AGER, 2(1): 1-13 基于水力流动单元的页岩油储层渗透率评价

已有 1352 次阅读 2018-1-15 19:48 |个人分类:AGER期刊|系统分类:论文交流| 期刊, 页岩油储层



1. 中国石油大学(华东) 非常规油气与新能源研究院 山东 青岛,266580

2. 中国石油大学(华东) 地球科学与技术学院 山东 青岛,266580


Advances in Geo-Energy Research, 2018, 2(1): 1-13 网址:

Permeability evaluation on oil-window shale based on hydraulic flow unit: A new approach

Pengfei Zhang, Shuangfang Lu, Junqian Li, Jie Zhang, Haitao Xue, Chen Chen

(Published: 2018-01-08)

Corresponding Author and Email: Shuangfang Lu,; Junqian Li,

Citation: Zhang, P., Lu, S., Li, J., Zhang, J., Xue, H., Chen, C. Permeability evaluation on oil-window shale based on hydraulic flow unit: A new approach. Advances in Geo-Energy Research, 2018, 2(1): 1-13, doi: 10.26804/ager.2018.01.01.

Article Type: Original article


Permeability is one of the most important petrophysical properties of shale reservoirs, controlling the fluid flow from the shale matrix to artificial fracture networks, the production and ultimate recovery of shale oil/gas. Various methods have been used to measure this parameter in shales, but no method effectively estimates the permeability of all well intervals due to the complex and heterogeneous pore throat structure of shale. A hydraulic flow unit (HFU) is a correlatable and mappable zone within a reservoir, which is used to subdivide a reservoir into distinct layers based on hydraulic flow properties. From these units, correlations between permeability and porosity can be established. In this study, HFUs were identified and combined with a back propagation neural network to predict the permeability of shale reservoirs in the Dongying Depression, Bohai Bay Basin, China. Well data from three locations were used and subdivided into modeling and validation datasets. The modeling dataset was applied to identify HFUs in the study reservoirs and to train the back propagation neural network models to predict values of porosity and flow zone indicator (FZI). Next, a permeability prediction method was established, and its generalization capability was evaluated using the validation dataset. The results identified five HFUs in the shale reservoirs within the Dongying Depression. The correlation between porosity and permeability in each HFU is generally greater than the correlation between the two same variables in the overall core data. The permeability estimation method established in this study effectively and accurately predicts the permeability of shale reservoirs in both cored and un-cored wells. Predicted permeability curves effectively reveal favorable shale oil/gas seepage layers and thus are useful for the exploration and the development of hydrocarbon resources in the Dongying Depression.

Keywords: Permeability, porosity, shale, hydraulic flow units, back propagation neural network.

Permeability evaluation on oil-window shale based on hydraulic flow unit_ A new .pdf

上一篇:Advances in Geo-Energy Research 公众号与官方交流群正式启动!
下一篇:AGER 基于随机时间成本权衡问题的油气田开发项目双目标优化


该博文允许注册用户评论 请点击登录 评论 (0 个评论)


Archiver|手机版|科学网 ( 京ICP备07017567号-12 )

GMT+8, 2020-4-6 01:27

Powered by

Copyright © 2007- 中国科学报社