基于自组织神经网络算法的低渗透砂岩孔隙结构自动分类

路研; 刘宗宾; 廖新武; 李超; 李扬

doi:10.19509/j.cnki.dzkq.tb20240056

基于自组织神经网络算法的低渗透砂岩孔隙结构自动分类

doi: 10.19509/j.cnki.dzkq.tb20240056

中海石油(中国)有限公司天津分公司, 天津 300452

基金项目:

国家科技重大专项 2017ZX05009001

详细信息

通讯作者:
路研, E-mail:luyan19@cnooc.com.cn

中图分类号: P618.13
计量
- 文章访问数: 59
- PDF下载量: 11
- 被引次数: 0
出版历程
- 收稿日期: 2024-02-14
- 录用日期: 2024-07-17
- 修回日期: 2024-04-07

Automatic classification of pore structures of low-permeability sandstones based on self-organizing-map neural network algorithm

Tianjin Branch of CNOOC China Limited, Tianjin 300452, China

More Information

Corresponding author: LU Yan, E-mail:luyan19@cnooc.com.cn

摘要

摘要:
低渗透砂岩储层的孔隙系统复杂, 孔隙-喉道大小分布多变, 是决定储层宏观岩石物理性质和控制流体在砂岩中渗流行为的关键因素。以往的低渗透砂岩孔隙结构分级评价工作多基于孔隙-喉道大小分布的几何形态或参数回归分析, 受人为因素干扰大, 缺乏精确的分级评价标准。以渤海湾盆地G油田沙四上亚段低渗透砂岩储层为研究对象, 综合运用岩相学分析、高压压汞、核磁共振及X射线CT扫描等技术手段, 详细探讨了低渗透砂岩微观孔隙结构特征。在此基础上, 选取了15个能够反映低渗透砂岩微观孔隙结构特征的储层评价参数, 并采用无监督模式下的自组织映射神经网络算法将取心层段的70组岩心样本自动划分为4类孔隙结构。研究结果表明, Ⅰ类孔隙结构以大孔喉为主, 中值喉道半径r₅₀主要分布在0.38~2.35 μm的范围内; 孔喉连通性好, 对渗透率贡献作用显著。Ⅱ类孔隙结构的渗流性能和连通性能仅次于Ⅰ类孔隙结构, 可动流体孔隙度在2.76%~5.61%之间, 中值喉道半径r₅₀主要分布在0.01~0.23 μm的范围内。Ⅲ类孔隙结构具有较好的孔喉连通性和较强的微观非均质性, 储集和渗流性能与Ⅰ类和Ⅱ孔隙结构相比明显较差。Ⅳ型孔隙结构内小孔喉占主导, 孔喉连通性差, 不利于流体在砂岩中的渗流。基于自组织映射神经网络算法可以实现多参数情况下的孔隙结构类型自动分类。分类结果不受不准确的用户自定义信息的影响, 并且对参与训练过程的参数数量没有限制, 在基于多参数的孔隙结构分类方面应用效果显著。建立的基于自组织特征映射(self-organizing feature map, 简称SOM)神经网络算法的孔隙结构分类评价标准, 对于研究低渗透砂岩储层的微观渗流行为和储层质量评价意义重大。
- 渤海湾盆地 /
- 低渗透砂岩 /
- 孔隙结构 /
- 自组织神经网络 /
- 无监督模式
Abstract: Objective
The pore system of low-permeability sandstone reservoirs is intricate, and the distribution of pore-throat sizes is highly variable. The microscopic pore structure significantly influences the reservoir′s petrophysical properties and plays a critical role in controlling fluid flow within sandstone reservoirs. Traditional approaches for evaluating pore structures primarily rely on morphological analyses of pore throat size distributions or regression analyses of pore structure parameters. These methods are significantly affected by human bias and often lack precise evaluation frameworks.
Methods
Poroperm analysis, mercury injection capillary pressure, nuclear magnetic resonance (NMR) measurements, and X-ray computed tomography (X-ray CT) scanning experiments were performed to characterize the pore structures of the Es₄^s low-permeability sandstones in the G oilfield, Bohai Bay Basin. On this basis, 15 parameters that reflect the microscopic features of low-permeability sandstones were selected, and four types of pore structures were classified by applying an unsupervised self-organizing-map neural network algorithm.
Results
The findings reveal that the Type Ⅰ pore structure predominantly features large pore throats, with a median throat radius (r₅₀) ranging from 0.38 to 2.35 μm. This type exhibits excellent pore connectivity, contributing significantly to permeability. The petrophysical properties and pore connectivity of Type Ⅱ pore structures are second only to those of Type Ⅰ pore structures. The movable fluid porosity ranges from 2.76% to 5.61%, and the median throat radius (r₅₀) is primarily distributed in the range of 0.01 to 0.23 μm. Type Ⅲ pore structures display good pore connectivity along with considerable microscopic heterogeneity. The petrophysical properties and seepage properties of Type Ⅲ pore structures are comparable to those of Type Ⅰ and Type Ⅱ pore structures. The Type Ⅳ pore structures are characterized by small pore throats and poor microscopic connectivity, which hinders fluid movement within the sandstones.
Conclusion
The self-organizing map neural network algorithm effectively classifies pore structure types in cases involving multiple parameters. The classification results are not affected by inaccurate user-defined information, and there is no limitation on the number of parameters involved in the training process, making the application effect in pore structure classification remarkable. The established pore structure evaluation scheme, which is based on a self-organizing feature map neural network algorithm, is vital for investigating the microscopic seepage behavior and reservoir quality of low-permeability sandstones.
- Bohai Bay Basin /
- low-permeability sandstone /
- pore structure /
- self-organizing map neural network /
- unsupervised mode
注释:

所有作者声明不存在利益冲突。

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图 1 研究区构造位置及地层发育特征

Es₁.沙一段; Es₂^s.沙二上亚段; Es₂^x.沙二下亚段; Es₃^s.沙三上亚段; Es₃^z.沙三中亚段; Es₃^x.沙三下亚段; Es₄^s.沙四上亚段; Es₄^x.沙四下亚段; Ek₁.孔店组一段; Ek₂.孔店组二段; Ek₃.孔店组三段

Figure 1. Structural location and stratigraphic characteristics of the study area

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图 2 滩坝砂岩岩石组分三角图

Figure 2. Rock compositions of the beach-bar sandstones

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图 3 沙四上亚段储层物性随埋深的变化

Figure 3. Plots of reservoir petrophysical characteristics versus burial depth in the Es₄^s sandstone reservoirs

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图 4 沙四上亚段砂岩储集空间及成岩作用特征

a.残余粒间孔隙、次生溶孔及中期碳酸盐胶结物，G89井, 2 599.2 m；b.残余粒间孔隙、次生溶孔及中期碳酸盐胶结物，G89井, 2 599.7 m；c.长石颗粒溶蚀孔与粒间孔组成的扩大孔，C37井, 2 686.9 m；d.基底式早期碳酸盐胶结作用和“浮粒结构”，F15井，2 728.5 m；e.中期孔隙充填式方解石胶结物，F15-1井，2 687.0 m；f.沿着长石解理溶蚀形成的微孔隙，Quanta200 FEG-SEM，F14-1井, 3 113.3 m

Figure 4. Pore systems and diagenesis in the Es₄^s sandstone reservoirs

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图 5 沙四上亚段储层高压压汞孔喉参数分布特征(N为样品数)

a~e.孔喉参数频率分布直方图；f.渗透率与分选系数图

Figure 5. Pore structure characteristics of the Es₄^s sandstone reservoirs derived from MICP measurement

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图 6 峰点孔喉半径(r_apex)确定示意图(样品F15_2817.2)

P_c.毛管压力; S_Hg.汞饱和度; S_Hg/P_c.汞饱和度和毛管压力比值; r_apex.峰点孔喉半径; 下同

Figure 6. Determination of peak radius of pore throat (r_apex)

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图 7 渗透率(k)与峰点孔喉半径(r_apex)之间的关系

Figure 7. Relationship between permeability(k) and peak radius of pore throat(r_apex)

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图 8 不同类型的毛管压力曲线(a, c, e, g)和孔喉大小分布特征(b, d, f, h) (k.渗透率；φ.孔隙度；下同)

Figure 8. Different types of capillary pressure curves(a,c,e,g) and pore throat size distributions(b,d,f,h)

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图 9 沙四上亚段低渗透砂岩储层典型T₂谱分布特征

Figure 9. Typical NMR T₂ spectral distributions for Es₄^s low-permeability sandstone reservoirs

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图 10 基于铸体薄片和X-ray CT获取的二维(a, b)和三维(c, d)孔隙结构(样品G89，2 598.2 m)

a.二维孔隙网络模型；b.基于二维孔隙网络模型获取的孔隙-喉道大小分布；c.三维孔隙网络模型; d.基于三维孔隙网络模型获取的孔隙-喉道大小分布

Figure 10. Two-dimensional(a, b) and three-dimensional(c, d) pore structure derived from thin sections and X-ray CT images

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图 11 SOM神经网络的网络结构(a)和竞争层(b) (据文献[27]修改)

Figure 11. Typical network structure(a) and competitive layer(b) of SOM neural network

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图 12 基于SOM神经网络算法的自组织映射拓扑图(a)、三维Sammon投影(b)和三维聚类树状图(c)

D_T.MICP分形维数; W_e.退汞效率; σ.分选系数; P_d.排驱压力; k.渗透率; φ.孔隙度; r_apex.峰点孔喉半径; r_max.最大孔喉半径; r₅₀.中值孔喉半径; RQI为储层品质指数(reservoir quality index)

Figure 12. Topological mapping(a), 3D Sammon mapping(b) and 3D clustering tree(c) based on SOM neural network algorithm

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图 13 沙四上亚段低渗透砂岩储层四类孔隙结构特征(FFI.可动流体体积分数)

Figure 13. Characteristics of four types of pore structures of Es₄^s low-permeability sandstone reservoirs

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