A stochastic permeability model for the shale-gas systems

Naraghi, Morteza Elahi; Javadpour, Farzam

doi:10.1016/j.coal.2015.02.004

Cited by 174 publications

(98 citation statements)

References 26 publications

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“…(Naraghi and Javadpour, 2015;Peng et al, 2015;Guo et al, 2015). During the shale gas exploitation, there is a change in the gas pressure and shale pore structure, which causing the gas permeability change.…”

Section: Influence Factors Of Gas Permeability Changementioning

confidence: 99%

Insights on the gas permeability change in porous shale

Chen

Xu³

et al. 2017

Adv. Geo-Energ. Res.

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Due to abundant nanoscale pores developed in shale, gas flow in shale presents a complex dynamic process. This paper summarized the effects from effective stress increase, shale matrix shrinkage, gas slippage and Knudsen diffusion on the gas permeability change in shale during shale gas recovery. With the reduce in gas pressure, effective stress increase leads to the decline of the permeability in an exponential form; the permeability increases due to the shale matrix shrinkage induced by gas desorption; appearances of gas slippage and Knudsen diffusion cause an additional increase in the gas permeability particularly in small pores at low pressures. In addition, some reported models evaluating the shale permeability were reviewed preliminarily. Models considering these four effects may be potentially effective to evaluate the gas permeability change in shale.

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Section: Influence Factors Of Gas Permeability Changementioning

confidence: 99%

Insights on the gas permeability change in porous shale

Chen

Xu³

et al. 2017

Adv. Geo-Energ. Res.

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“…In order to conduct quantitative comparisons with other results, we build a 2D model to keep the parameters (sample size, gas type, porosity, TOC, Temperature, pressure and pore size et al) consistent with Naraghi and Javadpour's work [16], as shown in Table 10.…”

Section: Quantitative Comparisons With Other Resultsmentioning

confidence: 99%

“…have noticed the big differences between kerogen and inorganic matrix in shale and treat them separately [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…A real shale is of intensely strong heterogeneity and anisotropy, calculating the permeability by a single pore size will bring errors and the pore-size distribution should be taken into consideration. Naraghi and Javadpour [16] put the pore-size distribution measured by the 3D experiment into a 2D model by stochastic classification method based on statistics, and applied ExpectationMaximization algorithm (EM) to separate two distributions of pores in organic matter and inorganic matrix, then discussed the impact of total organic content (TOC) and the pore-size distribution on AP. After that, Wang et al [17] employed EM algorithm to divide the pore size distribution and applied generalized lattice Boltzmann model (GLBM) to solve a 2D problem to analyze the effect of organic and inorganic matter distribution, TOC and surface diffusion on permeability.…”

Section: Introductionmentioning

confidence: 99%

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A 3D coupled model of organic matter and inorganic matrix for calculating the permeability of shale

Cao

Lin

Jiang

et al. 2017

Fuel

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h i g h l i g h t sA new 3D model coupling organic kerogen and inorganic matrix was developed. The sensitivity analysis on the apparent permeability of shale matrix were operated. Ignoring the contribution of kerogen to the permeability of shale will bring great error in most cases. When the TOC is less than 11% and the pressure is greater than 0.1 Mpa, the effect of kerogen distribution is negligible. a r t i c l e i n f o t r a c tMicrostructure-based quantitative computation of the shale permeability is challenging due to the presence of organic matter with nanoporous features. In this study, a new three-dimensional (3D) coupled model is proposed to investigate the micro-scale permeability of organic-rich-shale matrix. The coupled model consists of two subdomains, organic matter and inorganic matrix. They are described by the pore network model (PNM) and continuum model to capture the non-Darcy and Darcy flow, respectively, and the gas flow in the two subdomains are coupled by finite element mortars (Mortar). The convergence error, grid discretization and parallel scheme are also investigated to get the optimal computing parameters for this model. Then, the effects of the total organic content (TOC), kerogen distribution and poresize distribution on the apparent permeability (AP) of shale matrix are studied using the coupled model with computer-generated PNMs. And on the basis of FIB-SEM images, a Longmaxi shale sample from the Sichuan Basin, China is introduced to build a real shale model. Results show that AP is more sensitive to TOC and pore-size distribution than kerogen distribution. Additionally, in order to analyze the necessity of 3D model, a comparison of 3D and two-dimensional (2D) model is made and the error of 2D model is pointed out. The 3D couple model affords a foundation for further upscaling of shale permeability to REV scale and reservoir scale.

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“…The classical K-C equation has inherent limitations in estimating permeability because of with the porous media made up of capillary tubes is assumed to have fixed crosssectional area (Yu and Cheng, 2002;Xu and Yu, 2008;Cai et al, 2010;Chalmers et al, 2012;Asmussen et al, 2015;Naraghi and Javadpour, 2015;Mollajan et al, 2016;Verma and Pitchumani, 2017). Thus, improved methods based on the modified K-C equation were proposed considering tortuosity term in a more robust manner (Peng et al, 2015), irreducible (or connate) water saturation with capillary trapping (Izadi et Fig.…”

Section: Pore Type and Hfusmentioning

confidence: 99%

Applications of digital core analysis and hydraulic flow units in petrophysical characterization

Chen

Zhou

2017

Adv. Geo-Energ. Res.

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Conventional petrophysical characterizations are often based on direct laboratory measurements. Although they provide accurate results, such measurements are time-consuming and limited by instrument and environment. What's more, in the georesource energy industry, availability and cuttings of core plugs are difficult. Because of these reasons, virtual digital core technology is of increasing interest due to its capability of characterizing rock samples without physical cores and experiments. Virtual digital core technology, also known as digital rock physics, is used to discover, understand and model relationships between material, fluid composition, rock microstructure and macro equivalent physical properties. Based on actual geological conditions, modern mathematical methods and imaging technology, the digital model of the core or porous media is created to carry out physical field numerical simulation. In this review, the methods for constructing digital porous media are introduced first, then the characterization of thin rock cross section and the capillary pressure curve using scanning electron microscope image under mixed wetting are presented. Finally, we summarize the hydraulic flow unit methods in petrophysical classification.Keywords: Digital core, porous media, petrophysical characterization, thin section, mercury injection capillary pressure.Citation: Chen, X., Zhou, Y. Applications of digital core analysis and hydraulic flow units in petrophysical characterization. Adv.

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A stochastic permeability model for the shale-gas systems

Cited by 174 publications

References 26 publications

Insights on the gas permeability change in porous shale

Insights on the gas permeability change in porous shale

A 3D coupled model of organic matter and inorganic matrix for calculating the permeability of shale

Applications of digital core analysis and hydraulic flow units in petrophysical characterization

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