Fluvial deposits are a challenge for modelling flow in sub-surface
reservoirs. Connectivity and continuity of permeable bodies have
a major impact on fluid flow in porous media. Contemporary object-based
and multipoint statistics methods face a problem of robust representation
of connected structures. An alternative approach to model petrophysical
properties is based on machine learning algorithm ? Support Vector
Regression (SVR). Semi-supervised SVR is able to establish spatial
connectivity taking into account the prior knowledge on natural similarities.
SVR as a learning algorithm is robust to noise and captures dependencies
from all available data. Semi-supervised SVR applied to a synthetic
fluvial reservoir demonstrated robust results, which are well matched
to the flow performance