  The aim of this work was to predict the probability of the spread of rock
formations with hydrocarbon-collecting properties in the studied coastal area
using a stack of machine learning algorithms and data augmentation and
modification methods. This research develops the direction of machine learning
where training is conducted on well data and spatial attributes. Methods for
overcoming the limitations of this direction are shown, two methods -
augmentation and modification of the well data sample: Spindle and
Revers-Calibration. Considering the difficulties for seismic data
interpretation in coastal area conditions, the proposed approach is a tool
which is able to work with the whole totality of geological and geophysical
data, extract the knowledge from 159-dimensional space spatial attributes and
make facies spreading prediction with acceptable quality - F1 measure for
reservoir class 0.798 on average for evaluation of "drilling" results of
different geological conditions. It was shown that consistent application of
the proposed augmentation methods in the implemented technology stack improves
the quality of reservoir prediction by a factor of 1.56 relative to the
original dataset.
