Knowledge-based reconstruction of random porous media

Eschricht, N.; Hoinkis, E.; Mädler, Fritz; Schubert‐Bischoff, P.; Röhl-Kuhn, B.

doi:10.1016/j.jcis.2005.05.004

Cited by 26 publications

(32 citation statements)

References 48 publications

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“…Capillary condensation and evaporation take place gradually. An increase of the pore size for the disordered mesoporous silicas often causes an alteration in the hysteresis loop of type H2 to type H1 for the adsorption isotherm of nitrogen at 77 K, ,, as is the case for the present study. For the CPG sample with the average pore diameter of 13.3 nm, however, no changes in the mechanisms for capillary condensation and for capillary evaporation have been observed by neutron scattering measurements .…”

Section: Resultssupporting

confidence: 64%

Hysteresis Critical Point of Nitrogen in Porous Glass: Occurrence of Sample Spanning Transition in Capillary Condensation

Morishige

2009

Langmuir

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To examine the mechanisms for capillary condensation and for capillary evaporation in porous glass, we measured the hysteresis critical points and desorption scanning curves of nitrogen in four kinds of porous glasses with different pore sizes (Vycor, CPG75A, CPG120A, and CPG170A). The shapes of the hysteresis loop in the adsorption isotherm of nitrogen for the Vycor and the CPG75A changed with temperature, whereas those for the CPG120A and the CPG170A remained almost unchanged with temperature. The hysteresis critical points for the Vycor and the CPG75A fell on the common line observed previously for ordered mesoporous silicas. On the other hand, the hysteresis critical points for the CPG120A and the CPG170A deviated appreciably from the common line. This strongly suggests that capillary evaporation of nitrogen in the interconnected and disordered pores of both the Vycor and the CPG75A follows a cavitation process at least in the vicinity of their hysteresis critical temperatures in the same way as that in the cagelike pores of the ordered silicas, whereas the hysteresis critical points in the CPG120A and the CPG170A have origin different from that in the cagelike pores. The desorption scanning curves for the CPG75A indicated the nonindependence of the porous domains. On the other hand, for both the CPG120A and the CPG170A, we obtained the scanning curves that are expected from the independent domain theory. All these results suggest that sample spanning transitions in capillary condensation and evaporation take place inside the interconnected pores of both the CPG120A and the CPG170A.

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Section: Resultssupporting

confidence: 64%

Hysteresis Critical Point of Nitrogen in Porous Glass: Occurrence of Sample Spanning Transition in Capillary Condensation

Morishige

2009

Langmuir

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“…In contrast to MS and DFT work, we do not consider interactions between adsorbate molecules and the solid. Instead, we reconstruct a digitized periodic model of Gelsil 50 with a size of 100 3 nm 3 and then calculate the statistical average of the spatial nitrogen distribution with a recently implemented evolutionary minimization technique by use of three material desriptors gained from SANS and adsorption experiments: the fraction of liquid-free pore space φ, two-point correlation S 2 ( r ), and distribution Ψ corr ( D ) of the diameter D of the liquid-free pore space. Ψ corr ( D ) is derived from the pore size distribution Ψ( D ) of the empty pore system and is adapted to experimental data.…”

Section: Introductionmentioning

confidence: 99%

“…We briefly discuss the reason for choosing φ, S 2 ( r ), Ψ( D ), and Ψ corr ( D ) as descriptors. Before reconstructing Gelsil 50, we successfully tested the evolutionary method and the calculation of descriptors for 3D reconstructs by applying these algorithms to computer-generated random ensembles of penetrating equally sized spheres (RESs) . RESs are suitable test media because, in this case, analytical expressions are known for S 2 ( r ), the distribution p ( r ) of the chord length (CLD) in pores, and the lineal path function L ( r ).…”

Section: Introductionmentioning

confidence: 99%

Nitrogen Distribution at 77.7 K in Mesoporous Gelsil 50 Generated via Evolutionary Minimization with Statistical Descriptors Derived from Adsorption and In Situ SANS

2007

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Digitized periodic material models of size 100(3) nm3 of mesoporous xerogel Gelsil 50 are reconstructed by use of an evolutionary minimization technique, with two-point probability S2(r) and volume-based pore size distribution Psi(D) as a hybrid target function. S2(r) and Psi(D) are derived from small-angle neutron scattering (SANS) and adsorption data, respectively. The nitrogen distribution in Gelsil 50 is characterized in the multilayer adsorption and capillary-condensation regimes by S2(r) and statistical parameters obtained from in situ SANS data. The fraction of liquid-free pore space varphi is calculated from nitrogen adsorption data, and the distribution Psicorr(D) of the diameter of the liquid-free pore space at certain relative pressures is derived from Psi(D). The evolutionary algorithm is also used to generate the spatial nitrogen distribution by means of the descriptors varphi, S2(r), and Psicorr(D). The morphological parameters obtained from the reconstructs are compared to the respective SANS results.

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“…Subsequently, Yeong, Torquato and Manwart [105][106][107] proved that the SA method can approximate the evaluation function. Eschricht et al [108] improved the SA method and the accuracy of the reconstructed model. After building digital cores with the SA method, Zhao and Yao [109] suggested that good evaluation parameters can improve the reconstruction accuracy and consequently proposed an improved method to speed up the model optimization process.…”

Section: Status Of 3d Digital Core Technology Stochastic Reconstructimentioning

confidence: 99%

Challenges and Prospects of Digital Core-Reconstruction Research

et al. 2019

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The simulation of various rock properties based on three-dimensional digital cores plays an increasingly important role in oil and gas exploration and development. The accuracy of 3D digital core reconstruction is important for determining rock properties. In this paper, existing 3D digital core-reconstruction methods are divided into two categories: 3D digital cores based on physical experiments and 3D digital core stochastic reconstructions based on two-dimensional (2D) slices. Additionally, 2D slice-based digital core stochastic reconstruction techniques are classified into four types: a stochastic reconstruction method based on 2D slice mathematical-feature statistical constraints, a stochastic reconstruction method based on statistical constraints that are related to 2D slice morphological characteristics, a physics process-based stochastic reconstruction method, and a hybrid stochastic reconstruction method. The progress related to these various stochastic reconstruction methods, the characteristics of constructed 3D digital cores, and the potential of these methods are analysed and discussed in detail. Finally, reasonable prospects are presented based on the current state of this research area. Currently, studies on digital core reconstruction, especially for the 3D digital core stochastic reconstruction method based on 2D slices, are still very rough, and much room for improvement remains. In particular, we emphasize the importance of evaluating functions, multiscale 3D digital cores, multicomponent 3D digital cores, and disciplinary intersection methods in the 3D construction of digital cores. These four directions should provide focus, alongside challenges, for this research area in the future. This review provides important insights into 3D digital core reconstruction.

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Knowledge-based reconstruction of random porous media

Cited by 26 publications

References 48 publications

Hysteresis Critical Point of Nitrogen in Porous Glass: Occurrence of Sample Spanning Transition in Capillary Condensation

Hysteresis Critical Point of Nitrogen in Porous Glass: Occurrence of Sample Spanning Transition in Capillary Condensation

Nitrogen Distribution at 77.7 K in Mesoporous Gelsil 50 Generated via Evolutionary Minimization with Statistical Descriptors Derived from Adsorption and In Situ SANS

Challenges and Prospects of Digital Core-Reconstruction Research

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