石油物探2026，Vol.65Issue(3)：493-505,13.DOI:10.12431/issn.1000-1441.2025.0189
基于改进的U-Net网络的河道砂岩自动识别方法
Automatic channel identification based on improved U-Net
摘要
Abstract
Tight channel sands represent a significant reservoir type with high potential for hydrocarbon accumulation in continental basins.However,conventional methods often fall short in accurately characterizing the 3D distribution of these channel sands due to their multi-phase development,complex stacking relationships,and rapid lateral variations.To overcome this challenge,this study proposes an automated channel identification method based on an improved U-Net deep learning network.Guided by seismic sedimentology,the first step involves applying Wheeler transformation to the time-domain seismic data to incorporate sedimentary cycle characteristics,which facilitates the identification of sandstone stacking relationships and yields high-quality training samples.This is followed by the integration of a cascaded dilated convolution module and a spatial attention mechanism into the U-Net architecture.This integration strengthens the network's capacity to extract multi-scale features and thus improves the delineation of narrow,thin,and superimposed channel boundaries.Finally,data augmentation methods tailored to channel characteristics are employed to automatically generate a large number of training samples for model training and testing.Field application results demonstrate that the improved U-Net significantly enhances the accuracy of boundary identification for multi-phase superimposed channels and achieves 3D characterization of single-phase channel systems.This approach offers reliable technical support for the evaluation of tight channel sandstone reservoirs and the optimization of exploration strategies.关键词
深度学习/改进U-net网络/河道砂岩自动识别/期次剥离/三维空间雕刻Key words
deep learning/improved U-Net/automated channel identification/phase separation/3D channel sculpting分类
能源科技引用本文复制引用
张玉玺,缪志伟,李世凯,孙均..基于改进的U-Net网络的河道砂岩自动识别方法[J].石油物探,2026,65(3):493-505,13.基金项目
新型油气勘探开发国家科技重大专项(2025ZD1400400)资助. This research is financially supported by the National Science and Technology Major Project of China for New-Type Oil and Gas Exploration and Development(Grant No.2025ZD1400400). （2025ZD1400400）
