An Integrated Method for Upscaling Pore-Network Characterization and Permeability Estimation: Example from the Mississippian Barnett Shale

Peng, Sheng; Yang, Jijin; Xiao, Xianghui; Loucks, Bob; Ruppel, Stephen C.; Zhang, Tongwei

doi:10.1007/s11242-015-0523-8

Cited by 55 publications

(18 citation statements)

References 21 publications

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“…; Peng et al . ). However, neither the exact microstructure of the porous clay nor the properties of the solid unit of this composite have been fully evaluated.…”

Section: Implementation Of Multi‐scale Homogenisation Techniquesmentioning

confidence: 97%

Predicting the elastic response of organic‐rich shale using nanoscale measurements and homogenisation methods

Goodarzi

Rouainia

Aplin

et al. 2017

Geophysical Prospecting

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Block, M. (2017) 'Predicting the elastic response of organic-rich shale using nanoscale measurements and homogenisation methods.', Geophysical prospecting., 65 (6). pp. 1597-1614. Further information on publisher's website:https://doi.org/10.1111/ 1365-2478.12475 Publisher's copyright statement: This is the accepted version of the following article: Goodarzi, M., Rouainia, M., Aplin, A.C., Cubillas, P. and de Block, M. (2017), Predicting the elastic response of organic-rich shale using nanoscale measurements and homogenisation methods. Geophysical Prospecting., which has been published in nal form at https://doi.org/10.1111/1365-2478.12475. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving. Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractDetermination of the mechanical response of shales through experimental procedures is a practical challenge due to their heterogeneity and the practical difficulties of retrieving good quality core samples. Here, we investigate the possibility of using multi-scale homogenisation techniques to predict the macroscopic mechanical response of shales, based on quantitative mineralogical descriptions. We use the novel PeakForce Quantitative Nanomechanical Mapping (QNM ) technique to generate high resolution mechanical images of shales, allowing the response of porous clay, organic matter and mineral inclusions to be measured at the nanoscale. These observations support some of the assumptions previously made in the use of homogenisation methods to estimate the elastic properties of shale, and also earlier estimates of the mechanical properties of organic matter. We evaluate the applicability of homogenisation techniques against measured elastic responses of organic-rich shales, partly from published data and also from new indentation tests carried out in this work. Comparison of experimental values of the elastic constants of shale samples with those predicted by homogenisation methods showed that almost all predictions were within the standard deviation of experimental data. This suggests that the homogenisation approach is a useful way of estimating the elastic and mechanical properties of shales, in situations where conventional rock mechanics test data cannot be measured.

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“…; Peng et al . ). However, neither the exact microstructure of the porous clay nor the properties of the solid unit of this composite have been fully evaluated.…”

Section: Implementation Of Multi‐scale Homogenisation Techniquesmentioning

confidence: 97%

Predicting the elastic response of organic‐rich shale using nanoscale measurements and homogenisation methods

Goodarzi

Rouainia

Aplin

et al. 2017

Geophysical Prospecting

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“…More details on the micro-CT scanning conditions and tomographic volume reconstruction can be seen in Peng et al (2015). Figure 1 shows the schematic experimental setup of the MGE method.…”

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confidence: 99%

Permeability measurements in mudrocks using gas-expansion methods on plug and crushed-rock samples

Peng

Loucks

2016

Marine and Petroleum Geology

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Permeability is an important parameter relative to the production of hydrocarbons in shale oil/gas plays; however, the measurement of permeability in these nano-to microdarcy rocks remains a challenge. Results from different methods or from different laboratories are not consistent, and reasons are not fully understood. In the present study, permeability is measured for both plug and crushed-rock samples with different plug diameter or crushed-sample particle size to systematically investigate the permeability measurement to better understand and apply the measured results. A modified gas-expansion (MGE) method, which can measure permeability for plug samples under confining pressures, was established and applied to several Eagle Ford and Barnett Shale (mudrock) samples. Permeability results from this method are in fair agreement with those from the pulse-decay method. The traditional Gas Research Institute (GRI) method was applied to crushed-rock Eagle Ford Shale samples. The results were comparable to reported permeability for an Eagle Ford Shale sample. Particle or plug size has significant influence on permeability measurement. In general, permeability increases with increasing particle or plug size. For crushed sample with GRI method, the reason of increasing permeability is related to the limitation of the GRI technique and the data analysis method. Estimate of the permeability based on Kozeny-Carman Equation was conducted, and the results were used to evaluate the GRI permeability measurement. Particle size of 2-4 mm (5-10 meshes) is considered as an appropriate size for GRI permeability measurement. For plug sample, larger permeability with larger plug diameter is most likely caused by the artificial fractures. Higher confining pressure can reduce the influence of the fractures, but cannot fully remove it. A range of permeability, defined by the GRI permeability with 2-4 mm particles as the lower boundary and permeability of 1-in plug under high confining pressure (>5000 psi) as the upper boundary, 3 can be a more reliable measures to represent the shale matrix permeability. The range of the permeability also highlights the uncertainty in matrix permeability measurement for shale.

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“…The resulting pixel size was 1.0 μm. More details on micro-CT scan operational parameters and data analysis procedures are included in a previous study (Peng et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

Permeability estimation based on thin-section image analysis and 2D flow modeling in grain-dominated carbonates

Peng

Hassan

2016

Marine and Petroleum Geology

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Permeability estimation based on image analysis is a good alternative when an intact core-plug is not available for laboratory measurement. While there is much research on permeability estimation from 2D and 3D images or models, accurately estimating permeability for carbonates, a type of rock with substantial heterogeneity, is still challenging. In this study, a method for permeability estimation based on thin-section image analysis and 2D permeability simulation is developed for grain-dominated carbonates based on semi-theoretical analysis of 2D and 3D permeability (K2D and K3D) relationship. The mathematical expression of the K2D/K3D is examined and validated with a grainstone carbonate sample, for which both K2D and K3D were obtained through permeability simulation based on micro-CT images. The method was applied to 24 grain-dominated carbonate samples collected from different locations with a large variation in rock textures and fabrics and a broad range of permeability from 0.1 mD to 3200 mD. Representativeness of the thin section and the correct determination of the effective pores are the key for accurate estimation. The estimated permeability was compared to the measured value, and 92% of the results are within a factor of ±5, and 46% are with a factor of ±2. Potential sources of systematic error are discussed both qualitatively and quantitatively.

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An Integrated Method for Upscaling Pore-Network Characterization and Permeability Estimation: Example from the Mississippian Barnett Shale

Cited by 55 publications

References 21 publications

Predicting the elastic response of organic‐rich shale using nanoscale measurements and homogenisation methods

Predicting the elastic response of organic‐rich shale using nanoscale measurements and homogenisation methods

Permeability measurements in mudrocks using gas-expansion methods on plug and crushed-rock samples

Permeability estimation based on thin-section image analysis and 2D flow modeling in grain-dominated carbonates

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