Open AccessLateral scanning logging while drilling data processing using convolutional neural networks
Author(s) -
K. N. Danilovskii,
G. Loginov
Publication year - 2022
Publication title -
geofizičeskie tehnologii
Language(s) - English
Resource type - Journals
ISSN - 2619-1563
DOI - 10.18303/2619-1563-2021-2-24
Subject(s) - convolutional neural network , artificial neural network , computer science , noise (video) , electrical resistivity and conductivity , process (computing) , layer (electronics) , orientation (vector space) , artificial intelligence , pattern recognition (psychology) , algorithm , image (mathematics) , geometry , engineering , materials science , electrical engineering , mathematics , composite material , operating system
This article discusses a new approach to processing lateral scanning logging while drilling data based on a combination of three-dimensional numerical modeling and convolutional neural networks. We prepared dataset for training neural networks. Dataset contains realistic synthetic resistivity images and geoelectric layer boundary layouts, obtained based on true values of their spatial orientation parameters. Using convolutional neural networks two algorithms have been developed and programmatically implemented: suppression of random noise and detection of layer boundaries on the resistivity images. The developed algorithms allow fast and accurate processing of large amounts of data, while, due to the absence of full-connection layers in the neural networks’ architectures, it is possible to process resistivity images of arbitrary length.