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Forecasting the abnormal events at well drilling with machine learning

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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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Funding

The authors did not receive support from any organisation for the submitted work.

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Authors and Affiliations

  1. Skolkovo Institute of Science and Technology (Skoltech), 121205, Moscow, Russia

    Ekaterina Gurina, Nikita Klyuchnikov, Ksenia Antipova & Dmitry Koroteev

  2. Digital Petroleum, Skolkovo Innovation Center, 121205, Moscow, Russia

    Ekaterina Gurina, Nikita Klyuchnikov, Ksenia Antipova & Dmitry Koroteev

Authors
  1. Ekaterina Gurina
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  2. Nikita Klyuchnikov
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  3. Ksenia Antipova
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  4. Dmitry Koroteev
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Correspondence to Ekaterina Gurina.

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Appendices

Appendix A

In Fig. 13 one may see the example of mud telemetry data used during the drilling accident forecasting problem. After the feature generation procedure, the presented part of the time-series is transformed into a histogram, presented in Fig. 14.

Fig. 13
figure 13

Example of mud telemetry data that was used for feature generation procedure

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Fig. 14
figure 14

Features obtained from the mud telemetry data on Fig. 13, that used as input for the classification model

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Appendix B

Table 6 shows the results of the first stage of an optimization procedure for the top 40 parameter sets.

Table 6 Top 40 parameters sets that were tested during the first optimization stage of the Bag-of-features approach
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Gurina, E., Klyuchnikov, N., Antipova, K. et al. Forecasting the abnormal events at well drilling with machine learning. Appl Intell 52, 9980–9995 (2022). https://doi.org/10.1007/s10489-021-03013-x

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