基于TS-MAE与iTransformer的测井曲线表征学习及油层识别OA

To address the insufficient cross-well generalization of oil layer identification models caused by strong noise in well logging curves and pronounced inter-well distribution differences,an end-to-end reservoir identification method integrating TS-MAE self-supervised pre-training with the variable-wise attention mechanism of iTransformer is proposed.First,multichannel well-logging data are subjected to alignment,missing-value imputation,and standardization.Oil layer,water layer,and dry layer samples are then constructed using sliding-window segmentation with a window length of 64 and a stride of 16,together with center-point labeling.Second,a masking ratio of 0.75 is adopted during pre-training stage,and TS-MAE is employed to reconstruct the well-logging data a nd learn transferable joint representations of multiple logging curves.Finally,the pre-trained encoder is transferred to the downstream iTransformer classification network for fine-tuning.Experimental results based on 3,705 samples from four wells show that the proposed method achieves an accuracy of 93.7%,a Macro-F1 score of 93.1%,and an AUC of 0.971 on the test set,outperforming traditional formula-based methods,conventional machine-learning baselines,and an iTransformer trained from scratch.In cross-well validation,the model achieves a Macro-F1 score of 89.5%and an AUC of 0.956,demonstrating strong robustness to distribution shifts in unseen wells.Ablation experiments and visualization analyses further confirm the synergistic contributions of self-supervised pre-training and variable-wise attention to performance improvement,while enhancing the physical consistency and interpretability of the model predictions.The proposed method provides an efficient and deployable intelligent solution for multi-well reservoir evaluation.