石油地球物理勘探2025，Vol.60Issue(3)：576-587,12.DOI:10.13810/j.cnki.issn.1000-7210.20240239
基于互相关约束和CNN-GRU网络的井震自动标定
Automatic seismic-well tie based on cross-correlation constraints and CNN-GRU network
摘要
Abstract
Seismic-well tie is an important step in seismic data interpretation.The traditional seismic-well tie method synthesizes seismic records by using well logging data and extracted seismic wavelets and matches them with the seismic traces beside the well by dragging.This method has significant human factors,is highly time-consuming,and can easily cause overstretching.Therefore,a deep learning method based on convolutional neu-ral network(CNN)and gated recurrent unit(GRU)network is proposed to achieve automatic seismic-well tie.Firstly,seismic records are synthesized using typical models,and time correction quantities are introduced to correct the records of seismic traces beside the well.Secondly,the relationship between two seismic traces and the time correction quantities is established through a trained CNN-GRU network,and the correlation coeffi-cients of the two seismic traces are used as constraint conditions to directly predict the time correction quantities by using the synthetic seismic records and seismic traces beside the well.Finally,the neural network is tested using actual data from 30 wells,and the obtained results are compared with manual calibration results.The cor-relation coefficients between the calibrated synthetic seismic records and the seismic traces beside the wells are calculated.The following findings are obtained.① The correlation coefficients of automatic calibration with the network are greater than or equal to those of manual calibration for 25 wells and are basically consistent for the other wells.② Manually calibrating 30 wells takes about 30 min,while calibrating them with the network only takes 5 s.Therefore,compared with the traditional method,the proposed method has high accuracy and efficiency in seismic-well tie,which verifies the feasibility and progressiveness of the method.关键词
井震标定/深度学习/神经网络/时间校正量/相关系数Key words
seismic-well tie/deep learning/neural network/time correction quantities/correlation coefficient分类
地质学引用本文复制引用
李钦昭,刘洋,席念旭,张浩然,邸希..基于互相关约束和CNN-GRU网络的井震自动标定[J].石油地球物理勘探,2025,60(3):576-587,12.基金项目
本项研究受国家重点研发计划项目"深部煤系复合气藏地球物理预测方法"(2024YFC2909400)资助. （2024YFC2909400）
