【摘要】 根据测井资料计算油页岩含油率多采用△log R法或改进的△log R法,这些方法中参数获取过程中易产生诸多误差,且这些方法是建立在油页岩含油率与特征测井曲线值是线性关系的基础上的,而在实际非均质性地层中,测井对油页岩含油率参数的响应在本质上必然是非线性的。基于此,运用BP神经网络来预测柴达木盆地北部地区侏罗纪油页岩含油率。首先分析研究区段测井数据的数理统计分布特征,在优选学习样本的基础上再采用一种基于LM(Levenberg-Marquardt)算法的BP神经网络进行含油率预测,最后得出一组由40个连接权值与11个阈值组成的含油率参数解释模型,油页岩含油率预测值与岩心实验室分析值吻合很好,均方误差能控制在0.191 8。因此,运用此模型可以预测相同地质背景条件下的油页岩含油率。更多还原

【Abstract】 Method of △log R and advanced method of △log R arre usually adopted to calculate oil content of oil shale with log data. These methods easily cause some errors in the process of calculating parameters, and these methods are based on linear relation between oil content and characteristic log values. However, it was absolutely a nonlinear relation between them in the actual heterogeneous stratum. Therefore, BP neural network based on LM( Levenberg-Marquardt) algorithm was adopted to calculate the oil content in Jurassic strata of northern Qaidam basin. Firstly, mathematical statistics distribution feature of log data were analyzed with Matlab; Ssecondly, oil content values were predicted with BP neural network based on LM algorithm after the excellent samples had been chosen; finally, a matrix composed of 40 link weights and 11 thresholds was the parameter interpretation model of oil content. Results of the BP neural network prove that theoretical calculating values match well with the core experimental measuring values, and the mean square error can be controlled within 0. 191 8.Therefore, this parameter interpretation model can be promoted in the area of the same geology background.更多还原