Correlation of Geomechanical and Dynamic Elastic Properties with the P-Wave Velocity of Lower Gondwana Coal Measure Rocks of India

Rahman, Tabish; Sarkar, Kripamoy; Singh, Ashok Kumar

doi:10.1061/(asce)gm.1943-5622.0001828

Cited by 16 publications

(5 citation statements)

References 23 publications

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“…When the space is not enough for particles to expand, thermal stress damages the connection between the particles and weakens the cementation degree. New cracks are generated around the particles 20 , and the damage caused by thermal stress is irreversible. Particles cooled by water shrink rapidly and produce microcracks again, which causes the looser arrangement of particles and increased voids and hinders the propagation of P-waves in granite.…”

Section: Evaluation Index Of the Geothermal Exploitation Efficiencymentioning

confidence: 99%

Experimental study on evaluation of density, P-wave velocity, thermal conductivity, and thermal diffusion coefficient of granite after thermal treatments by using PCA

Wu,

Huang,

et al. 2024

Sci Rep

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Temperature significantly influences the physical parameters of granite, resulting in variations in the rock's thermal conductivity. In order to examine the impact of changes in multiple physical parameters of granite at different temperatures on the thermal conductivity of rocks, Principal Component Analysis (PCA) was employed to determine the correlation between granite at different temperatures and various physical parameters, including density (ρ), P-wave velocity (P), thermal conductivity (KT), and thermal diffusion coefficient (KD). Utilizing the linear contribution rate, a single indicator 'y' was derived to comprehensively represent the thermal conductivity of rocks. Research findings indicate that within the temperature range of 150–450 °C, the 'y'-value is relatively high, signifying favorable thermal conductivity of the rock. Notably, longitudinal wave velocity demonstrates higher sensitivity to temperature changes compared to other physical parameters.

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Section: Evaluation Index Of the Geothermal Exploitation Efficiencymentioning

confidence: 99%

Experimental study on evaluation of density, P-wave velocity, thermal conductivity, and thermal diffusion coefficient of granite after thermal treatments by using PCA

Wu,

Huang,

et al. 2024

Sci Rep

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“…Teymen and Menguc ( 2020 ) used 93 samples of different rock types and suggested a common regression equation. Rahman et al ( 2020 ) used Lower Gondwana sandstone and shale rocks and suggested separate regression equations for the 2 rock types, and recommended that each rock type follow a characteristic regression curve. The regression equations obtained by different researchers used in this study have been plotted and compared in Fig.…”

Section: Previous Studiesmentioning

confidence: 99%

“…Many researchers have proposed a general regression for multiple types of rocks (Kahraman 2001 ; Karakus et al 2005 ; Sharma and Singh 2008 ; Kilic and Teymen 2008 ; Sarkar et al 2012 ; Karakul and Ulusay 2013 ; Teymen and Menguc 2020 etc. ), whereas some researchers have proposed regression equations for single rock types (Tugrul and Zarif 1999 ; Yasar and Erdogan 2004 ; Chary et al 2006 ; Vasconcelos et al 2007 ; Minaeian and Ahangari 2011 ; Rahman et al 2020 etc.). In this paper, an attempt has been made to propose a generalised regression equation based on the lithology and evaluate the statistical acceptance of regression equations proposed for particular rock types.…”

Section: Introductionmentioning

confidence: 99%

Lithological Control on the Estimation of Uniaxial Compressive Strength by the P-Wave Velocity Using Supervised and Unsupervised Learning

Rahman

Sarkar

2021

Rock Mech Rock Eng

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Uniaxial compressive strength (UCS) is the most fundamental physico–mechanical parameter used for any rock mass classification in geotechnical and geological engineering. However, determining UCS is a very tough, expensive, time consuming and destructive method and requires experienced workers. On the other hand, P-wave velocity ( V P ) determination is cheap, precise, non-destructive and easy. There are many established relationships between UCS and V P but mostly are low in range or proposed for multiple rock types of different origin. In this paper, the correlation of UCS with V P has been assessed based on the rocks' lithology. The methodology used in this analysis was centred on the previous studies database, lithology-based data disintegration and data integration to establish lithology based simple regression (SR) equations. A total of 37 previous studies databases were processed, and 12 characteristic regression equations have been determined based on the lithology. The lithological control was also determined using the principal component analysis (PCA), which categorised the data into diverse rock types. Artificial neural network (ANN) has been used as a robust predictive tool to estimate the UCS using the V P and rock type information.

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“…The analysis and research on the mechanical characteristics of coal measure rocks have focused on exploring the strength and failure modes of coal measure rocks and the influence of the occurrence environment on the mechanical properties of rocks. Many researchers have tested the mechanical parameters of coal measure rocks, including the uniaxial compressive strength and tensile strength and investigated the relationships between the mechanical parameters of coal measure rocks, and between their mechanical parameters and acoustic wave velocity. − By comparing the uniaxial compressive strength and point load strength of coal measure rocks, disparities in the mechanical properties stemming from diverse lithologies have been revealed, and the failure modes and damage mechanisms within these rocks have been elucidated. , Furthermore, the influence of the occurrence environment (water, temperature, and other factors) on rock mechanical properties has attracted scholarly attention, and it was found that water can significantly diminish the strength parameters of coal measure rocks by absorbing and affecting the clay minerals in the rocks. Additionally, the seepage pressure substantially impacts the failure pattern and extent of damage within coal measure rocks under the action of water seepage. − Moreover, the increase in temperature exerts a discernible effect on the coal measure rock strength, presenting an initial increase followed by a subsequent decrease.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Rock Mechanical Characteristics and Establishment of an Evaluation System for Coal Measures in the Longtan Formation, Guizhou Province

Liang,

Cao,

Sang

et al. 2024

ACS Omega

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The mechanical properties of coal measure rocks and their evaluation significantly impact the process and efficacy of coal measure exploration and development. This study focuses on the Guizhou Longtan Formation coal measure. The mechanical and fracturing characteristics of coal measure rock samples are analyzed via well coring, geophysical logging, and indoor experiments. Additionally, predictive models for rock mechanical parameters are developed, and an evaluation system for the Longtan Formation coal measure rock mass is established. The findings are as follows: (1) Coal measure rocks in Guizhou's Longtan Formation exhibit a relatively low elastic modulus and tensile strength, but a substantial variation in compressive strength. The triaxial compressive strength, elastic modulus, and residual strength increase nonlinearly with increasing confining pressure. As the confining pressure increases, the failure mode of the mudstone and siltstone transitions from primarily splitting failure to shearing failure. (2) Strong correlations are calculated between logging parameters and rock mechanical parameters and are used to construct three regression prediction models, yielding an average prediction accuracy of approximately 85% for rock mechanical properties. (3) Considering the rock mechanical properties, rock mass structure and stratigraphic characteristics, and the occurrence environment related to the characteristics of rock mass affecting coal measure gas development, eight evaluation indices are selected. The analytic hierarchy process−entropy weighting method is used to determine the weights of the comprehensive evaluation indices, and a coal measure rock mass evaluation system is established by utilizing gray clustering analysis. The evaluation results categorize the mudstone group (mudstone and silty mudstone) as Classes III−IV, the fine sandstone group as Classes I and II, and the siltstone group (muddy siltstone and siltstone) as Classes II and III. A comparative analysis with fuzzy comprehensive evaluation results and extenics theory evaluation results demonstrated a high level of consistency. These findings benefit coal measure rock mechanics classification and quantitative research on rock mechanics properties, providing a solid foundation for efficient coal measure gas exploration and development.

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Correlation of Geomechanical and Dynamic Elastic Properties with the P-Wave Velocity of Lower Gondwana Coal Measure Rocks of India

Cited by 16 publications

References 23 publications

Experimental study on evaluation of density, P-wave velocity, thermal conductivity, and thermal diffusion coefficient of granite after thermal treatments by using PCA

Experimental study on evaluation of density, P-wave velocity, thermal conductivity, and thermal diffusion coefficient of granite after thermal treatments by using PCA

Lithological Control on the Estimation of Uniaxial Compressive Strength by the P-Wave Velocity Using Supervised and Unsupervised Learning

Analysis of Rock Mechanical Characteristics and Establishment of an Evaluation System for Coal Measures in the Longtan Formation, Guizhou Province

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