融合相似性度量加权核偏最小二乘的烷烃气体定量分析方法

Weighted kernel partial least squares method based on fusion of similarity measurement criteria for quantitative analysis of alkane gases

烃类气体含量的有效监测是油气勘探开采过程中安全保障的重要环节。红外光谱法作为一种安全高效的检测方法,受到现场工程师的关注,但主要采用离线模型进行测量,无法较好应对现场复杂的工况及变化多样的非线性影响因素,导致离线模型不更新而难以维持较高的预测精度。为此,提出了一种融合相似性度量加权核偏最小二乘的即时学习建模策略。首先设计了一种多相似性度量准则融合的样本相似性判别依据,有效筛选历史样本用于在线建模,其次在局部PLS模型中引入非线性核函数,实现非线性特征的有效提取,弥补线性偏最小二乘模型的非线性处理能力。在构建的多组分混合气体红外光谱数据上的实验结果验证了该方法的有效性,拟合优度R2达到0.994 1,RMSE和MRE相比PLS模型分别提升了43.6%和85.8%,可有效用于烃类气体红外光谱定量分析模型的在线更新与高精度预测。

The effective monitoring of hydrocarbon gas content is an important aspect of safety assurance in oil and gas exploration and production processes.Infrared spectroscopy,as a safe and efficient detection method,has attracted the attention of on-site engineers.However,it mainly uses offline models for measurement,which cannot cope with the complex working conditions and various nonlinear influencing factors on site,making it difficult for this non updated model to maintain high prediction accuracy.A weighted kernel partial least squares method based on fusion of similarity measurement criteria in just-in-time learning for quantitative analysis of alkane gases is proposed in this paper.Firstly,a similarity criterion based on fusion of multiple similarity measurement criteria is designed to effectively select historical samples for online modeling.Secondly,nonlinear kernel functions are introduced into local PLS models to effectively extract nonlinear features and compensate for the nonlinear processing ability of linear partial least squares models.The experimental results on the multi-component mixed gas infrared spectral data have verified the effectiveness of this method,with a goodness of R2 of 0.9941.Compared with that of the PLS model,the RMSE and MRE of the proposed model have improved by 43.6%and 85.8%,respectively.It can be effectively used for online updating and high-precision prediction of infrared spectral quantitative analysis models for hydrocarbon gas.