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基于关联规则与随机森林的地震多属性砂体厚度预测

曲志鹏 王芳芳 张云银 李晓晨

曲志鹏, 王芳芳, 张云银, 李晓晨. 基于关联规则与随机森林的地震多属性砂体厚度预测[J]. 地质科技通报, 2021, 40(3): 211-218. doi: 10.19509/j.cnki.dzkq.2021.0314
引用本文: 曲志鹏, 王芳芳, 张云银, 李晓晨. 基于关联规则与随机森林的地震多属性砂体厚度预测[J]. 地质科技通报, 2021, 40(3): 211-218. doi: 10.19509/j.cnki.dzkq.2021.0314
Qu Zhipeng, Wang Fangfang, Zhang Yunyin, Li Xiaochen. Thickness prediction of seismic multi-attributes sand based on association rules and random forests[J]. Bulletin of Geological Science and Technology, 2021, 40(3): 211-218. doi: 10.19509/j.cnki.dzkq.2021.0314
Citation: Qu Zhipeng, Wang Fangfang, Zhang Yunyin, Li Xiaochen. Thickness prediction of seismic multi-attributes sand based on association rules and random forests[J]. Bulletin of Geological Science and Technology, 2021, 40(3): 211-218. doi: 10.19509/j.cnki.dzkq.2021.0314

基于关联规则与随机森林的地震多属性砂体厚度预测

doi: 10.19509/j.cnki.dzkq.2021.0314
基金项目: 

国家重大科技专项 2017ZX05072

详细信息
    作者简介:

    曲志鹏(1981-), 男, 高级工程师, 主要从事油气勘探综合研究方面的工作。E-mail: quzhipeng@sinopec.com

    通讯作者:

    王芳芳(1986-), 女, 讲师, 主要从事地震储层预测方面的工作。E-mail: wff@cug.edu.cn

  • 中图分类号: TE122.2

Thickness prediction of seismic multi-attributes sand based on association rules and random forests

  • 摘要: 地震属性技术是砂体厚度预测的重要手段,由于目前可从地震数据中提取的地震属性种类较多,在利用地震属性技术前,必须优化出对砂体厚度最敏感的地震属性组合,以减少地震属性信息的重复与冗余。为此提出了一种联合关联规则与随机森林回归算法的地震多属性砂体厚度预测方法。随机森林回归算法能够建立地震多属性与砂体厚度之间的非线性关系,并能进行属性选择,但是该算法无法识别地震多种属性中的冗余特征。关联规则能够发现地震属性之间的非线性关联,并能借助卡方检验消除地震属性间的冗余性。分别采用了随机森林回归算法(RFR)、联合关联规则与随机森林回归(AR-RFR)及BP神经网络回归的算法(AR-BP)对滩坝砂岩合成模型和某实际工区进行了砂体厚度预测。对比结果表明,基于关联规则的属性优选得到的属性间相关性低,关联规则与随机森林算法的结合提高了砂体厚度的预测精度。数值实验证明了该方法的有效性。

     

  • 图 1  频繁模式树算法流程图

    Figure 1.  Flowchart of frequent pattern tree algorithm

    图 2  滩坝砂岩二维地质模型岩相分布图(a)及其合成地震数据(b)

    a中黑色竖直线表示随机抽取的井

    Figure 2.  2D geological model of beach bar sandstone (a) and synthetic seismic data (b)

    图 3  不同方法的砂体累计厚度预测结果的对比

    Figure 3.  Comparison of predicted sand thickness of different methods

    图 4  随机森林算法不同地震属性的重要性评估

    Figure 4.  Assessment of the importance of different seismic attributes in random forest

    图 5  井位处RFR、AR-BP、AR-RFR的砂体厚度的预测值与真实值的对比图

    Figure 5.  Comparison among RFR prediction, AR-BP prediction, AR-RFR prediction with real log of thickness

    图 6  基于关联规则方法优选的地震属性

    a.振幅加权频率;b.振幅加权相位;c.瞬时振幅的导数;d.15/20~25/30滤波器

    Figure 6.  Optimized seismic attributes based on association rules

    图 7  利用3种不同算法获得的砂体厚度层位切片

    a.RFR预测结果;b.AR-BP预测结果;c.AR-RFR预测结果

    Figure 7.  Prediction results of three different algorithms for sand thickness

    表  1  研究中采用地震属性

    Table  1.   Seismic attributes used in this study

    属性标识 属性名称 属性标识 属性名称 属性标识 属性名称
    1 振幅包络 9 导数的瞬时振幅 17 二阶导数的瞬时振幅
    2 平均频率 10 主频 18 振幅
    3 视极性 11 瞬时频率 19 5/10~15/20滤波器
    4 振幅加权余弦相位 12 瞬时相位 20 15/20~25/30滤波器
    5 振幅加权频率 13 积分 21 25/30~35/40滤波器
    6 振幅加权相位 14 绝对值振幅的包络 22 35/40~45/50滤波器
    7 余弦瞬时相位 15 正交道 23 45/50~55/60滤波器
    8 振幅导数 16 振幅二阶导数 24 55/60~65/70滤波器
    下载: 导出CSV

    表  2  基于随机森林方法优选的属性之间的相关关系

    Table  2.   Correlation analysis between various selected seismic attributes from random forest

    属性名称 导数 瞬时频率 积分 瞬时相位的余弦
    导数 1.000 0 0.348 2 -0.953 8 0.376 5
    瞬时频率 0.348 2 1.000 0 -0.356 8 0.293 0
    积分 -0.953 8 -0.356 8 1.000 0 -0.317 9
    瞬时相位的余弦 0.376 5 0.293 0 -0.317 9 1.000 0
    下载: 导出CSV

    表  3  基于关联规则方法优选的属性之间的相关关系

    Table  3.   Correlation analysis between various selected seismic attributes from association rules

    属性名称 振幅加权频率 振幅加权相位 瞬时振幅的导数 15/20~25/30滤波器
    振幅加权频率 1.000 0 0.018 6 -0.316 7 -0.400 9
    振幅加权相位 0.018 6 1.000 0 -0.452 5 -0.136 8
    瞬时振幅的导数 -0.316 7 -0.452 5 1.000 0 0.328 1
    15/20~25/30滤波器 -0.400 9 -0.136 8 0.328 1 1.000 0
    下载: 导出CSV
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