Well logging curves play a crucial role in oil and gas exploration and geological engineering, as they provide essential information about subsurface formations and reservoir properties. In recent years, with the growing adoption of deep learning techniques in geoscientific data analysis, well logging data have increasingly been modeled as depth-dependent sequences, enabling the application of sequential neural networks for their analysis. Among these approaches, attention mechanisms have been adopted in log interpretation tasks due to their ability to capture long-range dependencies within sequences. However, directly applying attention mechanisms without considering the intrinsic structure of logging data may introduce model redundancy and increase learning complexity, which can ultimately degrade predictive performance. To address this issue, this study proposes a Sequence-based Decoupling Encoder (SDE). The proposed encoder explicitly disentangles the interactions between logging curves and across depth, enabling the model to learn relationships along different dimensions separately, which allows more effective feature extraction and mapping into a latent space. The decoupling strategy also reduces the learning complexity of the attention mechanism and provides clearer learning objectives for the model. The proposed method is evaluated on the public dataset <em>FORCE2020</em> and applied to two common well log interpretation tasks: missing log reconstruction and lithology prediction. We compare SDE against several representative sequential baselines. Experimental results demonstrate that SDE achieves superior predictive performance in both tasks.