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Author : Siddharth Misra,Hao Li,Jiabo He
Publisher : Gulf Professional Publishing
Page : 128 pages
File Size : 43,8 Mb
Release : 2020
Category : Big data
ISBN : OCLC:1125154104
Author : Shuvajit Bhattacharya,Haibin Di
Publisher : Elsevier
Page : 378 pages
File Size : 50,9 Mb
Release : 2022-05-18
Category : Computers
ISBN : 9780128223086
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
Author : Hao Li
Publisher : Unknown
Page : 228 pages
File Size : 49,7 Mb
Release : 2020
Category : Engineering geology
ISBN : OCLC:1152200629
Author : Siddharth Misra,Hao Li,Jiabo He
Publisher : Gulf Professional Publishing
Page : 442 pages
File Size : 53,8 Mb
Release : 2019-10-12
Category : Technology & Engineering
ISBN : 9780128177372
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
Author : Shuvajit Bhattacharya
Publisher : Springer Nature
Page : 172 pages
File Size : 45,6 Mb
Release : 2021-05-03
Category : Technology & Engineering
ISBN : 9783030717681
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.
Author : Aria Abubakar
Publisher : Elsevier
Page : 0 pages
File Size : 55,8 Mb
Release : 2024-11-01
Category : Technology & Engineering
ISBN : 9780443224225
Artificial Intelligence for Subsurface Characterization and Monitoring provides an in-depth examination of how deep learning accelerates the process of subsurface characterization and monitoring and provides an end-to-end solution. In recent years, deep learning has been introduced to the geoscience community to overcome some longstanding technical challenges. This book explores some of the most important topics in this discipline to explain the unique capability of deep learning in subsurface characterization for hydrocarbon exploration and production and for energy transition. Readers will discover deep learning methods that can improve the quality and efficiency of many of the key steps in subsurface characterization and monitoring. The text is organized into five parts. The first two parts explore deep learning for data enrichment and well log data, including information extraction from unstructured well reports as well as log data QC and processing. Next is a review of deep learning applied to seismic data and data integration, which also covers intelligent processing for clearer seismic images and rock property inversion and validation. The closing section looks at deep learning in time lapse scenarios, including sparse data reconstruction for reducing the cost of 4D seismic data, time-lapse seismic data repeatability enforcement, and direct property prediction from pre-migration seismic data. Focuses on deep learning applications for geoscience provides a one-stop reference for deep learning applications for geoscience Provides comprehensive examples for state-of-art techniques throughout the subsurface characterization workflow Presented applications come with realistic field dataset examples so that readers can learn what to expect in real-life
Author : Daniel Asante Otchere
Publisher : CRC Press
Page : 368 pages
File Size : 45,7 Mb
Release : 2024-02-06
Category : Science
ISBN : 9781003860228
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.
Author : Srikanta Mishra
Publisher : CRC Press
Page : 379 pages
File Size : 52,6 Mb
Release : 2022-12-27
Category : Technology & Engineering
ISBN : 9781000823875
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.
Author : Soumyajit Mukherjee,Swagato Dasgupta,Chandan Majumdar,Subhadip Mandal,Troyee Dasgupta
Publisher : John Wiley & Sons
Page : 469 pages
File Size : 44,7 Mb
Release : 2022-10-12
Category : Science
ISBN : 9781119680109
HANDBOOK OF PETROLEUM GEOSCIENCE This reference brings together the latest industrial updates and research advances in regional tectonics and geomechanics. Each chapter is based upon an in-depth case study from a particular region, highlighting core concepts and themes as well as regional variations. Key topics discussed in the book are: Drilling solutions from the Kutch offshore basin Geophysical studies from a gas field in Bangladesh Exploring Himalayan terrain in India Tectonics and exploration of the Persian Gulf basin Unconventional gas reservoirs in the Bohemian Massif This book is an invaluable industry resource for professionals and academics working in and studying the fields of petroleum geoscience and tectonics.
Author : Goncalo Marques,Joshua O. Ighalo
Publisher : Academic Press
Page : 475 pages
File Size : 51,8 Mb
Release : 2022-03-20
Category : Computers
ISBN : 9780323855983
Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering merges computer engineering and environmental engineering. The book presents the latest finding on how data science and AI-based tools are being applied in environmental engineering research. This application involves multiple domains such as data science and artificial intelligence to transform the data collected by intelligent sensors into relevant and reliable information to support decision-making. These tools include fuzzy logic, knowledge-based systems, particle swarm optimization, genetic algorithms, Monte Carlo simulation, artificial neural networks, support vector machine, boosted regression tree, simulated annealing, ant colony algorithm, decision tree, immune algorithm, and imperialist competitive algorithm. This book is a fundamental information source because it is the first book to present the foundational reference material in this new research field. Furthermore, it gives a critical overview of the latest cross-domain research findings and technological developments on the recent advances in computer-aided intelligent environmental data engineering. Captures the application of data science and artificial intelligence for a broader spectrum of environmental engineering problems Presents methods and procedures as well as case studies where state-of-the-art technologies are applied in actual environmental scenarios Offers a compilation of essential and critical reviews on the application of data science and artificial intelligence to the entire spectrum of environmental engineering
Author : Mattia Bartoli,Mauro Giorcelli,Alberto Tagliaferro
Publisher : BoD – Books on Demand
Page : 394 pages
File Size : 47,5 Mb
Release : 2023-01-25
Category : Nature
ISBN : 9781803562513
Biochar is the carbonaceous residue produced from the pyrolytic conversion of biomass. It is generally used for agricultural applications as a soil amendment but has far wider potential. This book presents the use of biochar as a platform for the development of new intriguing solutions in several cutting-edge fields. The book is a useful reference volume for any reader with a strong scientific and technological background, ranging from scientific advisors in private companies to academic researchers promoting the spread of knowledge about biochar to anyone not already working with it.
Author : Jingzheng Ren,Weifeng Shen,Yi Man,Lichun Dong
Publisher : Elsevier
Page : 542 pages
File Size : 55,6 Mb
Release : 2021-06-05
Category : Technology & Engineering
ISBN : 9780128217436
Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering
Author : Sapna Sinha,Vishal Bhatnagar,Prateek Agrawal,Vikram Bali
Publisher : CRC Press
Page : 271 pages
File Size : 51,5 Mb
Release : 2023-10-30
Category : Computers
ISBN : 9781003802860
This book gives a complete overview of cloud computing: its importance, its trends, innovations, and its amalgamation with other technologies. Key Features: In-depth explanation of emerging technologies utilizing cloud computing Supplemented with visuals, flow charts, and diagrams Real-time examples included Caters to beginners, as well as advanced researchers, by explaining implications, innovations, issues, and challenges of cloud computing Highlights the need for cloud computing and the true benefits derived by its application and integration in emerging technologies Simple, easy language
Author : Camelia Delcea,Liviu-Adrian Cotfas
Publisher : Springer Nature
Page : 340 pages
File Size : 48,5 Mb
Release : 2023-03-02
Category : Business & Economics
ISBN : 9789811999321
This book focuses on the main advancements made in the economics and social sciences field through the use of grey systems theory. As a result, it addresses both the state of the art and the applications of grey systems theory in economics and social sciences. The book is structured in eight main chapters, covering the following topics: the state of the art in the grey systems theory research in economics and social sciences, which includes a bibliometric analysis, a selection of the most well-cited papers in the field, and a selection of applications in which the grey systems theory is used in the areas of suppliers selection, risk assessment, public opinion assessment, linear programming, complex projects management, social media analysis, and natural language processing Each chapter gives an overview of a particular economic or social sciences topic, providing an explanation on the main terms and methods used for solving the problem, including the notations, terminology, and the needed steps to solve it. A practical application is presented in most of the chapters, while in the others, a series of case studies are presented from the literature and discussed in depth in terms of methods used and advantages brought by each of these methods. The last chapter discusses the hybridization cases in which the grey systems theory has been or can be successfully used along with other artificial intelligence methods and techniques for a more advanced analysis in the economics and social sciences field. The reasoning and the explanations used in the book are easy to understand for the interested persons who are not familiar to the field and want to learn more related on how the grey systems theory can be applied to economics and social sciences. As for the experts in this field, this book can be a good referral point for developing new areas of research by combining the advantages of the grey systems theory with other theories within the field.
Author : John G Lyon,Lynn Lyon
Publisher : CRC Press
Page : 237 pages
File Size : 49,7 Mb
Release : 2022-12-21
Category : Technology & Engineering
ISBN : 9781000798913
1. Captures advanced technologies and applications for assimilation and implementation and addresses a wide spectrum of water issues. 2. Provides real world applications and case studies of advanced spectral and spatial sensors combined with geospatially driven water process modelling. 3. Details applications of the latest remote sensor systems including GRACE, SMAP, AVIRIS, Sentential, MODIS, Landsat 8, RapidEye, AirSWOT, and pays special attention to multidisciplinary cases studies. 4. It is global in coverage with applications demonstrated by more than 170 experts from around the world. 5. Edited by extremely qualified authors with lifelong expertise in water sciences and with an extensive record in books and journal publications.