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基于随机森林算法的二氧化碳驱油与封存主控因素研究

任俊帆 薛亮 聂捷 肖镭 廖广志

任俊帆, 薛亮, 聂捷, 肖镭, 廖广志. 基于随机森林算法的二氧化碳驱油与封存主控因素研究[J]. 地质科技通报, 2024, 43(3): 147-156. doi: 10.19509/j.cnki.dzkq.tb20230699
引用本文: 任俊帆, 薛亮, 聂捷, 肖镭, 廖广志. 基于随机森林算法的二氧化碳驱油与封存主控因素研究[J]. 地质科技通报, 2024, 43(3): 147-156. doi: 10.19509/j.cnki.dzkq.tb20230699
REN Junfan, XUE Liang, NIE Jie, XIAO Lei, LIAO Guangzhi. Research on the main control factors of carbon dioxide flooding and storage based on random forest algorithm[J]. Bulletin of Geological Science and Technology, 2024, 43(3): 147-156. doi: 10.19509/j.cnki.dzkq.tb20230699
Citation: REN Junfan, XUE Liang, NIE Jie, XIAO Lei, LIAO Guangzhi. Research on the main control factors of carbon dioxide flooding and storage based on random forest algorithm[J]. Bulletin of Geological Science and Technology, 2024, 43(3): 147-156. doi: 10.19509/j.cnki.dzkq.tb20230699

基于随机森林算法的二氧化碳驱油与封存主控因素研究

doi: 10.19509/j.cnki.dzkq.tb20230699
基金项目: 

国家自然科学基金项目 52274048

北京市自然科学基金项目 3222037

详细信息
    作者简介:

    任俊帆, E-mail: 19801212354@163.com

    通讯作者:

    薛亮, E-mail: xueliang@cup.edu.cn

  • 中图分类号: TE357.45

Research on the main control factors of carbon dioxide flooding and storage based on random forest algorithm

More Information
  • 摘要:

    在碳达峰、碳中和目标背景下, 二氧化碳驱油与封存是经济可行的碳减排的主要技术手段。明确影响二氧化碳驱油与封存效果的主控因素, 是实现二氧化碳高效驱油与封存的基础。在行业标准算例PUNQ-S3模型的基础上, 综合考虑二氧化碳与原油混相作用和二氧化碳构造、残余、溶解、矿化封存机理, 构建了二氧化碳提高原油采收率与地质封存一体化数值模拟模型, 结合随机森林智能算法, 开展了影响二氧化碳驱产油量和封存量的储层和生产参数特征重要性分析, 考虑驱油与封存时间尺度的差异, 建立了参数时序特征重要性分析方法, 实现了在不同二氧化碳驱油与封存阶段的主控因素分析。结果表明, 二氧化碳驱油与封存时序随机森林模型准确性高, 在二氧化碳驱油与封存前期, 二氧化碳构造封存量受储层含水饱和度控制, 溶解封存量受地层水矿化度控制; 在二氧化碳驱油与封存中、后期, 二氧化碳构造封存量则受储层渗透率控制, 溶解封存量则受储层渗透率与地层水矿化度控制; 残余封存量在二氧化碳驱油与封存前期较小, 导致其主控因素不明显, 在二氧化碳驱油与封存中后期受储层渗透率与含水饱和度控制; 二氧化碳矿化封存量在整个二氧化碳驱油与封存阶段受地层水pH值与矿化度控制; 二氧化碳驱油量在整个二氧化碳驱油与封存阶段受储层渗透率及含水饱和度控制。时序随机森林算法可以明确不同二氧化碳驱油与封存阶段的主控因素, 为二氧化碳提高原油采收率和地质封存的高效实施提供了技术支撑。

     

  • 图 1  PUNQ-S3地质模型示意图

    Figure 1.  Schematic diagram of PUNQ-S3 geological model

    图 2  原油恒组成膨胀实验(CCE)油相密度(a)以及原油相对体积(b)的拟合效果

    Figure 2.  CCE-oil phase density (a) and CCE-relative volume (b) fitting effects

    图 3  数值模拟模型样本累计产油量(a)与二氧化碳封存量(b~e)

    Figure 3.  Cumulative oil production (a) and CO2 sequestration (b-e) of numerical simulation model samples

    图 4  不同封存机理二氧化碳封存量占比的样本分布动态变化规律图

    Figure 4.  Dynamic variation of sample distribution of the proportion of CO2 storage of different storage mechanisms

    图 5  决策树示意图

    Sw.初始含水饱和度; i.样本数; K.储层渗透率;MSE.样本均方误差

    Figure 5.  Schematic diagram of the decision tree

    图 6  时序随机森林袋外数据误差估计图

    Figure 6.  Time series random forest OOB error estimation

    图 7  二氧化碳封存时序随机森林特征重要性分析

    Figure 7.  Importance analysis of random forest characteristics in carbon dioxide storage time series

    图 8  二氧化碳驱油时序随机森林特征重要性分析

    Figure 8.  Importance analysis of random forest characteristics in carbon dioxide flooding time series

    表  1  拟组分划分结果

    Table  1.   Pseudo-components partition

    序号 原油组分 拟组分 拟组分摩尔分数/%
    1 CO2 CO2 1.01
    2 H2S, N2, C1 C1_ 13.02
    3 C2, C3 C2_3 13.63
    4 IC4, NC4, IC5, NC5, FC6, C6H6, CC6 C4_6 22.71
    5 FC7, FC8, FC9, NC7, NC8, NC9 C7_9 26.26
    6 FC13-20 C13_20 12.65
    7 FC21+ C21+ 10.72
    注:IC.异烷烃; NC.正烷烃; FC.广义碳组分; C6H6.苯; CC6.环己烷
    下载: 导出CSV

    表  2  储层参数及生产参数

    Table  2.   Reservoir and production parameters

    类型 名称 范围
    储层参数 地层水pH值 6~8
    地层水矿化度/(g·L-1) 20~200
    初始含水饱和度 0.4~0.6
    储层渗透率/10-3 μm2 50~100
    生产参数 二氧化碳注入速率/(104 m3·d-1) 10~90
    生产井关井时二氧化碳见气百分比阈值/% 20~90
    下载: 导出CSV
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  • 收稿日期:  2023-12-18
  • 录用日期:  2024-03-13
  • 修回日期:  2024-03-07

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