面向原油总氢物性预测的数据扩增预处理方法

针对原油总氢物性回归预测中核磁共振光谱数据不足的问题,结合深度学习相关理论,提出一种光谱数据扩增预处理方法.根据样本输入和标签的相关系数,在原始样本中加入随机噪声以生成虚拟样本;处理样本数据结构以利于卷积神经网络特征提取,并加入数据冗余改进该结构以进一步提高数据特征提取的完整性;搭建实现原油总氢物性回归预测的卷积神经网络(Regression forecasting convolutional neural network,RF-CNN).实验结果表明,对于总氢物性的回归预测,该数据扩增预处理方法不但可以解决原始数据训练中的过拟合现象,而且相比于传统的偏最小二乘(PLS)回归方法,更具稳定性和精确性.

Data pretreatment approach for crude oil hydrogen properties prediction

Aiming at the problem of lack of nuclear magnetic resonance spectroscopy data in the prediction of total hydrogen activity of crude oil, combined with the theory of deep learning, a pre-processing method of spectral data amplification is proposed. According to the correlation coefficient of the sample input and the label, the random noise is added to the original sample to generate the virtual sample. The sample data structure is processed to facilitate the feature extraction of the convolutional neural network, and the data redundancy is added to improve the structure to further improve the integrity of the data feature extraction. A regression forecasting convolutional neural network(RF-CNN) is designed to realize the regression prediction of the total hydrogen content of crude oil. Experiments show that, for the regression prediction of total hydrogen properties, the amplified data not only solves the over-fitting phenomenon in the original data training, but also has more stability and accuracy than the traditional partial least squares(PLS) dimensionality reduction method.