南京大学学报(自然科学版) ›› 2019, Vol. 55 ›› Issue (6): 934–941.doi: 10.13232/j.cnki.jnju.2019.06.006

• • 上一篇    下一篇

基于相带划分的孔隙度预测

段友祥1,柳璠1(),孙歧峰1,李洪强1,2   

  1. 1. 中国石油大学(华东)计算机科学与技术学院,青岛,266580
    2. 中国石化集团胜利石油工程有限公司钻井工艺研究院,东营,257000
  • 收稿日期:2019-07-08 出版日期:2019-11-30 发布日期:2019-11-29
  • 通讯作者: 柳璠 E-mail:1134494842@qq.com
  • 基金资助:
    “十三五”国家科技重大专项(2017ZX05009-001)

Porosity prediction based on sedimentary facies

Youxiang Duan1,Fan Liu1(),Qifeng Sun1,Hongqiang Li1,2   

  1. 1. College of Computer Science and Technology,China University of Petroleum(East China), Qingdao,266580,China
    2. Drilling Technology Research Institute of Shengli Petroleum Engineering Corporation Limited, Sinopec (SLDTI),Dongying, 257000,China
  • Received:2019-07-08 Online:2019-11-30 Published:2019-11-29
  • Contact: Fan Liu E-mail:1134494842@qq.com

摘要:

孔隙度是油藏储层特性评价的重要指标,储层岩石组成成分不同,其孔隙结构也相应存在差异.研究沉积相对孔隙度影响这一重要因素,提出一种基于相带划分的孔隙度预测方法,首先利用阻抗数据、采用k?means聚类方法进行沉积相估计,获得储层的相带空间展布特征,然后对不同相带使用岭回归的方法对孔隙度进行预测.与其他方法相比,该方法较好地解决了因岩石物理特性意义不明确而造成的预测中的多解性问题,提高了预测准确度.使用实际区块油藏数据对该方法进行了实验验证,实验结果表明,该方法可以有效融合地质信息,预测稳定性高,受人为因素影响小,预测的符合度明显高于支持向量回归等其他方法.

关键词: 阻抗数据, 沉积相, 孔隙度预测, k?means方法, 岭回归

Abstract:

Porosity is an important index for evaluating reservoir characteristics,and the pore structure varies with the composition of reservoir rocks. This paper studies the important factor of relative porosity of sedimentary facies,and proposes a method of porosity estimation based on facies. Firstly,the impedance data and k?means clustering method are used to estimate sedimentary facies,and the spatial distribution characteristics of reservoir facies zones are obtained. Then,the porosity of different facies zones is predicted by ridge regression method. Compared with other methods,this method solves the problem of multi?solution in prediction,which caused by the uncertainty of the meaning of rock physical properties,and improves the accuracy of prediction. Experiments on real reservoir data show that the method can effectively fuse geological information,it has high prediction stability,and is less affected by human factors. The prediction coincidence is significantly higher than that of support vector regression.

Key words: impedance data, sedimentary facies, porosity estimation, k?means method, ridge regression

中图分类号: 

  • TE122.2

图1

孔隙度预测流程图"

图2

沉积模型"

图3

阻抗输入切片"

图4

轮廓系数分析"

图5

相估计"

图6

孔隙度预测结果"

表1

四种方法的性能比较"

方法 MAE MSE MADE R2
k?means+RR 0.92128 1.34466 0.76712 0.90566
MeanShift+RR 0.93917 1.34847 0.77563 0.90539
SVR 1.29394 2.22002 1.32370 0.83577
RR 1.33315 2.32215 1.37887 0.83708

图7

地质统计学与四种方法预测的孔隙度交汇图"

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