Analysis of heterogeneity and permeability anisotropy in carbonate rock samples using digital rock physics

Sun, Hongjuan; Vega, Sandra; Tao, Guo

doi:10.1016/j.petrol.2017.06.002

Cited by 92 publications

(33 citation statements)

References 39 publications

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“…The micro-CT data in this study encountered this problem. Histogram equalization (Gonzalez & Woods, 2002;Russ, 2006) has been the preferred choice to enhance image contrast (Sun et al, 2017). However, it has many disadvantages.…”

Section: Appendix B: Modified Automatic Contrast Adjustment Algorithmmentioning

confidence: 99%

Rock Characterization Using Gray‐Level Co‐Occurrence Matrix: An Objective Perspective of Digital Rock Statistics

Singh

Armstrong

Regenauer‐Lieb

et al. 2019

Water Resources Research

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Modeling flow and transport in porous media using pore‐scale modeling is reliant on rock properties derived from digital rock images using segmentation techniques. These digital rock images obtained using computed tomography incorporate the variation in the intensity of phases depending on the attenuation of X‐rays. A standard technique is the segmentation of tomographic images based on user‐selected grayscale thresholding, allowing the identification of different phases. This threshold is subjective based on the operator and results in loss of essential information about the grayscale variation after segmentation. This paper implements the gray‐level co‐occurrence matrix (GLCM) incorporating the full range of grayscale information. The GLCM captures the relative occurrence of grayscale values in a spatial map. These maps show visually connected/disconnected populations of different phases such as pore space, quartz grains, minerals, and other features. We show that each rock has its own GLCM signature depending on the variations in gray‐level intensities. Several statistical measures are calculated: (1) GLCM contrast describing local variation in the gray‐level intensities, (2) GLCM angular second moment, describing the rock homogeneity; (3) GLCM mean, describing weighted average of the probability of occurrence of features based on their location on the GLCM map; and (4) GLCM correlation, measuring the linear dependencies of grayscale values and the degree of (an) isotropy in the micro–computed tomographic images of each of the rock types. The GLCM method provides a pathway to alleviate user biases and allow automation of micro–computed tomography analyses.

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Section: Appendix B: Modified Automatic Contrast Adjustment Algorithmmentioning

confidence: 99%

Rock Characterization Using Gray‐Level Co‐Occurrence Matrix: An Objective Perspective of Digital Rock Statistics

Singh

Armstrong

Regenauer‐Lieb

et al. 2019

Water Resources Research

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“…Actually, the fractured rock permeability, calculated by Palabos code, is in dimensionless lattice (or image voxel) units and marked as K l hereafter. This value is easily converted to physical units K r by Equation (9) [45]:…”

Section: Fracture Permeability Calculationmentioning

confidence: 99%

“…Since the computation of permeability using the LBM has been evaluated as a reliable approximation to the real permeability, which have been validated by comparing simulated results with experimental data in reference [45]. In our work we also denote the permeability calculated by the LBM as K LB , i.e., K f = K LB .…”

Section: Fracture Permeability Calculationmentioning

confidence: 99%

Quantitative Study of the Geometrical and Hydraulic Characteristics of a Single Rock Fracture

Jiang

Min

2019

Energies

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Three-dimensional images of fractured rocks can be acquired by an X-ray micro-CT scanning technique, which allows researchers to investigate the ‘true’ inner void structure of a natural fracture without destroying the core. The 3D fractures in images can be characterised by measuring morphological properties on both fracture apertures and its trend surface, like the medial surface, that reveals the undulation of fracture planes. In a previous paper, we have proposed a novel method to generate fracture models stochastically. Based on a large number of such fracture models, in this work a modified factor was proposed for improving the performance of the cubic law by incorporating the flow-dominant characteristics, including two parameters (aperture roughness and spatial correlation length) for fracture apertures and two (surface undulation coefficient and spatial correlation length) for fracture trend-surface. We assess and validate the modified cubic law by applying it to natural fractures in images that have varying apertures and extremely bended trend-surfaces, with the permeabilities calculated by a Lattice Boltzmann Method as ‘ground truths’.

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“…On the other hand, 3D μ-CT images do not allow the visualization of pores smaller than the resolution limit, whereas experimental measurements are capable of measuring nanometer-scale pores. These differences in general are minor in homogeneous systems such as siliciclastic rocks (Appoloni et al, 2007), but are significant in heterogeneous rocks such as carbonates (Sun et al, 2017) and volcanic rocks. Total porosities obtained from Scale 1 images in μ-CT are lower than experimental values (Table 2).…”

Section: Fig 7 3d Representations Of Porosity Variation In Verticalmentioning

confidence: 99%

Pore System Quantification and Characterization in Volcanic Rocks: A Case Study From the Lower Cretaceous Serra Geral Group, Paraná Basin, Southern Brazil

Alv

Lima

Waichel

et al. 2019

Journal of Petroleum Geology

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Studies of pore systems in volcanic rocks are of increasing importance due to these rocks' potential as reservoirs for hydrocarbons. For this paper, samples of basaltic pahoehoe, rubbly pahoehoe and acidic lava from the Lower Cretaceous Serra Geral Group (Paraná Basin, southern Brazil) were analysed in order to quantify and characterize the constituent pore systems. The Serra Geral Group volcanics were erupted as part of the Paraná-Etendeka Igneous Province in the Early Cretaceous (Valanginian -Hauterivian). Analyses included experimental measurements by permo-porosimeter integrated with X-ray microtomography (μ-CT) image analysis of vertical and horizontal sample plugs. In addition petrographic analyses were carried out to characterize pore types in thin section. The experimental porosity values ranged from 0.11 to 13.08% and most permeability values were generally lower than 0.0004 mD. Values varied as much within flow zones as they did between them.Porosity and permeability values were not sufficient for the Serra Geral Group volcanics to be considered as a potential reservoir analogue. However the wide range in values was attributed to the processes which controlled the origin and development of the pore system. Primary pores observed included intracrystalline sieve, vesicular and interflow laminar types; secondary porosity, such as spongy, interclast and intra-matrix pore types, was related to dissolution and the precipitation of secondary minerals. The porosity values obtained by μ-CT (between <0.01 and 3.37%) were lower than those experimentally measured by permo-porosimeter. This was attributed to the presence of multi-scale pores in the volcanic rocks sampled, and also to limitations with image resolution. Even so, the use of μ-CT allowed the visualization of porosity variations and was useful in characterizing the pore system.The results presented in this paper demonstrate that the volcanic rocks in the Serra Geral Group have a heterogeneous pore system, similar to that in carbonate rocks.

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Analysis of heterogeneity and permeability anisotropy in carbonate rock samples using digital rock physics

Cited by 92 publications

References 39 publications

Rock Characterization Using Gray‐Level Co‐Occurrence Matrix: An Objective Perspective of Digital Rock Statistics

Rock Characterization Using Gray‐Level Co‐Occurrence Matrix: An Objective Perspective of Digital Rock Statistics

Quantitative Study of the Geometrical and Hydraulic Characteristics of a Single Rock Fracture

Pore System Quantification and Characterization in Volcanic Rocks: A Case Study From the Lower Cretaceous Serra Geral Group, Paraná Basin, Southern Brazil

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