In using generative adversarial networks to generate oil well production parameter data, this method causes the inconsistency between partially generated data characteristics and characteristics of oil well production process, which leads to the low quality of soft sensor modeling of dynamic liquid level. This paper presents an expansion method of soft sensor modeling data of oil well dynamic liquid level based on expert diagnosis-wasserstein generative adversarial networks. After the discriminator obtains the original loss value based on the real data and generated data, the rationality of the generated data is diagnosed by the expert diagnosis module in combination with the mechanism process of oil well production, and the discriminator judgment results are detected. The error results are compensated and added to the loss functions of the generator and discriminator for subsequent confrontation training, thus the better soft sensor modeling sample data of dynamic liquid level which consistent with the characteristics of oil well production process is generated. Through simulation experiments, the prediction accuracy of the dynamic liquid level improved by adding the generated data to the training data of soft sensor modeling, and the root mean square error is reduced by 5. 99%. It shows that the data generated by the generator after adding the expert diagnosis module has higher quality and can better meet the production needs of the oilfield.