Estimation of permeability for tight sandstone reservoir using conventional well logs based on mud-filtrate invasion model

被引:10
|
作者
Wang, Yuzhu [1 ]
Luo, Yang [2 ]
Liu, Hongping [2 ]
机构
[1] Univ New S Wales, Sch Petr Engn, Sydney, NSW 2052, Australia
[2] China Univ Geosci, Fac Earth Resources, Wuhan 430074, Hubei, Peoples R China
关键词
Permeability; Tight sandstone reservoir; Well log;
D O I
10.1260/0144-5987.33.1.15
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Permeability is an important but barely accessible parameter for fluid flow. In tight sandstone reservoir, it is more difficult to estimate permeability accurately because of the complexity of pore structure. Log interpretation is one of the most common and effective approaches to estimate permeability, especially nuclear magnetic resonance (NMR) log, which is considered the industry-leading technique for permeability determination in tight sandstone. However, this relatively new well log is missing in many oil-fields, which force us to explore new methods to estimate the permeability base on conventional well logs. This paper introduces a convenient and effective method to assess permeability using mud-filtrate invasion model which is established on the mechanic of mud-filtrate invasion. Only resistivity logs with different investigation radius are used in estimation. The difference of resistivity among different resistivity logs are considered to be the reflection of the saturation difference of mud-filtrate in undisturbed zone, invaded zone and flushed zone. On the one hand, the volume of mud filtrate is calculated using resistivity logs based on Archie's equation. On the other hand, the volume of mud filtrate is obtained based on Darcy's law. Then the volume of mud filtrate is used as a bridge to combine these two functions, where permeability will be the only unknown parameter in the equation and can be evaluated smoothly. This permeability estimation method was used in Sulige gas field. Results show that the estimated permeability matches well with the laboratory measurements.
引用
收藏
页码:15 / 24
页数:10
相关论文
共 32 条
  • [31] Estimation of pore structure and permeability in tight carbonate reservoir based on machine learning (ML) algorithm using SEM images of Jaisalmer sub-basin, India
    Pydiraju Yalamanchi
    Saurabh Datta Gupta
    Scientific Reports, 14
  • [32] Fracture characterization in oil-based mud boreholes using image logs: example form tight sandstones of Lower Cretaceous Bashijiqike Formation of KS5 well area, Kuqa Depression, Tarim Basin, China
    Yuan R.
    Han D.
    Tang Y.
    Wei H.
    Mo T.
    Wang C.
    Arabian Journal of Geosciences, 2021, 14 (6)