石油物探2026，Vol.65Issue(3)：469-477,9.DOI:10.12431/issn.1000-1441.2025.0044
基于时频域地震信号与深度全序列卷积神经网络的三维沉积微相预测方法
A deep full-sequence convolutional neural network for 3D sedimentary microfacies prediction using time-frequency seismic data
摘要
Abstract
To address the challenges of limited well control and the low accuracy of 3D sedimentary microfacies modeling using seismic data,this study proposes a prediction workflow based on a time-frequency deep full-sequence convolutional neural network(DFCNN).Building upon continuous wavelet transform for time-frequency decomposition,which expands the seismic information dimension and reduces interpretation uncertainties,a large,accurately labeled time-frequency spectrum dataset was constructed for sedimentary facies.The loss function,network architecture,and key parameters of the DFCNN were optimized,enabling accurate prediction of 3D microfacies.The method was applied to the Jurassic Sangonghe Formation in the MXZ area.The results show that the predicted 3D sedimentary microfacies agree well with the drilling data,achieving an accuracy of 82.6%,which demonstrates the applicability of the proposed workflow for sedimentary facies prediction.关键词
时频域地震样本/深度全序列卷积神经网络/网络结构/损失函数/三维沉积微相Key words
time-frequency seismic label/DFCNN/network architecture/loss function/3D sedimentary microfacies分类
能源科技引用本文复制引用
王月蕾,谭绍泉,穆星,张娟..基于时频域地震信号与深度全序列卷积神经网络的三维沉积微相预测方法[J].石油物探,2026,65(3):469-477,9.基金项目
中国石油化工股份有限公司课题"准中地区中生界隐蔽圈闭发育模式与精细描述"(P24029)资助. This research is financially supported by the Sinopec Research Project(Grant No.P24029). （P24029）
