Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Learning Applications for Drilling Performance: Forecasting, Diagnostics, Telemetry, and Predictive Maintenance

Deep Learning Applications for Drilling Performance: Forecasting, Diagnostics, Telemetry, and Predictive Maintenance

Wu, Xuqing
Urdaneta, Carlos
Shah, Aamir Bader

Hardcover

Series: Advances in Oil and Gas Exploration & Production

Technology & EngineeringGeneral SciencePhysics

Currently unavailable to order

ISBN10: 3032266475
ISBN13: 9783032266477
Publisher: Springer
Published: May 20 2026
Pages: 117
Weight: 0.98
Height: 0.57 Width: 7.38 Depth: 10.14
Language: English
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.

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