Non-core drilling has gradually become the primary exploration method in geological exploration engineering, and well logging curves have increasingly gained importance as the main carriers of geological information. However, factors such as geological environment, logging equipment, borehole quality, and unexpected events can all impact the quality of well logging curves. Previous methods of re-logging or manual corrections have been associated with high costs and low efficiency. This paper proposes a machine learning method that utilizes existing data to predict missing data, and its effectiveness and feasibility have been validated through field experiments. The proposed method builds on the traditional Long Short-Term Memory (LSTM) neural network by incorporating a self-attention mechanism to analyze the sequential dependencies of the data. It selects the dominant computational results in the LSTM, reducing the computational complexity from O(n^2) to O(nlogn) and improving model efficiency. Experimental results demonstrate that the proposed method achieves higher accuracy compared to traditional curve synthesis methods based on Fully Connected Neural Networks (FCNN) and vanilla LSTM. This accurate, efficient, and cost-effective prediction method holds a practical value in engineering applications.

翻译：非取心钻探已逐渐成为地质勘探工程中的主要勘探手段，测井曲线作为地质信息的主要载体其重要性日益凸显。然而，地质环境、测井设备、井眼质量及突发状况等因素均可能影响测井曲线质量。传统的重复测井或人工校正方法存在成本高、效率低等问题。本文提出一种利用现有数据预测缺失数据的机器学习方法，并通过现场实验验证了其有效性与可行性。该方法在传统长短期记忆神经网络基础上引入自注意力机制，通过分析数据的时序依赖关系，筛选LSTM中的主导计算结果，将计算复杂度从O(n²)降至O(nlogn)，有效提升了模型效率。实验结果表明，与传统基于全连接神经网络及标准LSTM的曲线合成方法相比，所提方法具有更高的预测精度。这种精确、高效且低成本的预测方法在工程应用中具有较强的实用价值。