  Adopting data-based approaches leads to model improvement in numerous Oil&Gas
logging data processing problems. These improvements become even more sound due
to new capabilities provided by deep learning. However, usage of deep learning
is limited to areas where researchers possess large amounts of high-quality
data. We present an approach that provides universal data representations
suitable for solutions to different problems for different oil fields with
little additional data. Our approach relies on the self-supervised methodology
for sequential logging data for intervals from well, so it also doesn't require
labelled data from the start. For validation purposes of the received
representations, we consider classification and clusterization problems. We as
well consider the transfer learning scenario. We found out that using the
variational autoencoder leads to the most reliable and accurate models.
approach We also found that a researcher only needs a tiny separate data set
for the target oil field to solve a specific problem on top of universal
representations.
