32 citations · 74 across the 5 of their papers we have counts for
5 papers
Making the black-box brighter: interpreting machine learning algorithm for forecasting drilling accidents
Ekaterina Gurina, Nikita Klyuchnikov, Ksenia Antipova +1
We present an approach for interpreting a black-box alarming system for forecasting accidents and anomalies during the drilling of oil and gas wells. The interpretation methodology…
Forecasting the abnormal events at well drilling with machine learning
Ekaterina Gurina, Nikita Klyuchnikov, Ksenia Antipova +1
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 representin…
Application of Machine Learning to accidents detection at directional drilling
Ekaterina Gurina, Nikita Klyuchnikov, Alexey Zaytsev +5
We present a data-driven algorithm and mathematical model for anomaly alarming at directional drilling. The algorithm is based on machine learning. It compares the real-time drilli…
Real-time data-driven detection of the rock type alteration during a directional drilling
Evgenya Romanenkova, Alexey Zaytsev, Nikita Klyuchnikov +8
During the directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20m between the bit and high-fidelity rock type sensors. The only way to…
Data-driven model for the identification of the rock type at a drilling bit
Nikita Klyuchnikov, Alexey Zaytsev, Arseniy Gruzdev +12
Directional oil well drilling requires high precision of the wellbore positioning inside the productive area. However, due to specifics of engineering design, sensors that explicit…