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 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 Guide for Oil and Gas Using Python

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

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.

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:

Reservoir Simulations

Reservoir Simulations
Author: Shuyu Sun,Tao Zhang
Publsiher: Gulf Professional Publishing
Total Pages: 340
Release: 2020-06-18
ISBN 10: 0128209623
ISBN 13: 9780128209622
Language: EN, FR, DE, ES & NL

Reservoir Simulations Book Review:

Reservoir Simulation: Machine Learning and Modeling helps the engineer step into the current and most popular advances in reservoir simulation, learning from current experiments and speeding up potential collaboration opportunities in research and technology. This reference explains common terminology, concepts, and equations through multiple figures and rigorous derivations, better preparing the engineer for the next step forward in a modeling project and avoid repeating existing progress. Well-designed exercises, case studies and numerical examples give the engineer a faster start on advancing their own cases. Both computational methods and engineering cases are explained, bridging the opportunities between computational science and petroleum engineering. This book delivers a critical reference for today’s petroleum and reservoir engineer to optimize more complex developments. Understand commonly used and recent progress on definitions, models, and solution methods used in reservoir simulation World leading modeling and algorithms to study flow and transport behaviors in reservoirs, as well as the application of machine learning Gain practical knowledge with hand-on trainings on modeling and simulation through well designed case studies and numerical examples.

Fuzzy Partial Differential Equations and Relational Equations

Fuzzy Partial Differential Equations and Relational Equations
Author: Masoud Nikravesh,Lofti A. Zadeh,Victor Korotkikh
Publsiher: Springer
Total Pages: 347
Release: 2013-04-17
ISBN 10: 3540396756
ISBN 13: 9783540396758
Language: EN, FR, DE, ES & NL

Fuzzy Partial Differential Equations and Relational Equations Book Review:

During last decade significant progress has been made in the oil indus try by using soft computing technology. Underlying this evolving technology there have, been ideas transforming the very language we use to describe problems with imprecision, uncertainty and partial truth. These developments offer exciting opportunities, but at the same time it is becoming clearer that further advancements are confronted by funda mental problems. The whole idea of how human process information lies at the core of the challenge. There are already new ways of thinking about the problems within theory of perception-based information. This theory aims to understand and harness the laws of human perceptions to dramatically im prove the processing of information. A matured theory of perception-based information is likely to be proper positioned to contribute to the solution of the problems and provide all the ingredients for a revolution in science, technology and business. In this context, Berkeley Initiative in Soft Computing (BISC), Univer sity of California, Berkeley from one side and Chevron-Texaco from another formed a Technical Committee to organize a Meeting entitled "State of the Art Assessment and New Directions for Research" to understand the signifi cance of the fields accomplishments, new developments and future directions. The Technical Committee selected and invited 15 scientists (and oil indus try experts as technical committee members) from the related disciplines to participate in the Meeting, which took place at the University of California, Berkeley, and March 15-17, 2002.

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: 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)

Data Mining

Data Mining
Author: Georg Zangl
Publsiher: Unknown
Total Pages: 222
Release: 2003
ISBN 10: 1928374650XXX
ISBN 13: STANFORD:36105119949589
Language: EN, FR, DE, ES & NL

Data Mining Book Review:

Machine Learning Guide for Oil and Gas Using Python

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

Project Finance for the International Petroleum Industry

Project Finance for the International Petroleum Industry
Author: Robert Clews
Publsiher: Academic Press
Total Pages: 416
Release: 2016-04-07
ISBN 10: 0128005297
ISBN 13: 9780128005293
Language: EN, FR, DE, ES & NL

Project Finance for the International Petroleum Industry Book Review:

This overview of project finance for the oil and gas industry covers financial markets, sources and providers of finance, financial structures, and capital raising processes. About US$300 billion of project finance debt is raised annually across several capital intensive sectors—including oil and gas, energy, infrastructure, and mining—and the oil and gas industry represents around 30% of the global project finance market. With over 25 year’s project finance experience in international banking and industry, author Robert Clews explores project finance techniques and their effectiveness in the petroleum industry. He highlights the petroleum industry players, risks, economics, and commercial/legal arrangements. With petroleum industry projects representing amongst the largest industrial activities in the world, this book ties together concepts and tools through real examples and aims to ensure that project finance will continue to play a central role in bringing together investors and lenders to finance these ventures. Combines the theory and practice of raising long-term funding for capital intensive projects with insights about the appeal of project finance to the international oil and gas industry Includes case studies and examples covering projects in the Arctic, East Africa, Latin America, North America, and Australia Emphasizes the full downstream value chain of the industry instead of limiting itself to upstream and pipeline project financing Highlights petroleum industry players, risks, economics, and commercial and legal arrangements

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.

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

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

AI and Learning Systems

AI and Learning Systems
Author: Konstantinos Kyprianidis,Erik Dahlquist
Publsiher: BoD – Books on Demand
Total Pages: 272
Release: 2021-02-17
ISBN 10: 1789858771
ISBN 13: 9781789858778
Language: EN, FR, DE, ES & NL

AI and Learning Systems Book Review:

Over the last few years, interest in the industrial applications of AI and learning systems has surged. This book covers the recent developments and provides a broad perspective of the key challenges that characterize the field of Industry 4.0 with a focus on applications of AI. The target audience for this book includes engineers involved in automation system design, operational planning, and decision support. Computer science practitioners and industrial automation platform developers will also benefit from the timely and accurate information provided in this work. The book is organized into two main sections comprising 12 chapters overall: •Digital Platforms and Learning Systems •Industrial Applications of AI

Machine Learning and Deep Learning in Real Time Applications

Machine Learning and Deep Learning in Real Time Applications
Author: Mahrishi, Mehul,Hiran, Kamal Kant,Meena, Gaurav,Sharma, Paawan
Publsiher: IGI Global
Total Pages: 344
Release: 2020-04-24
ISBN 10: 1799830977
ISBN 13: 9781799830979
Language: EN, FR, DE, ES & NL

Machine Learning and Deep Learning in Real Time Applications Book Review:

Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.

Artificial Intelligence in Process Engineering

Artificial Intelligence in Process Engineering
Author: Michael Mavrovouniotis
Publsiher: Elsevier
Total Pages: 382
Release: 2012-12-02
ISBN 10: 0323153143
ISBN 13: 9780323153140
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Process Engineering Book Review:

Artificial Intelligence in Process Engineering aims to present a diverse sample of Artificial Intelligence (AI) applications in process engineering. The book contains contributions, selected by the editors based on educational value and diversity of AI methods and process engineering application domains. Topics discussed in the text include the use of qualitative reasoning for modeling and simulation of chemical systems; the use of qualitative models in discrete event simulation to analyze malfunctions in processing systems; and the diagnosis of faults in processes that are controlled by Programmable Logic Controllers. There are also debates on the issue of quantitative versus qualitative information. The control of batch processes, a design of a system that synthesizes bioseparation processes, and process design in the domain of chemical (rather than biochemical) systems are likewise covered in the text. This publication will be of value to industrial engineers and process engineers and researchers.

Artificial Intelligence Concepts Methodologies Tools and Applications

Artificial Intelligence  Concepts  Methodologies  Tools  and Applications
Author: Management Association, Information Resources
Publsiher: IGI Global
Total Pages: 3048
Release: 2016-12-12
ISBN 10: 152251760X
ISBN 13: 9781522517603
Language: EN, FR, DE, ES & NL

Artificial Intelligence Concepts Methodologies Tools and Applications Book Review:

Ongoing advancements in modern technology have led to significant developments in artificial intelligence. With the numerous applications available, it becomes imperative to conduct research and make further progress in this field. Artificial Intelligence: Concepts, Methodologies, Tools, and Applications provides a comprehensive overview of the latest breakthroughs and recent progress in artificial intelligence. Highlighting relevant technologies, uses, and techniques across various industries and settings, this publication is a pivotal reference source for researchers, professionals, academics, upper-level students, and practitioners interested in emerging perspectives in the field of artificial intelligence.

The Quest for Artificial Intelligence

The Quest for Artificial Intelligence
Author: Nils J. Nilsson
Publsiher: Cambridge University Press
Total Pages: 135
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.

Dynamic Data Driven Applications Systems

Dynamic Data Driven Applications Systems
Author: Frederica Darema,Erik Blasch,Sai Ravela,Alex Aved
Publsiher: Springer Nature
Total Pages: 360
Release: 2020-11-02
ISBN 10: 3030617254
ISBN 13: 9783030617257
Language: EN, FR, DE, ES & NL

Dynamic Data Driven Applications Systems Book Review:

This book constitutes the refereed proceedings of the Third International Conference on Dynamic Data Driven Application Systems, DDDAS 2020, held in Boston, MA, USA, in October 2020. The 21 full papers and 14 short papers presented in this volume were carefully reviewed and selected from 40 submissions. They cover topics such as: digital twins; environment cognizant adaptive-planning systems; energy systems; materials systems; physics-based systems analysis; imaging methods and systems; and learning systems.