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arXiv · 2203.05378

Forecasting the abnormal events at well drilling with machine learning

Ekaterina Gurina·Nikita Klyuchnikov·Ksenia Antipova·Dmitry Koroteev
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Abstract

We present a data-driven and physics-informed algorithm for drilling accident forecasting. The core machine-learning algorithm uses the data from the drilling telemetry representing the time-series. We have developed a Bag-of-features representation of the time series that enables the algorithm to predict the probabilities of six types of drilling accidents in real-time. The machine-learning model is trained on the 125 past drilling accidents from 100 different Russian oil and gas wells. Validation shows that the model can forecast 70% of drilling accidents with a false positive rate equals to 40%. The model addresses partial prevention of the drilling accidents at the well construction.

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Ekaterina Gurina, Nikita Klyuchnikov, Ksenia Antipova, Dmitry Koroteev. 2022-03-10. Forecasting the abnormal events at well drilling with machine learning. https://doi.org/10.1007/s10489-021-03013-x

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