January 25, 2021

Download Ebook Free Machine Learning For Subsurface Characterization

Machine Learning for Subsurface Characterization

Machine Learning for Subsurface Characterization
Author : Siddharth Misra,Hao Li,Jiabo He
Publisher : Gulf Professional Publishing
Release Date : 2019-10-12
Category : Science
Total pages :440
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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

CO2 Injection in the Network of Carbonate Fractures

CO2 Injection in the Network of Carbonate Fractures
Author : J. Carlos de Dios
Publisher : Springer Nature
Release Date : 2021
Category :
Total pages :129
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Machine Learning for Spatial Environmental Data

Machine Learning for Spatial Environmental Data
Author : Mikhail Kanevski,Alexei Pozdnoukhov,Alexi Pozdnukhov,Vadim Timonin
Publisher : EPFL Press
Release Date : 2009-06-09
Category : Business & Economics
Total pages :377
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Accompanying CD-RM contains Machine learning office software, MLO guide (pdf) and examples of data.

Earth Observation Open Science and Innovation

Earth Observation Open Science and Innovation
Author : Pierre-Philippe Mathieu,Christoph Aubrecht
Publisher : Springer
Release Date : 2018-01-23
Category : Science
Total pages :330
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This book is published open access under a CC BY 4.0 license. Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites. This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. In addition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites.

geoENV VI – Geostatistics for Environmental Applications

geoENV VI – Geostatistics for Environmental Applications
Author : Amílcar Soares,Maria João Pereira,Roussos Dimitrakopoulos
Publisher : Springer Science & Business Media
Release Date : 2008-03-12
Category : Science
Total pages :511
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This volume contains 40 selected full-text contributions from the Sixth European Conference on Geostatistics for Environmental Applications, geoENV IV, held in Rhodes, Greece, October 25-26, 2006. The objective of the editors was to compile a set of papers from which the reader could perceive how geostatistics is applied within the environmental sciences. A few selected theoretical contributions are also included.

The Development of Comparative Information Yield Curves for Application to Subsurface Characterization

The Development of Comparative Information Yield Curves for Application to Subsurface Characterization
Author : Felipe Pereira Jorge De Barros
Publisher : Unknown
Release Date : 2009
Category :
Total pages :338
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Machine Learning and Big Data with KDB+/Q

Machine Learning and Big Data with KDB+/Q
Author : Jan Novotny,Paul A. Bilokon,Aris Galiotos,Frederic Deleze
Publisher : John Wiley & Sons
Release Date : 2019-11-11
Category : Business & Economics
Total pages :640
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Upgrade your programming language to more effectively handle high-frequency data Machine Learning and Big Data with KDB+/Q offers quants, programmers and algorithmic traders a practical entry into the powerful but non-intuitive kdb+ database and q programming language. Ideally designed to handle the speed and volume of high-frequency financial data at sell- and buy-side institutions, these tools have become the de facto standard; this book provides the foundational knowledge practitioners need to work effectively with this rapidly-evolving approach to analytical trading. The discussion follows the natural progression of working strategy development to allow hands-on learning in a familiar sphere, illustrating the contrast of efficiency and capability between the q language and other programming approaches. Rather than an all-encompassing “bible”-type reference, this book is designed with a focus on real-world practicality ­to help you quickly get up to speed and become productive with the language. Understand why kdb+/q is the ideal solution for high-frequency data Delve into “meat” of q programming to solve practical economic problems Perform everyday operations including basic regressions, cointegration, volatility estimation, modelling and more Learn advanced techniques from market impact and microstructure analyses to machine learning techniques including neural networks The kdb+ database and its underlying programming language q offer unprecedented speed and capability. As trading algorithms and financial models grow ever more complex against the markets they seek to predict, they encompass an ever-larger swath of data ­– more variables, more metrics, more responsiveness and altogether more “moving parts.” Traditional programming languages are increasingly failing to accommodate the growing speed and volume of data, and lack the necessary flexibility that cutting-edge financial modelling demands. Machine Learning and Big Data with KDB+/Q opens up the technology and flattens the learning curve to help you quickly adopt a more effective set of tools.

Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing

Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing
Author : Ni-Bin Chang,Kaixu Bai
Publisher : CRC Press
Release Date : 2018-02-21
Category : Technology & Engineering
Total pages :528
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Combining versatile data sets from multiple satellite sensors with advanced thematic information retrieval is a powerful way for studying complex earth systems. The book Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing offers complete understanding of the basic scientific principles needed to perform image processing, gap filling, data merging, data fusion, machine learning, and feature extraction. Written by two experts in remote sensing, the book presents the required basic concepts, tools, algorithms, platforms, and technology hubs toward advanced integration. By merging and fusing data sets collected from different satellite sensors with common features, we are enabled to utilize the strength of each satellite sensor to the maximum extent. The inclusion of machine learning or data mining techniques to aid in feature extraction after gap filling, data merging and/or data fusion further empowers earth observation, leading to confirm the whole is greater than the sum of its parts. Contemporary applications discussed in this book make all essential knowledge seamlessly integrated by an interdisciplinary manner. These case-based engineering practices uniquely illustrate how to improve such an emerging field of importance to cope with the most challenging real-world environmental monitoring issues.

Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling

Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling
Author : Y. Z. Ma
Publisher : Springer
Release Date : 2019-07-15
Category : Technology & Engineering
Total pages :640
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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.

Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization

Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization
Author : Siddharth Misra,Yifu Han,Pratiksha Tathed,Yuteng Jin
Publisher : Elsevier
Release Date : 2021-02-15
Category : Science
Total pages :275
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Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization focuses on the development and application of electromagnetic measurement methodologies and their interpretation techniques for subsurface characterization. The book guides readers on how to characterize and understand materials using electromagnetic measurements, including dielectric permittivity, resistivity and conductivity measurements. This reference will be useful for subsurface engineers, petrophysicists, subsurface data analysts, geophysicists, hydrogeologists, and geoscientists who want to know how to develop tools and techniques of electromagnetic measurements and interpretation for subsurface characterization. Includes case studies to add additional color to the presented content Provides codes for the mechanistic modeling of multi-frequency conductivity and relative permittivity of porous geomaterials Presents detailed descriptions of multifrequency electromagnetic data interpretation models and inversion algorithm

Data Science and Machine Learning

Data Science and Machine Learning
Author : J Tisa,J Nedumaan,Dr P S Jagadeesh Kumar
Publisher : Unknown
Release Date : 2020-01-25
Category :
Total pages :232
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Machine learning is entirely reasonable for data science as Data Science is a wide term for different arena. Machine Learning is accessible for different schemes, for example, regression and clustering. On the opposite side, Data science is a wide term that couldn't focus on complex algorithms. In any case, Data science is liable for carrying structure to big data, searching for persuasive patterns, and in surmising decisions so as to change the expert needs. This book's extreme objective is to bring the implication of Machine learning in Data Science.

Emerging Intelligent Technologies in Industry

Emerging Intelligent Technologies in Industry
Author : Dominik Ryżko,Piotr Gawrysiak,Henryk Rybinski,Marzena Kryszkiewicz
Publisher : Springer
Release Date : 2011-08-31
Category : Computers
Total pages :344
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Intelligent technologies are the essential factors of innovation, and enable the industry to overcome technological limitations and explore the new frontiers. Therefore it is necessary for scientists and practitioners to cooperate and inspire each other, and use the latest research results in creating new designs and products. The idea of this book came out with the industrial workshop organized at the ISMIS conference in Warsaw, 2011. The book covers several applications of emerging, intelligent technologies in various branches of the industry. The contributions describe modern intelligent tools, algorithms and architectures, which have the potential to solve real problems, experienced by practitioners in various industry sectors. We hope this volume will show new directions for cooperation between science and industry and will facilitate efficient transfer of knowledge in the area of intelligent information systems.

Applied Statistical Modeling and Data Analytics

Applied Statistical Modeling and Data Analytics
Author : Srikanta Mishra,Akhil Datta-Gupta
Publisher : Elsevier
Release Date : 2017-10-27
Category : Science
Total pages :250
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Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences provides a practical guide to many of the classical and modern statistical techniques that have become established for oil and gas professionals in recent years. It serves as a "how to" reference volume for the practicing petroleum engineer or geoscientist interested in applying statistical methods in formation evaluation, reservoir characterization, reservoir modeling and management, and uncertainty quantification. Beginning with a foundational discussion of exploratory data analysis, probability distributions and linear regression modeling, the book focuses on fundamentals and practical examples of such key topics as multivariate analysis, uncertainty quantification, data-driven modeling, and experimental design and response surface analysis. Data sets from the petroleum geosciences are extensively used to demonstrate the applicability of these techniques. The book will also be useful for professionals dealing with subsurface flow problems in hydrogeology, geologic carbon sequestration, and nuclear waste disposal. Authored by internationally renowned experts in developing and applying statistical methods for oil & gas and other subsurface problem domains Written by practitioners for practitioners Presents an easy to follow narrative which progresses from simple concepts to more challenging ones Includes online resources with software applications and practical examples for the most relevant and popular statistical methods, using data sets from the petroleum geosciences Addresses the theory and practice of statistical modeling and data analytics from the perspective of petroleum geoscience applications

Flood Forecasting Using Machine Learning Methods

Flood Forecasting Using Machine Learning Methods
Author : Fi-John Chang,Kuolin Hsu,Li-Chiu Chang
Publisher : MDPI
Release Date : 2019-02-28
Category : Technology & Engineering
Total pages :366
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Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.

Intelligent Digital Oil and Gas Fields

Intelligent Digital Oil and Gas Fields
Author : Gustavo Carvajal,Marko Maucec,Stan Cullick
Publisher : Gulf Professional Publishing
Release Date : 2017-12-14
Category : Technology & Engineering
Total pages :374
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Intelligent Digital Oil and Gas Fields: Concepts, Collaboration, and Right-time Decisions delivers to the reader a roadmap through the fast-paced changes in the digital oil field landscape of technology in the form of new sensors, well mechanics such as downhole valves, data analytics and models for dealing with a barrage of data, and changes in the way professionals collaborate on decisions. The book introduces the new age of digital oil and gas technology and process components and provides a backdrop to the value and experience industry has achieved from these in the last few years. The book then takes the reader on a journey first at a well level through instrumentation and measurement for real-time data acquisition, and then provides practical information on analytics on the real-time data. Artificial intelligence techniques provide insights from the data. The road then travels to the "integrated asset" by detailing how companies utilize Integrated Asset Models to manage assets (reservoirs) within DOF context. From model to practice, new ways to operate smart wells enable optimizing the asset. Intelligent Digital Oil and Gas Fields is packed with examples and lessons learned from various case studies and provides extensive references for further reading and a final chapter on the "next generation digital oil field," e.g., cloud computing, big data analytics and advances in nanotechnology. This book is a reference that can help managers, engineers, operations, and IT experts understand specifics on how to filter data to create useful information, address analytics, and link workflows across the production value chain enabling teams to make better decisions with a higher degree of certainty and reduced risk. Covers multiple examples and lessons learned from a variety of reservoirs from around the world and production situations Includes techniques on change management and collaboration Delivers real and readily applicable knowledge on technical equipment, workflows and data challenges such as acquisition and quality control that make up the digital oil and gas field solutions of today Describes collaborative systems and ways of working and how companies are transitioning work force to use the technology and making more optimal decisions