Machine Learning for Subsurface Characterization - Softcover

Misra

 
9780128177365: Machine Learning for Subsurface Characterization
Softcover
ISBN 10: 0128177365 ISBN 13: 9780128177365
Verlag: Gulf Professional Publishing, 2019

Inhaltsangabe

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.

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Über die Autorinnen und Autoren

Siddharth Misra is currently associate professor at the Harold Vance Department of Petroleum Engineering, Texas A&M University, College Station, Texas. His research work is in the area of data-driven predictive models, machine learning, geosensors, and subsurface characterization. He earned a PhD in petroleum engineering from the University of Texas and a bachelor of technology in electrical engineering from the Indian Institute of Technology in Bombay. He received the Department of Energy Early Career Award in 2018 to promote geoscience research.

Hao Li is a PhD-degree candidate in the Mewbourne College of Earth and Energy (MCEE) at the University of Oklahoma in Norman. He interned with Facebook on improving ranking models using machine learning. His research interests include machine learning, petrophysics, and data analytics. He holds an MS degree in petroleum engineering from China University of Petroleum in Beijing.

Jiabo He is currently a doctoral candidate in computer science at the University of Melbourne, Australia. Jiabo’s research area includes deep learning, reinforcement learning, and imitation learning. He earned an MS in petroleum engineering from the University of Oklahoma and a BS in petroleum engineering from the China University of Petroleum in Beijing.

Von der hinteren Coverseite

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.

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Verlag
Gulf Professional Publishing
Erscheinungsdatum
2019
Sprache
Englisch
ISBN 10 
0128177365
ISBN 13 
9780128177365
Einband
Taschenbuch
Auflage
1
Anzahl der Seiten
440
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
gpsr@libri.de
gpsr@libri.de

Friedensallee 273
Hamburg
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Deutschland