Kazakhstan journal for oil & gas industryKazakhstan journal for oil & gas industry2707-42262957-806XKMG Engineering9969010.54859/kjogi99690Research ArticleLithofacial analysis and possibilities for prediction of properties on geophysical research and seismic exploration data by methods of machine learningKolbikovaE. S.vestnik@niikmg.kzRoxar Paradigm E&P Software and Services LLC151220213432372801202228012022Copyright © 2021, Kolbikova E.S.2021<p>The success of a development strategy for any field depends on the degree of knowledge of the geological structure of its main reservoirs. As the area is drilled out, the concept of the structure of the hydrocarbon accumulation is refined, but in the case of a complex structure of the void space of the reservoirs and the lithological heterogeneity of the section over the area, geological uncertainties and risks during the subsequent placement of wells remain high. For these reasons, one of the main problems in hydrocarbon production is predicting rock types and the distribution of fluids throughout the reservoir away from wells, since the determination of rock properties is a major source of uncertainty in reservoir modeling studies [1, 2]. The proposed project will demonstrate algorithms based on machine learning methods that allow predicting the distribution of lithology and the uncertainty of lithofacies variability in the section.</p>classificationmachine learning methodslithotypingfacies forecastcluster modelproperty predictionspecification of reservoir propertiesжіктеумашиналық өңдеу әдістерілитолигиялық түрлерге бөлуфациялық болжаукластерлік нұсқа (модель)қасиеттерді болжауФКҚ нақтылауклассификацияметоды машинного обучениялитотипизацияпрогноз фацийкластерная модельпрогнозирование свойствуточнение ФЕС1.Hami-Eddine K., Klein P., and Richard L. Well Facies-based supervised classification on prestack. – SEG Annual Meeting, Houston, Texas, October 2009.2.Hami-Eddine K., Klein P., Richard L., de Ribet B. and Grout M., A new technique for lithology and fluid content prediction from prestack data: An application to a carbonate reservoir. – The 13th SEGJ International Symposium, Tokyo, Japan, April 2019.3.Ye Shin-Ju, Rabiller P. A new tool for electrofacies analysis: Multi-Resolution Graph-Based Clustering. – 41st Annual Logging Symposium SPWLA, 2000.4.Ye Shin-Ju, Rabiller P. Automated Electrofacies Ordering. – Petrophysics, 2005, v. 46, N 6.5.Zhou Y., and Goldman S. Democratic co-learning. – 16th IEEE International Conference on Tools with Artificial Intelligence, 2004.6.Kolbikova E., Gusev S., Garaev A., Malinovskaya O., Kamilevich R. Forecast of prospective oil saturation zones in the Devonian carbonate deposits of the Kharyaginsky field based on geological and geophysical information analysis by using machine learning methods. – SPE-206520, SPE Russian Petroleum Technology Conference, 2021.