基于粗集和神经网络的石油测井数据挖掘方法

由于石油测井数据存在着模糊性和噪声，在数据挖掘中单纯使用粗集方法会受噪 声干扰而直接影响分类精度，单纯使用神经网络会因输入信息空间维数较大时使网络结构复杂且训练时间长．为解决这些问题，根据测井解释原理，本文提出一种将两者结合起来的数据挖掘方法，即经过测井资料预处理、样本信息粗集方法简化、神经网络学习训练、待识信息网络识别和误差分析等步骤，其中使用的二层非线性连接权神经网络简化了网络的运算．通过岩性识别和储层参数定量计算两个应用实例，结果表明这种数据挖掘方法在测井解释中其识别率远高于其它单一数据挖掘方法，效果令人满意．

AN APPROACH TO OIL LOG DATA MINING BASED ON ROUGH SET & NEURAL NETWORK

For the ambiguity and noise of oil log data, only using the rough set in data mining would decrease the classification precision. For the large input information-dimension from database, only using the neural network in data mining would make the structure of neural network complex and the training overtime. To solve above problems, an approach to data mining integrated rough set and neural network is presented in this paper based on oil log interpretation principle. The process follows, log data preprocessing, samples information reduction by rough set, stud-ying-training of neural network, unknown information recognition by neural network, error analysis, and so on. A two-layer neural network with nonlinear connection weights make the operation simple. The examples of lithology recognition and reservoir parameters quantitative calculation show the coincident ratio by the data mining method is much more than that of other single methods, and the effect of log interpretation is satisfactory.