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arXiv · 1906.02667

Application of Machine Learning to accidents detection at directional drilling

Ekaterina Gurina·Nikita Klyuchnikov·Alexey Zaytsev·Evgenya Romanenkova·Ksenia Antipova·Igor Simon·Victor Makarov·Dmitry Koroteev
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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.

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Ekaterina Gurina, Nikita Klyuchnikov, Alexey Zaytsev, Evgenya Romanenkova, Ksenia Antipova, Igor Simon, Victor Makarov, Dmitry Koroteev. 2019-06-06. Application of Machine Learning to accidents detection at directional drilling. https://arxiv.org/abs/1906.02667

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