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    缝洞型油藏井间三维连通路径多目标搜索算法设计

    张振坤 张冬梅 康志江 姜文斌 刘坤岩

    张振坤, 张冬梅, 康志江, 姜文斌, 刘坤岩, 2023. 缝洞型油藏井间三维连通路径多目标搜索算法设计. 地球科学, 48(8): 3031-3043. doi: 10.3799/dqkx.2022.459
    引用本文: 张振坤, 张冬梅, 康志江, 姜文斌, 刘坤岩, 2023. 缝洞型油藏井间三维连通路径多目标搜索算法设计. 地球科学, 48(8): 3031-3043. doi: 10.3799/dqkx.2022.459
    Zhang Zhenkun, Zhang Dongmei, Kang Zhijiang, Jiang Wenbin, Liu Kunyan, 2023. Multi-Objective Search for Three-Dimensional Connectivity Paths between Wells in Fractured-Vuggy Reservoirs. Earth Science, 48(8): 3031-3043. doi: 10.3799/dqkx.2022.459
    Citation: Zhang Zhenkun, Zhang Dongmei, Kang Zhijiang, Jiang Wenbin, Liu Kunyan, 2023. Multi-Objective Search for Three-Dimensional Connectivity Paths between Wells in Fractured-Vuggy Reservoirs. Earth Science, 48(8): 3031-3043. doi: 10.3799/dqkx.2022.459

    缝洞型油藏井间三维连通路径多目标搜索算法设计

    doi: 10.3799/dqkx.2022.459
    基金项目: 

    国家自然科学基金联合基金重点项目 U19B6003

    国家重大科技专项 2016ZX05014-003-003

    详细信息
      作者简介:

      张振坤(1998-),男,硕士研究生,主要从事多目标优化、智慧油田研究. ORCID:0000-0003-4904-260X. E-mail:1202111251@cug.edu.cn

      通讯作者:

      张冬梅,ORCID:0000-0002-3377-7022.E-mail: cugzdm@foxmail.com

    • 中图分类号: P618.13

    Multi-Objective Search for Three-Dimensional Connectivity Paths between Wells in Fractured-Vuggy Reservoirs

    • 摘要: 深层海相碳酸盐岩油气藏储集体类型复杂多样,非均质性强,井间连通关系评价困难. 针对传统静动态方法主观性强、多解等问题,基于三层结构设计,采用地震多属性数据刻画不同类型储集空间,提出改进A*算法搜索符合地质构造的井间连通路径,根据优化目标研究自适应交叉变异概率改进NSGA Ⅲ算法自动获取井间三维沟通路径,细致刻画静态连通情况. 以塔河油田S80单元典型井组为研究对象,实验结果表明改进优化算法能有效提升多目标算法的全局搜索能力,自动搜索路径与地震资料分析、示踪剂测试情况基本吻合,能较好反映井间不同尺度缝洞空间配置关系,为缝洞型油藏注水开发阶段指导工作制度调整、提高采收率提供技术支撑.

       

    • 图  1  储集空间示意图

      Fig.  1.  Diagram of reservoir space

      图  2  井间连通路径示意图

      Fig.  2.  Diagram of inter-well connection paths

      图  3  多重介质油藏地质模型

      Fig.  3.  Multi-media reservoir geological model

      图  4  相邻单元连通结构

      Fig.  4.  Adjacent unit connectivity

      图  5  井间连通路径多层搜索策略框架图

      Fig.  5.  Multi-layer search strategy framework for inter-well connection paths

      图  6  闭环路径结构图

      Fig.  6.  Structure of closed-loop paths

      图  7  Pareto前沿分布对比

      Fig.  7.  Pareto frontier distribution comparison

      图  8  路径搜索收敛曲线

      Fig.  8.  Path search convergence curve

      图  9  TK636H井组三维连通路径搜索结果

      Fig.  9.  3D communication path search results of well group TK636H

      图  10  TK636H-TK747井对搜索路径分类结果

      a. 第一类路径;b. 第二类路径;c. 第三类路径

      Fig.  10.  Search path classification results of the well pair TK636H-TK747

      图  11  TK636H井组缝洞发育特征

      a. 三维路径图;b. 裂缝展布图;c. 溶洞展布图

      Fig.  11.  Fractured-vuggy development characteristics of well group TK636H

      图  12  TK636H井组示踪剂曲线图

      a. TK636H-S80井对;b. TK636H-TK611井对;c. TK636H-TK747井对

      Fig.  12.  Tracer curve of well group TK636H

      表  1  融合地震多属性井间三维连通路径搜索算法

      Table  1.   Search algorithm for 3D inter-well connection paths with fused seismic multi-attribute

      输入:控制参数:种群大小$ NP $,最大演化次数$ GE{N}_{\mathrm{m}\mathrm{a}\mathrm{x}} $,交叉概率最大最小值$ {p}_{c\mathrm{m}\mathrm{a}\mathrm{x}}、{p}_{c\mathrm{m}\mathrm{i}\mathrm{n}} $,变异概率最大最小值$ {p}_{m\mathrm{m}\mathrm{a}\mathrm{x}}、{p}_{m\mathrm{m}\mathrm{i}\mathrm{n}} $
      输出:最终种群$ {P}_{GE{N}_{\mathrm{m}\mathrm{a}\mathrm{x}}} $
      1: 生成初始种群$ {P}_{GEN}=\left\{\overrightarrow{{x}_{1}}, \overrightarrow{{x}_{2}}, \dots, \overrightarrow{{x}_{NP}}\right\} $,并初始化演化代数$ GEN\leftarrow 0 $,生成参考点$ Z $
      2: 根据改进A*算法搜索路径通过公式(8)(9)计算目标函数适应值$ f\left(\overrightarrow{{x}_{i}}\right)(i=\mathrm{1, 2}, \dots, NP) $
      3: while $ GEN < GE{N}_{\mathrm{m}\mathrm{a}\mathrm{x}} $ do
      4:   计算父代种群中的互异路径条数$ d $
      5:   通过公式(11)(12)(13)计算交叉变异概率
      6:   交叉变异产生子代种群$ {Q}_{GEN} $
      7:   合并父子代种群$ {R}_{GEN} $← $ {P}_{GEN}\bigcup {Q}_{GEN} $
      8:非支配排序$ \left({F}_{1}, {F}_{2}, \dots \right)=Non\_dominated\_sort\left({R}_{GEN}\right) $
      9:  初始化$ {S}_{GEN}\leftarrow \mathrm{\varnothing }, i=1 $
      10:   do
      11:     $ {S}_{GEN}\leftarrow {S}_{GEN}\bigcup {F}_{i} $, $ i=i+1 $
      12: while   $ \left|{S}_{GEN}\right| $≤$ NP $
      13:   关键层$ {F}_{l}\leftarrow {F}_{i+1} $
      14:   if $ \left|{S}_{GEN}\right|=NP $ then
      15:     $ {P}_{GEN+1}\leftarrow {S}_{GEN} $
      16:     break
      17:   else
      18:     $ {P}_{GEN+1}={\cup }_{j=1}^{l-1}{F}_{j} $
      19:     从$ {F}_{l} $挑选个体数$ K=NP-\left|{P}_{GEN+1}\right| $
      20:     自适应归一化$ Normalization\left({S}_{GEN}\right) $
      21:     参考点关联$ [\pi \left(s\right), d(s\left)\right]\leftarrow Associate({S}_{GEN}, Z) $, $ \pi \left(s\right) $为参考点序号,$ d\left(s\right) $为个体到参考线的距离
      22:   通过小生境保留$ {F}_{l} $层选择$ K $个个体$ {P}_{GEN+1}\leftarrow Niching(K, \pi, d, Z, {F}_{l}) $
      23:    $ GEN=GEN+1 $
      24: end while
      25: return $ {P}_{GE{N}_{\mathrm{m}\mathrm{a}\mathrm{x}}} $
      下载: 导出CSV

      表  2  算法参数设置

      Table  2.   Algorithm parameter setting

      算法 $ NP $ $ {P}_{c} $ $ {P}_{c\mathrm{m}\mathrm{a}\mathrm{x}} $ $ {P}_{c\mathrm{m}\mathrm{i}\mathrm{n}} $ $ {P}_{m} $ $ {P}_{m\mathrm{m}\mathrm{a}\mathrm{x}} $ $ {P}_{m\mathrm{m}\mathrm{i}\mathrm{n}} $ $ GE{N}_{\mathrm{m}\mathrm{a}\mathrm{x}} $
      NSAG Ⅲ 200 0.6 - - 0.05 - - 30
      改进算法 200 - 0.8 0.4 - 0.1 0.01 30
      下载: 导出CSV

      表  3  TK636H井组示踪剂测试结果

      Table  3.   Tracer test results of well group TK636H

      生产井 背景浓度(cd) 突破时间(d) 突破浓度(cd) 井距(m) 推进速度(m/d) 累积浓度(cd)
      S80 91.2 12 326.8 691 57.6 24 588.7
      TK611 101.4 11 244.5 1631 148.3 19 852.4
      TK747 144.7 17 374.1 1191 70.1 20 641.2
      下载: 导出CSV
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