Description
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, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume.
About the Author
Dr. Shuvajit Bhattacharya is a research associate at the Bureau of Economic Geology, the University of Texas at Austin. He is an applied geophysicist/petrophysicist specializing in seismic interpretation, petrophysical modeling, machine learning, and integrated subsurface characterization. Prior to joining the Bureau of Economic Geology, Dr. Bhattacharya worked as an Assistant Professor at the University of Alaska Anchorage. He has completed several projects in the USA, Netherlands, Australia, South Africa, and India. He has published and presented more than 70 technical articles in journals, books, and conferences. His current research focuses on energy resources exploration, development, and subsurface storage of carbon and hydrogen. He completed his Ph.D. at West Virginia University in 2016. Dr. Haibin Di is a Senior Data Scientist in the Digital Subsurface Intelligence team at Schlumberger. His research interest is in implementing machine learning algorithms, particularly deep neural networks, into multiple seismic applications, including stratigraphy interpretation, property estimation, denoising, and seismic-well tie. He has published more than 70 papers in seismic interpretation and holds seven patents on machine learning-assisted subsurface data analysis. Dr. Di received his Ph.D. in Geology from West Virginia University in 2016, worked as a postdoctoral researcher at Georgia Institute of Technology in 2016-2018, and joined Schlumberger in 2018.
Book Information
ISBN 9780128222959
Author Shuvajit Bhattacharya
Format Paperback
Page Count 376
Imprint Elsevier Science Publishing Co Inc
Publisher Elsevier Science Publishing Co Inc
Weight(grams) 770g
About the Author
Dr. Shuvajit Bhattacharya is a research associate at the Bureau of Economic Geology, the University of Texas at Austin. He is an applied geophysicist/petrophysicist specializing in seismic interpretation, petrophysical modeling, machine learning, and integrated subsurface characterization. Prior to joining the Bureau of Economic Geology, Dr. Bhattacharya worked as an Assistant Professor at the University of Alaska Anchorage. He has completed several projects in the USA, Netherlands, Australia, South Africa, and India. He has published and presented more than 70 technical articles in journals, books, and conferences. His current research focuses on energy resources exploration, development, and subsurface storage of carbon and hydrogen. He completed his Ph.D. at West Virginia University in 2016. Dr. Haibin Di is a Senior Data Scientist in the Digital Subsurface Intelligence team at Schlumberger. His research interest is in implementing machine learning algorithms, particularly deep neural networks, into multiple seismic applications, including stratigraphy interpretation, property estimation, denoising, and seismic-well tie. He has published more than 70 papers in seismic interpretation and holds seven patents on machine learning-assisted subsurface data analysis. Dr. Di received his Ph.D. in Geology from West Virginia University in 2016, worked as a postdoctoral researcher at Georgia Institute of Technology in 2016-2018, and joined Schlumberger in 2018.
Book Information
ISBN 9780128222959
Author Shuvajit Bhattacharya
Format Paperback
Page Count 376
Imprint Elsevier Science Publishing Co Inc
Publisher Elsevier Science Publishing Co Inc
Weight(grams) 770g
