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What is Drilling Anomaly Detection Workflow about?

This study presents a novel workflow for detecting drilling anomalies by integrating prior knowledge with an unsupervised learning algorithm, affinity propagation (AP). The framework includes real-time data preprocessing, drilling state identification, fluctuation sensing, and anomaly classification, achieving high sensitivity and low false alarm rates. Field tests demonstrate its effectiveness in detecting multiple types of anomalies with strong interpretability and real-time performance, making it a scala

Author

Keyvan

Language

EN