  The aim of this work is to create and apply a methodological approach for
predicting gas traps from 3D seismic data and gas well testing. The paper
formalizes the approach to creating a training dataset by selecting volumes
with established gas saturation and filtration properties within the seismic
wavefield. The training dataset thus created is used in a process stack of
sequential application of data processing methods and ensemble machine learning
algorithms. As a result, a cube of calibrated probabilities of belonging of the
study space to gas reservoirs was obtained. The high efficiency of this
approach is shown on a delayed test sample of three wells (blind wells). The
final value of the gas reservoir prediction quality metric f1 score was
0.893846.
