基于多源信息特征融合的抽油井动液面集成软测量建模

针对传统抽油井动液面(DLL)检测只能依靠人工操作回声仪测试,无法实时在线检测的问题,提出基于多源信息特征融合的抽油井动液面集成软测量新方法。采用快速傅里叶变换(FFT)将抽油机悬点载荷及振动时域信号转换成频域信号;采用核主元分析(KPCA)提取悬点载荷及振动频谱和电功率、井口油、套压时域信号非线性特征;利用改进的模糊交互式自组织数据分析聚类(ISODATA)和高斯过程回归(GPR)融合时频信息特征,建立多个动态子模型;利用权重优化证据理论(D-S)构造的概率分配函数作为权值因子,对子模型输出进行集成以得到最终的DLL预测值。油田现场应用证明了该方法的有效性。

Ensemble soft sensor modeling for dynamic liquid level of oil well based on multi-source information feature fusion

The dynamic liquid level (DLL) of an oil well is traditionally measured onsite by using the acoustic method. This method, however, has its limitation in determining real-time dynamic liquid level. A new ensemble soft-sensor approach of DLL based on the multi-source information feature fusion was proposed. The polish rod load and vibration signal in the time domain was transformed into the frequency domain using fast Fourier transform (FFT). The kernel principal component analysis (KPCA) was used to extract the nonlinear feature of the load and vibration spectral signal and the power, casing head pressure and tubing head pressure time signal. The improved fuzzy interactive self-organizing data analysis technique algorithm (ISODATA) and Gaussian process regression (GPR) were used to fuse time/frequency information feature and establish multiple sub-models. Then, the final DLL prediction model was obtained through the ensemble of the sub-models based on the weight factor calculated by optimized-weighted Dempster-Shafer (D-S) theory. The oil field application showed the validity of the proposed method.