基于掩码自监督Transformer的测井含油饱和度预测方法研究OA

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.