基于滑动平均预处理的BP网络储层识别方法

BP neural network of reservoir identification based on moving-weighted-average.

在油气勘探开发领域的储层识别研究中,神经网络技术是一种有效的工具。根据BP神经网络的逼近原理,提出了基于滑动平均预处理的BP神经网络储层识别方法。首先对学习样本中的每一组样本数据按照一定规则选取近邻点,然后根据近邻点信息,使用滑动平均的方法进行预处理得到新的样本数据,最后使用新的学习样本训练BP网络,进行储层判识。实验结果表明,该方法具有简单、高效、学习速度快的优点,能极大提高识别速度和预测精度。

Neural network is one of the efficient tools in the field of reservoir identification.A new method for reservoir identification is proposed which is hased on the combination of the approximation principle of BP network and the technology of preprocessing.The method,first selects the proper neighbor points for every training sample according to the certain rule,then calculates the new training data with moving-weighted-average method based on the information of the selected points,and finally obtains the identifying results.This method has faster learning speed and higher prediction precision than the traditional method without preprocessing,which uses one or two hidden layers.Experimental results show that the method can rapidly,accurately identify.