL |

**Internet of Things and Data Analytics Handbook Book Review:**

This book examines the Internet of Things (IoT) and Data Analytics from a technical, application, and business point of view. Internet of Things and Data Analytics Handbook describes essential technical knowledge, building blocks, processes, design principles, implementation, and marketing for IoT projects. It provides readers with knowledge in planning, designing, and implementing IoT projects. The book is written by experts on the subject matter, including international experts from nine countries in the consumer and enterprise fields of IoT. The text starts with an overview and anatomy of IoT, ecosystem of IoT, communication protocols, networking, and available hardware, both present and future applications and transformations, and business models. The text also addresses big data analytics, machine learning, cloud computing, and consideration of sustainability that are essential to be both socially responsible and successful. Design and implementation processes are illustrated with best practices and case studies in action. In addition, the book: Examines cloud computing, data analytics, and sustainability and how they relate to IoT overs the scope of consumer, government, and enterprise applications Includes best practices, business model, and real-world case studies Hwaiyu Geng, P.E., is a consultant with Amica Research (www.AmicaResearch.org, Palo Alto, California), promoting green planning, design, and construction projects. He has had over 40 years of manufacturing and management experience, working with Westinghouse, Applied Materials, Hewlett Packard, and Intel on multi-million high-tech projects. He has written and presented numerous technical papers at international conferences. Mr. Geng, a patent holder, is also the editor/author of Data Center Handbook (Wiley, 2015).