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
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

Artificial Intelligence in the Petroleum Industry

Artificial Intelligence in the Petroleum Industry
Author: Bertrand Braunschweig
Publsiher: Editions TECHNIP
Total Pages: 381
Release: 1996
ISBN 10: 9782710807032
ISBN 13: 2710807033
Language: EN, FR, DE, ES & NL

Artificial Intelligence in the Petroleum Industry Book Review:

Artificial Intelligent Approaches in Petroleum Geosciences

Artificial Intelligent Approaches in Petroleum Geosciences
Author: Constantin Cranganu,Henri Luchian,Mihaela Elena Breaban
Publsiher: Springer
Total Pages: 290
Release: 2015-04-20
ISBN 10: 3319165313
ISBN 13: 9783319165318
Language: EN, FR, DE, ES & NL

Artificial Intelligent Approaches in Petroleum Geosciences Book Review:

This book presents several intelligent approaches for tackling and solving challenging practical problems facing those in the petroleum geosciences and petroleum industry. Written by experienced academics, this book offers state-of-the-art working examples and provides the reader with exposure to the latest developments in the field of intelligent methods applied to oil and gas research, exploration and production. It also analyzes the strengths and weaknesses of each method presented using benchmarking, whilst also emphasizing essential parameters such as robustness, accuracy, speed of convergence, computer time, overlearning and the role of normalization. The intelligent approaches presented include artificial neural networks, fuzzy logic, active learning method, genetic algorithms and support vector machines, amongst others. Integration, handling data of immense size and uncertainty, and dealing with risk management are among crucial issues in petroleum geosciences. The problems we have to solve in this domain are becoming too complex to rely on a single discipline for effective solutions and the costs associated with poor predictions (e.g. dry holes) increase. Therefore, there is a need to establish a new approach aimed at proper integration of disciplines (such as petroleum engineering, geology, geophysics and geochemistry), data fusion, risk reduction and uncertainty management. These intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and mining, data analysis and interpretation, and knowledge discovery, from diverse data such as 3-D seismic, geological data, well logging, and production data. This book is intended for petroleum scientists, data miners, data scientists and professionals and post-graduate students involved in petroleum industry.

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
Publsiher: Gulf Professional Publishing
Total Pages: 300
Release: 2021-03-15
ISBN 10: 9780128207147
ISBN 13: 0128207140
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 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
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.

Intelligent Digital Oil and Gas Fields

Intelligent Digital Oil and Gas Fields
Author: Gustavo Carvajal,Marko Maucec,Stan Cullick
Publsiher: Gulf Professional Publishing
Total Pages: 374
Release: 2017-12-14
ISBN 10: 012804747X
ISBN 13: 9780128047477
Language: EN, FR, DE, ES & NL

Intelligent Digital Oil and Gas Fields Book Review:

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 to the value and experience industry has achieved from these in the last few years. The book then takes the reader on a journey first at a well level through instrumentation and measurement for real-time data acquisition, and then provides practical information on analytics on the real-time data. Artificial intelligence techniques provide insights from the data. The road then travels to the "integrated asset" by detailing how companies utilize Integrated Asset Models to manage assets (reservoirs) within DOF context. From model to practice, new ways to operate smart wells enable optimizing the asset. Intelligent Digital Oil and Gas Fields is packed with examples and lessons learned from various case studies and provides extensive references for further reading and a final chapter on the "next generation digital oil field," e.g., cloud computing, big data analytics and advances in nanotechnology. This book is a reference that can help managers, engineers, operations, and IT experts understand specifics on how to filter data to create useful information, address analytics, and link workflows across the production value chain enabling teams to make better decisions with a higher degree of certainty and reduced risk. Covers multiple examples and lessons learned from a variety of reservoirs from around the world and production situations Includes techniques on change management and collaboration Delivers real and readily applicable knowledge on technical equipment, workflows and data challenges such as acquisition and quality control that make up the digital oil and gas field solutions of today Describes collaborative systems and ways of working and how companies are transitioning work force to use the technology and making more optimal decisions

Applications of Artificial Intelligence Techniques in Industry 4 0

Applications of Artificial Intelligence Techniques in Industry 4 0
Author: Aydin Azizi
Publsiher: Springer
Total Pages: 61
Release: 2018-09-25
ISBN 10: 9811326401
ISBN 13: 9789811326400
Language: EN, FR, DE, ES & NL

Applications of Artificial Intelligence Techniques in Industry 4 0 Book Review:

This book is to presents and evaluates a way of modelling and optimizing nonlinear RFID Network Planning (RNP) problems using artificial intelligence techniques. It uses Artificial Neural Network models (ANN) to bind together the computational artificial intelligence algorithm with knowledge representation an efficient artificial intelligence paradigm to model and optimize RFID networks. This effort leads to proposing a novel artificial intelligence algorithm which has been named hybrid artificial intelligence optimization technique to perform optimization of RNP as a hard learning problem. This hybrid optimization technique consists of two different optimization phases. First phase is optimizing RNP by Redundant Antenna Elimination (RAE) algorithm and the second phase which completes RNP optimization process is Ring Probabilistic Logic Neural Networks (RPLNN). The hybrid paradigm is explored using a flexible manufacturing system (FMS) and the results are compared with well-known evolutionary optimization technique namely Genetic Algorithm (GA) to demonstrate the feasibility of the proposed architecture successfully.

Artificial Intelligence

Artificial Intelligence
Author: Marco Antonio Aceves-Fernandez
Publsiher: BoD – Books on Demand
Total Pages: 464
Release: 2018-06-27
ISBN 10: 178923364X
ISBN 13: 9781789233643
Language: EN, FR, DE, ES & NL

Artificial Intelligence Book Review:

Artificial intelligence (AI) is taking an increasingly important role in our society. From cars, smartphones, airplanes, consumer applications, and even medical equipment, the impact of AI is changing the world around us. The ability of machines to demonstrate advanced cognitive skills in taking decisions, learn and perceive the environment, predict certain behavior, and process written or spoken languages, among other skills, makes this discipline of paramount importance in today's world. Although AI is changing the world for the better in many applications, it also comes with its challenges. This book encompasses many applications as well as new techniques, challenges, and opportunities in this fascinating area.

Artificial Intelligence in Energy and Renewable Energy Systems

Artificial Intelligence in Energy and Renewable Energy Systems
Author: Soteris Kalogirou
Publsiher: Nova Publishers
Total Pages: 471
Release: 2007
ISBN 10: 9781600212611
ISBN 13: 1600212611
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Energy and Renewable Energy Systems Book Review:

This book presents state of the art applications of artificial intelligence in energy and renewable energy systems design and modelling. It covers such topics as solar energy, wind energy, biomass and hydrogen as well as building services systems, power generation systems, combustion processes and refrigeration. In all these areas applications of artificial intelligence methods such as artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above, called hybrid systems, are included. The book is intended for a wide audience ranging from the undergraduate level up to the research academic and industrial communities dealing with modelling and performance prediction of energy and renewable energy systems.

Gas Turbines Modeling Simulation and Control

Gas Turbines Modeling  Simulation  and Control
Author: Hamid Asgari,XiaoQi Chen
Publsiher: CRC Press
Total Pages: 176
Release: 2016-02-12
ISBN 10: 1498777546
ISBN 13: 9781498777544
Language: EN, FR, DE, ES & NL

Gas Turbines Modeling Simulation and Control Book Review:

Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks provides new approaches and novel solutions to the modeling, simulation, and control of gas turbines (GTs) using artificial neural networks (ANNs). After delivering a brief introduction to GT performance and classification, the book: Outlines important criteria to consider at the beginning of the GT modeling process, such as GT types and configurations, control system types and configurations, and modeling methods and objectives Highlights research in the fields of white-box and black-box modeling, simulation, and control of GTs, exploring models of low-power GTs, industrial power plant gas turbines (IPGTs), and aero GTs Discusses the structure of ANNs and the ANN-based model-building process, including system analysis, data acquisition and preparation, network architecture, and network training and validation Presents a noteworthy ANN-based methodology for offline system identification of GTs, complete with validated models using both simulated and real operational data Covers the modeling of GT transient behavior and start-up operation, and the design of proportional-integral-derivative (PID) and neural network-based controllers Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks not only offers a comprehensive review of the state of the art of gas turbine modeling and intelligent techniques, but also demonstrates how artificial intelligence can be used to solve complicated industrial problems, specifically in the area of GTs.

The Economics of Artificial Intelligence

The Economics of Artificial Intelligence
Author: Ajay Agrawal,Joshua Gans,Avi Goldfarb
Publsiher: National Bureau of Economic Re
Total Pages: 648
Release: 2019
ISBN 10: 022661333X
ISBN 13: 9780226613338
Language: EN, FR, DE, ES & NL

The Economics of Artificial Intelligence Book Review:

Advances in artificial intelligence (AI) highlight the potential of this technology to affect productivity, growth, inequality, market power, innovation, and employment. This volume seeks to set the agenda for economic research on the impact of AI. It covers four broad themes: AI as a general purpose technology; the relationships between AI, growth, jobs, and inequality; regulatory responses to changes brought on by AI; and the effects of AI on the way economic research is conducted. It explores the economic influence of machine learning, the branch of computational statistics that has driven much of the recent excitement around AI, as well as the economic impact of robotics and automation and the potential economic consequences of a still-hypothetical artificial general intelligence. The volume provides frameworks for understanding the economic impact of AI and identifies a number of open research questions. Contributors: Daron Acemoglu, Massachusetts Institute of Technology Philippe Aghion, Collège de France Ajay Agrawal, University of Toronto Susan Athey, Stanford University James Bessen, Boston University School of Law Erik Brynjolfsson, MIT Sloan School of Management Colin F. Camerer, California Institute of Technology Judith Chevalier, Yale School of Management Iain M. Cockburn, Boston University Tyler Cowen, George Mason University Jason Furman, Harvard Kennedy School Patrick Francois, University of British Columbia Alberto Galasso, University of Toronto Joshua Gans, University of Toronto Avi Goldfarb, University of Toronto Austan Goolsbee, University of Chicago Booth School of Business Rebecca Henderson, Harvard Business School Ginger Zhe Jin, University of Maryland Benjamin F. Jones, Northwestern University Charles I. Jones, Stanford University Daniel Kahneman, Princeton University Anton Korinek, Johns Hopkins University Mara Lederman, University of Toronto Hong Luo, Harvard Business School John McHale, National University of Ireland Paul R. Milgrom, Stanford University Matthew Mitchell, University of Toronto Alexander Oettl, Georgia Institute of Technology Andrea Prat, Columbia Business School Manav Raj, New York University Pascual Restrepo, Boston University Daniel Rock, MIT Sloan School of Management Jeffrey D. Sachs, Columbia University Robert Seamans, New York University Scott Stern, MIT Sloan School of Management Betsey Stevenson, University of Michigan Joseph E. Stiglitz. Columbia University Chad Syverson, University of Chicago Booth School of Business Matt Taddy, University of Chicago Booth School of Business Steven Tadelis, University of California, Berkeley Manuel Trajtenberg, Tel Aviv University Daniel Trefler, University of Toronto Catherine Tucker, MIT Sloan School of Management Hal Varian, University of California, Berkeley

Geological Prior Information

Geological Prior Information
Author: Andrew Curtis,Rachel Wood
Publsiher: Geological Society of London
Total Pages: 229
Release: 2004
ISBN 10: 9781862391710
ISBN 13: 1862391718
Language: EN, FR, DE, ES & NL

Geological Prior Information Book Review:

Earth Observation Open Science and Innovation

Earth Observation Open Science and Innovation
Author: Pierre-Philippe Mathieu,Christoph Aubrecht
Publsiher: Springer
Total Pages: 330
Release: 2018-01-23
ISBN 10: 3319656333
ISBN 13: 9783319656335
Language: EN, FR, DE, ES & NL

Earth Observation Open Science and Innovation Book Review:

This book is published open access under a CC BY 4.0 license. Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites. This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. In addition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites.

Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

Artificial Intelligence for Smart and Sustainable Energy Systems and Applications
Author: Miltiadis D. Lytras,Kwok Tai Chui
Publsiher: MDPI
Total Pages: 258
Release: 2020-05-27
ISBN 10: 303928889X
ISBN 13: 9783039288892
Language: EN, FR, DE, ES & NL

Artificial Intelligence for Smart and Sustainable Energy Systems and Applications Book Review:

Energy has been a crucial element for human beings and sustainable development. The issues of global warming and non-green energy have yet to be resolved. This book is a collection of twelve articles that provide strong evidence for the success of artificial intelligence deployment in energy research, particularly research devoted to non-intrusive load monitoring, network, and grid, as well as other emerging topics. The presented artificial intelligence algorithms may provide insight into how to apply similar approaches, subject to fine-tuning and customization, to other unexplored energy research. The ultimate goal is to fully apply artificial intelligence to the energy sector. This book may serve as a guide for professionals, researchers, and data scientists—namely, how to share opinions and exchange ideas so as to facilitate a better fusion of energy, academic, and industry research, and improve in the quality of people's daily life activities.

Machine Learning for Subsurface Characterization

Machine Learning for Subsurface Characterization
Author: Siddharth Misra,Hao Li,Jiabo He
Publsiher: Gulf Professional Publishing
Total Pages: 440
Release: 2019-10-12
ISBN 10: 0128177373
ISBN 13: 9780128177372
Language: EN, FR, DE, ES & NL

Machine Learning for Subsurface Characterization Book Review:

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 do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface. Learn from 13 practical case studies using field, laboratory, and simulation data Become knowledgeable with data science and analytics terminology relevant to subsurface characterization Learn frameworks, concepts, and methods important for the engineer’s and geoscientist’s toolbox needed to support

Proceedings of the Second Workshop Application of Artificial Intelligence Techniques in Seismology and Engineering Seismology

Proceedings of the Second Workshop  Application of Artificial Intelligence Techniques in Seismology and Engineering Seismology
Author: Mariano Garcia-Fernandez,Gaetano Zonno
Publsiher: Centre Europeen de Geodynamique Et de Seismologie
Total Pages: 304
Release: 1996
ISBN 10:
ISBN 13: STANFORD:36105112846261
Language: EN, FR, DE, ES & NL

Proceedings of the Second Workshop Application of Artificial Intelligence Techniques in Seismology and Engineering Seismology Book Review:

Data Mining

Data Mining
Author: Georg Zangl
Publsiher: Anonim
Total Pages: 222
Release: 2003
ISBN 10:
ISBN 13: STANFORD:36105119949589
Language: EN, FR, DE, ES & NL

Data Mining Book Review:

Green Technologies to Improve the Environment on Earth

Green Technologies to Improve the Environment on Earth
Author: Marquidia Pacheco
Publsiher: BoD – Books on Demand
Total Pages: 118
Release: 2019-04-10
ISBN 10: 1789856191
ISBN 13: 9781789856194
Language: EN, FR, DE, ES & NL

Green Technologies to Improve the Environment on Earth Book Review:

The aim of this book is to compile some of the green technologies applied to improve the environment on Earth. The success of these technologies is built from humility; from this ethical principle, the concept of honest broker is defined in this work. Some of the biggest environmental problems, such as soil pollution by heavy metals and pollution from the mining industry and massive coal plants, are also addressed. Additional subjects depicted here include geothermal energy, plasma technology, and the correct use of electric vehicles, and demonstrate a promising scenario to diminish greenhouse gases. Likewise, caring for wildlife is essential; the correct use of certain technologies depicted here can contribute to their conservation.

Data Mining

Data Mining
Author: Robert Groth
Publsiher: Prentice Hall
Total Pages: 266
Release: 2000
ISBN 10:
ISBN 13: UVA:X004401254
Language: EN, FR, DE, ES & NL

Data Mining Book Review:

PLEASE PROVIDE COURSE INFORMATION PLEASE PROVIDE

The Quest for Artificial Intelligence

The Quest for Artificial Intelligence
Author: Nils J. Nilsson
Publsiher: Cambridge University Press
Total Pages: 329
Release: 2009-10-30
ISBN 10: 1139642820
ISBN 13: 9781139642828
Language: EN, FR, DE, ES & NL

The Quest for Artificial Intelligence Book Review:

Artificial intelligence (AI) is a field within computer science that is attempting to build enhanced intelligence into computer systems. This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today's AI engineers. AI is becoming more and more a part of everyone's life. The technology is already embedded in face-recognizing cameras, speech-recognition software, Internet search engines, and health-care robots, among other applications. The book's many diagrams and easy-to-understand descriptions of AI programs will help the casual reader gain an understanding of how these and other AI systems actually work. Its thorough (but unobtrusive) end-of-chapter notes containing citations to important source materials will be of great use to AI scholars and researchers. This book promises to be the definitive history of a field that has captivated the imaginations of scientists, philosophers, and writers for centuries.