基于Regression GAN的原油总氢物性预测方法

Regression GAN Based Prediction for Physical Properties of Total Hydrogen in Crude Oil

针对生成对抗网络(Generative adversarial network,GAN)不适用于原油物性回归预测的问题,本文提出一种回归生成对抗网络(Regression GAN,RGAN)结构,该结构由生成模型G、判别模型D及回归模型R组成.通过判别模型D与生成模型G间的对抗学习,D提取原油物性核磁共振氢谱(~1H NMR)谱图的潜在特征.首层潜在特征是样本空间的浅层表示利于解决回归问题,采用首层潜在特征建立回归模型R,提高了预测的精度及稳定性.通过增加条件变量和生成样本间的互信息约束,并采用回归模型R的MSE损失函数估计互信息下界,生成模型G产生更真实的样本.实验结果表明,RGAN有效地提高了原油总氢物性回归预测精度及稳定性,同时加快了生成模型的收敛速度,提高了谱图的生成质量.

In view that generative adversarial network（GAN） is not applicable to prediction of physical properties of crude oil, a novel regression GAN（RGAN） framework is proposed in this study, which consists of a generator G, a discriminator D and a regression model R. Through adversarial learning between discriminator D and generator G, D extracts a series of latent features of ^1H nuclear magnetic resonance spectroscopy（^1H NMR） of crude oil. The first layer of latent features is shallow representation of the data space, which helps to solve the regression task. The regression model R is established using the first layer of latent features, which improves the accuracy and stability of the prediction.At the same time, the MSE loss function of the regression model R is applied to estimate the lower bound of the mutual information between conditional variables and generated samples, therefore generator G can produce more realistic samples.Experiment results demonstrate that RGAN can improve the prediction accuracy and stability of physical properties of total hydrogen in crude oil efficiently, and also improve the convergence speed of the generator as well as the quality of spectra generation.