基于互相关约束和CNN⁃GRU 网络的井震自动标定

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中图分类号：P631 文献标识码：A DOI：10. 13810/j. cnki. issn. 1000-7210. 20240239

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 correcting values are introduced to cor⁃ rect the records of seismic traces beside the well. Secondly，the relationship between two seismic traces and the time correcting values is established through a trained CNN ⁃ GRU network，and the correlation coefficients of the two seismic traces are used as constraint conditions to directly predict the time correcting values 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 correlation coef⁃ ficients 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 w ells. ② Manually calibrating 30 wells takes about 30min ，while calibrating them with the network only takes

5 s. Therefore，compared with the traditional method，the proposed method has higher accuracy and better effi⁃ ciency in seismic⁃well tie，which verifies the feasibility and progressiveness of the method.

Keywords：seismic⁃well tie，deep learning，neural network，time correcting values，correlation coefficient 李钦昭，刘洋，席念旭，等 . 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）：564‑575.

LI Qinzhao，LIU Yang，XI Nianxu，et al. Automatic seismic⁃well tie based on cross⁃correlation constraints and CNN⁃GRU network［J］. Oil Geophysical Prospecting，2025，60（3）：564‑575.

0 引言

地震资料是反映地下地层反射情况的时间域信息，而测井资料是展示地下地层地质情况的深度域资料，井震标定的目的是将时间域的地震资料与深度域的测井资料联系起来，从而实现可靠的速度建模、地震资料解释与储层预测等[1]。（剩余13742字）