The article analyzes the application of various classification algorithms (Random Forest, Extra Trees, Gradient Boosting, Multi-layer Perceptron, Gaussian Naive Bayes, Voting Classifier) based on supervised machine learning, both for individual seismic attributes (amplitude, phase, frequency) and for a set of seismic attributes calculated in the interval of channel system development for probabilistic assessment of presence of channels. The object of the study is the deposits of the Tyumen suite of a group of deposits located in the Khanty-Mansiysk Autonomous Okrug. The purpose of this work is to find the most effective seismic attributes and machine learning classification methods to optimize the methodology and improve the accuracy of forecasting the probability of channel system presence.

Maria Yu. Oreshkova
Leading Specialist
Gazprom neft company group
3-5, Pochtamtskaya str., St. Petersburg, 190000, Russia
е-mail: wintersurprise@mail.ru

Alexandr V. Butorin
Candidate of Geological and Mineralogical Sciences
Associate Professor
Institute of Earth Sciences, Saint Petersburg State University
7-9, Universitetskaya nab., St. Petersburg, 199034, Russia
Head of seismic discipline
Gazprom neft company group
3-5, Pochtamtskaya str., St. Petersburg, 190000, Russia
е-mail: a.butorin@spbu.ru
ORCID: 0000-0002-6074-1439
Scopus Author ID: 56370048400
Researcher ID: B-7405-2019
SPIN-код: 8474-6120
Author ID: 877389

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