  One of the first steps during the investigation of geological objects is the
interwell correlation. It provides information on the structure of the objects
under study, as it comprises the framework for constructing geological models
and assessing hydrocarbon reserves. Today, the detailed interwell correlation
relies on manual analysis of well-logging data. Thus, it is time-consuming and
of a subjective nature. The essence of the interwell correlation constitutes an
assessment of the similarities between geological profiles. There were many
attempts to automate the process of interwell correlation by means of
rule-based approaches, classic machine learning approaches, and deep learning
approaches in the past. However, most approaches are of limited usage and
inherent subjectivity of experts. We propose a novel framework to solve the
geological profile similarity estimation based on a deep learning model. Our
similarity model takes well-logging data as input and provides the similarity
of wells as output. The developed framework enables (1) extracting patterns and
essential characteristics of geological profiles within the wells and (2) model
training following the unsupervised paradigm without the need for manual
analysis and interpretation of well-logging data. For model testing, we used
two open datasets originating in New Zealand and Norway. Our data-based
similarity models provide high performance: the accuracy of our model is
$0.926$ compared to $0.787$ for baselines based on the popular gradient
boosting approach. With them, an oil\&gas practitioner can improve interwell
correlation quality and reduce operation time.
