Reassessing the Joint Roughness Coefficient (JRC) Estimation Using Z 2

Yang, Zon-Yee; Lo, S. C.; Di, Charles

doi:10.1007/s006030170012

Cited by 183 publications

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“…In this study, numeric data from the 3D scan were used for more precise evaluation of JRC by the following corelation [29]:…”

Section: Calcite Fractured Core Samplesmentioning

confidence: 99%

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Assessment of CO2 Injection in Fractured Calcite Rock Clogged by Pseudomonas putida Biofilm

Sygouni¹

2018

Int. J. Green Technol.

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Abstract:The effect of a possible accidental carbon dioxide (CO2) leakage from deep geologic storage reservoirs on shallow subsurface sources of potable water is receiving considerable attention, because groundwater quality can be compromised. In this work, the effect of a gas CO2 leakage on the permeability of a fractured calcite rock bioclogged with Pseudomonas (P.) putida biofilm was investigated experimentally under ambient conditions. Initially, a batch experiment of P. putida inactivation in the presence and absence of calcite was performed. Subsequently, P. putida biofilm was developed in a fractured calcite rock from Mons, Belgium. The fractured rock permeability was measured before and after bioclogging, as well as after CO2 injection. The experimental results indicated that calcite can enhance P. putida growth, and that a P. putida biofilm formation can practically eliminate fractured rock permeability (99% reduction). However, sudden CO2 injection increased the permeability of the bioclogged fractured calcite rock, but only to a level substantially lower than that corresponding to the initial permeability of the clean fractured rock.

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“…In this study, numeric data from the 3D scan were used for more precise evaluation of JRC by the following corelation [29]:…”

Section: Calcite Fractured Core Samplesmentioning

confidence: 99%

“…where Z 2 is the root mean square of the first derivative of the long axis profile, which is expressed in a discrete form as follows [29,30]:…”

Section: Calcite Fractured Core Samplesmentioning

confidence: 99%

Assessment of CO2 Injection in Fractured Calcite Rock Clogged by Pseudomonas putida Biofilm

Sygouni¹

2018

Int. J. Green Technol.

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“…Lee et al (2011) made a comparison of JRC with surface angularity θ s at SIs of 0.2, 0.3, 0.5, 1.0, 2.0, and 5.0 mm but suggested empirical equations with a fixed optimum sampling interval. However, it has been stated by Yang et al (2001), Li et al (2016), Zheng and Qi (2016), and Liu et al (2017) that sampling intervals might shift the relationship between the JRC and roughness parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating the three-dimensional joint roughness coefficient value of rock fractures

2017

Bull Eng Geol Environ

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Measurement and estimation of the joint roughness coefficient (JRC) is a critical but also difficult challenge in the field of rock mechanics. Parameters for estimating JRC based on a profile derived from a fracture surface are generally twodimensional (2D), where a single or multiple straight profiles derived from a surface cannot reflect the roughness of the entire surface. It is therefore necessary to derive the threedimensional (3D) roughness parameters from the entire surface. In this article, a detailed review is made on 3D roughness parameters along with classification and discussion of their usability and limitations. Methods using Triangulated Irregular Network (TIN) and 3D wireframe to derive 3D roughness parameters are described. Thirty-eight sets of fresh rock blocks with fractures in the middle were prepared and tested in direct shear. Based on these, empirical equations for JRC estimation using 3D roughness parameters have been derived. Nine parameters (θ s , θ g , θ 2s , S sT , S sF , V an , Z sa , Z rms , and Z range ) are found to have close correlations with JRC and are capable of estimating JRC of rock fracture surfaces. Other parameters (Z ss , Z sk , V svi , V sci , S dr and S ts ) show no good correlations with JRC. The sampling interval has little influence when using volume and amplitude parameters (V an , Z sa , Z rms , and Z range ) for JRC estimation, while it influences to some extent when other parameters (θ s , θ g , θ 2s , S sT and S sF ) are used. For their easy calculation, the equations with amplitude parameters are recommended to facilitate rapid estimation of JRC in engineering practice.

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“…These three methods all have some drawbacks. Beer et al (2002), Yang et al (2001), and Milne (1990) have assessed or cast doubts on them, respectively.…”

Section: Introductionmentioning

confidence: 98%

Measurement of joint roughness coefficient by using profilograph and roughness ruler

2009

J. Earth Sci.

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Joint roughness coefficient (JRC) is the key parameter for the empirical estimation of joint shear strength by using the JRC-JCS (joint wall compressive strength) model. Because JRC has such characteristics as nonuniformity, anisotropy, and unhomogeneity, directional statistical measurement of JRC is the precondition for ensuring the reliability of the empirical estimation method. However, the directional statistical measurement of JRC is time-consuming. In order to present an ideal measurement method of JRC, new profilographs and roughness rulers were developed according to the properties of rock joint undulating shape based on the review of measurement methods of JRC. Operation methods of the profilographs and roughness rulers were also introduced. A case study shows that the instruments and operation methods produce an effective means for the statistical measurement of JRC.

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Reassessing the Joint Roughness Coefficient (JRC) Estimation Using Z 2

Cited by 183 publications

References 0 publications

Assessment of CO2 Injection in Fractured Calcite Rock Clogged by Pseudomonas putida Biofilm

Assessment of CO2 Injection in Fractured Calcite Rock Clogged by Pseudomonas putida Biofilm

Estimating the three-dimensional joint roughness coefficient value of rock fractures

Measurement of joint roughness coefficient by using profilograph and roughness ruler

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