Machine Learning In The Oil And Gas Industry
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Machine Learning And Data Science In The Oil And Gas Industry
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Author : Patrick Bangert
language : en
Publisher: Gulf Professional Publishing
Release Date : 2021-03-04
Machine Learning And Data Science In The Oil And Gas Industry written by Patrick Bangert and has been published by Gulf Professional Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-04 with Science categories.
Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. - Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful - Gain practical understanding of machine learning used in oil and gas operations through contributed case studies - Learn change management skills that will help gain confidence in pursuing the technology - Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)
Machine Learning In The Oil And Gas Industry
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Author : Yogendra Narayan Pandey
language : en
Publisher: Apress
Release Date : 2020-11-03
Machine Learning In The Oil And Gas Industry written by Yogendra Narayan Pandey and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-03 with Computers categories.
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.
Adaptive Approach To Petroleum Reservoir Simulation
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Author : Stanislav Ursegov
language : en
Publisher: Springer Nature
Release Date : 2021-01-31
Adaptive Approach To Petroleum Reservoir Simulation written by Stanislav Ursegov and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-31 with Science categories.
This book presents unique features of the adaptive modeling approach based on new machine learning algorithms for petroleum exploration, development, and production. The adaptive approach helps simulation engineers and geoscientists to create adequate geological and hydrodynamic models. This approach is proven to be a real alternative to traditional techniques, such as deterministic modeling. Currently, machine-learning algorithms grow in popularity because they provide consistency, predictiveness, and convenience. The primary purpose of this book is to describe the theoretical state of the adaptive approach and show some examples of its implementation in simulation and forecasting different reservoir processes.
Proceedings Of The 2nd Vietnam Symposium On Advances In Offshore Engineering
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Author : Dat Vu Khoa Huynh
language : en
Publisher: Springer Nature
Release Date : 2021-12-24
Proceedings Of The 2nd Vietnam Symposium On Advances In Offshore Engineering written by Dat Vu Khoa Huynh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-24 with Technology & Engineering categories.
This book gathers a selection of refereed papers presented at the 2nd Vietnam Symposium on Advances in Offshore Engineering (VSOE 2021), held in 2022 in Ho Chi Minh City, Vietnam. The book consists of articles written by researchers, practitioners, policymakers, and entrepreneurs addressing the important topic of technological and policy changes intended to promote renewable energies and to generate business opportunities in oil and gas and offshore renewable energy. With a special focus on sustainable energy and marine planning, the book brings together the latest lessons learned in offshore engineering, technological innovations, cost-effective and safer foundations and structural solutions, environmental protection, hazards, vulnerability, and risk management. Its content caters to graduate students, researchers, and industrial practitioners working in the fields of offshore engineering and renewable energies.
Shale Analytics
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Author : Shahab D. Mohaghegh
language : en
Publisher: Springer
Release Date : 2017-02-09
Shale Analytics written by Shahab D. Mohaghegh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-09 with Technology & Engineering categories.
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.
Machine Learning For Societal Improvement Modernization And Progress
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Author : Pendyala, Vishnu S.
language : en
Publisher: IGI Global
Release Date : 2022-06-24
Machine Learning For Societal Improvement Modernization And Progress written by Pendyala, Vishnu S. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-24 with Computers categories.
Learning has been fundamental to the growth and evolution of humanity and civilization. The same concepts of learning, applied to the tasks that machines can perform, are having a similar effect now. Machine learning is evolving computation and its applications like never before. It is now widely recognized that machine learning is playing a similar role to electricity in the late 19th and early 20th centuries in modernizing the world. From simple high school science projects to large-scale radio astronomy, machine learning has revolutionized it all—however, a few of the applications clearly stand out as transforming the world and opening up a new era. Machine Learning for Societal Improvement, Modernization, and Progress showcases the path-breaking applications of machine learning that are leading to the next generation of computing and living standards. The focus of the book is machine learning and its application to specific domains, which is resulting in substantial civilizational progress. Covering topics such as lifespan prediction, smart transportation networks, and socio-economic data, this premier reference source is a dynamic resource for data scientists, industry leaders, practitioners, students and faculty of higher education, sociologists, researchers, and academicians.
Machine Learning For Subsurface Characterization
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Author : Siddharth Misra
language : en
Publisher: Gulf Professional Publishing
Release Date : 2019-10-12
Machine Learning For Subsurface Characterization written by Siddharth Misra and has been published by Gulf Professional Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-12 with Technology & Engineering categories.
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
Machine Learning And Data Science In The Power Generation Industry
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Author : Patrick Bangert
language : en
Publisher: Elsevier
Release Date : 2021-01-18
Machine Learning And Data Science In The Power Generation Industry written by Patrick Bangert and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-18 with Technology & Engineering categories.
Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study-driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting.
Applications Of Artificial Intelligence Ai And Machine Learning Ml In The Petroleum Industry
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Author : Manan Shah
language : en
Publisher: CRC Press
Release Date : 2022-09-02
Applications Of Artificial Intelligence Ai And Machine Learning Ml In The Petroleum Industry written by Manan Shah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-02 with Technology & Engineering categories.
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.
Machine Learning For Energy Systems
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Author : Denis Sidorov
language : en
Publisher: MDPI
Release Date : 2020-12-08
Machine Learning For Energy Systems written by Denis Sidorov and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-08 with Technology & Engineering categories.
This volume deals with recent advances in and applications of computational intelligence and advanced machine learning methods in power systems, heating and cooling systems, and gas transportation systems. The optimal coordinated dispatch of the multi-energy microgrids with renewable generation and storage control using advanced numerical methods is discussed. Forecasting models are designed for electrical insulator faults, the health of the battery, electrical insulator faults, wind speed and power, PV output power and transformer oil test parameters. The loads balance algorithm for an offshore wind farm is proposed. The information security problems in the energy internet are analyzed and attacked using information transmission contemporary models, based on blockchain technology. This book will be of interest, not only to electrical engineers, but also to applied mathematicians who are looking for novel challenging problems to focus on.