Machine Learning for Subsurface Characterization

Produk Detail:
  • Author : Siddharth Misra
  • Publisher : Gulf Professional Publishing
  • Pages : 440 pages
  • ISBN : 0128177373
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>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 page 440 pages. We cannot guarantee that Machine Learning for Subsurface Characterization book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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
  • Author : Siddharth Misra,Hao Li,Jiabo He
  • Publisher : Gulf Professional Publishing
  • Release : 12 October 2019
GET THIS BOOK 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 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

A Primer on Machine Learning in Subsurface Geosciences

A Primer on Machine Learning in Subsurface Geosciences
  • Author : Shuvajit Bhattacharya
  • Publisher : Springer
  • Release : 07 June 2021
GET THIS BOOK 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, 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

A Primer on Machine Learning in Subsurface Geosciences

A Primer on Machine Learning in Subsurface Geosciences
  • Author : Shuvajit Bhattacharya
  • Publisher : Springer Nature
  • Release : 04 June 2021
GET THIS BOOK 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, 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

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 : 01 February 2021
GET THIS BOOK 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 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

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 : 15 February 2021
GET THIS BOOK 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 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

3D Seismic Attribute Analysis and Machine Learning for Reservoir Characterization in Taranaki Basin New Zealand

3D Seismic Attribute Analysis and Machine Learning for Reservoir Characterization in Taranaki Basin  New Zealand
  • Author : Aamer Ali AlHakeem
  • Publisher : Unknown
  • Release : 22 June 2021
GET THIS BOOK 3D Seismic Attribute Analysis and Machine Learning for Reservoir Characterization in Taranaki Basin New Zealand

"The Kapuni group within the Taranaki Basin in New Zealand is a potential petroleum reservoir. The objective of the study includes building a sequential approach to identify different geological features and facies sequences within the strata, through visualizing the targeted formations by interpreting and correlating the regional geological data, 3D seismic, and well data by following a sequential workflow. First, seismic interpretation is performed targeting the Kapuni group formations, mainly, the Mangahewa C-sand and Kaimiro D-sand. Synthetic seismograms and well

Reservoir Simulations

Reservoir Simulations
  • Author : Shuyu Sun,Tao Zhang
  • Publisher : Gulf Professional Publishing
  • Release : 18 June 2020
GET THIS BOOK Reservoir Simulations

Reservoir Simulation: Machine Learning and Modeling helps the engineer step into the current and most popular advances in reservoir simulation, learning from current experiments and speeding up potential collaboration opportunities in research and technology. This reference explains common terminology, concepts, and equations through multiple figures and rigorous derivations, better preparing the engineer for the next step forward in a modeling project and avoid repeating existing progress. Well-designed exercises, case studies and numerical examples give the engineer a faster start on

Subsurface Hydrology

Subsurface Hydrology
  • Author : David W. Hyndman,Frederick D. Day-Lewis,Kamini Singha
  • Publisher : American Geophysical Union
  • Release : 09 January 2007
GET THIS BOOK Subsurface Hydrology

Published by the American Geophysical Union as part of the Geophysical Monograph Series, Volume 171. Groundwater is a critical resource and the PrinciPal source of drinking water for over 1.5 billion people. In 2001, the National Research Council cited as a "grand challenge" our need to understand the processes that control water movement in the subsurface. This volume faces that challenge in terms of data integration between complex, multi-scale hydrologie processes, and their links to other physical, chemical, and biological processes at multiple

A Machine Learning and Computer Vision Framework for Damage Characterization and Structural Behavior Prediction

A Machine Learning and Computer Vision Framework for Damage Characterization and Structural Behavior Prediction
  • Author : Rouzbeh Davoudi
  • Publisher : Unknown
  • Release : 22 June 2021
GET THIS BOOK A Machine Learning and Computer Vision Framework for Damage Characterization and Structural Behavior Prediction

This work focuses on using computer vision to relate surface (and limited subsurface) damage observations to quantitative damage and load levels in structural components. In particular, image processing and machine learning regression techniques have been used to build predictive models capable of estimating internal loads (e.g., shear and moment) and damage states in RC beams, slabs, and panels based on surface crack pattern images. The predictive models have been generated and tested using image data sets obtained from earlier

Enhance Oil and Gas Exploration with Data Driven Geophysical and Petrophysical Models

Enhance Oil and Gas Exploration with Data Driven Geophysical and Petrophysical Models
  • Author : Keith R. Holdaway,Duncan H. B. Irving
  • Publisher : John Wiley & Sons
  • Release : 04 October 2017
GET THIS BOOK Enhance Oil and Gas Exploration with Data Driven Geophysical and Petrophysical Models

Leverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this

Techniques for Real World Ground Penetrating Radar Data Analysis

Techniques for Real World Ground Penetrating Radar Data Analysis
  • Author : André Busche
  • Publisher : Unknown
  • Release : 13 March 2014
GET THIS BOOK Techniques for Real World Ground Penetrating Radar Data Analysis

Abstract Ground Penetrating Radar (GPR) Data Analysis deals with the problem of shallow subsurface imaging, which is motivated by the daily work of engineers, \eg those of municipalities.The concrete problem tackled in this thesis is motivated by the fact, that, at least in Germany, municipalities have knowledge about the existence of supply lines such as gas and water pipelines to cross and follow urban streets, while their actual position is often uncertain.The consequences are obvious: once a street

Study of AI Based Methods for Characterization of Geotechnical Site Investigation Data

Study of AI Based Methods for Characterization of Geotechnical Site Investigation Data
  • Author : Hui Wang,Xiangrong Wang,Robert Y. Liang
  • Publisher : Unknown
  • Release : 22 June 2021
GET THIS BOOK Study of AI Based Methods for Characterization of Geotechnical Site Investigation Data

Due to the inadequate knowledge of the soil forming histories and/or human activities, the subsurface soil layers are difficult to ascertain. Subsurface uncertainty and its influence on geotechnical design have long been a challenge facing practitioners. Recently, the ASCE Geo-institute has developed the Data Interchange for Geotechnical and Geoenvironmental Specialists (DIGGS), which is a standard schema for transferring geotechnical data between multiple organizations. It paves the way of sharing and unifying datasets and forms a structural database for further