32 citations · 99 across the 11 of their papers we have counts for
10 papers · 1 filter
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…
Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers
I. Fursov, A. Zaytsev, N. Kluchnikov +2
An adversarial attack paradigm explores various scenarios for the vulnerability of deep learning models: minor changes of the input can force a model failure. Most of the state of…
NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing
Nikita Klyuchnikov, Ilya Trofimov, Ekaterina Artemova +3
Neural Architecture Search (NAS) is a promising and rapidly evolving research area. Training a large number of neural networks requires an exceptional amount of computational power…
Multi-fidelity Neural Architecture Search with Knowledge Distillation
Ilya Trofimov, Nikita Klyuchnikov, Mikhail Salnikov +2
Neural architecture search (NAS) targets at finding the optimal architecture of a neural network for a problem or a family of problems. Evaluations of neural architectures are very…
Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world
Ivan Fursov, Alexey Zaytsev, Nikita Kluchnikov +2
Deep learning models suffer from a phenomenon called adversarial attacks: we can apply minor changes to the model input to fool a classifier for a particular example. The literatur…
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…
Gaussian Process Classification for Variable Fidelity Data
Nikita Klyuchnikov, Evgeny Burnaev
In this paper we address a classification problem where two sources of labels with different levels of fidelity are available. Our approach is to combine data from both sources by…
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…