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Personal profile

Research interests

My research interests lie broadly across spatial statistics, machine learning and uncertainty.

In particular my research is focused on:

  • uncertainty quantification in prediction modelling
  • inverse modelling for history matching
  • stochastic optimisation
  • Bayesian inference.
  • advance geostatistical techniques, such as multi-points statistics, and the problem of integration of relevant domain knowledge and data into statistical models.
  • machine learning and data mining approaches for reservoir modelling and uncertainty quantification. 

I lecture geostatistics to MSc students and also at IPE summer schools and EAGE educational days. I am a co-author of over 50 publications, including books: Geostatistics: Theory and Practice (Nauka, 2010, in Russian), Advanced Mapping of Environmental Data – Geostatistics, Machine Learning and Bayesian Maximum Entropy (Wiley, 2008).

Graduated Studen

International Research ts:


  • University of Lausanne, Switzerland: Prof M. Kanevski, Prof. I. Lunati, L. Josset, Prof. M. Maignan
  • CERENA, Institute Superior Technico, Lisbon, Portugal: Prof. A. Soares, Dr L. Azevedo,Dr. S. Focaccia, M.-H. Caeiero.
  • Gubkin Oil and Gas University (Moscow, Russia)
  • Tomsk Polytechnic University, Russia.
  • Dr. E. Savelieva (Nuclear Safety Institute, Moscow, Russia)
  • Prof. J. Caers, Stanford University, USA
  • Prof. George Christakos
  • Marc Serre, University of Chapel Hill, USA


2003: I joined Heriot-Watt University and was later promoted to Research Fellow and then to a Lecturer.

2000-2002: Post-doc on population dynamics modelling, University of St Andrews, Mathematical Institute,.

1998: IPhD degree in physics and mathematics from Russian Academy of Sciences (Nuclear Safety Institute)

Thesis on radioactive pollution modelling with geostatistics and artificial neural network.

1994: My first degree is in physics from Moscow State University. Thesis on atmospheric pollution transport modelling.

As an Associate Editor of Computers and Geosciences journal I acquire contributions bridging geoscience problems and advances in soft computing.

I am a member of the International Association for Mathematical Geosciences (IAMG) and European Association of Geoscientists and Engineers (EAGE).

I was a convener of the Machine learning session at Annual IAMG meetings in 2009 and 2013 and also a member of the scientific committees for a number of geostatistical and geosciences conferences.


  • QC Physics
  • uncertainty
  • machine learning
  • statistics
  • geostatistics
  • optimisation
  • Bayesian
  • prediction modelling
  • petroleum
  • geoscience
  • environmental mapping

Fingerprint Dive into the research topics where Vasily Demyanov is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 3 Similar Profiles
history Earth & Environmental Sciences
modeling Earth & Environmental Sciences
prediction Earth & Environmental Sciences
simulation Earth & Environmental Sciences
History Matching Mathematics
learning Earth & Environmental Sciences
methodology Earth & Environmental Sciences
Uncertainty Mathematics

Co Author Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 1995 2020

Multi-objective optimization under uncertainty of geothermal reservoirs using experimental design-based proxy models

Schulte, D. O., Arnold, D., Geiger, S., Demyanov, V. & Sass, I., 16 Jan 2020, In : Geothermics. 86, 101792.

Research output: Contribution to journalArticle

experimental design
geothermal energy
geothermal system

A consistent stochastic framework to quantify large-scale geological uncertainty in stochastic seismic inversion

Azevedo, L., Demyanov, V., Lopes, D., Soares, A. & Guerreiro, L., 2019.

Research output: Contribution to conferencePaper

Acoustic impedance
Particle swarm optimization (PSO)

Can machine learning reveal sedimentological patterns in river deposits?

Demyanov, V., Reesink, A. J. H. & Arnold, D. P., 2019, River to Reservoir: Geoscience to Engineering. Geological Society of London, p. 221-235 15 p. (Geological Society Special Publication; vol. 488).

Research output: Chapter in Book/Report/Conference proceedingChapter

Learning systems
sedimentary structure

Ensemble history matching enhanced with data analytics - A brown field study

Tolstukhin, E., Barrela, E., Khrulenko, A., Halotel, J. & Demyanov, V., 2019, 4th EAGE Conference on Petroleum Geostatistics. EAGE Publishing BV, ThPG04

Research output: Chapter in Book/Report/Conference proceedingConference contribution

History Matching
Field Study

Flow through fractured reservoirs under geological and geomechancial uncertainty

Steffens, B., Demyanov, V., Couples, G., Arnold, D. & Lewis, H., 3 Jun 2019, 81st EAGE Conference and Exhibition 2019. EAGE Publishing BV

Research output: Chapter in Book/Report/Conference proceedingConference contribution

fracture network
Electric power distribution
carbonate rock
decision making

Press / Media