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)

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.

Original languageEnglish
JournalApplied Intelligence
DOIs
Publication statusPublished - 2022

Keywords

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

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