Permeability calculations in unconsolidated homogeneous sands

Taheri, Saeed; Ghomeshi, Shahin; Kantzas, Apostolos

doi:10.1016/j.powtec.2017.08.014

Cited by 29 publications

(7 citation statements)

References 13 publications

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“…Medium 2 has the lowest average error of 2.7%, while medium 4 has the highest average error of 11.5%. Due to the method used to generate the synthetic media, the pore radius distribution widens from medium 1‐4 . Consequently, the level of heterogeneity of the reconstructed media increases, which leads to higher values of error for the calculated permeabilities, and this was observed in other studies .…”

Section: Resultsmentioning

confidence: 58%

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Scale‐up of pore‐level relative permeability from micro‐ to macro‐scale

Bashtani

Kantzas

2020

Can J Chem Eng

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Scaling up relative permeability curves of wetting and nonwetting phase of drainage and imbibition processes from pore scale to macro scale is a challenge. A new method for scaling up relative permeability from micro‐ to macro‐scale is proposed based on electrical analogy of multiphase fluid flow at pore scale. The method is validated against four synthetic porous media generated using homogeneous and heterogeneous grain size distributions, each of which were cut into eight sub‐segments. Single‐phase and two‐phase flow properties were calculated for the main blocks and the subsequent sub‐segments using random network modelling technique. Then, the subsegments were randomly distributed in space to reconstruct the main blocks and the proposed scale‐up method was employed to calculate the relative permeability curves of the reconstructed blocks. Results were compared to the ones obtained directly from the network model of the original blocks and show good agreement between the calculated and scaled‐up relative permeability curves of primary drainage and secondary imbibition. Furthermore, the model was tested on real media. Eight network models were extracted from pore size distribution of core samples obtained from the Green River basin located in the Mesaverde Formation. Flow properties obtained from the network models were validated against experimental data and good agreement was observed. These network models show a higher level of heterogeneity at micro‐scale. Then, the scale‐up methods were employed in order to reconstruct the macro‐scale sample and predict its properties. Scale‐up methods successfully predict the single‐phase and two‐phase flow properties of the sample.

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Section: Resultsmentioning

confidence: 58%

“…For the purpose of this study, four synthetic 3D granular porous media are reconstructed using the cooperative rearrangement and swelling of spheres algorithm, and each are divided into eight sub‐segments. A simple visualization of the main block of medium 4 is shown in Figure .…”

Section: Fluid Flow Propertiesmentioning

confidence: 99%

Scale‐up of pore‐level relative permeability from micro‐ to macro‐scale

Bashtani

Kantzas

2020

Can J Chem Eng

Self Cite

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“…The greatest overestimation was reported by Mostaghimi et al 42 for samples with porosities lower than 0.25, which were not observed in the samples used in this study. Taheri et al 43 reported high levels of agreement between KC and NS saturated conductivity estimations, but their work was related to a virtual homogeneous sand porous medium built from spherical particles. This medium very closely resembled the KC equation assumptions.…”

Section: Resultsmentioning

confidence: 99%

An intercomparison of the pore network to the Navier–Stokes modeling approach applied for saturated conductivity estimation from X-ray CT images

Gackiewicz

Lamorski

Sławiński

et al. 2021

Sci Rep

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Different modeling techniques can be used to estimate the saturated conductivity of a porous medium based on computed tomography (CT) images. In this research, two methods are intercompared: direct modeling using the Navier–Stokes (NS) approach and simplified geometry pore network (PN) modeling. Both modeling approaches rely on pore media geometry which was determined using an X-ray CT scans with voxel size 2 μm. An estimate of the saturated conductivity using both methods was calculated for 20 samples prepared from sand with diverse particle size distributions. PN-estimated saturated conductivity was found to be statistically equivalent to the NS-determined saturated conductivity values. The average value of the ratio of the PN-determined conductivity to the NS-determined conductivity (KsatPN/NS) was equal to 0.927. In addition to the NS and PN modeling approaches, a simple Kozeny-Carman (KC) equation-based estimate was made. The comparison showed that the KC estimate overestimated saturated conductivity by more than double (2.624) the NS estimate. A relationship was observed between the porous media specific surface and the KsatPN/NS ratio. The tortuosity of analyzed samples was estimated, the correlation between the porous media tortuosity and the specific surface of the samples was observed. In case of NS modelling approach the difference between pore media total porosity and total porosity of meshes, which were lower, generated for simulations were observed. The average value of the differences between them was 0.01. The method of NS saturated conductivity error estimation related to pore media porosity underestimation by numerical meshes was proposed. The error was on the average 10% for analyzed samples. The minimum value of the error was 4.6% and maximum 19%.

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“…Kozeny introduced the relation between permeability and the properties of the porous medium and it was later modified by Carman, and it is commonly known as the Kozeny-Carman equation [28]. Based on the basic theory, many other empirical equations focus on the relation of permeability and particle size [29]. Besides that, Istomina provided another prediction for permeability from the void ratio instead of particle size [30].…”

Section: )mentioning

confidence: 99%

Raking Effects on Aggregate Densification and Channelling in Thickening

Zhou

Wang

Jiao

et al. 2019

Advances in Materials Science and Engineering

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Thickening performance is greatly influenced by aggregate densification and channelling development; however, the microstructures of aggregates and channelling are unable to directly observe because of high turbidity mud bed during thickening. Computed tomography scan imaging technique has been introduced to investigate the effects of aggregate densification and channelling in the mud bed samples during unclassified tailings thickening. The samples were prepared through deep-freezing and freeze-drying techniques, immediately after sampling from the operation of a pilot thickener. Based on the information of void ratio, pore-size distribution, and permeability, obtained from 3D reconstruction images of the aggregates structure, aggregate densification and channelling development have been characterized. Channelling patterns have been classified by the size and shape of the connection throat of the pore between aggregates. As the aggregate structure broke and densified, the void ratio and pore size of the mud bed were decreased. Thickening performance enhancement was predicted based on solid flux and permeability of mud bed, and the result indicated raking on aggregate densification and channelling apparently improves the unclassified tailings dewatering extent.

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Permeability calculations in unconsolidated homogeneous sands

Cited by 29 publications

References 13 publications

Scale‐up of pore‐level relative permeability from micro‐ to macro‐scale

Scale‐up of pore‐level relative permeability from micro‐ to macro‐scale

An intercomparison of the pore network to the Navier–Stokes modeling approach applied for saturated conductivity estimation from X-ray CT images

Raking Effects on Aggregate Densification and Channelling in Thickening

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