摘要
研究区煤层较为发育,且煤层厚度差异大,导致目的层属性值存在区域性差异,采用同一种参数提取属性不能有效表征该区地质体特征;同时由于地震属性值是地质体特征(沉积特征,岩性特征、孔隙结构等)的综合反映,单属性参数具有多解性。在地质体分区的基础上,利用多地震属性优化下的神经网络技术对研究区进行分区砂岩储层预测。实践表明,该技术有效避免了单属性多解性和多属性综合判别精度低的缺点,提高了利用地震属性预测砂岩储层的精度。
Seismic attribute values are usually dependant on some geological parameters,such as sedimentary characteristics,lithology and pore structures.However,as the seismic reflection is a kind of integrated reflection of a geological body,a single attribute parameter may have multiple solutions.On the basis of individual geological body,the Neural network technique optimized with multi-seismic attributes is used by the authors to predict the reservoir in JHER well area.Our results show that this technique may avoid the disadvantages of multiple solution for single attribute,enhance the resolution of multi-attribute discrimination,and thus improve the accuracy and efficiency of seismic reservoir prediction.
作者
赵宇璇
黎小伟
袁春艳
范久霄
ZHAO Yuxuan;LI Xiaowei;YUAN Chunyan;FAN Jiuxiao(Green Energy Geothermal Development Co.,LTD.;Xianyang 712000,Shannxi,China;Petroleum Exploration and Production Research Institute of the North China Oil and Gas Company of Sinopec,Zhengzhou 450006,Henan,China)
出处
《海洋地质前沿》
CSCD
2019年第2期65-72,共8页
Marine Geology Frontiers
基金
国家科技重大专项"低丰度致密低渗油气藏开发关键技术"(2016ZX05048)
关键词
地震属性
优化
神经网络
储层预测
鄂尔多斯盆地
seismic attribute
optimization
neural network
reservoir prediction
Ordos Basin
作者简介
赵宇璇(1993—),女,助理工程师,主要从事构造地质和储层物性等方面的研究工作.E-mail:zhaoyuxuan.xxsx@sinopec.com;通讯作者:黎小伟(1984—),男,硕士,工程师,主要从事测井解释及地震资料解释方面的研究工作.E-mail:lixw.hbsj@sinopec.com.
