海相油气地质2026，Vol.31Issue(3)：264-276,13.DOI:10.3969/j.issn.1672-9854.2026.03.005
基于掩码自监督Transformer的测井含油饱和度预测方法研究
Research on oil saturation prediction from well logs based on a masked self-supervised Transformer
摘要
Abstract
Accurate interpretation of oil saturation(So)traditionally relies on core analysis and empirical formulas,yet this process is challenged by scarce labeled samples,missing logging curves,and significant noise in logging data.To address these issues,this paper proposes a masked self-supervised Transformer-based method for oil saturation prediction from well logs,termed MWLT-So.First,multiple conventional logging curves are thoroughly aligned and standardized,and a random masking reconstruction task is designed to enable self-supervised pretraining on a large amount of unlabeled well interval,thereby learning generalizable representations with cross-well transferability.The pretrained model is then fine-tuned on a limited number of labeled samples using a regression objective,while multi-scale positional encoding and feature fusion are incorporated to enhance long-range dependency modeling.Comparative experiments across different blocks and various logging-curve combinations demonstrate that:(1)Under both random data split and rigorous well-wise split evaluation settings,MWLT-So achieves the best performance on the primary regression task.(2)MWLT-So shows clear advantages in terms of geological profile consistency and error distribution,accurately capturing boundary transition zones and high-So plateau intervals,while effectively overcoming common deficiencies of traditional methods such as over-smoothing,overshooting,and phase lag.Its residuals are more concentrated with thinner tails,and both the median error and dispersion are lower,indicating superior accuracy and robustness.(3)In the oil-bearing/non-oil-bearing identification task based on the threshold So=0.5,MWLT-So attains the best classification performance with the lowest false-positive and false-negative rates,confirming its capability to reduce misclassification risk in threshold-based stratification scenarios.Overall,the proposed method provides effective technical support for rapid evaluation of complex reservoirs and identification of remaining oil.关键词
掩码自监督/Transformer/MWLT-So/测井曲线/含油饱和度/表征学习Key words
masked self-supervised learning/Transformer/MWLT-So/well logging curves/oil saturation/representa-tion learning分类
能源科技引用本文复制引用
黄俊杰,李全厚,段野,王子涵,张若渔,郑泽伟..基于掩码自监督Transformer的测井含油饱和度预测方法研究[J].海相油气地质,2026,31(3):264-276,13.基金项目
本文受国家科技重大专项"中高渗油田大幅提高采收率新方法与新技术"(编号:2025ZD1406100)、国家自然科学基金青年科学基金项目(C类)"陆相页岩游离油含量及可动性定量评价研究"(编号:42102200)和黑龙江省自然科学基金联合引导面上项目"基于马尔科夫链的厚度随机分布薄互层时频响应机理研究"(编号:LH2021D010)联合资助 （编号:2025ZD1406100）
