沉积模式约束的地震多属性水下扇岩相划分

Submarine fan lithofacies identification with depositional model and seismic attributes

水下扇是一类重要的岩性油气藏勘探目标,但对其内幕岩相进行精细刻画难度较大,导致勘探井位部署的成功率很低。为此,本文从"扇沉积模式"出发,建立一种模式指标来解决井数据缺乏的问题。以渤海湾盆地辽中凹陷南洼古近系东营组某一期湖底扇为例,通过经典水下扇模式分析地震相,综合选择出既有明确地质意义、又能一定程度上综合反映地震波反射特征的砂岩和砾岩两种组分含量指标来量化分类水下扇岩相。然后采用模糊径向基函数神经网络来映射"砂岩和砾岩含量—地震属性"间非线性关系,从而获得水下扇岩相的粗略划分,可满足勘探阶段大范围储层描述的精度要求。本文的主要目的是探讨如何将"扇沉积模式"与"地震数据"进行定量化衔接,从而为水下扇岩相的识别提供一种可能的解决方案。

Submarine fan is an important lithologic reservoir.However,it is still a challenge to precisely estimate the lithofacies of submarine fan in exploration geophysics.Therefore,we propose significance indicators based on the depositional model to solve lack of well data in the exploration period.With submarine fan facies model analysis,sandstone and conglomerate contents are selected as significance indicators to quantify lithofacies of submarine fan in Dongying Formation,Liaodong Depression,China.These indicators have not only clear geological significance,but also response of seismic reflection.The fuzzy radial function neural network maps the relation between'sandstone and conglomerate contents 'and'seismic attributes' to estimate lithofacies of submarine fan,which improve the precision of large-scale reservoir characterization in exploration period.Our objective is to find out quantitative indicators between fan depositional model and seismic data as a potential approach to estimate the submarine fan lithofacies.