摘要
鄂尔多斯盆地南部上古生界致密碎屑岩储集层的压实作用和胶结作用强烈,测井解释与实测的孔、渗参数相关性差。研究发现储集层岩性明显控制物性,在应用神经网络方法计算储集层孔、渗参数时,加入定量化岩性参数作为计算的输入参数,使计算精度大幅度提高。用累计产能系数法设定储集层参数下限,结合孔、渗之间的相关关系,对储集层进行分类。以计算层段的平均孔隙度和渗透率作为控制点,预测储集层参数的平面分布,预测结果与沉积相分布吻合度较高。
Under strong compaction and cementation, the clastic reservoir of Upper Paleozoic becomes very tight in south Ordos Basin, the correlation between log data and reservoir parameters could hardly be done. The research reveals that lithological character of reservoir has apparent controlling effect on porosity and permeability, by adding quantified lithological parameter into neural network as an input parameter. Cumulative productivity factor is used to define the permeability inferior boundary of effective reservoir, with the relation between permeability with porosity, which is used to sort reservoir into five categories. The distribution of reservoir parameters is predicted under domination of even porosity and permeability of each segment of the key wells.
出处
《石油勘探与开发》
SCIE
EI
CAS
CSCD
北大核心
2003年第6期75-77,共3页
Petroleum Exploration and Development
关键词
鄂尔多斯盆地
上古生界
神经网络
储集层下限
储集层参数
Ordos Basin
Upper Paleozoic
neural network
reservoir inferior boundary
reservoir parameter