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谭成仟 ,张建英 ,苏超 ,吴向红 ,赵丽敏 .井间参数预测的相控神经网络模型 .石油地球物理勘探 ,2 0 0 2 ,37(3) :2 5 4～ 2 5 7本文提出了一种基于微相研究的神经网络井间参数内插预测新方法。该方法结合油藏微相研究成果 ,采用井位和微相信息作为神经网络的输入参数 ,对储层参数进行空间预测。本文以孤岛油田渤 2 1断块油藏为例 ,利用空间分散井位点的渗透率资料和地区沉积微相信息进行井间渗透率内插预测。结果表明 ,该方法不仅可以方便地将一些先验的地区知识和专家经验用于井间参数预测之中 ,而且大大提高了井间参数的预测精度 ,为油藏建模提供了可靠的基础。
TAN Cheng-Qian, ZHANG Jian-Ying, SU Chao, WU Xiang-Hong, ZHAO Li-Min.Phase-Controlled Neural Network Model for Predicting Crosswell Parameters.Petroleum Geophysical Prospecting, 2 0 0 2, 37 (3): 2 5 4 ~ 2 5 7 In this paper, A Novel Method for Interpolating Interpolation of Intersection Parameters in Neural Networks Based on Microfacies. Based on the research results of reservoir microfacies, this method uses well location and microfacies information as the input parameters of neural network to predict the reservoir parameters. Taking the Bo 2 1 fault block reservoir in Gudao Oilfield as an example, this paper uses the permeability data of the dispersed well sites and the regional sedimentary microfacies information to predict the inter-well permeability interpolation. The results show that this method can not only be used to predict the crosswell parameters easily, but also provide a reliable basis for reservoir modeling.