# Forecasting the abnormal events at well drilling with machine learning

arXiv: https://arxiv.org/abs/2203.05378 · HTML: https://mlsanity.com/paper/2203.05378/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Multivariate LSTM-FCNs for Time Series Classification](https://mlsanity.com/paper/1801.04503/) (2018)
- [TimeNet: Pre-trained deep recurrent neural network for time series classification](https://mlsanity.com/paper/1706.08838/) (2017)
- [A review on distance based time series classification](https://mlsanity.com/paper/1806.04509/) (2018)
- [Hyperparameter Search in Machine Learning](https://mlsanity.com/paper/1502.02127/) (2015)
- [An Empirical Evaluation of Similarity Measures for Time Series Classification](https://mlsanity.com/paper/1401.3973/) (2014)
- [Influence of Resampling on Accuracy of Imbalanced Classification](https://mlsanity.com/paper/1707.03905/) (2017)
- [Bag of Recurrence Patterns Representation for Time-Series Classification](https://mlsanity.com/paper/1803.11111/) (2018)
- [Application of Machine Learning to accidents detection at directional drilling](https://mlsanity.com/paper/1906.02667/) (2019)

## Built on by (most cited first, 1 of 1)

- [Making the black-box brighter: interpreting machine learning algorithm for forecasting drilling accidents](https://mlsanity.com/paper/2209.02256/) (2022)

## 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.