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Applications of Artificial Intelligence Techniques in the Petroleum Industry

Applications of Artificial Intelligence Techniques in the Petroleum Industry
A Book

by Abdolhossein Hemmati Sarapardeh,Aydin Larestani,Nait Amar Menad,Sassan Hajirezaie

  • Publisher : Gulf Professional Publishing
  • Release : 2020-08-26
  • Pages : 322
  • ISBN : 0128223855
  • Language : En, Es, Fr & De
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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
Symbolic and ComputationaL...

by Bertrand Braunschweig

  • Publisher : Editions TECHNIP
  • Release : 1996
  • Pages : 381
  • ISBN : 9782710807032
  • Language : En, Es, Fr & De
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Artificial Intelligent Approaches in Petroleum Geosciences

Artificial Intelligent Approaches in Petroleum Geosciences
A Book

by Constantin Cranganu,Henri Luchian,Mihaela Elena Breaban

  • Publisher : Springer
  • Release : 2015-04-20
  • Pages : 290
  • ISBN : 3319165313
  • Language : En, Es, Fr & De
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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.

Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

Artificial Intelligence for Smart and Sustainable Energy Systems and Applications
A Book

by Miltiadis D. Lytras,Kwok Tai Chui

  • Publisher : MDPI
  • Release : 2020-05-27
  • Pages : 258
  • ISBN : 303928889X
  • Language : En, Es, Fr & De
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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.

Geological Prior Information

Geological Prior Information
Informing Science and Engineering

by Andrew Curtis,Rachel Wood

  • Publisher : Geological Society of London
  • Release : 2004
  • Pages : 229
  • ISBN : 9781862391710
  • Language : En, Es, Fr & De
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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
October 4th to 6th, 1995, Walferdange (Grand-Duchy of Luxemb[o]urg)

by Mariano Garcia-Fernandez,Gaetano Zonno

  • Publisher : Centre Europeen de Geodynamique Et de Seismologie
  • Release : 1996
  • Pages : 304
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Machine Learning in the Oil and Gas Industry

Machine Learning in the Oil and Gas Industry
Including Geosciences, Reservoir Engineering, and Production Engineering with Python

by Yogendra Narayan Pandey,Ayush Rastogi,Sribharath Kainkaryam,Srimoyee Bhattacharya,Luigi Saputelli

  • Publisher : Apress
  • Release : 2020-11-03
  • Pages : 300
  • ISBN : 9781484260937
  • Language : En, Es, Fr & De
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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.

Data Mining

Data Mining
Applications in the Petroleum Industry

by Georg Zangl

  • Publisher : Unknown Publisher
  • Release : 2003
  • Pages : 222
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Artificial Intelligence

Artificial Intelligence
Emerging Trends and Applications

by Marco Antonio Aceves-Fernandez

  • Publisher : BoD – Books on Demand
  • Release : 2018-06-27
  • Pages : 464
  • ISBN : 178923364X
  • Language : En, Es, Fr & De
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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 and Data Science in the Oil and Gas Industry

Machine Learning and Data Science in the Oil and Gas Industry
Best Practices, Tools, and Case Studies

by Patrick Bangert

  • Publisher : Gulf Professional Publishing
  • Release : 2021-03-15
  • Pages : 300
  • ISBN : 9780128207147
  • Language : En, Es, Fr & De
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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)

Shale Analytics

Shale Analytics
Data-Driven Analytics in Unconventional Resources

by Shahab D. Mohaghegh

  • Publisher : Springer
  • Release : 2017-02-09
  • Pages : 287
  • ISBN : 3319487531
  • Language : En, Es, Fr & De
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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.

Intelligent Digital Oil and Gas Fields

Intelligent Digital Oil and Gas Fields
Concepts, Collaboration, and Right-Time Decisions

by Gustavo Carvajal,Marko Maucec,Stan Cullick

  • Publisher : Gulf Professional Publishing
  • Release : 2017-12-14
  • Pages : 374
  • ISBN : 012804747X
  • Language : En, Es, Fr & De
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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

MICAI 2006: Advances in Artificial Intelligence

MICAI 2006: Advances in Artificial Intelligence
5th Mexican International Conference on Artificial Intelligence, Apizaco, Mexico, November 13-17, 2006, Proceedings

by Alexander Gelbukh,Carlos Alberto Reyes-Garcia

  • Publisher : Springer Science & Business Media
  • Release : 2006-11-07
  • Pages : 1236
  • ISBN : 3540490264
  • Language : En, Es, Fr & De
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This book constitutes the refereed proceedings of the 5th Mexican International Conference on Artificial Intelligence, MICAI 2006, held in Apizaco, Mexico in November 2006. It contains over 120 papers that address such topics as knowledge representation and reasoning, machine learning and feature selection, knowledge discovery, computer vision, image processing and image retrieval, robotics, as well as bioinformatics and medical applications.

Artificial Intelligence and Applied Mathematics in Engineering Problems

Artificial Intelligence and Applied Mathematics in Engineering Problems
Proceedings of the International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2019)

by D. Jude Hemanth,Utku Kose

  • Publisher : Springer Nature
  • Release : 2020-01-03
  • Pages : 1081
  • ISBN : 3030361780
  • Language : En, Es, Fr & De
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This book features research presented at the 1st International Conference on Artificial Intelligence and Applied Mathematics in Engineering, held on 20–22 April 2019 at Antalya, Manavgat (Turkey). In today’s world, various engineering areas are essential components of technological innovations and effective real-world solutions for a better future. In this context, the book focuses on problems in engineering and discusses research using artificial intelligence and applied mathematics. Intended for scientists, experts, M.Sc. and Ph.D. students, postdocs and anyone interested in the subjects covered, the book can also be used as a reference resource for courses related to artificial intelligence and applied mathematics.

Artificial Intelligence in Energy and Renewable Energy Systems

Artificial Intelligence in Energy and Renewable Energy Systems
A Book

by Soteris Kalogirou

  • Publisher : Nova Publishers
  • Release : 2007
  • Pages : 471
  • ISBN : 9781600212611
  • Language : En, Es, Fr & De
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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.

The Log Analyst

The Log Analyst
A Journal of Formation Evaluation and Reservoir Description

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1998
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Artificial Intelligence and Expert Systems in Petroleum Exploration

Artificial Intelligence and Expert Systems in Petroleum Exploration
A Book

by Marwan Simaan,Fred Aminzadeh

  • Publisher : Jai Press
  • Release : 1989
  • Pages : 307
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Artificial Intelligence Methods in the Environmental Sciences

Artificial Intelligence Methods in the Environmental Sciences
A Book

by Sue Ellen Haupt,Antonello Pasini,Caren Marzban

  • Publisher : Springer Science & Business Media
  • Release : 2008-11-28
  • Pages : 424
  • ISBN : 1402091192
  • Language : En, Es, Fr & De
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How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

Harness Oil and Gas Big Data with Analytics

Harness Oil and Gas Big Data with Analytics
Optimize Exploration and Production with Data-Driven Models

by Keith R. Holdaway

  • Publisher : John Wiley & Sons
  • Release : 2014-05-05
  • Pages : 384
  • ISBN : 1118910893
  • Language : En, Es, Fr & De
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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.

Handbook of Neural Computation

Handbook of Neural Computation
A Book

by Pijush Samui,Sanjiban Sekhar Roy,Valentina E. Balas

  • Publisher : Academic Press
  • Release : 2017-07-18
  • Pages : 658
  • ISBN : 0128113197
  • Language : En, Es, Fr & De
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Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods