Open AccessAPPLICATION OF CONVOLUTIONAL NEURAL NETWORKS FOR RESISTIVITY LOGS PROCESSING AND NON-ITERATIVE EXPRESS-INVERSION IN COMPLEX RESERVOIR ENVIRONMENTS
Author(s) -
Artem R. Leonenko,
K.N. Danilovskiy,
А. М. Петров
Publication year - 2021
Publication title -
interèkspo geo-sibirʹ
Language(s) - English
Resource type - Journals
ISSN - 2618-981X
DOI - 10.33764/2618-981x-2021-2-2-123-129
Subject(s) - electrical resistivity and conductivity , inversion (geology) , artificial neural network , convolutional neural network , computer science , data processing , interpretation (philosophy) , reliability (semiconductor) , well logging , software , data mining , artificial intelligence , algorithm , geology , geophysics , engineering , electrical engineering , programming language , seismology , power (physics) , physics , quantum mechanics , tectonics , operating system
The work is devoted to the development of techniques and software for the quantitative interpretation of resistivity oil well logs. The article considers the results of applying the neural network approach to the processing of resistivity logging data measured at intervals composed of thin layers with contrasting electrical properties. The proposed algorithms combine the advantages of data interpretation based on a two-dimensional axisymmetric medium model and high performance, which allows them to be used at the primary processing stage, increasing the reliability of express interpretation.