Real-Time Data-Driven Detection of the Rock-Type Alteration during a Directional Drilling

Evgeniya Romanenkova, Alexey Zaytsev, Nikita Klyuchnikov, Arseniy Gruzdev, Ksenia Antipova, Leyla Ismailova, Evgeny Burnaev, Artyom Semenikhin, Vitaliy Koryabkin, Igor Simon, Dmitry Koroteev

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


During directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20 m between the bit and high-fidelity rock-type sensors. The only way to detect the lithotype changes in time is the usage of measurements while drilling (MWD). However, there are no general mathematical modeling approaches that both well reconstruct the rock type based on MWD data and correspond to specifics of the oil and gas industry. In this letter, we present a data-driven procedure that utilizes MWD data for quick detection of changes in rock types. We propose the approach that combines traditional machine learning (ML) based on the solution of the rock-type classification problem with change detection procedures rarely used before in oil and gas industry. The data come from a newly developed oilfield in the north of western Siberia. The results suggest that we can detect a significant part of changes in rock types, reducing the change detection delay from 20 to 1.8 m and the number of false-positive alarms from 43 to 6 per well.

Original languageEnglish
Article number8945187
Pages (from-to)1861-1865
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number11
Publication statusPublished - Nov 2020


  • Change detection
  • classification
  • directional drilling
  • logging while drilling (LWD)
  • machine learning (ML)
  • measurements while drilling (MWD)
  • rock type


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