Advances In Subsurface Data Analytics
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Advances In Subsurface Data Analytics
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Author : Shuvajit Bhattacharya
language : en
Publisher: Elsevier
Release Date : 2022-05-18
Advances In Subsurface Data Analytics written by Shuvajit Bhattacharya and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-18 with Science categories.
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
Artificial Intelligence For A More Sustainable Oil And Gas Industry And The Energy Transition
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Author : Mohammadali Ahmadi
language : en
Publisher: Elsevier
Release Date : 2024-07-13
Artificial Intelligence For A More Sustainable Oil And Gas Industry And The Energy Transition written by Mohammadali Ahmadi and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-13 with Technology & Engineering categories.
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)
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
A Primer On Machine Learning In Subsurface Geosciences
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Author : Shuvajit Bhattacharya
language : en
Publisher: Springer
Release Date : 2021-06-07
A Primer On Machine Learning In Subsurface Geosciences written by Shuvajit Bhattacharya and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-07 with Technology & Engineering categories.
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.
Unconventional Resources
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Author : Cenk Temizel
language : en
Publisher: CRC Press
Release Date : 2025-02-27
Unconventional Resources written by Cenk Temizel and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-27 with Technology & Engineering categories.
The oil and gas sector is a vital player in the energy transition. With their vast resource potential, unconventional shale plays will be an essential part in enabling this change. Unconventional Resources serves as a comprehensive reference covering the latest technologies, methodologies, and applications of unconventional shale resources in the oil and gas industry, and their role in the evolution of the sector's energy transition. This book: Offers an overview of geophysics, geology, and reservoir characterization in unconventional resources Discusses drilling, well stimulation and completion, production engineering, and artificial lift Covers reservoir management and surveillance, recovery enhancement, production forecasting, and surface facilities and testing Details technical and technological advances, including machine learning, AI, data analytics, and Industry 4.0 Explores the latest methods/workflows in performance analysis in unconventional plays Employs integrated and hybrid approaches to the energy transition The book provides surface and subsurface technical professionals in the oil and gas industry a thorough overview of unconventionals along with the integrated/hybrid applications that will enable them to stay current with the industry's transition.
Preparatory Excavation Works In Mines Volume I
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Author : Prof. Dr. Bilal Semih Bozdemir
language : en
Publisher: Prof. Dr. Bilal Semih Bozdemir
Release Date :
Preparatory Excavation Works In Mines Volume I written by Prof. Dr. Bilal Semih Bozdemir and has been published by Prof. Dr. Bilal Semih Bozdemir this book supported file pdf, txt, epub, kindle and other format this book has been release on with Technology & Engineering categories.
Methodology: The project utilized down-the-hole (DTH) drilling techniques due to the steep slopes and limited access to machinery. Blast designs were customized to ensure stability while avoiding excessive disturbance to the surrounding environment. A combination of emulsion and water-gel explosives was used to ensure safety and effectiveness in the challenging conditions. Challenges: One of the most prominent challenges was the unpredictability of rock conditions, which necessitated ongoing assessments and adjustments to the drilling and blasting parameters. Furthermore, the environmental protocol required strict adherence to minimize impact on local fauna and flora. Results: The successful completion of the pipeline installation not only fulfilled the project timeline but also maintained compliance with environmental standards. This case underscores the necessity of adaptive management strategies in drilling and blasting, particularly in sensitive environments where both safety and ecological considerations are paramount.
Remote Sensing For Geophysicists
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Author : Mukesh Gupta
language : en
Publisher: CRC Press
Release Date : 2025-03-05
Remote Sensing For Geophysicists written by Mukesh Gupta and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-05 with Science categories.
Geophysical exploration methods are very expensive and invasive methods for surveys. Remote sensing methods are non-invasive and much cheaper for investigating the Earth’s surface. This book bridges this gap and aims to integrate exploration geophysics with remote sensing as a cost-effective method which is easy to implement for prospecting in different areas. It provides exploration geophysicists with the necessary information to use advanced remote sensing technology in the exploration of oil and gas, minerals, and groundwater. It describes the integration of remote sensing in each of the nine exploration methods based on over 11 case studies from different countries across the globe. Features: Describes the geophysical exploration methods that geophysicists frequently use, along with suitable remote sensing techniques Offers a well-structured one-stop guide for finding a suitable remote sensing technique for a specific geophysical exploration method Provides case studies on the exploration of oil, gas, and groundwater with step-by-step instructions using remote sensing technology Serves as a practical field book for exploration geophysicists who never used or rarely use remote sensing. Enables exploration geophysicists to understand and interpret remote sensing data for the assessment of complex explorations This book is an excellent resource for professionals, researchers, academics, and students with a background in remote sensing across many disciplines in Earth sciences such as geology, hydrology, petrology, mining, geography, geosciences, etc.
Quantitative Geosciences Data Analytics Geostatistics Reservoir Characterization And Modeling
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Author : Y. Z. Ma
language : en
Publisher: Springer
Release Date : 2019-07-15
Quantitative Geosciences Data Analytics Geostatistics Reservoir Characterization And Modeling written by Y. Z. Ma and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-15 with Technology & Engineering categories.
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.
Harness Oil And Gas Big Data With Analytics
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Author : Keith R. Holdaway
language : en
Publisher: John Wiley & Sons
Release Date : 2014-05-05
Harness Oil And Gas Big Data With Analytics written by Keith R. Holdaway and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-05 with Business & Economics categories.
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.
Machine Learning Applications In Subsurface Energy Resource Management
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Author : Srikanta Mishra
language : en
Publisher: CRC Press
Release Date : 2022-12-27
Machine Learning Applications In Subsurface Energy Resource Management written by Srikanta Mishra and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-27 with Technology & Engineering categories.
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.