一种新的非线性曲线平滑方法及在测井曲线识别中的应用

A New Nonlinear Smoothing and Recognizing Method of Well-Log Time Series

介绍了一种用不对称高斯型函数的非线性最小二乘拟合对时间序列进行平滑处理的方法,并将其应用于测井曲线的平滑处理。给出了一种针对油层测井曲线特点的简化平滑算法,减少了需要优化的参数个数。模型参数的连续变化能够描述形态各异的单峰测井曲线。利用模型参数直接作为特征值,将油层测井曲线单峰的形状分类为钟形、箱形、鸡蛋形、漏斗形和枣核形,分类结果较令人满意。这对其它时间序列的平滑和特征提取也有参考价值。

In this paper, a type of asymmetric Gaussian function fitted by nonlinear least square method is used for data smoothing of time series and this method is applied to the smoothing of well-log time series. A simplified algorithm of smoothing well-log curves of oil-layers is presented, which can reduce the number of parameters needed to optimize. Varying model parameters can describe the different one peak of well-log curves. The model parameters are directly used as feature values to classify the shape of one peak of well-log curves into bell, case, egg, funnel and date core. The recognition results are satisfactory. This method also helps for the smoothing and shape feature extraction of other time series.