Machine Learning For Subsurface Characterization
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Machine Learning For Subsurface Characterization
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Author : Siddharth Misra
language : en
Publisher: Gulf Professional Publishing
Release Date : 2020
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 2020 with Big data categories.
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 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.
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
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.
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.
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.
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
Analysing Cloud Ddos Attacks Using Supervised Machine Learning
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Author : Chisom Elizabeth Alozie
language : en
Publisher: Deep Science Publishing
Release Date : 2025-02-02
Analysing Cloud Ddos Attacks Using Supervised Machine Learning written by Chisom Elizabeth Alozie and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-02 with True Crime categories.
Cloud computing in its simplest form refers to the provision of hardware and software to deliver a service over an internet network. However, Cloud Computing has numerous issues, such as security attacks and distributed denial of service (DDoS). A DDoS attack is defined as a method of attack in which numerous computer systems are allowed to attack a target, such as a server, any resource, or website, resulting in a denial of service for the resource's intended users. This research analysed the normal traffic and DDoS attack traffic from cloud environments using machine learning technology to detect DDoS attacks. This work’s main contribution is the extraction of dataset features and the discovery of new flow features for DDoS attack detection. To create the dataset, novel features are stored in a CSV file using the CICFlowMeter tool. Features were selected using a correlation coefficient to get better model accuracy. Machine learning algorithms were trained on the resulting cloud dataset. The existing work reviews for detection of DDoS attacks either used a cloud dataset or another network data set, or the research findings were kept confidential. The methodology used to solve this problem is the CRISP-DM methodology. The proposed solution deployed a brand-new dataset with five machine-learning models for classification. The findings of this study help to improve knowledge of the ability of DDoS datasets to detect intrusions. Five performance metrics—accuracy, precision, recall, F1-score, and computation time were used to analyse the datasets. Based on the results achieved with the new dataset, the Random Forest, Support Vector Machine, Decision Tree, and K-NN achieved a 100% rate of 100% on the accuracy, precision, recall, and F1 score in a shorter computation time. With the open-source dataset, Random Forest, Decision Tree, and K-Nearest Neighbor achieved 100% accuracy.
Unraveling New Frontiers And Advances In Bioinformatics
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Author : Amit Chaudhary
language : en
Publisher: Springer Nature
Release Date : 2024-09-21
Unraveling New Frontiers And Advances In Bioinformatics written by Amit Chaudhary and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-21 with Computers categories.
This book describes the bioinformatics research field, from its historical roots to the cutting-edge technologies. Many readers can discover the power of next-generation sequencing and genomic data analysis, uncover the secrets of single-cell genomics and transcriptomics, explore the metagenomics and microbiome analysis, and predict the protein structures using structural bioinformatics. Several case studies witnessing the fusion of bioinformatics and artificial intelligence, driving insights from vast biological datasets have also been explored. Other important aspects listed in the book are integrating the omics data for a holistic view of biological systems; experiencing the future of medicine with precision healthcare and personalized treatments; accelerating drug discovery and repurposing through computational approaches; agricultural genomics; and exploring the role of immunoinformatics in designing effective vaccines against infectious diseases.