Machine Learning for Subsurface Characterization
  • Author : Siddharth Misra
  • Release Date : 12 October 2019
  • Publisher : Gulf Professional Publishing
  • Genre : Science
  • Pages : 440
  • ISBN 13 : 9780128177372

Machine Learning for Subsurface Characterization Book Summary

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
Genre : Science
Total View : 4754 Views
File Size : 49,9 Mb
Get Book

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 ...

Machine Learning for Subsurface Characterization

Machine Learning for Subsurface Characterization

Author : Siddharth Misra,Hao Li,Jiabo He
Publisher : Gulf Professional Publishing
Genre : Science
Total View : 6483 Views
File Size : 55,9 Mb
Get Book

Machine Learning for Subsurface Characterization focuses on the development and application of neural networks, deep learning, unsupervised learning, reinforcement learning, and clustering methods for subsurface characterization under constraints due to financial, operational, regulatory, risk, technological and environmental challenges. The book introduces readers to methods of generating subsurface signals and analyzing ...

Advances in Subsurface Data Analytics

Advances in Subsurface Data Analytics

Author : Shuvajit Bhattacharya,Haibin Di
Publisher : Elsevier
Genre : Computers
Total View : 4350 Views
File Size : 41,9 Mb
Get Book

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, ...

A Primer on Machine Learning in Subsurface Geosciences

A Primer on Machine Learning in Subsurface Geosciences

Author : Shuvajit Bhattacharya
Publisher : Springer Nature
Genre : Technology & Engineering
Total View : 1015 Views
File Size : 47,5 Mb
Get Book

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, ...

Machine Learning Applications in Subsurface Energy Resource Management

Machine Learning Applications in Subsurface Energy Resource Management

Author : Srikanta Mishra
Publisher : CRC Press
Genre : Technology & Engineering
Total View : 6404 Views
File Size : 45,9 Mb
Get Book

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 ...

Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization

Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization

Author : Siddharth Misra,Yifu Han,Yuteng Jin,Pratiksha Tathed
Publisher : Elsevier
Genre : Science
Total View : 2318 Views
File Size : 50,8 Mb
Get Book

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 ...

Handbook of Petroleum Geoscience

Handbook of Petroleum Geoscience

Author : Soumyajit Mukherjee,Swagato Dasgupta,Chandan Majumdar,Subhadip Mandal,Troyee Dasgupta
Publisher : John Wiley & Sons
Genre : Science
Total View : 1825 Views
File Size : 51,7 Mb
Get Book

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: ...