Advances in Subsurface Data Analytics: Traditional and Physics-Based Machine Learning - Tapa blanda

Acerca de los autores

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.

De la contraportada

Advances in Subsurface Data Analytics brings together the fundamentals of popular and emerging machine learning algorithms with their applications in subsurface analysis, including geology, geophysics, and petrophysics. Each chapter focuses on one machine learning algorithm and includes detailed workflow, applications, and case studies. In addition, some of the chapters contain a comparison of an algorithm with respect to others to better equip the readers with different strategies to implement automated workflows for subsurface analysis.

Advances in Subsurface Data Analytics will help researchers in academia and professional geoscientists working in the oil and gas industry to understand and appreciate the existence of several machine learning and deep learning models, how to optimize their performance, and their detailed applications in geosciences by bringing together several contributions in a single volume.