Xuqing (15 resultados)

Condición: New.

Condición: New. In English.

Condición: New. pp. VII, 79 43 illus., 41 illus. in color. 1 Edition NO-PA16APR2015-KAP.

Paperback. Condición: Brand New. 88 pages. 9.25x6.10x0.21 inches. In Stock.

Buch. Condición: Neu. 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. …

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Seismic imaging is a key component of subsurface exploration, and it depends on a high-quality seismic data acquisition system with effective seismic processing algorithms. Seismic data quality concerns various factors such as acquisition design, environmental constraints, sampling resolution, and noises. The focus of this book is to investigate efficient seismic data representation and signal enhancement solutions by leveraging the powerful feature engineering capability of deep learning. The book delves into seismic data representation and enhancement issues, ranging from seismic acquisition design to subsequent quality improvement and compression technologies. Given the challenges of obtaining suitable labeled training datasets for seismic data processing problems, we concentrate on exploring deep learning approaches that eliminate the need for labels. We combined novel deep learning techniques with conventional seismic data processing methods, and construct networks and frameworks tailored for seismic data processing. The editors and authors of this book come from both academia and industry with hands-on experiences in seismic data processing and imaging.…

Condición: New.

Condición: New. Print on Demand pp. VII, 79 43 illus., 41 illus. in color.

Condición: New. PRINT ON DEMAND pp. VII, 79 43 illus., 41 illus. in color.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Systematically discusses statistical inversion of electromagnetic logging data in oilfield drilling Explains the innovative Bayesian inference and sampling methods used for statistical inversion Includes a wealth of case studies, schematics.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Seismic imaging is a key component of subsurface exploration, and it depends on a high-quality seismic data acquisition system with effective seismic processing algorithms. Seismic data quality concerns various factors such as acquisition design, environmental constraints, sampling resolution, and noises. The focus of this book is to investigate efficient seismic data representation and signal enhancement solutions by leveraging the powerful feature engineering capability of deep learning. The book delves into seismic data representation and enhancement issues, ranging from seismic acquisition design to subsequent quality improvement and compression technologies. Given the challenges of obtaining suitable labeled training datasets for seismic data processing problems, we concentrate on exploring deep learning approaches that eliminate the need for labels. We combined novel deep learning techniques with conventional seismic data processing methods, and construct networks and frameworks tailored for seismic data processing. The editors and authors of this book come from both academia and industry with hands-on experiences in seismic data processing and imaging.…

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 132 pp. Englisch.…

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.