Forecasting the abnormal events at well drilling with machine learning
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 45 references that are in the map. First posted on arXiv 10 Mar 2022.
Builds on
10 of 45 references.
References · 10
- 2018Deep learning for time series classification: a reviewcited by 575
- 2016Time Series Classification from Scratch with Deep Neural Networks: A Strong Baselinecited by 358
- 2018Multivariate LSTM-FCNs for Time Series Classificationcited by 120
- 2017TimeNet: Pre-trained deep recurrent neural network for time series classificationcited by 46
- 2018A review on distance based time series classificationcited by 40
- 2015Hyperparameter Search in Machine Learningcited by 33
- 2014An Empirical Evaluation of Similarity Measures for Time Series Classificationcited by 12
- 2017Influence of Resampling on Accuracy of Imbalanced Classificationcited by 8
- 2018Bag of Recurrence Patterns Representation for Time-Series Classificationcited by 3
- 2019Application of Machine Learning to accidents detection at directional drillingcited by 3
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Citing papers per year: 2022: 1.
1 paper.