A deep learning multi-module fusion method for well logging curve reconstruction

Well logging data provide critical parameters to support the characterization of subsurface fluid properties. However, the complex downhole environment, harsh operating conditions, and various uncontrollable factors often lead to missing segments in logging curves, posing challenges to accurate analysis of subsurface fluid behavior. To address these challenges, this paper proposes a multi-module fusion approach for reconstructing logging curves. First, logging curve sequences are organized into a multi-channel feature map suitable for two-dimensional convolutional neural network input, enabling the extraction of local spatial features. Next, a squeeze-and-excitation module is employed to adaptively recalibrate channel-wise weights, thereby enhancing feature representation. Then, a temporal convolutional network is introduced to capture sequential information in the logging curves, further improving the model's ability to learn long-range dependencies. Finally, a Transformer is used to perform fine-grained refinement of the deep features for each sampling point and each logging curve. Comparative experiments based on actual logging data from the Dagang oil field demonstrate that the proposed approach achieves a Root Mean Square Error (RMSE) of 1.7302 and the R2 of 0.6160; compared with a vanilla Transformer, the RMSE is reduced by 0.3818, and the R2 is increased by 0.1882; compared with a Bidirectional Long Short-Term Memory model, the RMSE is reduced by 0.3261, and the R2 is increased by 0.1584. These results fully validate the superior feature-extraction capability and strong generalization performance of the proposed model. This study provides powerful support for reconstructing missing logging curves and investigating subsurface fluid behavior under complex geological conditions.