鱼群算法优化组合核函数GPR的油井动液面预测

Prediction for Dynamic Fluid Level of Oil Well Based on GPR with AFSA Optimized Combined Kernel Function

针对抽油井动液面(DFL)检测主要依靠人工操作回声仪测试,无法实时在线检测,而单一核函数的高斯过程回归(GPR)无法明显提高预测精度和泛化能力,提出了一种人工鱼群算法(AFSA)优化组合核函数的动态高斯过程回归动液面预测模型.采用多项式函数、线性函数与径向基函数组合构建核函数,利用人工鱼群算法对核函数模型参数进行寻优,采用快速傅里叶变换(FFT)和核主元分析(KPCA)融合提取时频数据非线性特征作为模型输入,提高模型的预测精度和泛化能力.油田现场应用验证了该方法的有效性.

The dynamic fluid level（ DFL） of an oil well is usually measured onsite by using the acoustic method. This method,however,has its limitation in determining the real-time DFL.Considering that Gaussian process regression（ GPR） with single kernel function cannot significantly improve the prediction accuracy and generalization ability,a dynamic GPR for DFL with the combined kernel function optimized by artificial fish-swarm algorithm（ AFSA） was proposed. The polynomial function,liner function and radial basis function were used to construct the combined kernel function of GPR in order to improve the generalization ability. The AFSA was used to optimize parameters of the combined kernel function in order to improve the prediction accuracy. The fast Fourier transform（ FFT） and kernel principal analysis（ KPCA） were used to extract nonlinear features of data in the time and frequency domain as the input variables of the model. The oil field application shows the validity of the proposed method.