Prediction of imbibition in unconsolidated granular materials

Gladkikh, Mikhail; Bryant, Steven L.

doi:10.1016/j.jcis.2005.03.029

Cited by 59 publications

(40 citation statements)

References 57 publications

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“…The consequence of this coarsening of the pore space is that sub-pore phenomena cannot be simulated directly; however, network modeling allows flow to be modeled over orders-of-magnitude larger physical domains. Network model simulations have been used to study processes such as imbibition (Fenwick and Blunt 1998;Gladkikh and Bryant 2005), drainage Fenwick and Blunt 1998), single phase and relative permeability computation (Bryant and Blunt 1992;Oren and Bakke 2002), the effects of pore structure on relative permeability (Bryant and Johnson 2003;Al-Kharusi and Blunt 2008), capillary pressure behavior (Fatt 1956;Silin and Patzek 2006;Al-Kharusi and Blunt 2008), residence time distributions (Thompson and Fogler 1997), dispersion (Sahimi et al 1986), measurement of interfacial area and fluid phase distribution (Dalla et al 2002), relationships between non-wetting phase distributions and pore geometry (Hilpert et al 2000), non-Newtonian flow in packed beds (Balhoff and Thompson 2004), and with continuum models in a multiscale framework (Balhoff et al 2007(Balhoff et al , 2008.…”

Section: Introductionmentioning

confidence: 99%

Effect of Network Structure on Characterization and Flow Modeling Using X-ray Micro-Tomography Images of Granular and Fibrous Porous Media

2011

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Image-based network modeling has become a powerful tool for modeling transport in real materials that have been imaged using X-ray computed micro-tomography (XCT) or other three-dimensional imaging techniques. Network generation is an essential part of image-based network modeling, but little quantitative work has been done to understand the influence of different network structures on modeling. We use XCT images of three different porous materials (disordered packings of spheres, sand, and cylinders) to create a series of four networks for each material. Despite originating from the same data, the networks can be made to vary over two orders of magnitude in pore density, which in turn affects network properties such as pore-size distribution and pore connectivity. Despite the orders-of-magnitude difference in pore density, single-phase permeability predictions remain remarkably consistent for a given material, even for the simplest throat conductance formulas. Detailed explanations for this beneficial attribute are given in the article; in general, it is a consequence of using physically representative network models. The capillary pressure curve generated from quasi-static drainage is more sensitive to network structure than permeability. However, using the capillary pressure curve to extract pore-size distributions gives reasonably consistent results even though the networks vary significantly. These results provide encouraging evidence that robust network modeling algorithms are not overly sensitive to the specific structure of the underlying physically representative network, which is important given the variety image-based network-generation strategies that have been developed in recent years. 123 364 P. Bhattad et al.

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Section: Introductionmentioning

confidence: 99%

Effect of Network Structure on Characterization and Flow Modeling Using X-ray Micro-Tomography Images of Granular and Fibrous Porous Media

2011

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“…The approach presented in this paper is based on the previous research [4][5][6][7][8][9][10][11][12][13][14]. We simulate physical processes at the pore scale in simple but physically representative model rocks.…”

Section: Pore Scale Modelmentioning

confidence: 99%

“…The approach is applied to compute capillary pressure curves [10][11][12], absolute and relative permeabilities [4,[8][9][10], electrical resistivity [6,10], acoustic velocities [4,7], and NMR transverse relaxation spectrum [4]. Corresponding algorithms have been described in the previous publications.…”

Section: Pore Scale Modelmentioning

confidence: 99%

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Applications of 2D-NMR maps and geometric pore scale modeling for petrophysical evaluation of a gas well

2008

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This paper presents the petrophysical evaluation of a gas reservoir from the Gulf of San Jorge basin, Argentina, by means of two-dimensional nuclear magnetic resonance (2D-NMR) maps and a geometric pore scale modeling of the NMR response of the wetting phase. The reservoir contains gas and irreducible water saturation at the top with a sharp transition into the water zone. The well has been logged with conventional tools such as gamma ray, neutron, density, and also the MRExplorer SM (MREX SM ) to acquire the NMR data for determining the irreducible water saturation. Data from special core analysis, scanning electron microscopy, thin sections, and capillary pressure tests have also been acquired. The core-log evaluation shows a very good agreement between the laboratory and log field data, especially in terms of irreducible water saturation, porosity, and permeability, which was also modeled using the Timur-Coates equation for purposes of field delivery. The 2D-NMR maps as T1 vs. T2 apparent (T2 app ) and diffusivity vs. T2 intrinsic (T2 int ) from both hydrocarbon and water zone lead to characterize the clay-bound and capillary-bound water and the under-called porosity due to the low hydrogen index of the gas. We use a pore scale model to quantify the effect of pendular water on T2 distribution.The methodology is based on constructing physically representative model rocks numerically, which allows precise geometric description of pore space. Unlike many other approaches to pore-level modeling, this approach introduces no adjustable parameters and can be used to produce quantitative, a priori predictions of the rock macroscopic behavior.

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“…A variety of models, such as capillary models [1][2][3][4] , process-based models [5][6][7] , random packing models [8][9][10][11] , pore-scale network models [12][13][14][15][16] , and statistical models [17][18][19][20][21][22][23] , have been proposed to characterize the porous structure and its physical, mechanical and chemical responses. Roughly, these models can be classified into reconstruction models and non-reconstruction models.…”

Section: Introductionmentioning

confidence: 99%

A statistical model for porous structure of rocks

Yang

Song

et al. 2008

Sci. China Ser. E-Technol. Sci.

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The geometric features and the distribution properties of pores in rocks were investigated by means of CT scanning tests of sandstones. The centroidal coordinates of pores, the statistic characterristics of pore distance, quantity, size and their probability density functions were formulated in this paper. The Monte Carlo method and the random number generating algorithm were employed to generate two series of random numbers with the desired statistic characteristics and probability density functions upon which the random distribution of pore position, distance and quantity were determined. A three-dimensional porous structural model of sandstone was constructed based on the FLAC 3D program and the information of the pore position and distribution that the series of random numbers defined. On the basis of modelling, the Brazil split tests of rock discs were carried out to examine the stress distribution, the pattern of element failure and the inosculation of failed elements. The simulation indicated that the proposed model was consistent with the realistic porous structure of rock in terms of their statistic properties of pores and geometric similarity. The built-up model disclosed the influence of pores on the stress distribution, failure mode of material elements and the inosculation of failed elements.porous structure, statistical model, random numbers, reconstruction, rocks

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Prediction of imbibition in unconsolidated granular materials

Cited by 59 publications

References 57 publications

Effect of Network Structure on Characterization and Flow Modeling Using X-ray Micro-Tomography Images of Granular and Fibrous Porous Media

Effect of Network Structure on Characterization and Flow Modeling Using X-ray Micro-Tomography Images of Granular and Fibrous Porous Media

Applications of 2D-NMR maps and geometric pore scale modeling for petrophysical evaluation of a gas well

A statistical model for porous structure of rocks

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