May 20, 2026
Deep Learning Applications for Drilling Performance Forecasting, Diagnostics, Telemetry, and Pred…
Deep Learning Applications for Drilling Performance Forecasting, Diagnostics, Telemetry, and Predictive Maintenance | 8.36 MB
Title: Deep Learning Applications for Drilling Performance: Forecasting,Diagnostics, Telemetry,andPredictive Maintenance
Author: Carlos Urdaneta · Aamir Bader Shah · Xuqing Wu · Xin Fu · Jiefu Chen
Category: Nonfiction, Science & Nature, Science, Earth Sciences, Geophysics, Technology, Engineering, Civil
Language: English | 123 Pages | ISBN: 9783032266484
Description:
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.
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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