|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Growth of MoS2 layers on the surface of multiwalled carbon nanotubes显示文摘 | V. O. Koroteev A. V. Okotrub Yu. V. Mironov O. G. Abrosimov Yu. V. Shubin L. G. Bulusheva | 2007 | Inorganic Materials2007，，3： | 1 |
| 2 | Fabrication of three-dimensional periodic microstruc-tures by means of two-photon polymerization显示文摘 | Borisov R A Dorojkina G N Koroteev N J | 1998 | Appl Phys1998，67，： | 1 |
| 3 | High-density three-dimensional optical data storage with photonic band-gap structures显示文摘 | Koroteev N I Magnitskii S A Tarasishin A V | 1999 | Laser Phys1999，9，6： | 1 |
| 4 | Enhancement of second-harmonic generation with femtosecond edge for different polarizations of incident light 显示文摘 | BALAKIN A V BUSHUEV V A KOROTEEV N I | 1999 | Opt Lett1999，24，12： | 1 |
| 5 | Space nuclear power systems : Yesterday, today, and tomorrow显示文摘 | Akimov V N Koroteev A A Koroteev A S | 2012 | Thermal Engineering2012，59，13： | 1 |
| 6 | Intense Nonlinear-Optics Excitation of Completely Symmetric Vibrations of Polyatomic Molecules:Study of the Fermi Resonance and Other Anharmonic Interactions显示文摘 | Gladrov S M Karimov M G Koroteev N I | 1982 | JETP Letter1982，35，9： | 1 |
| 7 | Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future显示文摘We analyze how artificial intelligence changes a significant part of the energy sector,the oil and gas industry.We focus on the upstream segment as the most capital-intensive part of oil and gas and the segment of enormous uncertainties to tackle.Basing on the analysis of AI application possibilities and the review of existing applica-tions,we outline the most recent trends in developing AI-based tools and identify their effects on accelerating and de-risking processes in the industry.We investigate AI approaches and algorithms,as well as the role and availability of data in the segment.Further,we discuss the main non-technical challenges that prevent the in-tensive application of artificial intelligence in the oil and gas industry,related to data,people,and new forms of collaboration.We also outline three possible scenarios of how artificial intelligence will develop in the oil and gas industry and how it may change it in the future(in 5,10,and 20 years). | Dmitry Koroteev Zeljko Tekic | 2021 | Energy and AI2021，3，1： | 1 |
| 8 | Compression of light pulses in photonic crystals显示文摘 | ZHELTIKOV A M KOROTEEV N I MAGNITSKII S A | 1998 | Quantum Electronics1998，28，10： | 1 |
| 9 | Solar power propulsion system adaptation to arlane 5 and preliminary development plan显示文摘 | Koroteev A S Kochetkov Y M Akimov V N | 2004 | AIAA2004，4139，： | 1 |
| 10 | Structure stability and electronic properties of the Zr-He system: First-principles calculations显示文摘 | Yu. M. Koroteev O. V. Lopatina I. P. Chernov | 2009 | Physics of the Solid State2009，，8： | 1 |
| 11 | Interaction of powerful laser radiation with the surfaces of semiconductors and metals:Nonlinear optical effects and nonlinear optical diagnostics显示文摘 | Akhmanov S A Emel'yanov V I Koroteev N I | 1985 | Sc-v Phys JETP1985，28，12： | 1 |
| 12 | Fabrication and Properties of Multilayer Ceramics in the SiC – TiB2 System显示文摘 | O. N. Grigor’ev A. V. Koroteev A. V. Klimenko E. E. Mayboroda E. V. Prilutskii N. D. Bega | 2000 | Refractories and Industrial Ceramics (-)2000，，11： | 1 |
| 13 | The (110) Surface Electronic Structure of FeTi,CoTi,and NiTi显示文摘 | KOROTEEV Y M LIPNITSKII A G CHULKOV E V | 2002 | Surf Sci2002，，： | 1 |
| 14 | Second harmonic generation by reflection of a two-dimensional laser beam from the surface of a ehiral medium显示文摘 | KOROTEEV N I MAKAROV V A VOLKOV S N | 1997 | Opt Comm1997，138，： | 1 |
| 15 | Compression of ultrashort light pulses inphotonic crystals: when envelopes cease to be slow显示文摘 | Koroteev N I Magnitskii S A Tarasishin A V | 1999 | Opt Commun1999，159，： | 1 |
| 16 | Machine learning for recovery factor estimation of an oil reservoir:A tool for derisking at a hydrocarbon asset evaluation显示文摘Well-known oil recovery factor estimation techniques such as analogy,volumetric calculations,material balance,decline curve analysis,hydrodynamic simulations have certain limitations.Those techniques are time-consuming,and require specific data and expert knowledge.Besides,though uncertainty estimation is highly desirable for this problem,the methods above do not include this by default.In this work,we present a data-driven technique for oil recovery factor(limited to water flooding)estimation using reservoir parameters and representative statistics.We apply advanced machine learning methods to historical worldwide oilfields datasets(more than 2000 oil reservoirs).The data-driven model might be used as a general tool for rapid and completely objective estimation of the oil recovery factor.In addition,it includes the ability to work with partial input data and to estimate the prediction interval of the oil recovery factor.We perform the evaluation in terms of accuracy and prediction intervals coverage for several tree-based machine learning techniques in application to the following two cases:(1)using parameters only related to geometry,geology,transport,storage and fluid properties,(2)using an extended set of parameters including development and production data.For both cases,the model proved itself to be robust and reliable.We conclude that the proposed data-driven approach overcomes several limitations of the traditional methods and is suitable for rapid,reliable and objective estimation of oil recovery factor for hydrocarbon reservoir. | Ivan Makhotin Denis Orlov Dmitry Koroteev Evgeny Burnaev Aram Karapetyan Dmitry Antonenko | 2022 | Petroleum2022，8，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员