摘要
东海Q构造古近系低孔低渗储层普遍发育,砂泥岩阻抗叠置严重,整体非均质性变化剧烈。落实致密碎屑岩“甜点”分布范围是当前该区勘探开发一体化研究亟需解决的关键问题。为此,在地震岩性学研究的基础上,提出了一种基于叠前敏感属性反演的地震“甜点”预测技术。采用振幅随偏移距变化(AVO)截距和梯度合成的扩展弹性阻抗(EEI35°)属性开展地震岩相预测。利用高孔砂岩储层的低密度特征解决地震物相预测的问题,并结合地震岩石物理定性和定量判析技术,优选复合属性进行地震流相识别。最终在层递式相控思想指导下,实现相对高孔高气饱优质储层的准确预测。实例应用表明:本方法能够有效刻画研究区致密碎屑岩“甜点”展布规律,为后续目标搜索和井位优化提供重要参考,具有较高的可靠性和推广应用价值。
Low-porosity low-permeability reservoirs are widely developed in the Paleogene Q structure in the East China Sea Basin,and the overall heterogeneity of sand body curtain changes dramatically.The key problem to be solved in the integration research of exploration and development of the Q structure is to delineate the superior reservoir development area of"sweet spot".A prediction method of seismic"sweet spot"was established by sensitive attribute inversion,of which the main idea is to realize the prediction of tight reservoir"sweet spot"by advanced optimization in seismic lithofacies,physical facies,and fluid facies.In this paper,the EEI(extended elastic impedance)attributes combining AVO(amplitude variation offset)intercept and gradient were used to predict lithofacies.The low-density characteristics of high-porosity sandstone reservoir were used to predict physical facies.Combining qualitative and quantitative analysis in rock physics,new composite attributes were optimized to identify fluid facies.Finally,"sweet spot"reservoir could be predicted accurately under the guidance of hierarchical phase control.Application examples showed that this method could be effectively characterize the distribution of tight clastic"sweet spots"in the research area,and provided an important reference for subsequent target search and well-location optimization in high reliability and application value.
作者
张岩
秦德文
ZHANG Yan;QIN Dewen(Shanghai Branch of CNOOC(China)Ltd.,Shanghai 200335,China)
出处
《海洋地质前沿》
CSCD
北大核心
2023年第5期93-100,共8页
Marine Geology Frontiers
基金
国家科技重大专项课题(KJGG2022-0304)。
关键词
地震岩性学
地震岩石物理
叠前AVO属性
有色反演
"甜点"预测
seismic lithology
seismic rock physics
pre-stack AVO attribute
colored inversion
"sweet spot"prediction
