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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:

Collaboration:

  • 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

Biography

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.

Keywords

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

Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

  • 5 Similar Profiles
history Earth & Environmental Sciences
modeling Earth & Environmental Sciences
prediction Earth & Environmental Sciences
simulation Earth & Environmental Sciences
learning Earth & Environmental Sciences
methodology Earth & Environmental Sciences
water chemistry Earth & Environmental Sciences
Sampling Engineering & Materials Science

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

Research Output 1996 2018

geostatistics
prediction
modeling
integrated approach
learning

Model selection for error generalization in history matching

Nezhad Karim Nobakht, B., Christie, M. & Demyanov, V., 2018, SPE Europec featured at 80th EAGE Conference and Exhibition 2018. Society of Petroleum Engineers , SPE-190778-MS

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

history
prediction
Linear regression
Oils
Decision making

Numerical Simulation of Polymer Flooding in a Heterogeneous Reservoir: Constrained versus Unconstrained Optimization

Ibiam, E., Geiger, S., Almaqbali, A., Demyanov, V. & Arnold, D., 2018, SPE Nigeria Annual International Conference and Exhibition 2018. Society of Petroleum Engineers , SPE-193400-MS

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

flooding
polymer
simulation
enhanced oil recovery
water
flooding
polymer
simulation
design flood
history

Uncertainty Quantification in Reservoir Prediction: Part 2—Handling Uncertainty in the Geological Scenario

Demyanov, V., Arnold, D., Rojas, T. & Christie, M., 19 Jul 2018, In : Mathematical Geosciences. p. 1-24 24 p.

Research output: Contribution to journalArticle

Open Access
File
prediction
inverse problem
sampling
history
geology

Press / Media