石油钻采工艺2026，Vol.48Issue(4)：475-487,13.DOI:10.13639/j.odpt.202601021
基于TS-MAE与iTransformer的测井曲线表征学习及油层识别
Well logging curve representation learning and oil-bearing layer identification based on TS-MAE and iTransformer
摘要
Abstract
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.关键词
测井曲线/油层识别/自监督预训练/TS-MAE/iTransformer/跨井泛化Key words
well logging curves/oil layer identification/self-supervised pre-training/TS-MAE/iTransformer/cross-well generalization分类
能源科技引用本文复制引用
黄俊杰,李全厚,邱林,宋振国..基于TS-MAE与iTransformer的测井曲线表征学习及油层识别[J].石油钻采工艺,2026,48(4):475-487,13.基金项目
油气藏地质及开发工程全国重点实验室2025年开放基金项目"基于数据驱动致密砂岩储层强鲁棒-泛化智能表征模型"(编号:PLN2025-15) （编号:PLN2025-15）
油气资源与勘探技术教育部重点实验室(长江大学)开放基金资助项目"基于常规测井驱动Kolmogorov-Arnold网络预测页岩储层物性和小样本泛化性能提升研究"(编号:K2025-04). （长江大学）
