Machine Learning for Subsurface Characterization

Download or Read online Machine Learning for Subsurface Characterization full in PDF, ePub and kindle. This book written by Siddharth Misra and published by Gulf Professional Publishing which was released on 12 October 2019 with total pages 440. We cannot guarantee that Machine Learning for Subsurface Characterization book is available in the library, click Get Book button to download or read online books. Join over 650.000 happy Readers and READ as many books as you like.

Machine Learning for Subsurface Characterization
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Publisher : Gulf Professional Publishing
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ISBN : 9780128177372
Pages : 440 pages
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Download or Read Online Machine Learning for Subsurface Characterization in PDF, Epub and Kindle

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

Machine Learning for Subsurface Characterization

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

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Machine Learning for the Subsurface Characterization at Core  Well  and Reservoir Scales

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A Primer on Machine Learning in Subsurface Geosciences

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,

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Advances in Subsurface Data Analytics

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,

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Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization

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

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Advances in Subsurface Data Analytics

Advances in Subsurface Data Analytics: Traditional and Physics-Based Machine Learning brings together popular, emerging machine learning algorithms and their applications in subsurface analysis, including geology, geophysics and petrophysics. Each chapter focuses on one machine learning algorithm and includes detailed workflow, applications and case studies. In addition, some of the chapters

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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Download or read online Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs written by Koenraad F. Beckers, published by Unknown which was released on 2021. Get Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Books now! Available in PDF, ePub and Kindle.

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CO2 Injection in the Network of Carbonate Fractures

This book presents guidelines for the design, operation and monitoring of CO2 injection in fractured carbonates, with low permeability in the rock matrix, for geological storage in permanent trapping. CO2 migration is dominated by fractures in formations where the hydrodynamic and geochemical effects induced by the injection play a key

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Advanced Network Technologies and Intelligent Computing

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Collaborative Computing  Networking  Applications and Worksharing

This two-volume set constitutes the refereed proceedings of the 16th International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020, held in Shanghai, China, in October 2020. The 61 full papers and 16 short papers presented were carefully reviewed and selected from 211 submissions. The papers reflect the conference sessions as follows: Collaborative Applications

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Geothermal Energy

This book focuses on the usage of geothermal energy in countries with low-enthalpy reservoirs. It begins with the fundamentals of geothermal energy and classification of geothermal resources and their importance, including enhanced geothermal systems (EGS). Further, it discusses the creation, production, potential assessment, perspective analysis, life cycle, and environmental assessments

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Machine Learning for Spatial Environmental Data

Accompanying CD-RM contains Machine learning office software, MLO guide (pdf) and examples of data.

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Sustainable Geoscience for Natural Gas SubSurface Systems

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

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