Machine Learning for Subsurface Characterization

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Siddharth Misra (Associate Professor, Harold Vance Department of Petroleum Engineering and the Department of Geology and Geophysics, Texas A&M University, USA) Hao Li (PhD-degree candidate in the Mewbourne College of Earth and Energy (MCEE) at the University of Oklahoma in Norman.)

Machine Learning for Subsurface Characterization

Siddharth Misra (Associate Professor, Harold Vance Department of Petroleum Engineering and the Department of Geology and Geophysics, Texas A&M University, USA), Hao Li (PhD-degree candidate in the Mewbourne College of Earth and Energy (MCEE) at the University of Oklahoma in Norman.), Jiabo He (A doctoral candidate in computer science at the University of Melbourne, Australia.)

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English

Gulf Professional Publishing
13 October 2019

Gas technology; Petroleum technology; Environmental science, engineering & technology; Machine learning

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.

By: Siddharth Misra (Associate Professor Harold Vance Department of Petroleum Engineering and the Department of Geology and Geophysics Texas A&M University USA), Hao Li (PhD-degree candidate in the Mewbourne College of Earth and Energy (MCEE) at the University of Oklahoma in Norman.), Jiabo He (A doctoral candidate in computer science at the University of Melbourne, Australia.)
Imprint: Gulf Professional Publishing
Country of Publication: United States
Dimensions: Height: 229mm, Width: 152mm,
Weight: 660g
ISBN: 9780128177365
ISBN 10: 0128177365
Pages: 440
Publication Date: 13 October 2019
Audience: College/higher education , Primary
Format: Paperback
Publisher's Status: Active

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.

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Machine Learning for Subsurface Characterization

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