摘要
针对渤南洼陷中深层扇三角洲前缘致密储层地震资料分辨率低、砂泥岩速度差异小、纵向含油层系多层薄、横向储层变化快、储层展布认识不清的难点,在地质沉积模式指导下,运用多体联合解释技术,建立地层等时格架,进行优势属性提取和分析。运用进化型神经网络技术,建立地震属性和砂地比的非线性关系,实现了对薄互层砂体的定量预测,预测结果既保证了与井点的吻合度,也保持了地震资料对沉积特征的反映能力。该方法可以为中深层薄互层储层预测提供借鉴,并指导了该块油藏开发。
The thin interbedded tight sandstone in the Bonan oilfield is a special kind of reservoirs.Multi-phase fans stacked each other with large reserves,but the utilization of reserve remains low in efficiency.Fine description is difficult to achieve since the large buried depth,low vertical resolution and fast lateral variations.To solve the problems mentioned above,joint interpretation technology for multi-sandbodies is used by the authors to establish the fine isochronous stratigraphic framework,and extract and analyze seismic attributes under the guidance of geological sedimentary model.Artificial neural network technology was adopted to establish the non-linear relationship between seismic attributes and reservoir thickness,and to carry out the quantitative prediction of thin interbedded sand body.The research results have successfully applied to the prediction of the thin interbedded tight reservoir in the 176 Block of the Bonan Oil field for well positioning.
作者
班丽
BAN Li(Exploration and Development Research Institute,Smopec Shengli Oilfield,Dongying 257015,Shandong,China)
出处
《海洋地质前沿》
CSCD
2019年第11期35-42,共8页
Marine Geology Frontiers
基金
国家科技重大专项大型油气田及煤层气开发项目“渤海湾盆地济阳坳陷致密油开发示范工程”(2017ZX05072)
关键词
扇三角洲前缘
薄互层
地震属性
非线性定量预测
fan deltaic front
thin interbedded sandstone
seismic attributes
non-linear quantitative reservoir prediction
