论文部分内容阅读
樊政军徐顺等：神经网络在评价塔北低阻油气储层参数中的应用，测井技术，１９９６（３）２０，２１５～２１８。本文阐述了塔里木盆地北部地区（简称塔北）低阻油气储层的成因及评价塔北低阻油气储层的新方法一神经网络法。该方法利用神经网络原理中前馈网络中的道传播（ＢＰ）学习算法求取孔隙度、渗透率和饱和度等储层参数。在选择解释模型时，考虑了各参数的不同性质选取了不同的网络结构。通过实际资料的处理结果与化验资料、测试结果相比较，认为神经网络法在塔北低阻油气储层测井解释中提高了储层参数的评价精度，具有良好的地质效果。
Fan Zhengjun, Xu Shun et al .: Application of neural network in the evaluation of low resistivity oil and gas reservoir parameters in Tarim Basin, Logging Technology, 1996 (3) 20,215 ~ 218. This paper describes the genesis of the low resistivity oil and gas reservoir in the northern part of the Tarim Basin and the new method and neural network method for evaluating the low resistivity and hydrocarbon reservoir in the Tarim Basin. In this method, the reservoir parameters such as porosity, permeability and saturation are obtained by using the BP learning algorithm in the feedforward network in the principle of neural networks. In the choice of interpretation model, taking into account the different nature of the parameters selected a different network structure. By comparing actual data with experimental data and test results, it is considered that neural network method improves the evaluation accuracy of reservoir parameters in logging interpretation of low resistivity oil and gas reservoir in Tarim Basin and has good geological effects.