Modeling pore size distribution of southern Sichuan shale gas reservoirs

2015 ◽  
Vol 26 ◽  
pp. 883-894 ◽  
Author(s):  
Botao Lin ◽  
Mian Chen ◽  
Yan Jin ◽  
Huiwen Pang
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Shale Gas ◽  
Gas Reservoirs ◽  
Shale Gas Reservoirs

scholarly journals Review of Formation and Gas Characteristics in Shale Gas Reservoirs

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5427
Author(s):  
Boning Zhang ◽  
Baochao Shan ◽  
Yulong Zhao ◽  
Liehui Zhang
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Shale Gas ◽  
Physical Parameters ◽  
Gas Reservoirs ◽  
Shale Formations ◽  
Adsorbed Gas ◽  
Shale Gas Reservoirs ◽  
Longmaxi Shale

An accurate understanding of formation and gas properties is crucial to the efficient development of shale gas resources. As one kind of unconventional energy, shale gas shows significant differences from conventional energy ones in terms of gas accumulation processes, pore structure characteristics, gas storage forms, physical parameters, and reservoir production modes. Traditional experimental techniques could not satisfy the need to capture the microscopic characteristics of pores and throats in shale plays. In this review, the uniqueness of shale gas reservoirs is elaborated from the perspective of: (1) geological and pore structural characteristics, (2) adsorption/desorption laws, and (3) differences in properties between the adsorbed gas and free gas. As to the first aspect, the mineral composition and organic geochemical characteristics of shale samples from the Longmaxi Formation, Sichuan Basin, China were measured and analyzed based on the experimental results. Principles of different methods to test pore size distribution in shale formations are introduced, after which the results of pore size distribution of samples from the Longmaxi shale are given. Based on the geological understanding of shale formations, three different types of shale gas and respective modeling methods are reviewed. Afterwards, the conventional adsorption models, Gibbs excess adsorption behaviors, and supercritical adsorption characteristics, as well as their applicability to engineering problems, are introduced. Finally, six methods of calculating virtual saturated vapor pressure, seven methods of giving adsorbed gas density, and 12 methods of calculating gas viscosity in different pressure and temperature conditions are collected and compared, with the recommended methods given after a comparison.

Depth and composition dependent nanopore structures of Indian shale gas reservoirs: An implication on storage potential

2021 ◽  
Author(s):  
Abinash Bal ◽  
Santanu Misra ◽  
Manab Mukherjee
Keyword(s):  
Fractal Dimension ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Negative Correlation ◽  
Fractal Dimensions ◽  
Clay Content ◽  
Pore Network Model ◽  
Cambay Basin ◽  
Gas Reservoirs

<p>We investigated the nanopore structures of shale samples obtained from Cambay and Krishna-Godavari (KG) basins in India using low-pressure N<sub>2</sub> sorption method. The samples occurred at variable depths (1403-2574m and 2599-2987m for Cambay and KG basins, respectively) and have wide ranges of clay contents (56-90%) both in volume and mineralogy. The results of this study indicate the specific surface area (SSA) and pore diameters of the samples share a non-linear negative correlation. The SSA is a strong function of the clay content over the samples’ depth. The specific micropore volumes of the KG basin have relatively higher (8.29-24.4%) than the Cambay basin (0.1-3.6%), which leads to higher SSA in the KG basin. From different statistical thickness equations, the Harkins Jura equation was found to be most suitable for the computation of BJH pore size distribution and t-plot inversion in shale. Shale samples from Cambay basin show unimodal pore size distribution, with a modal diameter of 4-5nm, while in the KG basin, show bi-modal to multimodal pore size distribution, mostly ranges from 3-12 nm. In the fractal FHH method, fractal exponent D<sub>f</sub>-3 provides a better realistic result than fractal dimensions calculated from (D<sub>f</sub>-3)/3. In our samples, pore surface fractal dimension (D<sub>f1</sub>) show a positive correlation with SSA and a negative correlation with pore diameter, and pore structure fractal dimension (D<sub>f2</sub>) shows a negative correlation both with clay(%) and depth. The experimental data obtained in this study are instrumental in developing the pore-network model to assess the hydrocarbon reserve and recovery in shale.</p>

Investigating Effects of Pore Size Distribution and Pore Shape on Radon Production in Marcellus Shale Gas Formation

2019 ◽  
Vol 33 (2) ◽  
pp. 700-707
Author(s):  
Wei Tian ◽  
Xingru Wu ◽  
Dehua Liu ◽  
Amanda Knaup ◽  
Changlong Chen ◽  
...  
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Shale Gas ◽  
Marcellus Shale ◽  
Pore Shape ◽  
Gas Formation

Pore Size Distribution and Fractal Characteristics of the Nanopore Structure in Organic-Rich Shales Using the Neimark-Kiselev Fractal Approach

2021 ◽  
Vol 21 (1) ◽  
pp. 646-658
Author(s):  
Mingming Wei ◽  
Li Zhang ◽  
Yongqiang Xiong ◽  
Ping’an Peng ◽  
Yiwen Ju
Keyword(s):  
Pore Structure ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Shale Gas ◽  
Fractal Dimensions ◽  
Clay Content ◽  
Scientific Basis ◽  
Fractal Approach ◽  
Fractal Characteristics

Shale gas has been playing an increasingly important role in meeting global energy demands. The heterogeneity of the pore structure in organic-rich shales greatly affects the adsorption, desorption, diffusion and flow of gas. The pore size distribution (PSD) is a key parameter of the heterogeneity of the shale pore structure. In this study, the Neimark-Kiselev (N-K) fractal approach was applied to investigate the heterogeneity in the PSD of the lower Silurian organic-rich shales in South China using low-pressure N2 adsorption, total organic carbon (TOC) content, maturity analysis, X-ray diffraction (XRD) and field emission scanning electron microscopy (FE-SEM) measurements. The results show that (1) the fractal dimension DN-K obtained by N-K theory better represents the heterogeneity of the PSD in shale at an approximately 1–100 nm scale. The DN-K values range from 2.3801 to 2.9915, with a mean of 2.753. The stronger the PSD heterogeneity is, the higher the DN-K value in shale is. (2) The clay-rich samples display multimodal patterns at pore sizes greater than 20 nm, which strongly effect the PSD heterogeneity. Quartz-rich samples display major peaks at less than or equal to a 10 nm pore size, with a smaller effect on the PSD heterogeneity in most cases. In other brittle mineral-rich samples, there are no obvious major peaks, and a weak heterogeneity of the PSDs is displayed. (3) A greater TOC content, maturity, clay content and pore size can cause stronger heterogeneity of the PSD and higher fractal dimensions in the shale samples. This study helps to understand and compare the PSD and fractal characteristics from different samples and provides important theoretical guidance and a scientific basis for the exploration and development of shale gas resources.

A FRACTAL PERMEABILITY MODEL FOR SHALE MATRIX WITH MULTI-SCALE POROUS STRUCTURE

Fractals ◽  
2016 ◽  
Vol 24 (01) ◽  
pp. 1650002 ◽  
Author(s):  
MAO SHENG ◽  
GENSHENG LI ◽  
SHOUCENG TIAN ◽  
ZHONGWEI HUANG ◽  
LIQIANG CHEN
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Shale Gas ◽  
Gas Permeability ◽  
Pore Space ◽  
Fractal Theory ◽  
Permeability Model ◽  
Multi Scale ◽  
Nanopore Structure

Nanopore structure and its multiscale feature significantly affect the shale-gas permeability. This paper employs fractal theory to build a shale-gas permeability model, particularly considering the effects of multiscale flow within a multiscale pore space. Contrary to previous studies which assume a bundle of capillary tubes with equal size, in this research, this model reflects various flow regimes that occur in multiscale pores and takes the measured pore-size distribution into account. The flow regime within different scales is individually determined by the Knudsen number. The gas permeability is an integral value of individual permeabilities contributed from pores of different scales. Through comparing the results of five shale samples, it is confirmed that the gas permeability varies with the pore-size distribution of the samples, even though their intrinsic permeabilities are the same. Due to consideration of multiscale flow, the change of gas permeability with pore pressure becomes more complex. Consequently, it is necessary to cover the effects of multiscale flow while determining shale-gas permeability.

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