Kazakhstan journal for oil & gas industryKazakhstan journal for oil & gas industry2707-42262957-806XKMG Engineering9561910.54859/kjogi95619Research ArticleApplication of convolutional neural networks in the lithological description of the coreMurtazayevI. D.i.murtazayev@niikmg.kzKonyssovN. Zh.n.konyssov@niikmg.kzSaliyevN. B.n.saliyev@niikmg.kz150620202220272912202129122021Copyright © 2020, Murtazayev I.D., Konyssov N.Z., Saliyev N.B.2020<p>The article describes a method for training a convolutional neural network for rock lithology recognition based on images of core material. High Resolution (Hi-Res) photos were used for training models. The principles of convolutional neural networks and their practical application in geology are considered. As an outcome of this work, the model of neural networks for recognizing rock lithology was created and applied in practice using a smartphone. It was established that many ML and DL technologies potentially can be applicable for oil and gas industry.</p>neural networkcorelithologyнейронная сетькернлитология[https://ru.wikipedia.org/wiki/Нейронная_сеть.][Мак-Каллок У.С., Питтс В. Логическое исчисление идей, относящихся к нервной активности. – Архивная копия, 1956, с. 363–384.][https://ru.wikipedia.org/wiki/Свёрточная_нейронная_сеть.][Aphex34 – собственная работа: https://commons.wikimedia.org/w/index.php?curid=45679374.][Keras API.https://github.com/keras-team/keras.][TensorFlow API. https://github.com/tensorflow/tensorflow.][Архитектура MobileNet. https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet.][Утилита для обучения ИНС TeachebleMachine. https://github.com/googlecreativelab/teachablemachine-community.][Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. https://arxiv.org/abs/1704.04861.][Приложение TFL Classify. https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/android.][Портативный формат TensorFlow для мобильных устройств. https://www.tensorflow.org/lite.]