Variation of Permeability with Porosity in Sandstone Diagenesis Interpreted with a Fractal Pore Space Model

2000 ◽  
pp. 603-619 ◽  
Author(s):  
Hansgeorg Pape ◽  
Christoph Clauser ◽  
Joachim Iffland
Keyword(s):  
Pore Space ◽  
Space Model ◽  
Sandstone Diagenesis

Variation of Permeability with Porosity in Sandstone Diagenesis Interpreted with a Fractal Pore Space Model

2000 ◽  
Vol 157 (4) ◽  
pp. 603-619 ◽  
Author(s):  
H. Pape ◽  
C. Clauser ◽  
J. Iffland
Keyword(s):  
Pore Space ◽  
Space Model ◽  
Sandstone Diagenesis

A pore space model for rock permeability and bulk modulus

1984 ◽  
Vol 89 (B1) ◽  
pp. 527 ◽  
Author(s):  
Donald A. Seeburger ◽  
Amos Nur
Keyword(s):  
Bulk Modulus ◽  
Pore Space ◽  
Rock Permeability ◽  
Space Model

scholarly journals Modeling the spectral features of acoustic noise produced by gas flow in rock samples based on the theory of ensemble of interconnected pore resonators

2019 ◽  
pp. 126-134
Author(s):  
E. A. Ivanova ◽  
D. N. Mikhailov
Keyword(s):  
Gas Flow ◽  
Acoustic Noise ◽  
Pore Space ◽  
Comsol Multiphysics ◽  
Space Model ◽  
Helmholtz Resonators ◽  
Frequency Calculation ◽  
Flow Through ◽  
Indiana Limestone ◽  
Interconnected Pore

A new model describing the pore space as an ensemble of interconnected Helmholtz resonators is proposed. This model makes it possible to improve the description of spectral peculiarities of the experimentally recorded acoustic noise during gas flow through a porous medium. The results of the resonance frequency calculation are presented by the example of the pore space model of the Indiana Limestone. Microvortexes in the pores are considered as the main mechanism of acoustic noise generation by gas flow. The presented numerical simulations on COMSOL Multiphysics show that the generation of microvortexes begins when the Reynolds number in the pores reaches 1 to 10.

Correlation of Pore Structure and Permeability by SEM-Image Analysis

1990 ◽  
Vol 48 (1) ◽  
pp. 428-429
Author(s):  
C. A. Callender ◽  
Wm. C. Dawson ◽  
J. J. Funk
Keyword(s):  
Image Analysis ◽  
Pore Structure ◽  
Imaging System ◽  
Pore Space ◽  
Simulation Models ◽  
Image Analyzer ◽  
Imaging Data ◽  
Sem Images ◽  
Thin Sections ◽  
Rock Types

The geometric structure of pore space in some carbonate rocks can be correlated with petrophysical measurements by quantitatively analyzing binaries generated from SEM images. Reservoirs with similar porosities can have markedly different permeabilities. Image analysis identifies which characteristics of a rock are responsible for the permeability differences. Imaging data can explain unusual fluid flow patterns which, in turn, can improve production simulation models.Analytical SchemeOur sample suite consists of 30 Middle East carbonates having porosities ranging from 21 to 28% and permeabilities from 92 to 2153 md. Engineering tests reveal the lack of a consistent (predictable) relationship between porosity and permeability (Fig. 1). Finely polished thin sections were studied petrographically to determine rock texture. The studied thin sections represent four petrographically distinct carbonate rock types ranging from compacted, poorly-sorted, dolomitized, intraclastic grainstones to well-sorted, foraminiferal,ooid, peloidal grainstones. The samples were analyzed for pore structure by a Tracor Northern 5500 IPP 5B/80 image analyzer and a 80386 microprocessor-based imaging system. Between 30 and 50 SEM-generated backscattered electron images (frames) were collected per thin section. Binaries were created from the gray level that represents the pore space. Calculated values were averaged and the data analyzed to determine which geological pore structure characteristics actually affect permeability.

Positional Estimation Within a Latent Space Model for Networks

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff
Keyword(s):  
Random Effects ◽  
Network Data ◽  
Random Effects Models ◽  
Space Model ◽  
Network Analyses ◽  
Recent Advances ◽  
Latent Space ◽  
Latent Space Model ◽  
Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

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