Artificial Intelligent Approaches In Petroleum Geosciences

Author : Constantin Cranganu,Henri Luchian,Mihaela Elena Breaban
Publisher : Springer
Page : 290 pages
File Size : 48,9 Mb
Release : 2015-04-20
Category : Technology & Engineering
ISBN : 9783319165318

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Artificial Intelligent Approaches in Petroleum Geosciences by Constantin Cranganu,Henri Luchian,Mihaela Elena Breaban Pdf

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.

Author : Constantin Cranganu,Henri Luchian,Mihaela Elena Breaban
Publisher : Unknown
Page : 128 pages
File Size : 52,7 Mb
Release : 2015
Category : Electronic
ISBN : 3319165321

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Artificial Intelligent Approaches in Petroleum Geosciences by Constantin Cranganu,Henri Luchian,Mihaela Elena Breaban Pdf

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.

Author : Bertrand Braunschweig,Bernt-A Bremdal
Publisher : Editions TECHNIP
Page : 404 pages
File Size : 55,6 Mb
Release : 1996
Category : Technology & Engineering
ISBN : 2710807033

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Artificial Intelligence in the Petroleum Industry by Bertrand Braunschweig,Bernt-A Bremdal Pdf

Author : Fred Aminzadeh,Cenk Temizel,Yasin Hajizadeh
Publisher : John Wiley & Sons
Page : 613 pages
File Size : 47,9 Mb
Release : 2022-09-21
Category : Science
ISBN : 9781119879695

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

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

Author : M. Nikravesh,L.A. Zadeh,Fred Aminzadeh
Publisher : Elsevier
Page : 755 pages
File Size : 40,5 Mb
Release : 2003-04-22
Category : Science
ISBN : 9780080541327

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Soft Computing and Intelligent Data Analysis in Oil Exploration by M. Nikravesh,L.A. Zadeh,Fred Aminzadeh Pdf

This comprehensive book highlights soft computing and geostatistics applications in hydrocarbon exploration and production, combining practical and theoretical aspects. It spans a wide spectrum of applications in the oil industry, crossing many discipline boundaries such as geophysics, geology, petrophysics and reservoir engineering. It is complemented by several tutorial chapters on fuzzy logic, neural networks and genetic algorithms and geostatistics to introduce these concepts to the uninitiated. The application areas include prediction of reservoir properties (porosity, sand thickness, lithology, fluid), seismic processing, seismic and bio stratigraphy, time lapse seismic and core analysis. There is a good balance between introducing soft computing and geostatistics methodologies that are not routinely used in the petroleum industry and various applications areas. The book can be used by many practitioners such as processing geophysicists, seismic interpreters, geologists, reservoir engineers, petrophysicist, geostatistians, asset mangers and technology application professionals. It will also be of interest to academics to assess the importance of, and contribute to, R&D efforts in relevant areas.

Author : Yogendra Narayan Pandey,Ayush Rastogi,Sribharath Kainkaryam,Srimoyee Bhattacharya,Luigi Saputelli
Publisher : Apress
Page : 300 pages
File Size : 41,9 Mb
Release : 2020-11-03
Category : Computers
ISBN : 1484260937

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Machine Learning in the Oil and Gas Industry by Yogendra Narayan Pandey,Ayush Rastogi,Sribharath Kainkaryam,Srimoyee Bhattacharya,Luigi Saputelli Pdf

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.

Author : Keith R. Holdaway,Duncan H. B. Irving
Publisher : John Wiley & Sons
Page : 368 pages
File Size : 43,8 Mb
Release : 2017-10-09
Category : Business & Economics
ISBN : 9781119215103

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

Leverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this alternative analytical workflow, and in-depth discussion addresses the many Big Data issues in geophysics and petrophysics. From data collection and context through real-world everyday applications, this book provides an essential resource for anyone involved in oil and gas exploration. Recent and continual advances in machine learning are driving a rapid increase in empirical modeling capabilities. This book shows you how these new tools and methodologies can enhance geophysical and petrophysical data analysis, increasing the value of your exploration data. Apply data-driven modeling concepts in a geophysical and petrophysical context Learn how to get more information out of models and simulations Add value to everyday tasks with the appropriate Big Data application Adjust methodology to suit diverse geophysical and petrophysical contexts Data-driven modeling focuses on analyzing the total data within a system, with the goal of uncovering connections between input and output without definitive knowledge of the system's physical behavior. This multi-faceted approach pushes the boundaries of conventional modeling, and brings diverse fields of study together to apply new information and technology in new and more valuable ways. Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models takes you beyond traditional deterministic interpretation to the future of exploration data analysis.

Author : K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh
Publisher : CRC Press
Page : 250 pages
File Size : 44,5 Mb
Release : 2020-10-07
Category : Computers
ISBN : 9781000179514

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Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches by K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh Pdf

Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning

Author : Shuvajit Bhattacharya
Publisher : Springer Nature
Page : 172 pages
File Size : 44,9 Mb
Release : 2021-05-03
Category : Technology & Engineering
ISBN : 9783030717681

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A Primer on Machine Learning in Subsurface Geosciences by Shuvajit Bhattacharya Pdf

This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, machine learning will ultimately benefit these users. The book is written by a practitioner of machine learning and statistics, keeping geoscientists in mind. It highlights the need to go beyond concepts covered in STAT 101 courses and embrace new computational tools to solve complex problems in geosciences. It also offers practitioners, researchers, and academics insights into how to identify, develop, deploy, and recommend fit-for-purpose machine learning models to solve real-world problems in subsurface geosciences.

Author : Ahmed El-Banbi,Ahmed Alzahabi,Ahmed El-Maraghi
Publisher : Gulf Professional Publishing
Page : 432 pages
File Size : 45,9 Mb
Release : 2018-04-20
Category : Technology & Engineering
ISBN : 9780128125731

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PVT Property Correlations by Ahmed El-Banbi,Ahmed Alzahabi,Ahmed El-Maraghi Pdf

PVT properties are necessary for reservoir/well performance forecast and optimization. In absence of PVT laboratory measurements, finding the right correlation to estimate accurate PVT properties could be challenging. PVT Property Correlations: Selection and Estimation discusses techniques to properly calculate PVT properties from limited information. This book covers how to prepare PVT properties for dry gases, wet gases, gas condensates, volatile oils, black oils, and low gas-oil ration oils. It also explains the use of artificial neural network models in generating PVT properties. It presents numerous examples to explain step-by-step procedures in using techniques designed to deliver the most accurate PVT properties from correlations. Complimentary to this book is PVT correlation calculator software. Many of the techniques discussed in this book are available with the software. This book shows the importance of PVT data, provides practical tools to calculate PVT properties, and helps engineers select PVT correlations so they can model, optimize, and forecast their assets. Understand how to prepare PVT data in absence of laboratory reports for all fluid types Become equipped with a comprehensive list of PVT correlations and their applicability ranges Learn about ANN models and their applications in providing PVT data Become proficient in selecting best correlations and improving correlations results

Author : Ehsan Khamehchi,Mohammad Reza Mahdiani
Publisher : Springer
Page : 46 pages
File Size : 54,7 Mb
Release : 2016-12-31
Category : Technology & Engineering
ISBN : 9783319514512

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Gas Allocation Optimization Methods in Artificial Gas Lift by Ehsan Khamehchi,Mohammad Reza Mahdiani Pdf

This Brief offers a comprehensive study covering the different aspects of gas allocation optimization in petroleum engineering. It contains different methods of defining the fitness function, dealing with constraints and selecting the optimizer; in each chapter a detailed literature review is included which covers older and important studies as well as recent publications. This book will be of use for production engineers and students interested in gas lift optimization.

Author : Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein
Publisher : John Wiley & Sons
Page : 436 pages
File Size : 40,6 Mb
Release : 2021-08-18
Category : Technology & Engineering
ISBN : 9781119646167

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Deep Learning for the Earth Sciences by Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein Pdf

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

Author : Y. Z. Ma
Publisher : Springer
Page : 640 pages
File Size : 45,7 Mb
Release : 2019-07-15
Category : Technology & Engineering
ISBN : 9783030178604

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Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling by Y. Z. Ma Pdf

Earth science is becoming increasingly quantitative in the digital age. Quantification of geoscience and engineering problems underpins many of the applications of big data and artificial intelligence. This book presents quantitative geosciences in three parts. Part 1 presents data analytics using probability, statistical and machine-learning methods. Part 2 covers reservoir characterization using several geoscience disciplines: including geology, geophysics, petrophysics and geostatistics. Part 3 treats reservoir modeling, resource evaluation and uncertainty analysis using integrated geoscience, engineering and geostatistical methods. As the petroleum industry is heading towards operating oil fields digitally, a multidisciplinary skillset is a must for geoscientists who need to use data analytics to resolve inconsistencies in various sources of data, model reservoir properties, evaluate uncertainties, and quantify risk for decision making. This book intends to serve as a bridge for advancing the multidisciplinary integration for digital fields. The goal is to move beyond using quantitative methods individually to an integrated descriptive-quantitative analysis. In big data, everything tells us something, but nothing tells us everything. This book emphasizes the integrated, multidisciplinary solutions for practical problems in resource evaluation and field development.

Author : Ibrahim Palaz,Sailes K. Sengupta
Publisher : Springer Science & Business Media
Page : 315 pages
File Size : 46,7 Mb
Release : 2012-12-06
Category : Science
ISBN : 9781461243885

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Automated Pattern Analysis in Petroleum Exploration by Ibrahim Palaz,Sailes K. Sengupta Pdf

Here is a state-of-the-art survey of artificial intelligence in modern exploration programs. Focussing on standard exploration procedures, the contributions examine the advantages and pitfalls of using these new techniques, and, in the process, provide new, more accurate and consistent methods for solving old problems. They show how expert systems can provide the integration of information that is essential in the petroleum industry when solving the complicated questions facing the modern petroleum geoscientist.

Author : Jia’en Lin
Publisher : Springer Nature
Page : 875 pages
File Size : 46,7 Mb
Release : 2024-06-30
Category : Electronic
ISBN : 9789819704798

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Proceedings of the International Field Exploration and Development Conference 2023 by Jia’en Lin Pdf