The Pressure Dependence of the Archie Cementation Exponent for Samples from the Ordovician Goldwyer Shale Formation in Australia

SPE Journal ◽  
2021 ◽  
pp. 1-11
Author(s):  
Zhiqi Zhong ◽  
Lionel Esteban ◽  
Reza Rezaee ◽  
Matthew Josh ◽  
Runhua Feng
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Complex Impedance ◽  
Clay Content ◽  
Exchange Capacity ◽  
Shale Formations ◽  
Fluid Saturation ◽  
Confining Pressures ◽  
Log Interpretation

Summary Applying the realistic cementation exponent (m) in Archie’s equation is critical for reliable fluid-saturation calculation from well logs in shale formations. In this study, the cementation exponent was determined under different confining pressures using a high-salinity brine to suppress the surface conductivity related to the cation-exchange capacity of clay particles. A total of five Ordovician shale samples from the Canning Basin, Australia, were used for this study. The shale samples are all illite-rich with up to 60% clay content. Resistivity and porosity measurements were performed under a series of confining pressures (from 500 to 8,500 psi). Nuclear magnetic resonance (NMR) was used to obtain porosity and pore-size distribution and to detect the presence of residual oil. The complex impedance of samples was determined at 1 kHz to verify the change in pore-size distribution using the POLARIS model (Revil and Florsch 2010). The variation of shale resistivity and the Archie exponent m at different pressures is caused by the closure of microfractures at 500 psi, the narrowing of mesopores/macropores between 500 and 3,500 psi, and the pore-throat reduction beyond 3,500 psi. This study indicates that unlike typical reservoirs, the Archie exponent m for shale is sensitive to depth of burial because of the soft nature of the shale pore system. An equation is developed to predict m under different pressures after microfracture closure. Our study provides recommended experimental procedures for the calculation of the Archie exponent m for shales, leading to improved accuracy for well-log interpretation within shale formations when using Archie-basedequations.

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>

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.

Microstructure and water retention of Oxisols in Thailand

Soil Research ◽  
2005 ◽  
Vol 43 (8) ◽  
pp. 973 ◽  
Author(s):  
S. Tawornpruek ◽  
I. Kheoruenromne ◽  
A. Suddhiprakarn ◽  
R. J. Gilkes
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Water Retention ◽  
Field Capacity ◽  
Exchange Capacity ◽  
Scanning Electron Micrographs ◽  
North East ◽  
The North ◽  
Pore Size Distributions

The moisture characteristics and related pore size distributions of Thai Oxisols were determined for 11 representative pedons. These soils are Kandiustox and Kandiudox located in the North-east Plateau, South-east Coast, and Peninsular Thailand. They are generally acidic (pH 5–6), clayey, have low cation exchange capacity (5–15 cmolc/kg) and have low bulk densities (0.77–1.36 Mg/m3). The microstructures of these soils are mostly granular with compound packing 10–1500 μm voids between aggregates. Water retention curves for all soils are similar with amounts of water retained at 33 kPa (field capacity) ranging from 22 to 43 (%weight) and 1500 kPa (permanent wilting point) ranging from 17 to 34%. Water available to plants is 3–12%. There is a strongly bimodal pore size distribution with the modal macropore and micropore diameters calculated from the soil moisture retention characteristic curves being about 100 μm and 0.02 μm. The pore size distribution determined by N2-BET method indicates a modal micropore of about 0.01 μm. The total volume of pores >5 μm estimated by image analysis of scanning electron micrographs ranges from 20 to 50% with most of the variation in porosity being due to large differences in macropore (>75 μm) volume.

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.

Sign in / Sign up

Export Citation Format

Share Document