AMS, ultrasonic P-wave velocity and rock strength analysis in quartzites devoid of mesoscopic foliations – implications for rock mechanics studies

Vishnu, C. S.; Mamtani, Manish A.; Basu, Arindam

doi:10.1016/j.tecto.2010.09.017

Cited by 29 publications

(9 citation statements)

References 69 publications

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“…As far as the modulus of elasticity (E t ) is concerned, the descending order of materials with respect to the mean E t value is quartz, marble, quartzite and metasandstone (Table 4); this indicates, as expected, that quartz is the stiffest material and thus more resistant to deformation. Similar UCS values have been reported in other studies for quartz [57], quartzite [58], marble and sandstone [59]. The results indicate that breakage follows a first order law i.e., the breakage rate S i is independent of time and the S i values can be determined from the slope of the straight lines.…”

Section: Uniaxial Compressive Strength and Modulus Of Elasticitysupporting

confidence: 89%

Correlation between Material Properties and Breakage Rate Parameters Determined from Grinding Tests

Petrakis

Komnitsas

2018

Applied Sciences

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Abstract:The present study investigates four materials, namely quartz, marble, quartzite and metasandstone and aims to establish correlations, with the use of simple and multiple regression analysis, between their properties and breakage rate parameters. The material properties considered in this study derived from the application of destructive and non-destructive tests and include P-wave velocity (V p ), Schmidt rebound value (R L ), uniaxial compressive strength (UCS) and tangent modulus of elasticity (E t ), while the breakage rate parameters determined from batch grinding tests, include breakage rate S i , maximum breakage rate S m , α T and α, and optimum particle size x m . The results indicate that the properties of all materials examined show very good correlation and can be used to predict S i or α T . Furthermore, parameter α is well correlated with V p , R L and E t using inverse exponential functions, while S m is strongly correlated with R L and UCS. Overall, it is deduced that multiple regression analysis involving two independent variables is a reliable approach and can be used to identify correlations between properties and breakage rate parameters for quartz, quartzite and metasandstone, which are silica rich materials. The only exception shown is the determination of x m for marble.

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Section: Uniaxial Compressive Strength and Modulus Of Elasticitysupporting

confidence: 89%

Correlation between Material Properties and Breakage Rate Parameters Determined from Grinding Tests

Petrakis

Komnitsas

2018

Applied Sciences

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“…Many researchers have used ultrasonic velocity index to predict rock strength by measurement of ultrasonic velocities in directions parallel and perpendicular to weakness planes of anisotropic rocks (Vasconcelos et al 2007;Sharma and Singh 2008;Vasconcelos et al 2008;Khaksar et al 2009;Moradian and Behnia 2009;Vishnu et al 2010;Kelessidis 2011, Rigopoulos et al 2011. Shalabi et al (2007) proved that the relationships from this test were weaker than the results obtained by Schmidt hammer and shore tests.…”

Section: Ucs Of Rocks: Measurement and Prediction Methodsmentioning

confidence: 96%

A Neuro-Genetic Network for Predicting Uniaxial Compressive Strength of Rocks

2012

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Uniaxial Compressive Strength (UCS) is considered as one of the most important parameters in designing rock structures. Determination of this parameter requires preparation of rock samples which is costly and time consuming. Moreover discrepancy of laboratory test results is often observed. To overcome the drawbacks of traditional method of UCS measurement, in this paper, predictive models based on neuro-genetic approach and multivariable regression analysis have been developed for predicting compressive strength of different type of rocks. Coefficient of determinatoin (R2) and the Mean Square Error (MSE) were calculated for comparison of the models' efficiency. It was observed that accuracy of the neuro-genetic model is significantly better than regression model. For the neuro-genetic and regression models, R2 and MSE were equal to 95.89 % and 0.0045 and 77.4 % and 1.61, respectively. According to sensitivity analysis for neurogenetic model, Schmidt rebound number is the most effective parameter in predicting UCS.

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“…The presence of fissures always has a significant impact on pulse wave transmission, and wave velocity increases parallel to the bedding planes, but decreases in the perpendicular direction [49][50][51]. In this paper, coal porosity φ was calculated using a weighing method described as follows:…”

Section: Correlations Between the Upvp Values And Porositymentioning

confidence: 99%

Investigation of the Velocities of Coals of Diverse Rank under Water- or Gas-Saturated Conditions for Application in Coalbed Methane Recovery

Zhai

Ranjith

et al. 2019

Geofluids

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Coalbed methane recovery enhanced by hydraulic or nonaqueous fracturing methods has been studied for decades, and it is of significance to evaluate fracturing results and scope for field applications. Monitoring variation in velocity is one way to explain fracturing effects. However, the existence of residual water or gas within cracks or pores may affect velocity measurements, and the correlation between velocity and inherent coal attributes (such as density and porosity) has not been studied comprehensively. In this paper, coal of different ranks (lignite, bituminite, and anthracite) was prepared under water and gas saturation to approximately simulate the residual water and gas in cracks under field applications. Correlations between the velocity and coal attributes were studied. For both water- and gas-saturated cores, the diverse velocity distributions were highly correlated to rank and saturation media. The longitudinal ultrasonic pulse velocity (UPVp) and transverse ultrasonic pulse velocity (UPVs) of different cores were distributed differently. For coal saturated with water or gas, the UPVp values of lignite, bituminite, and anthracite had positive linear correlations with the corresponding UPVs values. The discrete velocity ratio data were fit as negative linear correlations with UPVs, and different coals had different declining degrees, the difference of which might be attributed to the characteristics of structural cracks and the inherent properties of the coal, such as grain size and pore shape, which result in decreasing coal integrity and strength. Moreover, the difference in acoustic resistance between coal and fluids might have an inverse impact on the acoustic energy, and a larger difference might cause a large amount of energy to dissipate and finally cause the velocity to decrease. Under water and gas saturation conditions, the UPVp showed a positive linear correlation with density and a negative linear correlation with porosity. Finally, a potential field application was designed on the relations between the velocity and the elastic parameters to estimate fracturing effects by monitoring the petrophysical parameters of coal lithologies.

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AMS, ultrasonic P-wave velocity and rock strength analysis in quartzites devoid of mesoscopic foliations – implications for rock mechanics studies

Cited by 29 publications

References 69 publications

Correlation between Material Properties and Breakage Rate Parameters Determined from Grinding Tests

Correlation between Material Properties and Breakage Rate Parameters Determined from Grinding Tests

A Neuro-Genetic Network for Predicting Uniaxial Compressive Strength of Rocks

Investigation of the Velocities of Coals of Diverse Rank under Water- or Gas-Saturated Conditions for Application in Coalbed Methane Recovery

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