Email this article Application of convolutional neural networks in the lithological description of the core
- Authors: Murtazayev I.D.1, Konyssov N.Z.1, Saliyev N.B.1
-
Affiliations:
- ТОО «КМГ Инжиниринг»
- Issue: Vol 2, No 2 (2020)
- Pages: 20-27
- Section: Articles
- URL: https://vestnik-ngo.kz/2707-4226/article/view/95619
- DOI: https://doi.org/10.54859/kjogi95619
- ID: 95619
Cite item
Full Text
Abstract
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.
Keywords
About the authors
I. D. Murtazayev
ТОО «КМГ Инжиниринг»
Author for correspondence.
Email: i.murtazayev@niikmg.kz
инженер департамента промысловой геологи и геологического моделирования
Kazakhstan, г. Нур-СултанN. Zh. Konyssov
ТОО «КМГ Инжиниринг»
Email: n.konyssov@niikmg.kz
старший инженер департамента промысловой геологи и геологического моделирования
Kazakhstan, г. Нур-СултанN. B. Saliyev
ТОО «КМГ Инжиниринг»
Email: n.saliyev@niikmg.kz
директор департамента промысловой геологи и геологического моделирования
Kazakhstan, г. Нур-СултанReferences
- 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.
Supplementary files
