利用神经网络确定储层参数:孔隙度和渗透率。本文综合地质、测井、钻井、岩芯分析资料,在岩芯归位、测井数据标准化、归一化后,挑选合适的建模学习样本,利用BP神经网络建立孔隙度和渗透率预测模型,并用误差统计法和叠合图比较法对储层参数预测效果进行评价。 Determine Reservoir Parameters Using Neural Networks: Porosity and Permeability. In this paper, geology, well logging, drilling and core analysis data are integrated. After the core is repositioned and the logging data are normalized and normalized, a suitable modeling learning sample is selected, and a BP neural network is used to establish a prediction model of porosity and permeability The prediction results of reservoir parameters were evaluated by error statistics method and superimposed graph comparison method.