A Primer on Machine Learning in Subsurface Geosciences
A Primer on Machine Learning in Subsurface Geosciences

Author: Shuvajit Bhattacharya

Publisher: Springer Nature

Published: 2021-05-03

Total Pages: 172

ISBN-13: 3030717682

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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.

Machine Learning Applications in Subsurface Energy Resource Management
Machine Learning Applications in Subsurface Energy Resource Management

Author: Srikanta Mishra

Publisher: CRC Press

Published: 2022-12-27

Total Pages: 388

ISBN-13: 100082389X

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The utilization of machine learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines, including geo-energy. Motivated by these developments, Machine Learning Applications in Subsurface Energy Resource Management presents a current snapshot of the state of the art and future outlook for ML applications to manage subsurface energy resources (e.g., oil and gas, geologic carbon sequestration, and geothermal energy). Covers ML applications across multiple application domains (reservoir characterization, drilling, production, reservoir modeling, and predictive maintenance) Offers a variety of perspectives from authors representing operating companies, universities, and research organizations Provides an array of case studies illustrating the latest applications of several ML techniques Includes a literature review and future outlook for each application domain This book is targeted at practicing petroleum engineers or geoscientists interested in developing a broad understanding of ML applications across several subsurface domains. It is also aimed as a supplementary reading for graduate-level courses and will also appeal to professionals and researchers working with hydrogeology and nuclear waste disposal.

Artificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition
Artificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition

Author: Mohammadali Ahmadi

Publisher: Elsevier

Published: 2024-07-13

Total Pages: 517

ISBN-13: 0443240116

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Artificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition: Case Studies and Code Examples presents a package for academic researchers and industries working on water resources and carbon capture and storage. This book contains fundamental knowledge on artificial intelligence related to oil and gas sustainability and the industry's pivot to support the energy transition and provides practical applications through case studies and coding flowcharts, addressing gaps and questions raised by academic and industrial partners, including energy engineers, geologists, and environmental scientists. This timely publication provides fundamental and extensive information on advanced AI applications geared to support sustainability and the energy transition for the oil and gas industry. - Reviews the use and applications of AI in energy transition of the oil and gas sectors - Provides fundamental knowledge and academic background of artificial intelligence, including practical applications with real-world examples and coding flowcharts - Showcases the successful implementation of AI in the industry (including geothermal energy)

Encyclopedia of Mathematical Geosciences
Encyclopedia of Mathematical Geosciences

Author: B. S. Daya Sagar

Publisher: Springer Nature

Published: 2023-07-13

Total Pages: 1744

ISBN-13: 3030850404

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The Encyclopedia of Mathematical Geosciences is a complete and authoritative reference work. It provides concise explanation on each term that is related to Mathematical Geosciences. Over 300 international scientists, each expert in their specialties, have written around 350 separate articles on different topics of mathematical geosciences including contributions on Artificial Intelligence, Big Data, Compositional Data Analysis, Geomathematics, Geostatistics, Geographical Information Science, Mathematical Morphology, Mathematical Petrology, Multifractals, Multiple Point Statistics, Spatial Data Science, Spatial Statistics, and Stochastic Process Modeling. Each topic incorporates cross-referencing to related articles, and also has its own reference list to lead the reader to essential articles within the published literature. The entries are arranged alphabetically, for easy access, and the subject and author indices are comprehensive and extensive.

Machine Learning and Artificial Intelligence in Geosciences
Machine Learning and Artificial Intelligence in Geosciences

Author:

Publisher: Academic Press

Published: 2020-09-22

Total Pages: 318

ISBN-13: 0128216840

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Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more. - Provides high-level reviews of the latest innovations in geophysics - Written by recognized experts in the field - Presents an essential publication for researchers in all fields of geophysics

Machine Learning in the Oil and Gas Industry
Machine Learning in the Oil and Gas Industry

Author: Yogendra Narayan Pandey

Publisher: Apress

Published: 2020-11-03

Total Pages: 300

ISBN-13: 9781484260937

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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.

Advances in Subsurface Data Analytics
Advances in Subsurface Data Analytics

Author: Shuvajit Bhattacharya

Publisher: Elsevier

Published: 2022-05-18

Total Pages: 378

ISBN-13: 0128223081

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Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume. - Covers fundamentals of simple machine learning and deep learning algorithms, and physics-based approaches written by practitioners in academia and industry - Presents detailed case studies of individual machine learning algorithms and optimal strategies in subsurface characterization around the world - Offers an analysis of future trends in machine learning in geosciences

Data Analytics in Reservoir Engineering
Data Analytics in Reservoir Engineering

Author: Sathish Sankaran

Publisher:

Published: 2020-10-29

Total Pages: 108

ISBN-13: 9781613998205

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Data Analytics in Reservoir Engineering describes the relevance of data analytics for the oil and gas industry, with particular emphasis on reservoir engineering.

Biogeochemical Cycles
Biogeochemical Cycles

Author: Katerina Dontsova

Publisher: John Wiley & Sons

Published: 2020-04-14

Total Pages: 336

ISBN-13: 1119413303

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Elements move through Earth's critical zone along interconnected pathways that are strongly influenced by fluctuations in water and energy. The biogeochemical cycling of elements is inextricably linked to changes in climate and ecological disturbances, both natural and man-made. Biogeochemical Cycles: Ecological Drivers and Environmental Impact examines the influences and effects of biogeochemical elemental cycles in different ecosystems in the critical zone. Volume highlights include: Impact of global change on the biogeochemical functioning of diverse ecosystems Biological drivers of soil, rock, and mineral weathering Natural elemental sources for improving sustainability of ecosystems Links between natural ecosystems and managed agricultural systems Non-carbon elemental cycles affected by climate change Subsystems particularly vulnerable to global change The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals. Find out more about this book from this Q&A with the Author. Book Review: http://www.elementsmagazine.org/archives/e16_6/e16_6_dep_bookreview.pdf

Handbook of Liquefied Natural Gas
Handbook of Liquefied Natural Gas

Author: Saeid Mokhatab

Publisher: Gulf Professional Publishing

Published: 2013-10-15

Total Pages: 617

ISBN-13: 0124046452

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Liquefied natural gas (LNG) is a commercially attractive phase of the commodity that facilitates the efficient handling and transportation of natural gas around the world. The LNG industry, using technologies proven over decades of development, continues to expand its markets, diversify its supply chains and increase its share of the global natural gas trade. The Handbook of Liquefied Natural Gas is a timely book as the industry is currently developing new large sources of supply and the technologies have evolved in recent years to enable offshore infrastructure to develop and handle resources in more remote and harsher environments. It is the only book of its kind, covering the many aspects of the LNG supply chain from liquefaction to regasification by addressing the LNG industries' fundamentals and markets, as well as detailed engineering and design principles. A unique, well-documented, and forward-thinking work, this reference book provides an ideal platform for scientists, engineers, and other professionals involved in the LNG industry to gain a better understanding of the key basic and advanced topics relevant to LNG projects in operation and/or in planning and development. - Highlights the developments in the natural gas liquefaction industries and the challenges in meeting environmental regulations - Provides guidelines in utilizing the full potential of LNG assets - Offers advices on LNG plant design and operation based on proven practices and design experience - Emphasizes technology selection and innovation with focus on a "fit-for-purpose design - Updates code and regulation, safety, and security requirements for LNG applications