测井数据是油气田开发和评价的基础，然而实际开采过程中因井壁稳定性不佳、设备故障等因素导致测井数据的失真或缺失，针对现有预测模型结果不稳定、精度不高等问题，提出一种基于变分模态分解（variational mode decomposition, VMD）改进的由卷积神经网络（convolutional neural networks, CNN）、双向长短期记忆网络（bidirectional long short-term memory, Bi-LSTM）与注意力机制Attention组合的预测模型对测井曲线缺失段进行预测。以测井序列数据作为输入，利用VMD算法将测井序列分解成一系列调幅调频信号子序列；通过CNN网络提取测井序列的特征并利用Bi-LSTM网络进行训练，训练过程使用Attention机制来动态地学习每个时间步的重要性权重；最后输出测井曲线预测值。将该方法应用于河南泌阳区块测井曲线预测，并与其他常用机器学习预测模型进行对比分析，结果显示，基于VMD改进的CNN-BiLSTM-Att模型的测井曲线预测方法应用效果显著，误差仅10^-3数量级，预测精度可达92.02%，研究成果为测井曲线的准确预测提供了新思路。

Logging data constitutes the basis for oil and gas field development and evaluation. However, in actual mining, factors like poor wellbore stability and equipment failure give rise to the distortion or loss of logging data. A prediction model based on variational mode decomposition (VMD) was proposed to address the issues of unstable and inaccurate results in existing prediction models. The model combines convolutional neural networks (CNN), bidirectional long short term memory (Bi-LSTM), and attention mechanism to predict missing sections in well logging curves. With logging sequence data as input, the VMD algorithm was employed to decompose the sequence into a series of amplitude-modulated and frequency-modulated signal subsequences. The features were extracted by the CNN network and trained by the Bi-LSTM network. During training, the Attention mechanism was utilized to learn the importance weight of each time step dynamically. Finally, the predicted value of the logging curve was outputted. The method was applied to predict logging curves in the Biyang Block of Henan Province and compared with other common machine learning prediction models. The results show that the application effect of the CNN-BiLSTM-Att model improved based on VMD is remarkable, with an error of only the order of 10^-3 and a prediction accuracy of 92.02%. The research results provide new ideas for accurate prediction of logging curves.