Personal Websites

Denis Orlov

Professor:  Dmitry Koroteev 

Denis has more than 15 years of experience in fundamental and applied research in Hydrodynamics and Continuum Mechanics. He started his scientific work at the Lomonosov Moscow State University (MSU) as an intern at the Faculty of Physics in 2004. Denis graduated from the MSU with a Master’s degree in 2007 and obtained his PhD degree in Chemical Physics in 2010. Since 2010 he has worked at Gazprom VNIIGAZ LLC as a research scientist, head of the sector and head of the laboratory, where he performed and managed experimental, numerical and analytical investigations of multiphase filtration in ordinary and complex porous media for reservoir engineering needs.

Denis’ scientific interests in Skoltech dedicates to hydrocarbon recovery and oil/gas field development optimization, including Digital Rock Physics (DRP). Recent research activities he was involved in relate to the reinforcement of DRP technology with Machine Learning and Deep Learning approaches.

Denis led or took part at least in 15 R&D projects (including state grants). He is the author of over 70 publications, including 20 papers in top international and Russian peer-reviewed journals. He is also a Co-Founder and Business Development Officer in Digital Petroleum Company – a start-up founded together with Skoltech to develop new digital technologies. 

Research Interests :

  • Digital Rock Physics
  • Digital twin of lab core studies
  • Production forecast with real-time history matching
  • Predictive models based on well treatment history and physical features of near wellbore zone
  • Predictive maintenance
  • Objective data-driven recommending systems
  • System engineering and continuous optimization
  • Design Chemistry
  1. Orlov D., Koroteev D., Sitnikov A. Self-Colmatation in terrigenic oil reservoirs of Eastern Siberia // Journal of Petroleum Science and Engineering. – 2018. – Т. 163. – С. 576-589.
  2. Erofeev, A., Orlov, D., Ryzhov, A., & Koroteev, D. (2019). Prediction of porosity and permeability alteration based on machine learning algorithms. Transport in Porous Media, 128(2), 677-700.
  3. Orlov, D., & Koroteev, D. (2019). Advanced analytics of self-colmatation in terrigenous oil reservoirs. Journal of Petroleum Science and Engineering, 182, 106306.
  4. Baraboshkin, E. E., Ismailova, L. S., Orlov, D. M., Zhukovskaya, E. A., Kalmykov, G. A., Khotylev, O. V., … & Koroteev, D. A. (2020). Deep convolutions for in-depth automated rock typing. Computers & Geosciences, 135, 104330.
  5. Orlov, D., Ebadi, M., Muravleva, E., Volkhonskiy, D., Erofeev, A., Savenkov, E., … & Koroteev, D. (2021). Different Methods of Permeability Calculation in Digital Twins of Tight Sandstones. Journal of Natural Gas Science and Engineering, 87, 103750.
  6. Ebadi, M., Orlov, D., Makhotin, I., Krutko, V., Belozerov, B., & Koroteev, D. (2021). Strengthening the Digital Rock Physics, Using Downsampling for Sub-Resolved Pores in Tight Sandstones. Journal of Natural Gas Science and Engineering, 103869.
  7. Sidorenko, M., Orlov, D., Ebadi, M., & Koroteev, D. (2021). Deep learning in denoising of micro-computed tomography images of rock samples. Computers & Geosciences, 104716.
  8. Erofeev, A. S., Orlov, D. M., Perets, D. S., & Koroteev, D. A. (2021). AI-Based Estimation of Hydraulic Fracturing Effect. SPE Journal, 1-12.
  9. Alekseev, V.V., Orlov, D.M. and Koroteev, D.A., 2021, October. Multi-Mineral Segmentation of SEM Images Using Deep Learning Techniques. In SPE Russian Petroleum Technology Conference. OnePetro.
  10. Gubanova, A. E., Khabibullin, B. A., Orlov, D. M., & Koroteev, D. A. (2021, October). Modified CR-Type Material Balance Model for Well Production Forecasts in Case of Well Treatments. In SPE Russian Petroleum Technology Conference. OnePetro.
  11. Makhotin, I., Orlov, D., Koroteev, D., Burnaev, E., Karapetyan, A., & Antonenko, D. (2021). Machine learning for recovery factor estimation of an oil reservoir: a tool for de-risking at a hydrocarbon asset evaluation. Petroleum.
  12. Illarionov, E., Temirchev, P., Voloskov, D., Kostoev, R., Simonov, M., Pissarenko, D., Orlov, D. and Koroteev, D., 2022. End-to-end neural network approach to 3D reservoir simulation and adaptation. Journal of Petroleum Science and Engineering, 208, p.109332.
  13. Makhotin, I., Orlov, D., & Koroteev, D. (2022). Machine Learning to Rate and Predict the Efficiency of Waterflooding for Oil Production. Energies, 15(3), 1199.
  14. Volkhonskiy, D., Muravleva, E., Sudakov, O., Orlov, D., Burnaev, E., Koroteev, D., … & Krutko, V. (2022). Generative adversarial networks for reconstruction of three-dimensional porous media from two-dimensional slices. Physical Review E, 105(2), 025304.

Master’s degree in Physics in Lomonosov Moscow State University (MSU), 2007

PhD degree in Chemical Physics in Lomonosov Moscow State University (MSU), 2010

Participation in Industrial Projects:

  1. Primary computation of transport and capacity properties of rocks (Gazpromneft STC LLC)
  2. Regional petrophysics (Gazpromneft STC LLC)
  3. 2D-3D reconstructor (Gazpromneft STC LLC)
  4. Analysis  of oil films distribution in porous media (ZARUBEZHNEFT JSC)
  5. Development of CT method for pore structure analysis  (LUKOIL PJSC)
  6. Complex of laboratory tests for In-situ combustion technology improvement on Mayorovskaya field (LUKOIL PJSC)
  7. Complex of laboratory tests for TermoEOR technology improvement on Sredne-Nazimskoe field (LUKOIL PJSC)
  8. Development of a direct multiphase simulator for micro flows in rock’s porous media (Gazpromneft STC LLC)
  9. Development of algorithms and techniques for metamodeling of reservoir flows (Gazpromneft STC LLC)
  10. Creation of the models for predicting the effectiveness of well operations (Innopraktika Company)