Machine Learning For The Subsurface Characterization At Core Well And Reservoir Scales
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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
Implementation And Interpretation Of Machine And Deep Learning To Applied Subsurface Geological Problems
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Author : David A. Wood
language : en
Publisher: Elsevier
Release Date : 2025-02-18
Implementation And Interpretation Of Machine And Deep Learning To Applied Subsurface Geological Problems written by David A. Wood and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-18 with Technology & Engineering categories.
Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems: Prediction Models Exploiting Well-Log Information explores machine and deep learning models for subsurface geological prediction problems commonly encountered in applied resource evaluation and reservoir characterization tasks. The book provides insights into how the performance of ML/DL models can be optimized—and sparse datasets of input variables enhanced and/or rescaled—to improve prediction performances. A variety of topics are covered, including regression models to estimate total organic carbon from well-log data, predicting brittleness indexes in tight formation sequences, trapping mechanisms in potential sub-surface carbon storage reservoirs, and more.Each chapter includes its own introduction, summary, and nomenclature sections, along with one or more case studies focused on prediction model implementation related to its topic. - Addresses common applied geological problems focused on machine and deep learning implementation with case studies - Considers regression, classification, and clustering machine learning methods and how to optimize and assess their performance, considering suitable error and accuracy metric - Contrasts the pros and cons of multiple machine and deep learning methods - Includes techniques to improve the identification of geological carbon capture and storage reservoirs, a key part of many energy transition strategies
Machine Learning For The Subsurface Characterization At Core Well And Reservoir Scales
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Author : Hao Li
language : en
Publisher:
Release Date : 2020
Machine Learning For The Subsurface Characterization At Core Well And Reservoir Scales written by Hao Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Engineering geology categories.
Reservoir Characterization
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Author : Fred Aminzadeh
language : en
Publisher: John Wiley & Sons
Release Date : 2022-01-06
Reservoir Characterization written by Fred Aminzadeh 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 2022-01-06 with Science categories.
RESERVOIR CHARACTERIZATION The second volume in the series, “Sustainable Energy Engineering,” written by some of the foremost authorities in the world on reservoir engineering, this groundbreaking new volume presents the most comprehensive and updated new processes, equipment, and practical applications in the field. Long thought of as not being “sustainable,” newly discovered sources of petroleum and newly developed methods for petroleum extraction have made it clear that not only can the petroleum industry march toward sustainability, but it can be made “greener” and more environmentally friendly. Sustainable energy engineering is where the technical, economic, and environmental aspects of energy production intersect and affect each other. This collection of papers covers the strategic and economic implications of methods used to characterize petroleum reservoirs. Born out of the journal by the same name, formerly published by Scrivener Publishing, most of the articles in this volume have been updated, and there are some new additions, as well, to keep the engineer abreast of any updates and new methods in the industry. Truly a snapshot of the state of the art, this groundbreaking volume is a must-have for any petroleum engineer working in the field, environmental engineers, petroleum engineering students, and any other engineer or scientist working with reservoirs. This outstanding new volume: Is a collection of papers on reservoir characterization written by world-renowned engineers and scientists and presents them here, in one volume Contains in-depth coverage of not just the fundamentals of reservoir characterization, but the anomalies and challenges, set in application-based, real-world situations Covers reservoir characterization for the engineer to be able to solve daily problems on the job, whether in the field or in the office Deconstructs myths that are prevalent and deeply rooted in the industry and reconstructs logical solutions Is a valuable resource for the veteran engineer, new hire, or petroleum engineering student
A Primer On Machine Learning In Subsurface Geosciences
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Author : Shuvajit Bhattacharya
language : en
Publisher: Springer Nature
Release Date : 2021-05-03
A Primer On Machine Learning In Subsurface Geosciences written by Shuvajit Bhattacharya and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-03 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.
Data Science And Machine Learning Applications In Subsurface Engineering
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Author : Daniel Asante Otchere
language : en
Publisher: CRC Press
Release Date : 2024-02-06
Data Science And Machine Learning Applications In Subsurface Engineering written by Daniel Asante Otchere and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-06 with Science categories.
This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments. This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.
Sustainable Geoscience For Natural Gas Subsurface Systems
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Author : David A. Wood
language : en
Publisher: Gulf Professional Publishing
Release Date : 2021-10-30
Sustainable Geoscience For Natural Gas Subsurface Systems written by David A. Wood 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 2021-10-30 with Science categories.
Sustainable Geoscience for Natural Gas SubSurface Systems delivers many of the scientific fundamentals needed in the natural gas industry, including coal-seam gas reservoir characterization and fracture analysis modeling for shale and tight gas reservoirs. Advanced research includes machine learning applications for well log and facies analysis, 3D gas property geological modeling, and X-ray CT scanning to reduce environmental hazards. Supported by corporate and academic contributors, along with two well-distinguished editors, the book gives today's natural gas engineers both fundamentals and advances in a convenient resource, with a zero-carbon future in mind. - Includes structured case studies to illustrate how new principles can be applied in practical situations - Helps readers understand advanced topics, including machine learning applications to optimize predictions, controls and improve knowledge-based applications - Provides tactics to accelerate emission reductions - Teaches gas fracturing mechanics aimed at reducing environmental impacts, along with enhanced oil recovery technologies that capture carbon dioxide
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)
Core Values The Role Of Core In Twenty First Century Reservoir Characterization
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Author : A. Neal
language : en
Publisher: Geological Society of London
Release Date : 2023-11-21
Core Values The Role Of Core In Twenty First Century Reservoir Characterization written by A. Neal and has been published by Geological Society of London this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-21 with Science categories.
Deep subsurface characterization technologies and demands are changing rapidly within the energy industry. In this swiftly evolving landscape, the wide range of analyses performed on the rocks and fluids obtained from cores remain fundamental tools in managing subsurface uncertainty and associated risk. During the energy transition large volumes of newly acquired and legacy core will be accessed to better understand both existing hydrocarbon resources and other subsurface energy-related systems, particularly for carbon capture, utilization and storage (CCUS), geothermal energy and the long-term storage of nuclear waste. Through state-of-the-art reviews and case studies this volume illustrates how innovative approaches continue to create value from both new and historical cores recovered for deep subsurface reservoir characterization and storage complex evaluation. Such an assessment is timely given that the sector sits at a pivotal point in terms of changing technologies, economics, demographics, skillsets and energy solutions.
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