130 citations
- Skolkovo Institute of Science and TechnologyRU8 papers
- Lomonosov Moscow State UniversityRU3 papers
- Peter the Great St. Petersburg Polytechnic UniversityRU2 papers
- A.P. Ershov Institute of Informatics Systems, Siberian Branch of the Russian Academy of Sciences1 paper
- Dalhousie UniversityCA1 paper
- IBM (United States)US1 paper
- Inception Institute of Artificial IntelligenceAE1 paper
- Independent University of MoscowRU1 paper
- Institute of Informatics ProblemsRU1 paper
- ITMO UniversityRU1 paper
- National Research Tomsk State UniversityRU1 paper
- Novosibirsk State UniversityRU1 paper
6 papers · 1 filter
Data-driven model for hydraulic fracturing design optimization: focus on building digital database and production forecast
A. D. Morozov, D. O. Popkov, V. M. Duplyakov +6
Growing amount of hydraulic fracturing (HF) jobs in the recent two decades resulted in a significant amount of measured data available for development of predictive models via mach…
Deep Convolutions for In-Depth Automated Rock Typing
E. E. Baraboshkin, L. S. Ismailova, D. M. Orlov +5
The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a metho…
Wave-ice interaction in the North-West Barents Sea
Aleksey Marchenko, Peter Wadhams, Clarence O Collins +2
The results of field work on drift ice during wave propagation are analyzed and presented. The field work was performed in the Barents Sea, and the main focus of the paper is on wa…
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…
Prediction of Porosity and Permeability Alteration based on Machine Learning Algorithms
Andrei Erofeev, Denis Orlov, Alexey Ryzhov +1
The objective of this work is to study the applicability of various Machine Learning algorithms for prediction of some rock properties which geoscientists usually define due to spe…