摘要
四川盆地南部官渡构造下沙溪庙组属致密砂岩储层,常规速度反演难以将有效储层从致密砂岩中分道辨出来,而孔隙度可以较好地将其区分。作者在测井曲线校正、测井孔隙度精细解释的基础上,结合扩展的地震体属性分析法和神经网络拟合法开展储层孔隙度预测,计算了下沙溪庙组孔隙度数据体,并以交互验证法作为质量控制手段,验证了结果的正确性。该数据体可以直观地反映出高孔隙砂岩的展布特征。在此基础上,根据下沙溪庙组储层地质特征,针对下沙溪庙组一砂组进行了孔隙度平面解释,描述了有效储层的平面展布特征,指出该地区下一步勘探的有利区域。
The Jurassic Xiashaximiao Formation （Jest） reservoir of the Guandu structure in South Sichuan is a compacted clastic rock reservoir. Its porosity can better discriminate the effective reservoir than the conventional velocity inversion does. Based on the well logs correction and detailed well porosity interpretation, this paper combines the extended seismic attribute analysis and the artificial neural network fitting method to predict the porosity, compute porosity data volume of J2s1, and demonstrate validity of the results using the cross-validation method. This data volume can directly describe the high pore sand distribution. On this basis, the porosity plan character of No. 1 sand group is interpreted according to the reservoir geological features of J2s1, the plane distribution of effective reservoir described, and the favorable exploration area pointed out.
出处
《成都理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2008年第5期508-511,共4页
Journal of Chengdu University of Technology: Science & Technology Edition
基金
国家自然科学基金委员会与中国石油化工股份有限公司联合基金资助项目(40739903)
关键词
官渡构造
致密砂岩储层
孔隙度
预测
Guandu structure
compacted clastic rock reservoir
porosity
prediction
