Forecasting the abnormal events at well drilling with machine learning

Ekaterina Gurina, Nikita Klyuchnikov, Ksenia Antipova, Dmitry Koroteev

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
JournalApplied Intelligence
Publication statusPublished - 2022


  • Bag-of-features
  • Classification
  • Directional drilling
  • Machine learning
  • Telemetry


Dive into the research topics of 'Forecasting the abnormal events at well drilling with machine learning'. Together they form a unique fingerprint.

Cite this