scholarly journals A permeability prediction method based on pore structure and lithofacies

2019 ◽  
Vol 46 (5) ◽  
pp. 935-942 ◽  
Author(s):  
Lideng GAN ◽  
Yaojun WANG ◽  
Xianzhe LUO ◽  
Ming ZHANG ◽  
Xianbin LI ◽  
...  
Keyword(s):  
Pore Structure ◽  
Prediction Method ◽  
Permeability Prediction

scholarly journals Pore Structure Characteristics and Permeability Prediction Model in a Cretaceous Carbonate Reservoir, North Persian Gulf Basin

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Hao Lu ◽  
Hongming Tang ◽  
Meng Wang ◽  
Xin Li ◽  
Liehui Zhang ◽  
...  
Keyword(s):  
Pore Structure ◽  
Oil Recovery ◽  
Prediction Models ◽  
Carbonate Reservoir ◽  
Good Prediction ◽  
Type I ◽  
Permeability Prediction ◽  
Rock Types ◽  
Fluid Saturation ◽  
Pore Types

Due to the diversity of pore types, it is challenging to characterize the Middle East’s Cretaceous carbonate reservoir or accurately predict its petrophysical properties. In this paper, pore structure in the reservoir is first classified using a comprehensive method. Then, based on the identified pore structure types, a new permeability model with high prediction precision is established. The reservoir is dominated by 6 pore types, such as intergrain pores and moldic pores, and 6 rock types. Grainstone, algal packstone, algal wackestone, and foraminifera wackestone are porous rock types, and echinoderm wackestone and mudstone are nonporous rock types. The types of pore structure in the study area can be divided into four types. Type I has midhigh porosity and medium-high permeability due to its large throat, while type II has a fine throat type with midhigh porosity and midpermeability. Due to their isolated pores, the permeability is low in types III and IV, and out of these two, type III has better storage capacity. Movable fluid saturation calculated by the spectral coefficient method and r apex can characterize the boundary between the connected pores and unconnected pores very well in the research area. It is not accurate enough to simply classify the pore structure by permeability and porosity. The combination of porosity, permeability, r apex , flow zone indicator, and the reservoir quality index can effectively distinguish and classify pore structure types in noncoring wells. The characteristics of each pore structure type are consistent with those of the fractal dimension, which thereby proves the effectiveness of the pore structure classification. New permeability prediction models are proposed for different pore structure types, and good prediction results have been obtained. This study is of great significance for enhancing oil recovery.

scholarly journals Multiscale Research on Pore Structure Characteristics and Permeability Prediction of Sandstone

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
M. A. Shi-Jia ◽  
L. I. N. Yuan-Jian ◽  
L. I. U. Jiang-Feng ◽  
Kundwa Marie Judith ◽  
Ishimwe Hubert ◽  
...  
Keyword(s):  
Pore Structure ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Target Material ◽  
Future Research ◽  
Distribution Method ◽  
Binary Images ◽  
Permeability Prediction

The random existence of many irregular pore structures in geotechnical materials has a decisive influence on its permeability and other macroscopic properties. The analysis and characterization of the micropore structure of the material and its permeability are of great significance for geotechnical engineering. In this study, digital images with different magnifications were used to examine the pore structure and permeability of sandstone samples. The image processing method is used to obtain binary images, and then, the pore size distribution method is used to calculate the pore size distribution. Therefore, based on the Hagen-Poiseuille formula, we get the prediction value of material’s permeability and compare it with the value obtained from mercury intrusion porosimetry (MIP). It is found that different microscopic images with different magnification and various statistical methods of pore size have a specific influence on the characterization of pore structure and permeability prediction. The porosity of different magnifications is not the same, and the results obtained at higher magnifications are more consistent with the results obtained with MIP. With the increase of magnification, we can observe more pores in large sizes. The effect of CPSD (continuous pore size distribution) in pore size statistics is better than that of DPSD (discrete pore size distribution). In permeability prediction, the prediction result of higher magnification images are closer to the instrument test value, and the value of DPSD is more significant than that of CPSD. In future research, an appropriate method should be selected to obtain a reasonable prediction of the permeability of the target material.

Pore structure characterization and permeability prediction of coal samples based on SEM images

2019 ◽  
Vol 67 ◽  
pp. 160-171 ◽  
Author(s):  
Shuai-Bing Song ◽  
Jiang-Feng Liu ◽  
Dian-Sen Yang ◽  
Hong-Yang Ni ◽  
Bing-Xiang Huang ◽  
...  
Keyword(s):  
Pore Structure ◽  
Structure Characterization ◽  
Sem Images ◽  
Permeability Prediction
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