致密储层裂缝气水微观渗流过程研究

侯世伟; 吕寻庆; 孟素云; 张皓; 杜修力

doi:10.11781/sysydz2025030671

致密储层裂缝气水微观渗流过程研究

doi: 10.11781/sysydz2025030671

1.
沈阳建筑大学土木工程学院, 沈阳 110168
2.
北京工业大学城市与工程安全减灾教育部重点实验室, 北京 100124

基金项目:

国家自然科学基金项目 52208356

辽宁省教育厅项目 LJ242410153045

辽宁省科技厅项目 2024-MSLH-388

北京工业大学教育部重点实验室项目 2022B05

沈阳市中青年科技创新人才支持计划 RC220171

详细信息

作者简介:
侯世伟(1982—)，女，博士，副教授，从事岩土工程防灾减灾研究。E-mail: hsw1375@126.com

中图分类号: TE311
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- 文章访问数: 25
- HTML全文浏览量: 6
- PDF下载量: 6
- 被引次数: 0
出版历程
- 收稿日期: 2024-09-24
- 修回日期: 2025-04-10
- 刊出日期: 2025-05-28

Microscopic seepage process of gas and water in fractures of tight reservoirs

1.
School of Civil Engineering, Shenyang Jianzhu University, Shenyang, Liaoning 110168, China
2.
Key Laboratory of Urban and Engineering Safety and Disaster Reduction of Ministry of Education, Beijing University of Technology, Beijing 100124, China

摘要

摘要: 为研究致密储层裂缝空间内流体的动态渗流机理，基于深度学习分割结果，构建真实储层三维数字岩心裂隙结构。首先评价其连通性，然后模拟单相流渗透率，利用水平集方法耦合N-S方程进行气、水两相流驱替过程研究，并采用有限元方法求解。结果显示：深度学习方法可高效自动分割岩心图像中的裂隙，准确率达85%；连通裂隙对于岩石渗透性有重要作用，流体性质的不同，影响流动压力和速度，进而影响其渗透率。驱替模拟过程中可清晰观察到气、水两相分布特征，随驱替时间变化直至渗流结束，狭窄裂隙通道流体饱和度几乎无变化，是残余气相的主要赋存空间；而连通性相对较好的裂隙成为主渗流通道，其具有宽且笔直的特征，气体采收率趋于稳定。该研究结果对微观条件下致密储层裂缝空间内的气、水两相流动研究具有一定的指导意义。
- 渗流过程 /
- 裂隙结构 /
- 裂隙连通性 /
- 数字岩心 /
- 深度学习分割 /
- 致密储层
Abstract: To investigate the dynamic seepage mechanisms of fluids within fractures of tight reservoirs, a three-dimensional digital core fracture structure of an actual reservoir was constructed based on deep learning segmentation results. First, the fracture connectivity was evaluated. Then, single-phase flow permeability simulation was conducted, and gas-water two-phase flow displacement was studied using a level-set method coupled with Navier-Stokes (N-S) equations, with solutions obtained using the finite element method. The results showed that the deep learning method efficiently and automatically segmented fractures in core images with an accuracy of 85%. Connected fractures played an important role in rock permeability. Different fluid properties affected flow pressure and velocity, thereby affecting permeability. During the displacement simulation, the distribution characteristics of gas and water phases were clearly observed. As the displacement progressed until seepage completion, the fluid saturation in narrow fracture channels remained nearly unchanged, serving as the primary storage space for residual gas phase. Fractures with relatively good connectivity, characterized by great width and straightness, became the main seepage channels where gas recovery rates tended to stabilize. The research findings provide guidance for studying gas-water two-phase flow in fracture spaces of tight reservoirs under microscopic conditions.
- gas-water seepage process /
- fracture structure /
- fracture connectivity /
- digital core /
- deep learning segmentation /
- tight reservoir
注释:

利益冲突声明/Conflict of Interests

所有作者声明不存在利益冲突。

All authors declare no relevant conflict of interests.

作者贡献/Authors’Contributions

侯世伟参与数值试验设计、文章写作与修改；吕寻庆完成实验操作，参与论文写作和修改；孟素云提供资料与修改建议；张皓参与实验设计与论文修改；杜修力参与论文结构设计，提出修改建议。所有作者均阅读并同意最终稿件的提交。

HOU Shiwei contributed to the numerical experiment design, paper writing, and revision. LV Xunqing completed the experimental operation and contributed to the paper writing and revision. MENG Suyun provided data and revision suggestions. ZHANG Hao contributed to the experimental design and paper revision. DU Xiuli contributed to the paper structure design and provided revision suggestions. All authors have read the final version of the paper and consented to its submission.

HTML全文

图 1 U-Net网络结构

Figure 1. U-Net network structure

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图 2 切片图像物质组成

Figure 2. Material composition in slice image

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图 3 U-Net训练结果

Figure 3. U-Net training results

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图 4 砂岩RVE重构模型

Figure 4. RVE reconstruction model of sandstone

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图 5 裂隙分割提取过程

Figure 5. Fracture segmentation extraction process

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图 6 三维重构结果

Figure 6. Three-dimensional reconstruction results

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图 7 最大连通裂隙结构及其骨架化模型

Figure 7. Largest connected fracture structure and its skeletonized model

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图 8 COMSOL生成计算模型

Figure 8. Computational models generated by COMSOL

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图 9 模型在不同流体下的速度和压力云图

Figure 9. Contour maps of model velocity and pressure with different fluids

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图 10 不同时刻模型速度与压力分布

Figure 10. Model velocity and pressure distributions at different moments

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图 11 不同时刻流体状态分布

Figure 11. Fluid state distributions at different moments

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图 12 流体饱和度变化曲线

Figure 12. Fluid saturation variation curves

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表 1 渗流模拟参数设定

Table 1. Parameter settings for seepage simulation

流体材料	密度/(g/cm³)	动力黏度/(Pa·s)	压差/Pa
水	1	0.001	1 000
甲烷	6.5×10^-4	1.1×10^-5	1 000

下载: 导出CSV

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