致密砂岩储层渗透率预测技术研究进展

路萍; 王浩辰; 高春云; 郝昱宇; 谭富荣; 刘杰; 刘伟刚; 白勇; 高建平

doi:10.6038/pg2022FF0236

致密砂岩储层渗透率预测技术研究进展

路萍^1,,
王浩辰³,
高春云^{4, ,},
郝昱宇⁵,
谭富荣⁶,
刘杰¹,
刘伟刚¹,
白勇¹,
高建平¹

- 1.
  陕西省能源化工研究院, 西安 710021
- 2.
  西北大学地质学系, 大陆动力国家重点实验室, 西安 710069
- 3.
  Cardiff University, Wales CF10 3AT, UK
- 4.
  陕西榆能集团能源化工研究院有限公司, 榆林 719000
- 5.
  西安科技大学, 西安 710054
- 6.
  陕西省矿产地质调查中心, 西安 710068
基金项目:
陕西省自然科学基础研究计划项目(2022JQ-231)和国家重点研发计划项目(2019YFE0100100)联合资助

详细信息

作者简介:
路萍，女，1988年生，博士研究生，主要从事测井解释和机器学习研究. E-mail: lupyyhappy@126.com

通讯作者: 高春云, 男, 1992年生, 博士, 主要从事测井解释及地震解释研究.E-mail: 214440756@qq.com

中图分类号: P631

收稿日期: 2022-02-21

修回日期: 2022-09-19

刊出日期: 2022-12-20

Research progress of permeability prediction technology for tight sandstone reservoirs

LU Ping^1,,
WANG HaoChen³,
GAO ChunYun^{4, ,},
HAO YuYu⁵,
TAN FuRong⁶,
LIU Jie¹,
LIU WeiGang¹,
BAI Yong¹,
GAO JianPing¹

- 1.
  Shaanxi Provincial Institute of Energy Resources and Chemical Engineering, Xi'an 710021, China
- 2.
  Department of Geology, State Key Laboratory Dynamics, Northwest University, Xi'an 710069, China
- 3.
  Cardiff University, Wales CF10 3AT, UK
- 4.
  Shaanxi Yuneng Group Energy and Chemical Research Institute Co., Ltd., Yulin 719000, China
- 5.
  Xi'an University of Science and Technology, Xi'an 710054, China
- 6.
  Shaanxi Mineral Geological Survey Center, Xi'an 710068, China

More Information

Corresponding author: GAO ChunYun, E-mail: 214440756@qq.com

Received Date: 21 February 2022

Revised Date: 19 September 2022

Publish Date: 20 December 2022

摘要

摘要:
对于致密砂岩储层而言，渗透率是评价储层物性、渗流特征的重要参数，也是储层产能挖潜和提高采收率的关键.致密砂岩储层孔隙类型多样，孔隙结构复杂，非均质性强，孔渗关系变化复杂，并非简单的线性关系.利用孔渗统计回归和测井解释方法预测精度较低，致密砂岩储层渗透率预测成为一项重要而艰巨的任务.本研究基于孔喉结构是致密砂岩储层渗透率的主控因素，重点利用毛管压力(MICP)、核磁共振(NMR)、岩心物性实验数据及测井资料，对基于统计回归理论、流体流动单元划分(FZI)储层分类理论、分形几何理论及人工智能理论的渗透率预测方法进行综述.最后指出，基于MICP、NMR及常规测井，将流体流动单元划分(FZI)储层分类技术、分形几何数字岩心技术以及人工智能机器学习技术相结合，可形成一套具有岩石物理学意义的致密砂岩储层渗透率评价和预测的有效方法.
- 致密砂岩储层 /
- 渗透率预测 /
- FZI技术 /
- 分形几何技术 /
- 机器学习技术
Abstract:
For tight sandstone reservoirs, permeability is an important parameter to evaluate the physical properties and seepage characteristics of the reservoir, and it is also the key to explore reservoir productivity and improve oil recovery. Tight sandstone reservoirs have diverse pore types, complex pore structure, strong heterogeneity, and complex changes in the porosity-permeability relationship, which is not a simple linear relationship. The prediction accuracy of using porosity and permeability statistical regression and logging interpretation methods is low, and the permeability prediction of tight sandstone reservoirs has become an important and arduous task. According to the pore throat structure is the main controlling factors of tight sandstone reservoir permeability, this study summarizes permeability prediction methods based on the statistical regression theory, the FZI reservoir classification theory, fractal geometry theory and artificial intelligence theory by using the capillary pressure (MICP), Nuclear Magnetic Resonance (NMR), core physical property experimental data and logging data. Finally, it is pointed out that based on MICP, NMR and conventional logging, a combination of fluid Flow Unit Division (FZI) reservoir classification technology, fractal geometric digital core technology and artificial intelligence machine learning technology can form an effective method for evaluating and predicting the permeability of tight sandstone reservoirs with petro-physical significance.
- Tight sandstone reservoir /
- Permeability prediction /
- FZI technology /
- Fractal geometry technology /
- Machine learning technology

HTML全文

图 1

NMR实验中关键参数说明

Figure 1.

Determination method of key parameters in NMR experiments

下载: 全尺寸图片幻灯片

图 2

基于FZI的渗透率预测流程图

Figure 2.

Flow chart of permeability prediction based on FZI

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图 3

分形维数计算流程图

Figure 3.

Flow chart of fractal dimension calculation

下载: 全尺寸图片幻灯片

图 4

机器学习的程序示意图

Figure 4.

Schematic diagram of the machine learning program

下载: 全尺寸图片幻灯片

图 5

BP-ANN示意图

Figure 5.

Schematic diagram of BP-ANN

下载: 全尺寸图片幻灯片

图 6

高斯过程回归示意图( 知乎，2021)

Figure 6.

Schematic diagram of Gaussian process regression (Zhihu, 2021)

下载: 全尺寸图片幻灯片

图 7

集成学习示意图

Figure 7.

Schematic diagram of integrated learning

下载: 全尺寸图片幻灯片

表 1

基于MICP和NMR的经典渗透率模型

Table 1.

Classical permeability model based onMICP and NMR

渗透率模型	表达式	模型所需实验数据
Winland模型	lgR₃₅=0.732+0.588lgK-0.864lgØ	毛管压力(MICP)
Swanson模型		毛管压力(MICP)
W-A模型		毛管压力(MICP)
Pittman模型	lgR_apex=-0.117+0.475lgK-0.099lgØ	毛管压力(MICP)
SDR模型	K=aØ⁴T_2LM²	核磁共振(NMR)
Coates模型		核磁共振(NMR)

下载: 导出CSV

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致密砂岩储层渗透率预测技术研究进展

作者简介: 路萍，女，1988年生，博士研究生，主要从事测井解释和机器学习研究. E-mail: lupyyhappy@126.com

通讯作者: 高春云, 男, 1992年生, 博士, 主要从事测井解释及地震解释研究.E-mail: 214440756@qq.com

Research progress of permeability prediction technology for tight sandstone reservoirs

Corresponding author: GAO ChunYun, E-mail: 214440756@qq.com

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作者简介:
路萍，女，1988年生，博士研究生，主要从事测井解释和机器学习研究. E-mail: lupyyhappy@126.com