Mohlo by se vám také líbit

Více informací o knize AI-Driven Drilling Performance Optimization

• Parametry knihy
• Anotace
• Oblíbené z jiného soudku

Nákupem získáte 400 bodů

Anotace knihy

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.

Parametry knihy

• Plný název: AI-Driven Drilling Performance Optimization
• Podnázev: DE
• Autor: Carlos Urdaneta, Aamir Bader Shah, Xuqing Wu, Xin Fu, Jiefu Chen
• Edice: Advances in Oil and Gas Exploration & Production,
• Jazyk: Angličtina
• Vazba: Pevná
• Počet stran: 190
• EAN: 9783032266477
• ID: 51764550
• Nakladatelství: Springer, Berlin
• Hmotnost: 389 g
• Rozměry: 254 × 178 mm
• Datum vydání: 10. December 2026

Oblíbené z jiného soudku

---

---

---