基于随机森林算法的陆相沉积烃源岩定量地震刻画:以东海盆地长江坳陷为例

赵峦啸, 刘金水, 姚云霞, 钟锴, 麻纪强, 邹采枫, 陈远远, 付晓伟, 朱晓军, 朱伟林, 耿建华. 2021. 基于随机森林算法的陆相沉积烃源岩定量地震刻画:以东海盆地长江坳陷为例. 地球物理学报, 64(2): 700-715, doi: 10.6038/cjg2021O0123
引用本文: 赵峦啸, 刘金水, 姚云霞, 钟锴, 麻纪强, 邹采枫, 陈远远, 付晓伟, 朱晓军, 朱伟林, 耿建华. 2021. 基于随机森林算法的陆相沉积烃源岩定量地震刻画:以东海盆地长江坳陷为例. 地球物理学报, 64(2): 700-715, doi: 10.6038/cjg2021O0123
ZHAO LuanXiao, LIU JinShui, YAO YunXia, ZHONG Kai, MA JiQiang, ZOU CaiFeng, CHEN YuanYuan, FU XiaoWei, ZHU XiaoJun, ZHU WeiLin, GENG JianHua. 2021. Quantitative seismic characterization of source rocks in lacustrine depositional setting using the Random Forest method: An example from the Changjiang sag in East China Sea basin. Chinese Journal of Geophysics (in Chinese), 64(2): 700-715, doi: 10.6038/cjg2021O0123
Citation: ZHAO LuanXiao, LIU JinShui, YAO YunXia, ZHONG Kai, MA JiQiang, ZOU CaiFeng, CHEN YuanYuan, FU XiaoWei, ZHU XiaoJun, ZHU WeiLin, GENG JianHua. 2021. Quantitative seismic characterization of source rocks in lacustrine depositional setting using the Random Forest method: An example from the Changjiang sag in East China Sea basin. Chinese Journal of Geophysics (in Chinese), 64(2): 700-715, doi: 10.6038/cjg2021O0123

基于随机森林算法的陆相沉积烃源岩定量地震刻画:以东海盆地长江坳陷为例

  • 基金项目:

    国家重大科技专项"长江坳陷油气资源潜力评价"(ZX05027001-008)和国家自然科学基金面上项目(41874124)资助

详细信息
    作者简介:

    赵峦啸, 男, 1986年生, 副教授, 主要从事岩石物理和储层地球物理方面的教学和科研工作.E-mail:zhaoluanxiao@tongji.edu.cn

    通讯作者: 耿建华, 教授, 主要从事地震数据处理、储层地球物理和地震岩石物理方面的教学和科研工作.E-mail:jhgeng@tongji.edu.cn
  • 中图分类号: P631

Quantitative seismic characterization of source rocks in lacustrine depositional setting using the Random Forest method: An example from the Changjiang sag in East China Sea basin

More Information
  • 烃源岩的定量地震刻画对于勘探开发区块的优选、盆地油气资源量的估算都具有重要意义.陆相沉积环境下的浅湖或半深湖相的烃源岩横向变化快,其空间展布需要依靠钻井约束下的反射地震进行刻画,但是其地震弹性特征与岩性和有机质含量的映射关系呈现高度非线性化,因而很难利用传统基于地震岩石物理模型驱动的烃源岩地震预测方法进行有效刻画.本文以低勘探区的东海盆地长江坳陷为例,提出了一种在数据驱动的机器学习框架下,综合利用地质约束、钻井录井、测井、地球化学和叠前地震数据进行烃源岩的定量地震刻画的工作流程.其核心思想是利用随机森林集成学习算法对小样本数据表现优异的特征,以井位处的测井弹性数据(纵波速度和密度)、岩性、地球化学标定的总有机碳含量(TOC)为样本标签数据,在地质导向约束下通过随机森林算法生成学习网络,并将该网络与叠前地震反演结果相结合,采取先预测泥岩再预测总有机碳含量的"两步走"策略,完成对烃源岩空间分布及其非均质性的定量地震刻画,并对预测结果的不确定性进行评价.测试结果显示,随机森林算法相较于其他的机器学习算法能够更准确的识别陆相沉积地层的泥岩,并比传统的利用阻抗转化方法获得更可靠的总有机碳含量预测结果.

  • 加载中
  • 图 1 

    (a) 随机森林算法的训练及验证过程;(b)随机森林算法的预测过程

    Figure 1. 

    (a) Training and verification process of random forest; (b) Prediction process of random forest

    图 2 

    机器学习(随机森林)框架下综合叠前地震、录井、测井和地球化学测试数据进行烃源岩定量地震刻画的工作流程

    Figure 2. 

    The work flow of quantitative seismic characterization of source rocks using random forest algorithm by integration of prestack seismic, drilling, logging, and geochemistry data

    图 3 

    MRF-1井的测井数据

    Figure 3. 

    MRF-1 well logging data

    图 4 

    MRF-1井(a)所有地质层段混合在一起的砂泥岩纵波速度和密度交会图;(b)不同地质层段的砂泥岩纵波速度-密度交会图及压实效应对弹性特征的影响

    Figure 4. 

    (a) Crossplot of P-wave velocity and density of sandstone and shale for all the geological strata in MRF-1 well; (b) Crossplots of P-wave velocity and density of sandstone and shale separated by different geological strata.

    图 5 

    基于随机森林算法采取分段训练分段预测的方法对MRF-1井砂泥岩的岩性预测结果

    Figure 5. 

    Lithofacies prediction results based on random forest algorithm (The training and prediction are performed at each geological section separately)

    图 6 

    基于(a)模糊逻辑; (b)概率神经网络; (c)支持向量机; (d)深度神经网络,采取分段训练分段预测的方法对MRF-1井砂泥岩岩性预测的结果

    Figure 6. 

    Lithofacies prediction results based on (a) fuzzy logic, (b) probabilistic neural network, (c) support vector machine, and (d) deep neural network. The training and prediction are performed at each geological strata separately

    图 7 

    利用不同的方法进行测井数据总有机碳含量预测的结果对比

    Figure 7. 

    Comparison of TOC prediction using different methods

    图 8 

    研究工区内某条地震侧线的叠前弹性参数AVO同时反演结果

    Figure 8. 

    Simultaneous AVO inversion results o of a prestack seismic line in the study area

    图 9 

    基于随机森林算法得到的砂泥岩岩性地震预测结果(a)和岩性预测的不确定性概率结果(b)

    Figure 9. 

    (a) Seismic lithofacies prediction results of sandstone and shale, and (b) uncertainty probability results of lithofacies prediction based on random forest algorithm

    图 10 

    基于随机森林算法得到的总有机碳含量地震预测结果

    Figure 10. 

    Seismic prediction results of TOC based on random forest algorithm

    表 1 

    不同机器学习算法进行砂泥岩岩性预测相关参数

    Table 1. 

    Parameterization of different machine learning algorithms for sand and mudstone lithology prediction

    机器学习算法 参数
    模糊逻辑 fuzzy gamma, gamma=0.5
    概论神经网络 spread=1.0
    支持向量机 c=1024, g=1024
    深度神经网络 hidden_units=[10, 20, 10]
    随机森林 ntree=500, mtry=1
    下载: 导出CSV

    表 2 

    各种机器学习算法对MRF-1井岩性预测准确率对比

    Table 2. 

    Comparison of the prediction accuracy of various machine learning algorithms for the lithology prediction of MRF-1 well

    机器学习算法 岩性预测准确率/%
    模糊逻辑 75.69
    概论神经网络 74.56
    支持向量机 76.66
    深度神经网络 72.32
    随机森林 95.10
    下载: 导出CSV

    表 3 

    不同算法对MRF-1井TOC预测相关系数对比

    Table 3. 

    Comparison of correlation coefficients for TOC prediction at MRF-1 well based on different algorithms

    算法 相关系数
    美人峰四、五、六段 随机森林 0.9715
    线性回归 0.6979
    非线性回归 0.7207
    美人峰一、二、三段 随机森林 0.9367
    线性回归 0.3508
    非线性回归 0.3407
    下载: 导出CSV
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出版历程
收稿日期:  2020-04-02
修回日期:  2020-12-15
上线日期:  2021-02-10

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