Modification of U-Net Network Architecture for Core Samples Analysis Automation; Industrial Engineering, Applications and Manufacturing (ICIEAM)

Modification of U-Net Network Architecture for Core Samples Analysis Automation

Détails bibliographiques
Parent link:Industrial Engineering, Applications and Manufacturing (ICIEAM).— 2025.— P. 863-868
Auteur principal: Denisov V. I. Vladislav Igorevich
Autres auteurs: Kochegurov A. I. Aleksandr Ivanovich, Semenchenko N. E. Nikita Evgenjevich
Résumé:Title screen
For oil and gas facilities, digital twins are a promising technology that can improve the quality of forecasts, enhance production safety, and determine the most optimal development strategy for enterprises. To reduce operational costs, increase production volumes, and improve oil processing efficiency, oil companies are now digitizing their assets. The development of digital field concept involves creating a model where not only wells but also the data under study, as well as the results of the studies themselves, are digitized. Furthermore, the direction of modern industrial development also implies the automation of these processes. To achieve these goals, artificial intelligence is being actively implemented. This work focuses on one aspect of automating the core analysis process, specifically the task of modifying the U-Net network architecture to improve the efficiency of working with rock samples. The proposed changes to the original architecture are as follows: adding intermediate connections between decoder layers (for more accurate transfer of features at different levels of abstraction), introducing residual blocks (to address the vanishing gradient problem), replacing 2D convolution with depthwise separable convolution (to make the model lighter and faster) and incorporating attention mechanisms (to increase the network's accuracy). Additionally, the article discusses an “online” augmentation algorithm developed as part of this work. As shown in the study, these modifications enable higher accuracy, better resistance to overfitting, and improved computational efficiency. These achievements can be further utilized in solving classification and segmentation tasks on rock samples
Текстовый файл
Langue:anglais
Publié: 2025
Sujets:
U-Net
neural networks
rock segmentation
rock classification
core
architecture modification
rock samples
электронный ресурс
труды учёных ТПУ
Accès en ligne:https://doi.org/10.1109/ICIEAM65163.2025.11028233
Format: Électronique Chapitre de livre
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=683153

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