Vasily Demyanov is a Professor and a leader of GeoDataScience research group at Institute of GeoEnergy Engineering with Heriot-Watt University. He leads industry and government funded research in machine learning, geomodelling and uncertainty quantification for reservoir prediction. He also teaches a graduate course on geostatistics and is a EAGE EET lecturer (EET 12). Vasily has over 20 years of experience in geostatistics and has published over 100 publications. He is currently a Guest Editor of a Special Issue "Data Science in Reservoir Modelling Workflows" of Energies open access MDPI journal.
Vasily’s research interests lie broadly across spatial statistics, machine learning and uncertainty. In particular, his research is focused on uncertainty quantification in prediction modelling, inverse modelling for history matching, stochastic optimisation, Bayesian inference, and the problem of integration of reservoir knowledge and relevant data into statistical modelling workflows with machine learning and data analytics approaches.
The growing volume of digital information on reservoir monitoring and development opens new opportunities for more efficient exploitation and decision making. The lecture will demonstrate the use of machine learning in a number of typical reservoir modelling workflow tasks: identifying the geological features from seismic and outcrop imagery interpretation under uncertainty; description complex geological heterogeneity with respect to uncertainty; reservoir dynamics forecasting and model update based on production data analytics; making optimal decisions on the development of natural resources based on a forecast under conditions of uncertainty.