流场压缩感知渗透率计算

doi:10.16180/j.cnki.issn1007-7820.2021.05.001

Abstract

Abstract:

The petroleum industry needs to obtain the permeability of porous material model by CT scanning, but the number of meshes in the calculation model is usually very large, which is usually larger than 2 000×2 000×2 000 voxels, Therefore, it is impossible to solve the hydrodynamic problems by a microcomputer in a short time. If the low-density grid can be reduced with the amount of simulation decreased accordingly, and then a mathematical fitting recovery algorithm can be implied to restore the simulation results to the high-precision grid, the low-performance equipment may complete the fluid simulation task in a short time. In this study, compressed sensing is introduced into the Euler grid computation to recover the flow field data of the grid with low precision sampling meshes. Three sandstone cores are randomly selected and compared with the results of high-precision grid calculation, and the error is less than 4%. Therefore, this method can optimize the calculation efficiency in the permeability calculation of fluid simulation of porous rock.

Key words: algorithm optimization, CT scanning, digital core model, grid optimization, computational fluid dynamics, compressive sensing, LBM, sparse reconstruction

CLC Number:

TP311.1

GUO Long,YAO Shuxin,ZHENG Fawei. Application of Compressive Sensing of CFD in Permeability Calculation[J].Electronic Science and Technology, 2021, 34(5): 1-6.

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References 20

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小波基	分层数	稀疏度
Haar	2	0.099
Haar	3	0.061
Daubechies	2	0.081
Daubechies	3	0.052
Symlets Biorthogonal	2	0.076
Symlets Biorthogonal	3	0.051

稀疏变换方法	分层数	稀疏度
小波变换	3	0.051
曲波变换	3	0.042

稀疏优化方法	渗透率/md	MSE
SPGL1	16.2	0.004 92
GPSR	16.1	0.005 26
FISTA	16.2	0.004 21
POCS	16.3	0.004 85

稀疏优化方法	切片重构时间 /ms	节约流场计算时间/ms
SPGL1	122	1 743
GPSR	35	1 830
FISTA	152	1 713
POCS	58	1 807

Application of Compressive Sensing of CFD in Permeability Calculation

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