摘要
对姬塬地区C98井长8段(分为长81,长82小层)储层岩心分析孔隙度数据进行筛选、处理,且等间距均值化取值作为特征序列;然后与C98井测井资料孔隙度进行了精细的评价、匹配和拟合,利用灰色关联分析方法计算了孔隙度与各测井曲线的邓氏灰色关联度、灰色绝对关联度、灰色相对关联度和灰色综合关联度,按照关联度大小提取了较大的7个参数:AC,CAL,CNL,DEN,GR,RT,SP作为孔隙度预测的影响因素序列;最后建立GM(1,n)模型对目的层孔隙度进行了模拟和预测,并做了残差和相对误差分析.从整体上来看,模型预测结果与钻井取心分析资料基本趋于一致,达到了纵向上预测的目的,同时也体现了灰色预测方法具有涉及数据量小、操作简便、运算速度快的特点.
The reservoir core porosity analysis data in Chang 8 of Jiyuan C98 well （divided into long 81 and 82 small layer） was filtered and processed so as to result in spacing averaged values as a sequence of features. Then the C98’s well-logging information on its porosity was precisely evaluated, matched and fitted, so that the gray relational analysis method could be applied to calculate the porosity log curve Deng’s, gray relational grade, absolute of gray, gray relative correlation degree and gray comprehensive correlation degree. In accordance with the size of the associated degree, the following seven parameters AC, CAL, CNL, DEN, GR, RT, SP were taken as a sequence of factors affecting the porosity prediction. Lastly, GM（1, n） model was established on simulating and predicting the target layer porosity. Meanwhile, the residual analysis and that of relative error were also conducted. Generally, the result predicted by the model is mostly consistent with drilling coring analysis data, which therefore achieves the purpose of prediction on the vertical. In addition, the advantages of the grey prediction method, i.e. involving a small amount of data, operating easily, with high operation speed, etc, are embodied.
出处
《兰州大学学报（自然科学版）》
CAS
CSCD
北大核心
2014年第1期39-45,共7页
Journal of Lanzhou University(Natural Sciences)
基金
中国科学院西部行动计划项目(KZCX2-XB3-02)
中国科学院"西部之光"联合学者项目(Y133WQ1-WQ)
