  The representation learning problem in the oil & gas industry aims to
construct a model that provides a representation based on logging data for a
well interval. Previous attempts are mainly supervised and focus on similarity
task, which estimates closeness between intervals. We desire to build
informative representations without using supervised (labelled) data. One of
the possible approaches is self-supervised learning (SSL). In contrast to the
supervised paradigm, this one requires little or no labels for the data.
Nowadays, most SSL approaches are either contrastive or non-contrastive.
Contrastive methods make representations of similar (positive) objects closer
and distancing different (negative) ones. Due to possible wrong marking of
positive and negative pairs, these methods can provide an inferior performance.
Non-contrastive methods don't rely on such labelling and are widespread in
computer vision. They learn using only pairs of similar objects that are easier
to identify in logging data.
  We are the first to introduce non-contrastive SSL for well-logging data. In
particular, we exploit Bootstrap Your Own Latent (BYOL) and Barlow Twins
methods that avoid using negative pairs and focus only on matching positive
pairs. The crucial part of these methods is an augmentation strategy. Our
augmentation strategies and adaption of BYOL and Barlow Twins together allow us
to achieve superior quality on clusterization and mostly the best performance
on different classification tasks. Our results prove the usefulness of the
proposed non-contrastive self-supervised approaches for representation learning
and interval similarity in particular.
