电子测量与仪器学报

王通,陈延彬.基于改进生成对抗网络的动液面建模数据扩充[J].电子测量与仪器学报,2023,37(2):99-109

基于改进生成对抗网络的动液面建模数据扩充

Dynamic liquid level modeling data augmentation based onimproved generative adversarial networks

DOI：

英文关键词:data augmentation dynamic liquid level generative adversarial networks

基金项目:国家自然科学基金(62173073)项目资助

作者	单位
王通	1.沈阳工业大学电气工程学院
陈延彬	1.沈阳工业大学电气工程学院

Author	Institution
Wang Tong	1.School of Electrical Engineering, Shenyang University of Technology
Chen Yanbin	1.School of Electrical Engineering, Shenyang University of Technology

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中文摘要:

针对采用生成对抗网络进行油井生产参数数据生成时,部分生成数据不符合油井生产过程特性,导致动液面软测量建模质量不高的问题,提出一种基于专家诊断的生成对抗网络油井动液面软测量建模数据扩充方法。在判别器基于真实数据与生成数据得到原始损失值后,结合油井生产的机理过程对生成数据的合理性进行专家诊断,检测判别器判别结果。对错误结果进行补偿并加入生成器与判别器的损失函数中进行后续对抗训练,从而生成较优的符合油井生产过程特性的动液面软测量建模样本数据。通过仿真实验,生成数据补充到软测量建模的训练数据中能够提高动液面的预测精度,均方根误差降低了 5. 99%。表明加入专家诊断模块后生成器生成数据质量更高,能够更好地满足油田生产需求。

英文摘要:

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.

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