  The gamma-ray logging method is one of the mandatory well logging methods for
geophysical exploration of wells. However, during the conduct of such a study,
the sensor, for one reason or another, may stop recording observations in the
well. If a small number of values are missing, you can restore these values
using standard methods to fill in gaps like in time series. If data miss a
large number of values, observations usually are made again, which leads to
additional financial costs. This work proposes an alternative solution, in the
form of filling missed observations in data with the help of machine learning
methods. The main idea of this method is to construct a simple two- layer
neural network that is trained on data from the well, and then synthesise the
missing values based on the trained neural network. This work evaluates the
effectiveness of the proposed method, and gives reasons for the appropriateness
of using different methods of filling gaps, depending on the number of missed
values.
