Effect of the variance of pore size distribution on the transport properties of heterogeneous networks

1998 ◽  
Vol 103 (B1) ◽  
pp. 513-525 ◽  
Author(s):  
Yves Bernabé ◽  
Céline Bruderer
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Transport Properties ◽  
Size Distribution ◽  
Heterogeneous Networks

Dissolution and precipitation reactions during acidic fluid percolation through different limestone samples

2020 ◽  
Author(s):  
Linda Luquot
Keyword(s):  
Diffusion Coefficient ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Transport Properties ◽  
Size Distribution ◽  
High Capacity ◽  
Co2 Injection ◽  
Geometrical Parameters ◽  
Calcite Dissolution ◽  
Geological Formation

<p>CO2 sequestration in deep geological formation is considered an option to reduce CO2 emissions in the atmosphere. After injection, the CO2 will slowly dissolve into the pore water producing low pH fluids with a high capacity for dissolving carbonates. Limestone rock dissolution induces geometrical parameters changes such as porosity, pore size distribution, or tortuosity which may consequently modify transport properties (permeability, diffusion coefficient). Characterizing these changes is essential for modelling flow and CO2 transport during and after the CO2 injection. Indeed, these changes can affect the storage capacity and injectivity of the formation.</p><p>Very few published studies evaluate the transport properties changes (porosity, permeability, pore size distribution, diffusion coefficient) due to fluid-rock interactions (dissolution and/or precipitation).</p><p>Here we report experimental results from the injection of acidic fluids (some of them equilibrated with gypsum) into limestone core samples of 25.4 mm diameter and around 25 mm length. We studied two different limestone samples: one composed of 73% of calcite and 27% of quartz, and the second one of 100% of dolomite. Experiments were realized at room temperature. Before and after each acidic rock attack, we measure the sample porosity, the diffusion coefficient and the pore size distribution.</p><p>We also imaged the 3D pore network by X-ray microtomography to evaluate the same parameters. During percolation experiments, the permeability changes are recorded and chemical samples taken to evaluate calcite dissolution and gypsum precipitation. Several dissolution/precipitation-characterization cycles are performed on each sample in order to study the evolution and relation of the different parameters.</p><p>These experiments show different dissolution regimes depending of the fluid acidity and of the</p><p>limestone samples in particular the initial local heterogeneity, and pore size distribution.</p>

scholarly journals PORE SIZE DISTRIBUTION IN SIMULATION OF MASS TRANSPORT IN POROUS MEDIA: A CASE STUDY IN RESERVOIR ANALYSIS

2018 ◽  
Vol 56 (2A) ◽  
pp. 24-30
Author(s):  
Vu Hong Thai
Keyword(s):  
Porous Media ◽  
Mass Transport ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Transport Properties ◽  
Size Distribution ◽  
Control Volume ◽  
Mass Conservation ◽  
Absolute Permeability ◽  
Transport In Porous Media

The modeling and numerical simulation of mass transport in porous media is discussed in this work by using the so-called pore size distribution for computing transport properties. The pore-size distribution is a property of the pore structure of a porous medium. This can be used to estimate the different transport properties, amongst other, the permeability. By starting with a formula for the absolute permeability, the simulation of water and oil transport in reservoirs is considered by solving mass conservation equations with the help of the control volume method. The influence of the pore size distribution on the transport behavior is discussed to demonstrate the adequacy of the use of pore size distribution in studying the behavior of reservoirs.

pyGAPS: A Python-Based Framework for Adsorption Isotherm Processing and Material Characterisation

2019 ◽  
Author(s):  
Paul Iacomi ◽  
Philip L. Llewellyn
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
Isosteric Heat ◽  
Material Characterisation ◽  
Mixture Adsorption ◽  
Surface Energetics ◽  
Adsorption Processing ◽  
User Friendly

Material characterisation through adsorption is a widely-used laboratory technique. The isotherms obtained through volumetric or gravimetric experiments impart insight through their features but can also be analysed to determine material characteristics such as specific surface area, pore size distribution, surface energetics, or used for predicting mixture adsorption. The pyGAPS (python General Adsorption Processing Suite) framework was developed to address the need for high-throughput processing of such adsorption data, independent of the origin, while also being capable of presenting individual results in a user-friendly manner. It contains many common characterisation methods such as: BET and Langmuir surface area, t and α plots, pore size distribution calculations (BJH, Dollimore-Heal, Horvath-Kawazoe, DFT/NLDFT kernel fitting), isosteric heat calculations, IAST calculations, isotherm modelling and more, as well as the ability to import and store data from Excel, CSV, JSON and sqlite databases. In this work, a description of the capabilities of pyGAPS is presented. The code is then be used in two case studies: a routine characterisation of a UiO-66(Zr) sample and in the processing of an adsorption dataset of a commercial carbon (Takeda 5A) for applications in gas separation.

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