Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Klyuchnikov, Nikita"'
Autor:
Agustsson, Steinn Ymir, Haque, Mohammad Ahsanul, Truong, Thi Tam, Bianchi, Marco, Klyuchnikov, Nikita, Mottin, Davide, Karras, Panagiotis, Hofmann, Philip
Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique to determine the electronic structure of solids. Advances in light sources for ARPES experiments are currently leading to a vast increase of data acquisition rates
Externí odkaz:
http://arxiv.org/abs/2407.04631
Publikováno v:
Journal of Petroleum Science and Engineering, 2022, 111041, ISSN 0920-4105
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 aims to explain the local behavior of the accident predictive model t
Externí odkaz:
http://arxiv.org/abs/2209.02256
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
Externí odkaz:
http://arxiv.org/abs/2203.05378
Autor:
Fursov, Ivan, Zaytsev, Alexey, Burnyshev, Pavel, Dmitrieva, Ekaterina, Klyuchnikov, Nikita, Kravchenko, Andrey, Artemova, Ekaterina, Burnaev, Evgeny
Robustness of huge Transformer-based models for natural language processing is an important issue due to their capabilities and wide adoption. One way to understand and improve robustness of these models is an exploration of an adversarial attack sce
Externí odkaz:
http://arxiv.org/abs/2107.11275
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 time-consuming. One of the possible ways to mitigate this issue is to
Externí odkaz:
http://arxiv.org/abs/2006.08341
Autor:
Klyuchnikov, Nikita, Trofimov, Ilya, Artemova, Ekaterina, Salnikov, Mikhail, Fedorov, Maxim, Burnaev, Evgeny
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, which makes NAS unreachable for those researchers who have limited o
Externí odkaz:
http://arxiv.org/abs/2006.07116
Autor:
Gurina, Ekaterina, Klyuchnikov, Nikita, Zaytsev, Alexey, Romanenkova, Evgenya, Antipova, Ksenia, Simon, Igor, Makarov, Victor, Koroteev, Dmitry
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 drilling telemetry with one corresponding to past accidents and analyses the
Externí odkaz:
http://arxiv.org/abs/1906.02667
Autor:
Romanenkova, Evgenya, Zaytsev, Alexey, Klyuchnikov, Nikita, Gruzdev, Arseniy, Antipova, Ksenia, Ismailova, Leyla, Burnaev, Evgeny, Semenikhin, Artyom, Koryabkin, Vitaliy, Simon, Igor, Koroteev, Dmitry
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 detect the lithotype changes in time is the usage of Measurements Whil
Externí odkaz:
http://arxiv.org/abs/1903.11436
Autor:
Klyuchnikov, Nikita, Burnaev, Evgeny
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 applying a co-kriging schema on latent functions, which allows the mod
Externí odkaz:
http://arxiv.org/abs/1809.05143
Autor:
Klyuchnikov, Nikita, Zaytsev, Alexey, Gruzdev, Arseniy, Ovchinnikov, Georgiy, Antipova, Ksenia, Ismailova, Leyla, Muravleva, Ekaterina, Burnaev, Evgeny, Semenikhin, Artyom, Cherepanov, Alexey, Koryabkin, Vitaliy, Simon, Igor, Tsurgan, Alexey, Krasnov, Fedor, Koroteev, Dmitry
Directional oil well drilling requires high precision of the wellbore positioning inside the productive area. However, due to specifics of engineering design, sensors that explicitly determine the type of the drilled rock are located farther than 15m
Externí odkaz:
http://arxiv.org/abs/1806.03218