首页|多属性神经网络反演在重力流储层预测中的应用——以歧口凹陷歧南斜坡沙一段为例

多属性神经网络反演在重力流储层预测中的应用——以歧口凹陷歧南斜坡沙一段为例

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歧口凹陷歧南斜坡沙一段属于典型的重力流水道沉积,受湖盆深陷扩张、沉积坡度陡等因素影响,各层砂体普遍发育,但纵向相互叠置、横向变化大,地震属性分析对纵向相互叠置砂体的区分能力较差、精度不高.基于特征曲线重构的多属性神经网络反演技术先通过叠后地震资料拓频处理,提高薄层砂岩识别能力,再利用基于高阶统计量的时频分析技术构建五级层序格架,细化地质研究对象,最后采用基于GR曲线重构的多属性神经网络反演进行储层预测.该技术使得砂体叠置区分辨能力更强,边界刻画更清晰,提高了反演结果的精度和准确性,实践证实储层预测符合率可达到80%以上,为纵横向变化大的砂体分布预测提供了一种新的技术思路.
Application of multi-attribute neural network inversion in gravity flow reservoir prediction—a case study of Es1 Formation in Qinan Slope,Qikou Sag
Es1 Formation of Qinan Slope in Qikou Sag is a typical gravity channel deposit,which is affected by factors such as deep basin expansion and steep sedimentary slopes.The sand bodies in each layer are generally developed,but the vertical overlapping and horizontal changes are large.While the seismic attrib-ute analysis has poor ability and accuracy in distinguishing vertically overlapping sand bodies.The multi-attribute neural network inversion technique based on feature curve reconstruction first improves the ability to identify thin sandstone layers through frequency processing of stacked seismic data.Then,using time-fre-quency analysis techniques based on high-order statistics,a fifth-order sequence framework is construc-ted to refine the geological research object.Finally,the multi-attribute neural network inversion based on GR curve reconstruction is used for reservoir prediction.This technology makes the resolution of sand body overlapping area stronger,the boundary characterization clearer,and improves the accuracy and precision of inversion results.Practice has shown that the accuracy of reservoir prediction can reach over 80%,provi-ding a new technical approach for the prediction of sand body distribution with large vertical and horizontal changes.

gravity flowhigh order statisticsfifth-order sequencecurve reconstructionneural network

赵林丰、李晓静、王晶晶、纪建峥、赵永峰、周连敏

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中国石油大港油田公司勘探开发研究院,天津 300280

重力流 高阶统计量 五级层序 曲线重构 神经网络

2024

石油地质与工程
中国石化河南油田分公司

石油地质与工程

影响因子:0.453
ISSN:1673-8217
年,卷(期):2024.38(5)