Download or read book Applications of Artificial Intelligence AI and Machine Learning ML in the Petroleum Industry written by Manan Shah and published by CRC Press. This book was released on 2022-09-02 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, raw data on any industry is widely available. With the help of artificial intelligence (AI) and machine learning (ML), this data can be used to gain meaningful insights. In addition, as data is the new raw material for today’s world, AI and ML will be applied in every industrial sector. Industry 4.0 mainly focuses on the automation of things. From that perspective, the oil and gas industry is one of the largest industries in terms of economy and energy. Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry analyzes the use of AI and ML in the oil and gas industry across all three sectors, namely upstream, midstream, and downstream. It covers every aspect of the petroleum industry as related to the application of AI and ML, ranging from exploration, data management, extraction, processing, real-time data analysis, monitoring, cloud-based connectivity system, and conditions analysis, to the final delivery of the product to the end customer, while taking into account the incorporation of the safety measures for a better operation and the efficient and effective execution of operations. This book explores the variety of applications that can be integrated to support the existing petroleum and adjacent sectors to solve industry problems. It will serve as a useful guide for professionals working in the petroleum industry, industrial engineers, AI and ML experts and researchers, as well as students.

Download or read book Applications of Artificial Intelligence Techniques in the Petroleum Industry written by Abdolhossein Hemmati-Sarapardeh and published by Gulf Professional Publishing. This book was released on 2020-08-26 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Machine Learning Guide for Oil and Gas Using Python written by Hoss Belyadi and published by Gulf Professional Publishing. This book was released on 2021-04-09 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Machine Learning in the Oil and Gas Industry written by Yogendra Narayan Pandey and published by Apress. This book was released on 2020-11-03 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning and Data Science in the Oil and Gas Industry written by Patrick Bangert and published by Gulf Professional Publishing. This book was released on 2021-03-04 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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)

Download or read book Shale Analytics written by Shahab D. Mohaghegh and published by Springer. This book was released on 2017-02-09 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Reservoir Geomechanics written by Mark D. Zoback and published by Cambridge University Press. This book was released on 2010-04-01 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: This interdisciplinary book encompasses the fields of rock mechanics, structural geology and petroleum engineering to address a wide range of geomechanical problems that arise during the exploitation of oil and gas reservoirs. It considers key practical issues such as prediction of pore pressure, estimation of hydrocarbon column heights and fault seal potential, determination of optimally stable well trajectories, casing set points and mud weights, changes in reservoir performance during depletion, and production-induced faulting and subsidence. The book establishes the basic principles involved before introducing practical measurement and experimental techniques to improve recovery and reduce exploitation costs. It illustrates their successful application through case studies taken from oil and gas fields around the world. This book is a practical reference for geoscientists and engineers in the petroleum and geothermal industries, and for research scientists interested in stress measurements and their application to problems of faulting and fluid flow in the crust.

Download or read book Applications of Machine Learning written by Prashant Johri and published by Springer Nature. This book was released on 2020-05-04 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics.

Download or read book Artificial Intelligence in Chemical Engineering written by Thomas E. Quantrille and published by Elsevier. This book was released on 2012-12-02 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) is the part of computer science concerned with designing intelligent computer systems (systems that exhibit characteristics we associate with intelligence in human behavior). This book is the first published textbook of AI in chemical engineering, and provides broad and in-depth coverage of AI programming, AI principles, expert systems, and neural networks in chemical engineering. This book introduces the computational means and methodologies that are used to enable computers to perform intelligent engineering tasks. A key goal is to move beyond the principles of AI into its applications in chemical engineering. After reading this book, a chemical engineer will have a firm grounding in AI, know what chemical engineering applications of AI exist today, and understand the current challenges facing AI in engineering. - Allows the reader to learn AI quickly using inexpensive personal computers - Contains a large number of illustrative examples, simple exercises, and complex practice problems and solutions - Includes a computer diskette for an illustrated case study - Demonstrates an expert system for separation synthesis (EXSEP) - Presents a detailed review of published literature on expert systems and neural networks in chemical engineering

Download or read book Artificial Intelligent Approaches in Petroleum Geosciences written by Constantin Cranganu and published by Springer Nature. This book was released on with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advanced Analytics and Artificial Intelligence Applications written by Ali Soofastaei and published by BoD – Books on Demand. This book was released on 2019-11-13 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computers and machines were developed to reduce time consumption and manual human efforts to complete projects efficiently. With fast-growing technologies in the field, we have finally reached a stage where almost everyone in the world has access to these high technologies. However, this is just a starting phase because future development is taking a more advanced route in the shape of artificial intelligence (AI). Although AI is under the computer science umbrella, nowadays there is no field unaffected by this high technology. The overall aim of using intelligence learning methods is to train machines to think intelligently and make decisions in different situations the same as humans. Previously, machines were doing what they were programmed to do, but now with AI, devices can think and behave like a human being. This book aims to present the application of advanced analytics and AI in different industries as practical tools to develop prediction, optimization, and make decision models.

Download or read book Engineering Applications of Artificial Intelligence written by Aziza Chakir and published by Springer Nature. This book was released on with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Artificial Intelligence Aided Materials Design written by Rajesh Jha and published by CRC Press. This book was released on 2022-03-15 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the application of artificial intelligence (AI)/machine learning (ML) concepts to develop predictive models that can be used to design alloy materials, including hard and soft magnetic alloys, nickel-base superalloys, titanium-base alloys, and aluminum-base alloys. Readers new to AI/ML algorithms can use this book as a starting point and use the MATLAB® and Python implementation of AI/ML algorithms through included case studies. Experienced AI/ML researchers who want to try new algorithms can use this book and study the case studies for reference. Offers advantages and limitations of several AI concepts and their proper implementation in various data types generated through experiments and computer simulations and from industries in different file formats Helps readers to develop predictive models through AI/ML algorithms by writing their own computer code or using resources where they do not have to write code Covers downloadable resources such as MATLAB GUI/APP and Python implementation that can be used on common mobile devices Discusses the CALPHAD approach and ways to use data generated from it Features a chapter on metallurgical/materials concepts to help readers understand the case studies and thus proper implementation of AI/ML algorithms under the framework of data-driven materials science Uses case studies to examine the importance of using unsupervised machine learning algorithms in determining patterns in datasets This book is written for materials scientists and metallurgists interested in the application of AI, ML, and data science in the development of new materials.

Download or read book Building Business Knowledge for Complex Modern Business Environments written by Sedky, Ahmed and published by IGI Global. This book was released on 2024-10-15 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: As business technology advances, global competition results in shifting consumer expectations. Businesses with effective knowledge management have become essential for organizations seeking to thrive. Modern business environments are complex, requiring leaders and teams to navigate data, insights, and strategies. As businesses confront challenges like market volatility, regulatory changes, and the need for sustainable practices, cultivating an understanding of industry dynamics, emerging trends, and innovative practices is pivotal. Further research into the intersectionality of business essentials with emerging technologies, socio-economic factors, and environmental sustainability may foster interdisciplinary collaboration and innovation. Building Business Knowledge for Complex Modern Business Environments explores essential components necessary for navigating contemporary business landscapes. It delves into crucial aspects such as strategic planning, financial management, technological integration, marketing strategies, and sustainable practices through detailed analysis and practical examples for business success. This book covers topics such as management science, digital technology, and entrepreneurship, and is a useful resource for business owners, managers, administrators, data scientists, computer engineers, academicians, and researchers.

Download or read book Big Data Analysis New Algorithms for a New Society written by Nathalie Japkowicz and published by Springer. This book was released on 2015-12-16 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued concerning the potential dangers of Big Data Analysis along with its pitfalls and challenges.

Download or read book Robot Rules written by Jacob Turner and published by Springer. This book was released on 2018-10-29 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains why AI is unique, what legal and ethical problems it could cause, and how we can address them. It argues that AI is unlike any other previous technology, owing to its ability to take decisions independently and unpredictably. This gives rise to three issues: responsibility--who is liable if AI causes harm; rights--the disputed moral and pragmatic grounds for granting AI legal personality; and the ethics surrounding the decision-making of AI. The book suggests that in order to address these questions we need to develop new institutions and regulations on a cross-industry and international level. Incorporating clear explanations of complex topics, Robot Rules will appeal to a multi-disciplinary audience, from those with an interest in law, politics and philosophy, to computer programming, engineering and neuroscience.

Download or read book Data Driven Analytics for the Geological Storage of CO2 written by Shahab Mohaghegh and published by CRC Press. This book was released on 2018-05-20 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven analytics is enjoying unprecedented popularity among oil and gas professionals. Many reservoir engineering problems associated with geological storage of CO2 require the development of numerical reservoir simulation models. This book is the first to examine the contribution of artificial intelligence and machine learning in data-driven analytics of fluid flow in porous environments, including saline aquifers and depleted gas and oil reservoirs. Drawing from actual case studies, this book demonstrates how smart proxy models can be developed for complex numerical reservoir simulation models. Smart proxy incorporates pattern recognition capabilities of artificial intelligence and machine learning to build smart models that learn the intricacies of physical, mechanical and chemical interactions using precise numerical simulations. This ground breaking technology makes it possible and practical to use high fidelity, complex numerical reservoir simulation models in the design, analysis and optimization of carbon storage in geological formations projects.