Comparison of NMR simulations of porous media derived from analytical and voxelized representations

Jin, Guodong; Torres‐Verdín, Carlos; Toumelin, Emmanuel

doi:10.1016/j.jmr.2009.07.021

Cited by 35 publications

(9 citation statements)

References 26 publications

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“…If more than four of the original voxels were in the pore ͑grain͒ space, the new voxel is assigned to be pore ͑grain͒; if the breakdown of the original voxels is four grain and four pore, then the new voxel is assigned randomly. Note that this is slightly different than the procedure used by Jin et al ͑2009͒; they assign the four-four case to the grain space, which leads to decreasing porosity as the resolution is degraded. The five models then vary from the original 400 3 with resolution of 2.8 m to 25 3 with resolution of 44.8 m. Four downscaled cubes from the original 400 3 cube ͑subset 3 in Figure 1a͒ are shown in Figure 7.…”

Section: Image Resolutionmentioning

confidence: 99%

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Pore-scale modeling of electrical and fluid transport in Berea sandstone

et al. 2010

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The purpose of this paper is to test how well numerical calculations can predict transport properties of porous permeable rock, given its 3D digital microtomography ͑CT͒ image. For this study, a Berea 500 sandstone sample is used, whose CT images have been obtained with resolution of 2.8 m. Porosity, electrical conductivity, permeability, and surface area are calculated from the CT image and compared with laboratory-measured values. For transport properties ͑electrical conductivity, permeability͒, a finite-difference scheme is adopted. The calculated and measured properties compare quite well. Electrical transport in Berea 500 sandstone is complicated by the presence of surface conduction in the electric double layer at the grain-electrolyte boundary. A three-phase conductivity model is proposed to compute surface conduction on the rock CT image. Effects of image resolution and computation sample size on the accuracy of numerical predictions are also investigated. Reducing resolution ͑i.e., increasing the voxel dimensions͒ decreases the calculated values of electrical conductivity and hydraulic permeability. Increasing computation sample volume gives a better match between laboratory measurements and numerical results. Large sample provides a better representation of the rock.

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Section: Image Resolutionmentioning

confidence: 99%

“…Recent authors have devoted considerable attention to treating surface conduction at the pore scale ͑Devarajan et al, Jin et al, 2009;Motealleh et al, 2007͒. In these papers, shaly sands are modeled with surface-conductive clay coating the grains. To represent shale, they assume the grains to be comprised entirely of conductive clays.…”

Section: Introductionmentioning

confidence: 99%

Pore-scale modeling of electrical and fluid transport in Berea sandstone

et al. 2010

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“…However, our pore-scale simulation, which assume voxelized grain boundary, shows a fracture T 2 peak at 274 msec ( Figure 1a and Table 1). This difference comes from the discretization of the pore space in the micro-CT images, which overestimates the grain-fluid interface surface area by about 1.5 times (Jin et al, 2009). Equation (5) can be modified for a "surface-area-corrected" coefficient of 1.5 given by…”

Section: Diffusional Coupling Effectmentioning

confidence: 99%

Quantifying the Impact of Natural Fractures and Pore Structure on NMR Measurements in Multiple-Porosity Systems

Heidari

2014

All Days

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This paper quantitatively evaluates the hierarchical structure of multiple-porosity systems, including natural micro-fractures, inter-granular, and intra-granular pores, using nuclear magnetic resonance (NMR) measurements. Conventional well logs cannot distinguish between different pore structures. NMR measurements, although counted among the most reliable methods to measure formation porosity, has been conventionally considered as insensitive to the existence of natural fractures. Thus, the impact of natural micro-fractures on NMR measurement has rarely been investigated.We simulated NMR response in porous media using a random-walk algorithm. We randomly distributed and oriented natural micro-fractures in various porous rock matrices. We then quantified the sensitivity of NMR T 2 (spin-spin relaxation time) distribution to the presence of natural fractures within (a) three-dimensional (3D) Micro-Computed Tomography (micro-CT) images of carbonate and sandstone rock samples and (b) synthetic organic shale matrices.Results from synthetic rock samples showed that NMR T 2 distribution can be significantly affected by aperture, concentration, and shape (e.g., needle-like or planar shape) of natural fractures, as well as size and connectivity of inter-granular pores. We also quantified the impact of diffusional coupling effect between the micro-fractures and inter-granular pores on NMR T 2 distribution. Applications of this research include reliable reservoir characterization in challenging multiple-porosity systems such as naturally fractured carbonates and organic-rich source rocks, where (a) conventional well logs cannot reliably characterize the complex pore structure and (b) interpretation techniques for unconventional logs such as NMR are not fully developed.

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“…However, in digital domains the surface area, S, does not converge to the true value when resolution increases, in contrast with the behaviour of pore volume, and the methods for treating the surfaces in numerical simulations are critical. Different approaches have been suggested to meet this difficulty [13,14].…”

Section: Introductionmentioning

confidence: 99%

Numerical simulations of NMR relaxation in chalk using local Robin boundary conditions

Ögren

Jha

Dobberschütz

et al. 2019

Journal of Magnetic Resonance

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The interpretation of nuclear magnetic resonance (NMR) data is of interest in a number of fields. InÖgren [Eur. Phys. J. B (2014) 87: 255] local boundary conditions for random walk simulations of NMR relaxation in digital domains were presented. Here, we have applied those boundary conditions to large, three-dimensional (3D) porous media samples. We compared the random walk results with known solutions and then applied them to highly structured 3D domains, from images derived using synchrotron radiation CT scanning of North Sea chalk samples. As expected, there were systematic errors caused by digitalization of the pore surfaces so we quantified those errors, and by using linear local boundary conditions, we were able to significantly improve the output. We also present a technique for treating numerical data prior to input into the ESPRIT algorithm for retrieving Laplace components of time series from NMR data (commonly called T -inversion).

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Comparison of NMR simulations of porous media derived from analytical and voxelized representations

Cited by 35 publications

References 26 publications

Pore-scale modeling of electrical and fluid transport in Berea sandstone

Pore-scale modeling of electrical and fluid transport in Berea sandstone

Quantifying the Impact of Natural Fractures and Pore Structure on NMR Measurements in Multiple-Porosity Systems

Numerical simulations of NMR relaxation in chalk using local Robin boundary conditions

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