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. 2024 Aug 1;9(32):34380–34396. doi: 10.1021/acsomega.4c01643

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

Yu Liang †, Liwen Cao ‡,*, Shuxun Sang §,*, Yudong Zhang ∥
PMCID: PMC11325512  PMID: 39157144

Abstract

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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.

1. Introduction

Coal measures serve as important hydrocarbon source rocks and reservoirs for coal measure gas (coalbed methane, tight sandstone gas, and shale gas, which coexist and can accumulate into valuable resources).1−3 Currently, the exploration and development of coal measure gas remain in their nascent stages, and Guizhou Province is emerging as a pivotal coal measure gas exploration and development reserve region within China. This province boasts substantial geological coalbed methane reserves amounting to 3.15 × 1012 m3, accompanied by a formidable 4.55 × 1012 m3 of Permian and Carboniferous coal measure shale gas resources, signifying the extensive developmental potential of coal measure gas.4−7 Within the process of coal measure gas exploration and development, understanding the mechanical and fracturing characteristics of coal measure rocks and establishing a comprehensive evaluation system of rock masses applicable to coal measures are conducive to selecting suitable construction programs and parameters in the process of coal measure gas development (reservoir fracturing and reforming as well as combined layer drainage and mining), which will foster the efficient development of coal measure gas.

Currently, the analysis and investigation of rock mechanical characteristics within domestic coal measures are predominantly concentrated in regions of North China and Northwest China, where coal mining and coal measure gas development are industrialized. Notable examples include the Qinshui and the Ordos Basin. These coal measures predominantly involve Jurassic-Cretaceous and Carboniferous-Diapiric strata. 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.8−10 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.11,12 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.13−16 Moreover, the increase in temperature exerts a discernible effect on the coal measure rock strength, presenting an initial increase followed by a subsequent decrease. Observations pertaining to the composition of the rock material and changes in the fine structure illustrate that augmented strength emanates from the thermal expansion of crystalline minerals, while the ongoing temperature rise leads to the decomposition of kaolinite and the thermal cracking of mineral grains, ultimately diminishing the rock strength.17−19 Guizhou’s coal measure gas resources are primarily located in western Guizhou. The main coal measure is the Permian Upper Longtan Formation, characterized by thin to medium-thick coal seams. Distinctively, the regional coal measure superposed reservoirs exhibit a unique composition, featuring coal seams interbedded with sandstones and mudstones, which markedly deviate from the coal measure superposed reservoirs identified in North China and Northeast China.20,21 The exploration and development of coal measure gas include coal measure gas resource evaluation and favorable area selection, geological surveying within target zones, and coal measure gas development (well drilling and completion, reservoir enhancement and renovation, and integrated layer drainage and extraction). The research efforts pertinent to Guizhou’s coal measure gas exploration and development have predominantly focused on aspects such as coal measure gas resource evaluation and geological selection within the block,22−26 reservoir characteristics and geochemical characteristics of coal measure gas well water,27−31 coal measure gas development process models, and production control factors associated with coal measure gas wells.20,32−34 However, the current rock mechanics and damage characterization for the varied lithologies found within Guizhou’s coal measures, as well as the establishment of a comprehensive evaluation system for coal measure rock masses, remain markedly inadequate, which is extremely mismatched with the development of coal measure gas in the region and is in urgent need of research and enrichment.

In light of these considerations, this study is focused on the Dahebian block in Guizhou. By means of on-site sampling and geophysical logging of parameter wells within the Dahebian block, the rock mechanics and failure characterization of the Longtan Formation coal measure rocks were performed, and a coal measure rock evaluation system was established by selecting eight key indices from the aspects of rock mechanics properties, rock structure and stratigraphic features, and the occurrence environment that affect the characteristics of the rock mass. A comprehensive evaluation of the coal measure rock masses was also carried out. The purpose of this research is to gain an in-depth understanding of the characteristics of rock mechanical properties and establish a comprehensive evaluation system tailored to superposed gas reservoirs in Guizhou (coal seams interbedded with sandstones and mudstones). This work not only lays the foundation for formulating a rock mechanical stratigraphic framework and stratigraphic model, but also promotes the in-depth study of the theory of coal measure rock mechanical stratigraphy and provides references and guidance for the high-efficiency exploration and development of coal measure gas.

2. Geological Background of the Study Area

The study area is situated in the northern sector of Zhongshan district, Liupanshui city, Guizhou Province, and is dominated by solifluction geomorphology, with the terrain generally high in the surrounding area and low in the central area. The core drilling hole, identified as well DH Can 1, is positioned within the confines of the designated block (Figure 1). The block ranges between the Dahebian coal mine in the south, the Wangjiazhai coal mine in the west, the Naluozhai coal mine-Lixin well 1 exploration area in the north, and the Lianshan well field in the east, spanning elevations ranging from 1640 to 2122 m. The research block is located on two wings of the Dahebian syncline; the syncline is a tectonic unit belonging to the northwest tectonic deformation area of Weining in the Liupanshui fault of the Qianbei uplift within the Yangtze River Land Mass, and the major tectonic line is NWW-SEE. Under the control of the tectonic effect, the internal folds of the syncline are not developed. The inclination angle of the strata in the two wings to the axial part increases gradually, the inclination angle of the strata in the east wing of the tilt is steeper, and the inclination angle of the strata in the west wing is slower. The research block exhibits a moderate tectonic complexity. Faults are developed in the block, and the prominent among these are seven water faults, F1–4, F2–1, F9, F10, F20, F21, F22, and F24. Within the southern reaches of the syncline, the majority of faults take the form of low-angle normal faults, often aligned along the NE direction, while the syncline’s northern region is typified by high-angle reverse faults, chiefly oriented in the NNW direction.

Figure 1.

Figure 1

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Geographic location of the Dahebian block and coal measures of the Longtan Formation.

The lithostratigraphy within the block includes the Emeishan Basalt Formation (P2β), the Longtan Formation (P3l), the Feixianguan Formation (T1f), the Yongningzhen Formation (T1yn), the Guanling Formation (T2g), and the Quaternary (Q), in order from oldest to youngest. The Longtan Formation is a coal measure that was deposited at the intersection of land and sea, with a thickness ranging from 200.70 to 257.28 m, hosting 14–29 coal seams with a cumulative thickness ranging from 23.94–28.77 m. Seven mineable coal seams of the Longtan Formation are in this area, with combined thicknesses ranging from 14.24 to 17.54 m (Zhao et al., 2022), and the coefficient of the coal content of the mineable coal seams is 6.90%. Due to its combination of lithological and coal-bearing characteristics, the Longtan Formation is segregated into three sections: P3l1, P3l2, and P3l3. With respect to the results of well drilling parameters, the depth of the Longtan Formation in the area ranges from 737–943 m. Lithologically, the Longtan Formation predominantly comprises siltstone, argillaceous siltstone, fine sandstone, mudstone, and silty mudstone. In addition, there are a few carbonaceous mudstones and breccia and there is bauxite mudstone at the bottom of the Longtan Formation. The coal measure superposed reservoir is characterized by the combination of interbedded coal seams, sandstone, and mudstone. The total coal resources of this block is 100006 × 104 t, with a potential coalbed methane reserve estimated at 122.62 × 108 m3, and a resource abundance of 2.43 × 108 m3/km2, and there is a demonstration project for the exploration and development of coalbed methane in the southeastern part of the research area in the Panzhou Tucheng syncline. The Dahebian syncline’s coalbed methane block has favorable resource attributes and an elevated recoverable coefficient. Therefore, it is favorable for coalbed methane development and has great potential for development.35

3. Analysis of Rock Mechanical Properties and Failure Characteristics

3.1. Sample Preparation and Experimental Design

3.1.1. Parametric Well Sampling, Logging, and Sample Preparation

Drilling and coring operations are centered at well DH Can 1 within the Dahebian block, Guizhou. The target layer is the Longtan Formation, and the drilling completion layer is the Emeishan Basalt Formation at 972.02 m. Wireline core drilling tools were used in all well construction with diamond bits selected as the drill heads. The drilling parameters were moderate pressure and high speed (4–8 kN drilling pressure, speed greater than 300–1300 r/min). According to the “Quality Standard of Drill for Coal Geology Exploration” (MT/T 1042–2007),36 the initial wellbore quality was good. A total of 244 cores were extracted from the parameter well, each exhibiting a diameter of 60 mm and an impressive core recovery rate of 98% (reaching 99% within the coal measure strata). The cores originating from the Yongningzhen Formation, Feixianguan Formation, and Longtan Formation, obtained through on-site sampling, were cataloged following the criteria delineated by the “Description Standard of Core of Coal-bearing Series” (DZ/T 0002–2017)37 and the “Specifications for Coal, Peatexploration” (DZ/T 0215–2002).38 In accordance with the oil and gas industry standard “Quality Specification for Original Petroleum Logging Information” (SY/T 5132–2012),39 the enterprise standard “The Operation Regulation of Witeline Logging of Coalbed Methan Well” (Q/CUCBM 0401–2002)40 and geological design imperatives, field logging of the coalbed methane at the parameter well was carried out to obtain the natural potential, natural Gamma, and 10 other logging curves.

Since the main lithologies of the coal measures in the Longtan Formation are mudstone, fine sandstone, siltstone, silty mudstone, and argillaceous siltstone, core samples of these main lithologies of the Longtan Formation obtained from on-site drilling were used. A core drilling rig was used to drill rock samples with a diameter of 25 mm. Subsequently, these samples were cut into standard core samples measuring φ25 mm × 50 mm with a core cutter to prepare the rock for compression testing, and there were 104 core samples in total. Considering that certain core samples such as mudstone and silty mudstone used in the experiments have high mud contents and are prone to softening upon exposure to water, the success rate of the coal measure rock sample preparation was not high, so φ60 mm × 30 mm test specimens were prepared for the Brazilian splitting experiments. There were 52 samples in total.

3.1.2. Experimental Design

The experiments included tests of the basic physical parameters of the rock (including density tests and sonic velocity tests) and tests of its mechanical properties (uniaxial compression tests, triaxial compression tests, and Brazilian splitting tests). The core samples and experimental apparatuses are shown in Figure 2. The density of each Longtan Formation core sample was obtained by testing, and a KON-NM-4A nonmetal ultrasonic testing analyzer was employed to conduct rock acoustic wave velocity experiments. An MTS816 electrohydraulic servo pressure testing machine was used to carry out the uniaxial compression tests, for which 18 groups of core samples were selected, with 3 samples in each group. The experimental loading rate was 0.005 mm/s. The triaxial compression tests were performed with an MTS815 electrohydraulic servo triaxial pressure testing machine, and 10 groups of core samples were selected for these tests, with 5 samples in each group. The confining pressure of each group of cores in these experiments was set to 5, 10, 15, 20, and 25 MPa, and the experimental loading rate was set at 0.0015 mm/s. The splitting tests were performed with an MTS816 electrohydraulic servo pressure testing machine. Fourteen groups of core samples were selected for the splitting tests, with 3 samples in each group, and the experimental loading rate was 50 N/s.

Figure 2.

Figure 2

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Core samples and experimental devices.

3.2. Analysis of Basic Physical Parameters of Coal Measure Rocks

Density tests and acoustic wave velocity tests were carried out on the cores of each of the main lithologies of the Longtan Formation obtained from drilling, and Figure 3 shows the density and longitudinal wave velocities across the different rock types. Among the prevalent rock types within the Longtan Formation, mudstone and silty mudstone exhibit P-wave velocities ranging from 1.96 to 3.80 km/s and 2.66 to 3.61 km/s, respectively, with average values of 3.02 and 3.03 km/s, respectively. In the case of siltstone and argillaceous siltstone, their respective P-wave velocities range from 2.56 to 3.90 and 2.75 to 3.90 km/s, with average values of 3.31 and 3.30 km/s, respectively. The fine sandstone, characterized by a P-wave velocity range of 3.07 to 3.70 km/s, has an average P-wave of 3.35 km/s. The average densities of the mudstone and silty mudstone are 2.75 and 2.79 g/cm3, respectively, while the average densities of the siltstone and argillaceous siltstone are 2.85 and 2.84 g/cm3, respectively. The average density of fine sandstone is 2.86 g/cm3. The inherent presence of muddy bands within the mudstone strata, coupled with the greater development of pores and fissures compared to the sandstone strata, leads to the more discrete density and P-wave velocity of the mudstone, while the fine sandstone is dense and intact, so the changes in the P-wave velocity and density of the fine sandstone samples are small. The average P-wave velocity and density results of the mudstone, siltstone, and fine sandstone increasing in that order. The density and P-wave velocity of the silty mudstone and argillaceous siltstone vary between those of the mudstone and siltstone.

Figure 3.

Figure 3

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Rock basic physical parameters: (a) P-wave velocity of various rocks in the Longtan Formation and (b) densities of various rocks in the Longtan Formation.

3.3. Analysis of Rock Failure Characteristics

3.3.1. Experimental Features

Uniaxial compression tests, triaxial compression tests, and Brazilian splitting tests were carried out on the selected rock samples to determine the rock failure modes (Figures 4–6). When subjected to uniaxial compressive stress, all rock types experience brittle failure, exhibiting a four-stage process of deformation and failure: (1) With the progressive increase in axial stress, the structural planes or microcracks within the rock gradually close, leading to nonlinear deformation. (2) As the loading continues, the rocks undergo elastic deformation followed by a plastic deformation stage, resulting in the emergence of new microcracks. (3) The microfractures persistently develop until the samples reach their maximum load-bearing capacity. (4) After the load-bearing capacity of the rock samples reaches the peak, the cracks develop and penetrate to form macrofracture surfaces, and the load-bearing capacity of the samples decreases rapidly; however, the load-bearing capacity does not reach zero. At this time, rocks retain a certain level of loading-bearing capability.

Figure 4.

Figure 4

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Typical failure modes and stress–strain curves of rocks in a uniaxial compression experiment: (a) No. 61 mudstone, (b) No. 6 argillaceous siltstone, and (c) No. 35 fine sandstone.

Figure 6.

Figure 6

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Typical failure modes of rocks in the Brazilian splitting experiment: (a) No. 61 mudstone, (b) No. 74 silty mudstone, (c) No. 38 argillaceous siltstone, (d) No. 65 siltstone, and (e) No. 60 fine sandstone.

Figure 5.

Figure 5

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Typical failure modes of rock in a triaxial compression experiment: (a) No. 14 mudstone, (b) No. 39 argillaceous siltstone, and (c) No. 60 fine sandstone.

When subjected to uniaxial compressive stress, multiple cracks oriented parallel to the axial direction from the mudstone and silty mudstone. Secondary cracks develop alongside the primary cracks, primarily resulting in splitting failure. In argillaceous siltstone and siltstone, most cracks run parallel to the axial direction, with a minority of samples displaying obliquely intersecting cracks relative to the main cracks. The failure mode is predominantly splitting failure with a small number of samples demonstrating mixed splitting-shear failure. For the fine sandstone, cracks manifest either parallel or oblique to the axial direction, yielding a spectrum of failure modes, including splitting failure and mixed splitting-shear failure. Under the influence of triaxial stress, the mudstone and silty mudstone exhibit mixed splitting-shear failure or shear failure at a low confining pressure (5 MPa). As the confining pressure increases, the failure mode transitions toward shear failure, encompassing double-shear plane failure and single-shear plane failure. The failure modes of the siltstone and argillaceous siltstone evolve from splitting-shear compound failure to shear failure (double-shear plane failure or single-shear plane failure) under a low confining pressure (5 MPa). The fine sandstone predominantly experiences shear failure (double-shear plane failure or single-shear plane failure). The results from the splitting tests revealed that when subjected to radial tensile stress, the failure mode of the tested rock is mainly failure via the formation of a vertical tensile crack. Owing to a rock’s heterogeneity (including mineral composition, structural plane development, etc.), the growth of tensile cracks exhibits a certain degree of deflection. Furthermore, some samples display secondary cracks near the termini of the tensile cracks.

3.3.2. Numerical Simulation Features

To better analyze the propagation of microscopic cracks within the studied rocks and the impact of confining pressure on the rock damage characteristics, a Particle Flow Code simulation was employed to simulate the compression process of the coal measure rocks. The simulation adopts a parallel bond model, which establishes an elastic interaction between the pieces and transmits both forces and moments between them and is widely used in rock mechanics.

Following model establishment, parameter calibration (Table 1), and loading simulation,41 an analysis of crack propagation was conducted. The stress–strain curves and failure modes of the rocks obtained by numerical simulation of uniaxial and triaxial compression experiments are in good agreement with the actual experimental results (Figures 4, 7, and 8). However, the simulated stress–strain curves tend to deviate from the measured curves due to the absence of pores and cracks in the simulated rock samples. This omission results in the simulated curves shifting to the left of the experimental stress–strain curves, as the simulated samples lack the pore-fissure compaction that actual rock undergoes during compression. The observation of the process of crack development during rock compression reveals that cracks are typically initiated from the sample ends. Prior to failure, the number of cracks is generally limited, but this number sharply increases once the stress reaches its peak value. Throughout the loading process, microcracks originating from both the ends and middle of the samples persistently expand, interact, and eventually coalesce to form macroscopic cracks. A crack-type analysis (distinguishing tensile cracks in blue and shear cracks in red, as depicted in Figure 7) illustrates that tensile cracks persistently propagate as loading progresses and that the shear cracks begin to manifest and propagate once the rocks enter the phase of unstable fracture development. Within regions with densely distributed tensile cracks, shear cracks and tensile cracks interconnect and expand, ultimately culminating in the formation of macroscopic fracture surfaces. The presence of a confining pressure hinders the opening of vertical fractures, increasing the susceptibility of rocks to shear failure under the combined influence of the confining pressure and axial stress. The observed patterns of crack propagation and failure indicate that rock deformation and failure characteristics are complex and influenced by factors such as confining pressure, lithology, and structural planes.42

Table 1. Discrete Element Model Parameter Calibration.
parameter No. 61 mudstone No. 6 argillaceous siltstone No. 35 fine sandstone
density (g/cm3) 2.64 2.64 2.96
radius ratio 1.66 1.66 1.66
friction coefficient 0.5 0.5 0.5
stiffness ratio 2.0 3.0 2.5
Young’s modulus (GPa) 6.0 3.0 5.5
bond stiffness ratio 2.0 3.0 2.5
bond Young’s modulus (GPa) 6.0 3.0 5.5
bond tensile strength (MPa) 36.0 18.5 25.0
bond cohesion strength (MPa) 42 22 29
friction angle (deg) 30 30 30
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Figure 7.

Figure 7

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Rock failure modes and cracks development in uniaxial compression experiment (numerical simulation): (I) initial stage, (II) elastic stage, (III) peak stage, and (IV) postfailure stage.

Figure 8.

Figure 8

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Rock failure modes and crack development in the triaxial compression experiment (numerical simulation).

3.4. Analysis of Rock Mechanical Characteristics

The rock mechanical parameters of the Longtan Formation coal measuring samples are detailed in Table 2. The uniaxial compressive strength of the Longtan Formation rocks is 17.17–186.05 MPa, the elastic modulus is 3.06–17.92 GPa, the tensile strength is 0.71–3.99 MPa, the cohesion is 1.73–45.53 MPa, and the internal friction angle is 18.19–51.00°. Evidently, the rock mechanical parameters exhibit considerable variability, a consequence attributed to divergences in the development degree of structural planes and the mineral composition inherent to rock formations. The mechanical parameters of rocks, even those of the same lithology, exhibit substantial variances. If the average values of uniaxial compressive strength, elastic modulus, and tensile strength of all kinds of rocks represent the mechanical properties across common rock types, a distinct order emerges. Specifically, the uniaxial compressive strength increases in the sequence of mudstone, silty mudstone, argillaceous siltstone, fine sandstone, and siltstone. The elastic modulus follows a similar pattern, with ascending values corresponding to silty mudstone, mudstone, argillaceous siltstone, fine sandstone, and siltstone. Moreover, the tensile strength progressively increases following the order of mudstone, silty mudstone, siltstone, argillaceous siltstone, and fine sandstone. Generally, the mechanical attributes of sandstone tend to outperform those of mudstone. Concurrently, the mechanical properties of silty mudstone and argillaceous siltstone typically fall between those of mudstone and fine sandstone. Compared with the coal measures of the Longtan Formation in eastern Yunnan and the Shanxi Formation in Huainan (Figure 9),43,44 the coal measure rocks of the Longtan Formation in Guizhou have rock mechanical characteristics such as a wide range of uniaxial compressive strengths and relatively low elastic moduli and tensile strengths.

Table 2. Mechanical Parameters of Coal Measure Rocksa.

lithology uniaxial compressive strength (MPa) elastic modulus (GPa) tensile strength (MPa) cohesion (MPa) internal friction angle (deg)
mudstone 17.17–103.21 4.17–10.91 0.71–2.08 32.18–36.33 18.77–47.23
57.3 7.63 1.64 34.25 33
silty mudstone 18.17–90.11 3.06–11.29 0.88–3.99 1.73–3.40 42.00–51.00
62.46 7.6 2.43 2.56 46.5
argillaceous siltstone 26.70–174.69 4.21–17.92 2.30–2.88 24.35–45.53 18.19–33.99
82.2 9.54 2.63 34.94 26.09
siltstone 64.18–186.05 7.35–17.69 1.96–3.26 16.39 37.77
137.61 13.62 2.6
fine sandstone 35.98–127.11 7.12–12.65 1.87–3.46 7.57–36.74 21.90–50.28
84.74 10.26 2.93 20.41 34.78
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a

Below the horizontal line is the average value of each parameter.

Figure 9.

Figure 9

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Comparison of mechanical parameters of multicoal measures: (a) comparison of uniaxial compressive strength of multicoal measures, (b) comparison of elastic modulus of multicoal measures, and (c) comparison of tensile strength of multicoal measures.

Figure 10 presents the typical triaxial compression curves of the samples. The deformation and failure progression of the coal measure rocks are also distinctly characterized by four phases: pore and fissure compaction, elastic deformation-microfracture development, unstable fracture development, and postdestruction stage. After reaching the peak stress, most of the rock samples experience rapid stress drop, mainly exhibiting brittle failure. A small part of the sandstone has shown ductility under the control of confining pressure and failure strength. Figure 11 illustrates the changes in the triaxial compression test parameters induced by varying the confining pressure. As the confining pressure increases from 5 to 25 MPa, the triaxial compressive strength increases by 46.70%, 451.47%, 62.67%, 90.00%, and 189.93% for mudstone, silty mudstone, argillaceous siltstone, siltstone, and fine sandstone, respectively. Correspondingly, the elastic modulus increases by 10.22%, 250.17%, 26.05%, 44.22%, and 217.05%, respectively. The triaxial compressive strength, elastic modulus, and residual strength collectively exhibit a nonlinear upsurge.

Figure 10.

Figure 10

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Typical triaxial compression test curves: (a) No. 68 mudstone triaxial compression experimental curves, (b) No. 31 fine sandstone triaxial compression experimental curve, and (c) No. 65 siltstone triaxial compression experimental curve.

Figure 11.

Figure 11

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Relationship between triaxial experimental parameters and confining pressure: (a) relationship between triaxial compressive strength and confining pressure, (b) relationship between elastic modulus and confining pressure, and (c) relationship between residual strength and confining pressure.

4. Establishment of Logging-Based Prediction Models for Rock Mechanics

4.1. Acquisition and Analysis of the Logging Parameters of Coal Measures

In the exploration and development of coal measure gas, the geophysical logging curves can be used to identify the specific coal rock types and evaluate the coal measure gas content in the reservoir. According to the conductivity, radioactivity, acoustic characteristics, and other characteristics of coal measures, the mud content, mineral composition, and porosity of coal measure rocks can be obtained through various logging methods,45,46 which can significantly contribute to the comprehensive understanding of the mechanical properties of coal measure rocks. Based on the on-site geophysical logging results, the geophysical well logging curves and six fundamental logging parameters of the Longtan Formation were obtained (Table 3, Figure 12).

Table 3. Well Logging Parameters of the Longtan Formation Coal Measure the Rocks in the Dahebian Block.

lithology natural potential (MV) natural gamma (API) acoustic time difference (μs/m) compensated neutron (%) compensated density (g/cm3) deep lateral resistivity (Ω·m)
mudstone –19.01–3.99 45.37–225.86 214.00–492.99 8.14–89.03 1.75–2.72 8.40–322.00
silty mudstone –8.46–3.97 47.93–182.37 194.00–461.00 11.77–45.47 1.93–2.71 10.00–231.00
siltstone –12.74–3.44 52.83–158.12 202.00–425.00 11.98–27.02 2.04–2.64 11.00–276.99
argillaceous siltstone –11.27–2.42 50.13–197.47 209.00–457.98 12.28–44.50 1.94–2.67 11.88–266.99
fine sandstone –23.46–0.61 30.13–124.40 199.00–457.00 7.85–25.61 2.09–2.78 13.00–1993.00
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Figure 12.

Figure 12

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Logging curves of the Longtan Formation in the Dahebian block.

The natural potential of coal measure rocks is −23.46–3.99 MV, the natural gamma is 30.13–225.86 API, the acoustic time difference is 194.00–492.99 μm/s, the compensation neutron is 7.85–89.03%, the compensation density is 1.75–2.78 g/cm3, and the deep lateral resistivity is 8.40–1993.00 Ω·m. Analyzing these logging parameters reveals characteristics associated with different rock types within the Longtan Formation. According to the main range of the various logging parameters, fine sandstone and siltstone typically exhibit medium to low natural potential and natural gamma values, along with medium to low acoustic time differences and compensated neutron values. These rocks also display a substantial variation range in compensated density with medium to high resistivity. In contrast, the mudstone has the characteristics of medium to high natural potential and natural gamma values, coupled with a high acoustic time difference, high compensated neutron, low compensated density, and low resistivity. The logging parameters of argillaceous siltstone and silty mudstone generally fall between those of sandstone and mudstone. Compared with those of the sandstone section, the natural potential curve of the mudstone section is convex to the right side; the fluctuation range of the compensated neutron and acoustic time difference curve is larger, and the resistivity curve is smaller. These differences can be attributed to the acoustic time difference, compensated neutrons, and resistivity logging curves, which are related to the development of pore fractures and rock compactness within each rock type. The natural potential is influenced by the shale content.47,48

4.2. Correlation Analysis of Logging Parameters and Rock Mechanical Parameters

The acquisition of the logging and rock mechanical parameters was accomplished through on-site logging and indoor mechanical experiments. Among the various logging parameters, three critical mechanical parameters (uniaxial compressive strength, elastic modulus, and tensile strength) were chosen as dependent variables and six logging parameters (compensated density, acoustic time difference, compensated neutron, natural gamma, deep lateral resistivity, and natural potential) were chosen as independent variables. Correlation analysis was conducted to discern the relationships between these logging parameters and rock mechanical parameters, focusing on those with at least moderately strong correlations. The results of this analysis are presented in Table 4. The acoustic time difference, compensated neutron, and deep lateral resistivity exhibit good correlations with the uniaxial compressive strength and elastic modulus. Moreover, the acoustic time difference, natural gamma ray, and deep lateral resistivity are correlated to the tensile strength. In summary, the acoustic time difference and deep lateral resistivity are significant factors that are correlated with the rock mechanical parameters (Figure 13), and can better reflect the mechanical properties of coal measure rocks.

Table 4. Correlation between Well Logging Parameters and Rock Mechanical Parameters.

  uniaxial compressive strength (MPa)
 elastic modulus (GPa)
 tensile strength (MPa)
R acoustic time difference (μs/m) compensated neutron (%) deep lateral resistivity (Ω·m) acoustic time difference (μs/m) compensated neutron (%) deep lateral resistivity (Ω·m) acoustic time difference (μs/m) natural gamma (API) deep lateral resistivity (Ω·m)
index –0.7051 –0.6372 0.6686 –0.7723 –0.7817 0.6641 –0.8168 0.5963 0.5007
polynomial –0.7213 –0.6390 0.6699 –0.7733 –0.7895 0.6833 –0.8447 0.6080 0.6365
power –0.6943 –0.6284 0.6755 –0.7693 –0.7755 0.6681 –0.8344 0.5663 0.5863
linear –0.7176 –0.6303 0.6699 –0.7680 –0.7672 0.6740 –0.7881 0.6024 0.5236
logarithm –0.7103 –0.6355 0.6708 –0.7717 –0.7796 0.6549 –0.8147 0.5808 0.5970
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Figure 13.

Figure 13

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Relationship between well logging parameters and rock mechanics parameters: (a) acoustic time difference–uniaxial compressive strength, (b) acoustic time difference–elastic modulus, (c) acoustic time difference–tensile strength, (d) deep lateral resistivity–uniaxial compressive strength, (e) deep lateral resistivity–elastic modulus, and (f) deep lateral resistivity–tensile strength.

4.3. Well Logging-Based Model Establishment and Verification

Based on the correlation findings between logging parameters and rock mechanical parameters, as discussed in section 3.2, and considering the relationships among rock mechanical parameters and various logging parameters, multiple linear regression models for each rock mechanical parameter were developed and binary linear regression models for rock mechanical parameters were obtained (Table 5). Overall, the binary regression models exhibit greater correlation coefficients (R) than did the univariate regression models. Moreover, all P values were less than 0.05, indicating that the correlation of the established model is significant. To validate the accuracy of the established logging-based models, they were applied to three different types of lithologies, as detailed in Table 6. The relative errors of the rock mechanical parameters are 1.59∼35.65%, the average relative error of the uniaxial compressive strength is 17.18%, the average relative error of the elastic modulus is 13.21%, the relative error of the tensile strength is 14.86%, the average relative error of all the considered mechanical parameters is 15.08%, and the accuracy rate of the prediction results of the rock mechanical parameters is approximately 85%.

Table 5. Multiple Linear Regression Models.

multiple regression linear models formula |R| |P|
uniaxial compressive strength y, acoustic time difference x1, and compensated neutron x2 y = −0.248x1 – 1.065x2 + 157.281 0.7313 0.0218
elastic modulus y, acoustic time difference x1, and compensated neutron x2 y = −0.019x1 – 0.215x2 + 18.284 0.8210 0.0037
tensile strength y, acoustic time difference x1, and deep lateral resistivity x2 y = −0.006x1 + 0.001x2 + 3.888 0.7954 0.0301
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Table 6. Well Logging Models Verification.

      uniaxial compressive strength (MPa)
 elastic modulus (GPa)
 tensile strength (MPa)
No. lithology depth (m) exptl value predictive value relative error (%) exptl value predictive value relative error (%) exptl value predictive value relative error (%)
78-1 argillaceous siltstone 910.69 75.35 76.55 1.59 10.57 9.96 5.80 2.51 2.44 2.93
78-2   ∼912.18 99.01 78.98 20.24 12.51 10.28 17.86 2.3 2.48 7.82
78-3     77.77 81.16 4.36 9.69 10.58 9.15 2.73 2.51 3.37
60-1 fine sandstone 880.23 110.6 71.17 35.65 11.61 9.44 18.66 3.26 2.26 30.66
60-2   ∼881.23 97.57 71.55 26.67 10.74 9.35 12.94 3.12 2.31 22.28
60-3     111.98 72.81 34.98 12.13 9.33 23.11 2.98 2.38 20.26
74-1 siltstone mudstone 907.9 66.2 69.1 4.38 8.79 9.15 4.09 2.31 2.2 4.91
74-2   ∼910.69 55.96 69.48 24.15 7.56 9.13 20.81 3.03 2.22 26.56
74-3     68.21 69.99 2.61 8.66 9.22 6.49 2.63 2.24 14.94
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5. Establishment and Comprehensive Evaluation of a Coal Measure Rock Mass Evaluation System Based on Gray Clustering Analysis

5.1. Establishment of a Comprehensive Evaluation System for Coal Measure Rock Mass Based on Gray Clustering Analysis

A comprehensive evaluation of rock masses is a comprehensive reflection and summary of the physical and mechanical properties of engineering rock masses within the region. This process aids researchers in gaining an intuitive understanding of the properties of engineering rock masses, enabling them to swiftly devise practical engineering solutions. Presently, research findings related to the comprehensive evaluation of rock masses have played a significant role in various engineering activities including tunnel construction and coal mining. By analyzing and evaluating the deformation and failure characteristics of the surrounding rock in tunnels and coal mine passageways, appropriate support measures have been identified to manage surrounding rock deformations.49−51 The primary steps involved in constructing a comprehensive evaluation system for rock masses include selecting indicators that influence rock mass properties based on project characteristics, categorizing these indicators, determining their respective weights, and selecting the appropriate comprehensive evaluation methodology. Common comprehensive evaluation systems for engineering rock masses include the RMR, Q, and BQ classifications. These systems are characterized by easily obtainable indicators and straightforward methods. There are also modified RMR classifications, modified Q classifications, and other evaluation systems that are tailored to specific engineering and regional attributes.52,53 Additionally, mathematical and computer-based approaches, such as fuzzy mathematics and machine learning, are increasingly being employed in the comprehensive evaluation of rock masses. Researchers select pertinent index parameters to establish an evaluation system leveraging the strengths of mathematical and computer-based methods.54,55 These methods have proven effective in addressing complex multifactor and multilevel problems encountered in the comprehensive evaluation of rock masses.56 Given the distinct variations in rock mass properties, construction objectives, and construction environments across different ventures, the previously established comprehensive rock mass evaluation systems may not be universally applicable. This is especially true in regards to scientifically evaluating coal measures and selecting the design and construction methodology for coal measure gas extraction processes.

Gray system theory can consider various problems, such as incomplete and uncertain information encountered in projects and process project information with incomplete information systems. This method does not require a large amount of sample data and has a small computational workload.52 Currently, gray system theory has applications across various domains including ecology, management, transportation, and other fields. Given that coal measure rock masses have various factors influencing their properties, resulting in the difficult acquisition of their mechanical properties, and discrete parameters, the comprehensive evaluation of coal measure rock masses is often treated as a gray system. Gray clustering analysis, which fuses gray theory with clustering techniques, can be used for a comprehensive evaluation. Gray clustering analysis can determine the gray class of each clustering object in a gray system based on comprehensive evaluation indices and whitening weight functions. Considering that different comprehensive evaluation indices possess varying levels of significance and dimensions, the sample values of the different indices are quite different. To avoid the weak effect of some indices in clustering, gray fixed weight clustering analysis is used to evaluate the coal measure rock mass. Furthermore, a combination of the analytic hierarchy process (AHP) and entropy weight method (EWM) is adopted to determine composite weightings for these indices, avoiding subjectivity in weight assignments. The primary steps of conducting gray cluster analysis for coal measure rock masses include the selection of comprehensive evaluation indicators specific to coal measure rock masses, the delineation of gray categories for these indices, the establishment of composite clustering weights, the definition of whitening weight functions, the computation of clustering coefficients for clustering objects assigned to each gray category, and ultimately, the comprehensive evaluation of coal measure rock masses.

5.2. Comprehensive Evaluation Index Selection

The productivity of coal measure gas is intricately affected by factors such as geological characteristics, reservoir fracturing, drainage, and other factors. The principal geological control factors include the physical and mechanical attributes of coal measure reservoirs, the extent of pore and fissure development, the prevailing hydrogeological conditions, and the intricate geological structures.20,57,58 The influence of geological factors on the development of coal measure gas is comprehensively considered, and the common evaluation indices and methods of rock mass strength are summarized.59−61 A comprehensive evaluation of coal measure rock mass is carried out in terms of rock mechanical properties, rock mass structure and stratigraphic characteristics, and the occurrence environment of the rock mechanics stratigraphic unit. The uniaxial compressive strength, elastic modulus, tensile strength, integrity coefficient, block index, sedimentary thickness, hydrogeological influence coefficient, and structural influence coefficient were selected as comprehensive evaluation indices. The occurrence environment (hydrogeological conditions and geological structure) is described qualitatively. The hydrogeological influence coefficient is determined according to the occurrence environment and takes into account the type and amount of groundwater, aquifer characteristics, etc. The structural influence coefficient considers the degree of development of structural planes (faults, fissures, and weak interlayers, etc.). Through field investigation and access to geological data from across the block, quantitative evaluation criteria for each quantitative index and two qualitative indicators were established (Table 9). Fine sandstone, siltstone, and mudstone with different sampling depths were selected to obtain sample values of the clustering objects (Table 7).

Table 9. Comprehensive Evaluation Table.

parameter gray class evaluation result value range whitening wt function
uniaxial compressive strength (MPa) I excellent >150 f11[125,175,–,−]
II good 100–150 f21[75,125,–,175]
III medium 50–100 f31[37.5,75,–,125]
IV poor 25–50 f41[12.5,37.5,–,75]
V very poor 0–25 f51[−,–,12.5,37.5]
elastic modulus (GPa) I excellent >16 f12[14,18,–,−]
II good 12–16 f22[10,14,–,18]
III medium 8–12 f32[6,10,–,14]
IV poor 4–8 f42[2,6,–,10]
V very poor 0–4 f52[−,–,2,6]
tensile strength (MPa) I excellent >4 f13[3.5,4.5,–,−]
II good 3–4 f23[2.5,3.5,–,4.5]
III medium 2–3 f33[1.5,2.5,–,3.5]
IV poor 1–2 f43[0.5,1.5,–,2.5]
V very poor 0–1 f53[−,–,0.5,1.5]
integrity factor I excellent 0.8–1.0 f14[0.7,0.9,–,−]
II good 0.6–0.8 f24[0.5,0.7,–,0.9]
III medium 0.4–0.6 f34[0.3,0.5,–,0.7]
IV poor 0.2–0.4 f44[0.1,0.3,–,0.5]
V very poor 0–0.2 f54[−,–,0.1,0.3]
block index I excellent >30 f15[20,40,–,−]
II good 10–30 f25[6.5,20,–,40]
III medium 3–10 f35[2,6.5,–,20]
IV poor 1–3 f45[0.5,2,–,6.5]
V very poor 0–1 f55[−,–,0.5,2]
sediment thickness (m) I excellent >8 f16[7,9,–,−]
II good 6–8 f26[5,7,–,9]
III medium 4–6 f36[3,5,–,7]
IV poor 2–4 f46[1,3,–,5]
V very poor 0–2 f56[−,–,1,3]
hydrogeological influence coefficient I excellent 0–0.2 f17[−,–,0.1,0.3]
II good 0.2–0.4 f27[0.1,0.3,–,0.5]
III medium 0.4–0.6 f37[0.3,0.5,–,0.7]
IV poor 0.6–0.8 f47[0.5,0.7,–,0.9]
V very poor 0.8–1.0 f57[0.7,0.9,–,−]
structural influence coefficient I excellent 0–0.2 f18[−,–,0.1,0.3]
II good 0.2–0.4 f28[0.1,0.3,–,0.5]
III medium 0.4–0.6 f38[0.3,0.5,–,0.7]
IV poor 0.6–0.8 f48[0.5,0.7,–,0.9]
V very poor 0.8–1.0 f58[0.7,0.9,–,−]
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Table 7. Sample Values of the Clustering Objects.

No. depth (m) lithology uniaxial compressive strength (MPa) elastic modulus (GPa) tensile strength (MPa) integrity factor block index sediment thickness (m) hydrogeological influence coefficient structural influence coefficient
1 821.37–823.20 fine sandstone 53.37 9.55 2.78 0.67 29.73 1.83 0.20 0.40
2 850.74–855.48 argillaceous siltstone 94.06 10.93 2.57 0.63 8.68 4.74 0.20 0.30
3 855.48–860.45 fine sandstone 101.64 11.33 2.66 0.93 25.52 4.97 0.10 0.10
4 874.05–875.17 silty mudstone 53.75 5.90 1.84 0.58 7.98 1.12 0.10 0.20
5 880.23–881.23 fine sandstone 106.72 11.49 3.07 0.61 9.42 1.00 0.10 0.20
6 882.00–882.81 mudstone 46.34 7.50 1.94 0.66 9.65 0.81 0.10 0.70
7 893.60–897.70 siltstone 94.30 13.49 2.60 0.77 9.01 4.10 0 0.20
8 907.90–910.69 silty mudstone 63.46 8.34 2.66 0.64 7.41 2.79 0 0.20
9 910.69–912.18 argillaceous siltstone 84.05 10.93 2.47 0.72 7.48 1.49 0 0.20
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5.3. Combination Clustering Weight Determination

The indicator weight is determined by the subjective and objective combination weighting method. In the subjective weighting method, AHP is employed to determine the weight distribution of each indicator. The objective weighting method employs EWM for weight determination. For the comprehensive evaluation of the coal measure rock mass, there are m objects (i = 1, 2, ..., m), with m = 9, and n indicators (j = 1, 2, ..., n), with n = 8. The AHP method is utilized to ascertain the contribution degree of each indicator to the characteristics of the coal measure rock mass, and the comprehensive evaluation indicators are categorized into three layers: the target layer, the first-level indicator layer, and the secondary indicator layer, thus forming a multilevel evaluation system (Figure 14). The 1–9 scale method is employed to compare the rock mechanical property parameters, rock mass structure and stratigraphic characteristic parameters, occurrence environment characteristic parameters at the first-level indicator layer, as well as the index importance between each secondary indicator layer, and the judgment matrix is constructed. By calculating the maximum eigenvalue and CR value of each judgment matrix, following a consistency test, the matrix column vector is normalized. Subsequently, the row vectors are summed, and the weights of the AHP are obtained after normalization (Table 8):

5.3.

Figure 14.

Figure 14

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Multilevel evaluation system.

Table 8. Weight of Each Comprehensive Evaluation Index (AHP Method).

judgment matrix parameters wt
O–P   P1 P2 P3  
P1 1 1 3 0.43
P2 1 1 3 0.43
P3 1/3 1/3 1 0.14
P1–Q1   Q11 Q12 Q13  
Q11 1 2 3 0.54
Q12 1/2 1 2 0.30
Q13 1/3 1/2 1 0.16
P2–Q2   Q21 Q22 Q23  
Q21 1 2 4 0.56
Q22 1/2 1 3 0.32
Q23 1/4 1/3 1 0.12
P3–Q3   Q31 Q32    
Q31 1 1   0.50
Q32 1 1   0.50
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The EWM normalizes the data in Table 7 and calculates the information entropy of each index to obtain the entropy weight of each index. The equations are as follows:

5.3. 1
5.3. 2
5.3. 3

where xij is the normalized sample value, Hj is the information entropy of each index, and βj is the weight of each index obtained by the entropy weight method.

According to eqs 1–3, the index weight βj of the EWM is obtained: βj = (0.13, 0.08, 0.08, 0.09, 0.17, 0.15, 0.22, 0.08)T. The subjective and objective combination weighting method is based on the linear weighted sum method. Researchers tend to use the AHP to determine the preference of the weight is μj, and the preference of the EWM to determine the weight is (1 – μj).62 Thus, the combination weight γj is obtained:

5.3. 4
5.3. 5

According to eqs 4 and 5, the combined weight of the comprehensive evaluation indices is obtained:

5.3.

5.4. Gray Class and Whitening Weight Function Determination

In the process of employing gray clustering for rock mass evaluation, it is essential to ascertain the gray class for each index. There are many classifications of engineering rock mass evaluation at home and abroad, such as the four-category, five-category, six-category, etc. Since the five-category classification method contains different types of rocks ranging from good to poor, it is currently used most in the evaluation of engineering rock masses at home and abroad. Assuming that the evaluation index consists of s gray classes (k = 1, 2, ..., s), with a total of five subclasses. The whitening weight function of the index j belonging to the gray class k is denoted by fkj(x), and xkj is the turning point of the whitening weight function.63 The conventional whitening weight functions include the typical whitening weight function, lower limit measure whitening weight function, moderate measure whitening weight function, and upper limit measure whitening weight function (Figure 15). Each whitening weight function can be expressed as

5.4.

Assuming that the value range of index j is [aj,bj], the turning point xkj of each gray class k of index j is determined. The moderate measure whitening weight function, the lower limit measure whitening weight function and the upper limit measure whitening weight function are selected to construct the mixed whitening weight function64 as the whitening weight function of the comprehensive evaluation system of the coal measure rock mass (Figure 16). The first six comprehensive evaluation indices f1j(x) correspond to the upper limit measure of whitening weight function, and fsj(x) is the lower limit measure of whitening weight function (f1j(x) of the hydrogeological influence coefficient and tectonic influence coefficient is the lower limit measure of whitening weight function, and fsj(x) is the upper limit measure of whitening weight function). When k = 2, 3, ..., s – 1, the moderate measure whitening weight function fkj(x) is adopted. The probability that the measured sample data of each index belong to each gray class, k is evaluated. The range of each gray class and the corresponding whitening weight functions are shown in Table 9.

Figure 15.

Figure 15

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Whitening weight functions: (a) typical whitening weight function, (b) upper limit measure whitening weight function, (c) moderate measure whitening weight function, and (d) lower limit measure whitening weight function.

Figure 16.

Figure 16

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Whitening weight functions of a comprehensive evaluation of coal measure rock mass.

5.5. Gray Clustering Evaluation and Its Application

5.5.1. Gray Clustering Evaluation Results

The sample values of the comprehensive evaluation indices in Table 7 belonging to the gray class probability of each index are calculated and combined with the weight γj to obtain the clustering coefficient:

5.5.1. 6

where σki is the clustering coefficient of object i belonging to subclass k.

Determine whether object i belongs to gray class k1 (k1 = 1, 2, ..., s):

5.5.1. 7

According to eqs 6 and 7, the clustering coefficient matrix and the comprehensive evaluation results for the coal measured rock mass are obtained (Table 10). These results are then compared with previous research results via comprehensive fuzzy evaluation and extenics theory evaluation.65,66

Table 10. Comprehensive Evaluation Results of Coal Measure of the Rock Mass.
No. lithology clustering coefficient matrix gray clustering evaluation fuzzy comprehensive evaluation65 extenics theory evaluation66
1 fine sandstone [0.142,0.341,0.314,0.194,0.009] II III III
2 argillaceous siltstone [0.099,0.377,0.514,0.010,0.000] III III III
3 fine sandstone [0.390,0.286,0.323,0.001,0.000] I I I
4 silty mudstone [0.155,0.118,0.359,0.317,0.050] III III III
5 fine sandstone [0.110,0.417,0.393,0.000,0.080] II II II
6 mudstone [0.110,0.141,0.309,0.360,0.080] IV IV IV
7 siltstone [0.125,0.435,0.403,0.036,0.000] II II II
8 silty mudstone [0.155,0.171,0.500,0.174,0.000] III III III
9 argillaceous siltstone [0.171,0.244,0.502,0.022,0.060] III III III
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Table 9 reveals the comprehensive evaluation results for the coal measure rock mass within the Longtan Formation in Guizhou, categorized as Classes I–IV. Specifically, the fine sandstone falls within Classes I–II, the argillaceous siltstone is Class III, the silty mudstone is Class III, the mudstone is Class IV, and the siltstone is class II. When divided into rock groups, the mudstone group (mudstone, silty mudstone) is classified as Class III–IV, the fine sandstone group is classified as Class I–II, and the siltstone group (siltstone, argillaceous siltstone) is classified as Class II–III. Since the fine sandstone and siltstone have characteristics of low mudcontents, favorable rock mechanical properties, relatively intact rock mass, and substantial stratigraphic thicknesses, the probability that the gray category of these lithologies correspond to Classes I−II is high, and thus the evaluationresult is Classes I−II. Because the mudstone contains argillaceous bands, bedding, and pores and fissures, the gray class of evaluation parameters belongs to Classes III–V, and thus the evaluation result is Class IV. The silty mudstone and argillaceous siltstone have intermediate degrees of bedding and pore and fissure development compared to those of mudstone and sandstone, and thus the corresponding evaluation result is Class III. According to the analysis of the mechanical and failure characteristics of these coal measure rocks, the mechanical properties, of the sandstone are generally higher than those of the mudstone, and the mechanical properties, of the silty mudstone and argillaceous siltstone are generally between those of the mudstone and sandstone. These consistent conclusions regarding the mechanical properties underscore the reasonableness of the established evaluation system. Furthermore, a comparison of the gray clustering evaluation results for the Longtan Formation coal measure rock mass with the results from the commonly used fuzzy comprehensive evaluation method and extenics theory evaluation for coal measure rock mass indicates a high degree of consistency. This concordance underscores the scientific validity and reliability of the established gray clustering evaluation system.

5.5.2. Potential Application of Evaluation Method and Results

Based on the research results of the rock mechanical characteristics analysis of the parameter well in the Dahebian block, it is evident that the regional coal measure rocks exhibit relatively low elastic modulus and tensile strength, while the uniaxial compressive strength shows a wide variation range. Moreover, significant differences in mechanical properties are observed among different types of rocks. Guizhou boasts abundant coal measure gas resources, with coal interbedding frequently occurring with sandstone and mudstone. By integrating the understanding of coal measure rock mechanical characteristics and comprehensive evaluation of rock masses, multiple rock mechanical stratigraphic units with similar rock mechanical properties can be delineated. These units can control the development and extension of fractures, thereby facilitating the establishment of a coal measure rock mechanical stratigraphic framework and a three-dimensional model. This model reflects the internal structure, physical properties, and other characteristics of the coal measure reservoir. Regarding coal measure gas production simulation, establishing a realistic reservoir geomechanical model necessitates considering reservoir rock mechanical parameters and fracture distribution characteristics. However, due to the coring process and rock properties, some samples are challenging to produce, rendering the acquisition of rock mechanical parameters difficult. Nevertheless, the rock mechanical parameter logging prediction model, established through logging parameter inversion and comparative analysis with actual test parameters, can significantly contribute to improving the mechanical parameters of each reservoir.

The coal measure superimposed gas reservoirs in Guizhou exhibit a significant development, with each rock type demonstrating distinct mechanical properties. Merely considering the failure characteristics of a single lithology rock when establishing an actual reservoir geomechanical model does not accurately reflect reality. Moving forward, there is a need to delve deeper into the rock mechanical properties and failure characteristics across multiple types of coal measure reservoirs with superimposed layers. Additionally, building upon the division of rock mechanical stratigraphic units and the establishment of coal measure rock mechanical stratigraphic models, further research can focus on conducting numerical simulations of coal measure superimposed gas reservoir production. This research aims to evaluate gas production effectiveness under current development technologies.

6. Conclusion

Using a comprehensive approach encompassing on-site drilling coring, geophysical logging, geological data compilation, and indoor physical and mechanical experiments, through the analysis of the mechanical and failure characteristics of the coal measure rock in the Longtan Formation and the testing of the corresponding logging-based prediction models, a comprehensive evaluation system of the Longtan Formation coal measure was established. The following conclusions were obtained:

  • (1)

    Compared with coal measures from the Longtan Formation in eastern Yunnan and the Shanxi Formation in Huainan, the Longtan Formation coal measure in the block has a more discrete compressive strength, along with a relatively lower elastic modulus and tensile strength. Confining pressure can significantly affect the mechanical properties and failure characteristics of rock (the triaxial compressive strength, elastic modulus, and residual strength of the coal measure rock increase nonlinearly; the failure modes of the mudstone and siltstone samples evolve from splitting failure to either single-shear surface failure or double-shear surface failure).

  • (2)

    Logging parameters, including the acoustic time difference, compensated neutron, and deep lateral resistivity, exhibit significant correlations with the uniaxial compressive strength and elastic modulus. Moreover, the acoustic time difference, deep lateral resistivity, and natural gamma show strong correlation with tensile strength. The predictions of rock mechanical parameters were made, yielding an average relative error of 15.08%.

  • (3)

    Utilizing the gray clustering analysis method, which integrates gray system theory and clustering method, we formulated a comprehensive evaluation system for the Longtan Formation coal measure rock mass. The evaluation results for the whole coal measure rock mass are I–IV. The results generated by this newly established evaluation system are basically the same as those with those obtained using the fuzzy comprehensive evaluation method and extenics theory evaluation, confirming the validity of the established evaluation system.

Author Contributions

# These authors contributed equally to this work (Y.L. and L.C.).

The authors declare no competing financial interest.

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