Advances in Subsurface Data Analytics: Traditional and Physics-based Machine Learning - Brossura

Brossura

ISBN 10: 0128222956 ISBN 13: 9780128222959

Casa editrice: Elsevier, 2022

Riassunto

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.

• Covers fundamentals of simple machine learning and deep learning algorithms, and physics-based approaches written by practitioners in academia and industry
• Presents detailed case studies of individual machine learning algorithms and optimal strategies in subsurface characterization around the world
• Offers an analysis of future trends in machine learning in geosciences

Su questo libro

Informazioni sugli autori

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.

Dalla quarta di copertina

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.

Dettagli dell?edizione

• Editore: Elsevier
• Data di pubblicazione: 2022
• ISBN 10: 0128222956
• ISBN 13: 9780128222959
• Rilegatura: Copertina flessibile
• Lingua: Inglese
• Numero edizione: 1
• Numero di pagine: 376
• Redattore: Bhattacharya, Di
• Contatto del produttore: non disponibile