Logging Curve Reconstruction Method Based on CNN-BiLSTM With Integrated Attention Mechanism

Traditional logging methods often encounter complications like collapses of wellbores and instrument malfunctions, leading to the loss or misalignment of logging data. Relogging is expensive, particularly for oil and gas wells that have already been cemented; thus, reconstructing logging curves presents a more economical alternative. Although traditional methods like empirical modeling and multiple fitting are effective in some cases, they are limited by geographic location and rock type, making it difficult to meet the demands for precise interpretation of logging data and detailed descriptions of reservoirs. In this study, a convolutional neural network (CNN)-bidirectional long and short-term memory network (BiLSTM)-Attention model is proposed that combines a BiLSTM, a CNN, and an integrated attention mechanism. The model optimizes the ability of traditional deep learning methods to process time-series data and effectively establishes complex nonlinear mapping relationships between inputs and outputs. In this letter, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination R2 are used to evaluate the performance of the CNN-BiLSTM-Attention model. The results demonstrate that the divergence between the reconstructed value and the actual value is negligible in the log reconstruction experiment of CNN-BiLSTM-Attention, particularly under difficult geological settings. This study not only provides an innovative solution for the accurate prediction and effective reconstruction of logging curves but also provides valuable practical experience and a foundation for the implementation of deep learning in geophysics.