Application of Machine Learning to accidents detection at directional drilling

2019/06/06 by Ekaterina Gurina, Nikita Klyuchnikov, Gurina, Ekaterina +12
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Drilling and Well Engineering #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Oil and Gas Production Techniques

paper · pdf · doi:10.48550/arxiv.1906.02667

openalex publication_date 2019/06/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a data-driven algorithm and mathematical model for anomaly alarming at directional drilling. The algorithm is based on machine learning. It compares the real-time drilling telemetry with one corresponding to past accidents and analyses the level of similarity. The model performs a time-series comparison using aggregated statistics and Gradient Boosting classification. It is trained on historical data containing the drilling telemetry of 80 wells drilled within 19 oilfields. The model can detect an anomaly and identify its type by comparing the real-time measurements while drilling with the ones from the database of past accidents. Validation tests show that our algorithm identifies half of the anomalies with about 0.53 false alarms per day on average. The model performance ensures sufficient time and cost savings as it enables partial prevention of the failures and accidents at the well construction.

Citations

• An efficient k-means clustering algorithm: analysis and implementation

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