Deep Learning Applications for Drilling Performance: Forecasting, Diagnostics, Telemetry, and Predictive Maintenance
Language: English
Published by Springer, 2026
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 224.69
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Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - This book presents data driven approaches to improve drilling performance in geothermal, coiled tubing, and conventional operations. It begins with transformer models for forecasting rate of penetration in geothermal wells, followed by methods for predicting both penetration and downhole shock in coiled tubing drilling. A variational autoencoder framework is introduced for diagnosing resistivity tool anomalies to support reliable geosteering. Subsequent chapters examine the use of deep autoencoders and separation networks to improve electromagnetic telemetry signals. This book also details synthetic data driven models combined with physics-based degradation approaches to forecast the remaining useful life of drilling equipment. Hybrid strategies for generating synthetic data are discussed to extend model training in scenarios with limited failure records. Each chapter blends technical insights with real-world case studies, demonstrating how these methods reduce non-productive time, improve tool reliability, and strengthen decision making in drilling operations. …
Seller Inventory # 9783032266477
- Title
- Deep Learning Applications for Drilling Performance: Forecasting, Diagnostics, Telemetry, and Predictive Maintenance
- Author
- Xuqing Wu
- Publisher
- Springer
- Publication year
- 2026
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3032266475
- ISBN 13
- 9783032266477
- Item weight
- 458 grams
- Dimensions
- 260x183x13 mm
This book presents data driven approaches to improve drilling performance in geothermal, coiled tubing, and conventional operations. It begins with transformer models for forecasting rate of penetration in geothermal wells, followed by methods for predicting both penetration and downhole shock in coiled tubing drilling. A variational autoencoder framework is introduced for diagnosing resistivity tool anomalies to support reliable geosteering. Subsequent chapters examine the use of deep autoencoders and separation networks to improve electromagnetic telemetry signals. This book also details synthetic data driven models combined with physics-based degradation approaches to forecast the remaining useful life of drilling equipment. Hybrid strategies for generating synthetic data are discussed to extend model training in scenarios with limited failure records. Each chapter blends technical insights with real-world case studies, demonstrating how these methods reduce non-productive time, improve tool reliability, and strengthen decision making in drilling operations.
"Synopsis" may belong to another edition of this title.
About the Author
Carlos Urdaneta received his Masters degree in Electrical Engineering from Rice University. He has worked for SLB in new product development since 2011. He is a Ph.D. candidate at the department of Electrical and Computer Engineering, University of Houston. His current research focuses on integrating AI models into drilling workflows, emphasizing predictive maintenance, dynamic forecasting, and telemetry signal improvement.
Aamir Bader Shah received his BS. degree in Electrical Engineering at the NUST University and a Masters degree in Embedded System and Controls from the University of Leicester. He is currently a Ph.D. candidate at the department of Electrical and Computer Engineering at the University of Houston. His current research focuses on predicting remaining useful life in downhole drilling equipment.
Xuqing Wu received the Ph.D. degree in Computer Science from the University of Houston. He is currently an Associate Professor of Computer Information Systems with the College of Technology, University of Houston. Prior to joining the University of Houston in 2015, he was a Data Scientist and Software Engineer of the Energy and IT industry. His research interests include scientific machine learning, probabilistic modeling, and subsurface sensing.
Xin Fu received the Ph.D. degree in computer engineering from the University of Florida, Gainesville, in 2009. She is currently a Professor with the Electrical and Computer Engineering Department, University of Houston, Houston, TX, USA. Her research interests include computer architecture, high-performance computing, hardware reliability and variability, energy-efficient computing, and mobile computing.
Jiefu Chen is an Associate Professor with the Department of Electrical and Computer Engineering, University of Houston. He received the Ph.D. degree in Electrical Engineering from Duke University. From 2011 to 2015, he was a Staff Scientist with Weatherford. He has published over 100 papers in computational electromagnetics, inverse problems, machine learning, oilfield data analytics, seismic data processing, subsurface wireless communication, and well logging.
"About the title" may belong to another edition of this title.
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