Machine learning applications for well-logging interpretation of the Vikulov Formation

Nowadays well logging curves are interpreted by geologists who preprocess the data and normalize the curves for this purpose. The preparation process can take a long time, especially when hundreds and thousands of wells are involved. This paper explores the applicability of Machine Learning methods to geology tasks, in particular the problem of lithology interpretation using well-logs, and also reveals the issue of the quality of such predictions in comparison with the interpretation of specialists. The authors of the article deployed three groups of Machine Learning algorithms: Random Forests, Gradient Boosting and Neural Networks, and also developed its own metric that takes into account the geological features of the study area and statistical proximity of lithotypes based on log curves values. As a result, it was proved that Machine Learning algorithms are able to predict lithology from a standard set of well logs without calibration on reference layers, which significantly saves time spent on preliminary preparation of curves.