Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams

Abstract

This document introduces a novel equivalent algorithm for forecasting gas content within deep coal seams, which is subject to constraints stemming from the advancements and precision achieved in well and roadway engineering endeavors. This algorithm meticulously acknowledges that coal seam gas content comprises three fundamental components: the inherent gas emission rate of the equivalent stratum, the residual gas content retained within the coal seam itself, and the influence imparted by the gas content within the coal seam. Furthermore, the approach thoroughly considers variations in the level of porosity development within the coal seam and its surrounding rock formations, as well as the occurrence of gas within these structures. The equivalent layer is classified into two distinct groups: the sandstone zone and the clay zone. The sandstone zone utilizes pertinent parameters pertaining to fine sandstone, whereas the clay zone distinguishes between clay rock and thick mudstone. The influencing factor considerations solely encompass natural elements, such as the coal seam’s occurrence and geological structure. The residual gas content employs either existing measured parameters or acknowledged experimental parameters specific to the coal seam. Based on this predictive approach, an intelligent auxiliary software (V1.0) for mine gas forecasting was devised. The software calculates the gas content of deep coal seams within the mine at intervals of 100 m × 100 m, subsequently fitting the contour lines of gas content across the entire area. The gas content predictions derived from this equivalent algorithm demonstrate robust adaptability to variations in gas content caused by construction activities, and the prediction results exhibit an acceptable level of error on-site. Notably, the prediction process is not constrained by the progress of tunnel engineering, ensuring that the prediction outcomes can accurately represent the distribution characteristics of deep coal seam gas content. After a year of application, the prediction results have consistently met on-site requirements, providing a scientific foundation for the implementation of effective gas prevention and control measures in the mining area. Furthermore, this approach can effectively guide the formulation of medium- and long-term gas prevention and control plans for mines.

1. Introduction

As the depth of coal mining steadily increases, the gas pressure and gas content have also undergone a corresponding augmentation. As a result, coal and gas outbursts, gas explosions, and other associated disasters have emerged as increasingly pressing issues. The gas content of coal seams serves as a pivotal parameter in analyzing the distribution characteristics of coal seam gas within mines and assessing the potential risk of coal and gas outbursts. As a vital preventive measure, gas drainage plays a crucial role in mitigating the likelihood of such outbursts. Furthermore, coalbed methane (CBM) represents a potentially viable resource that can be harnessed. Consequently, research efforts have been directed towards enhancing the efficiency of CBM extraction, thereby ensuring the safe and efficient operation of coal mines [1]. In the design of gas drainage, the method of gas drainage must be taken into careful consideration. Numerous studies have been conducted to investigate the permeability of coal and rock, which is a crucial aspect in determining effective gas drainage strategies [2,3,4,5,6,7,8,9]. The gas content is a crucial influencing factor in coal–gas outbursts. A number of studies have been conducted with the aim of devising novel methods for the prevention and prediction of such occurrences. Certain scholars have conducted research on the correlation between the gas content and the coal structure [10]. The findings of these studies indicate that coal seams located in regions with more extensive coal structural damage exhibit higher levels of gas content. The primary methodologies utilized for forecasting the gas content within coal seams encompass the direct measurement approach [11,12,13,14,15] as well as the quantitative theoretical algorithm [16,17,18,19,20,21,22]. The former necessitates a significant number of on-site drilling projects, and its predictive scope is inherently restricted by the advancement of current tunnel engineering endeavors. In contrast, the latter is unconstrained by production progress and constitutes the primary algorithmic instrument for the compilation of mine gas geological maps. Nevertheless, owing to the inadequate comprehensiveness of the selected geological factors, the accuracy of gas content prediction encounters difficulties in satisfying the field operational requirements of mines characterized by intricate geological structures. The prediction method proposed in this paper considers many geological factors, such as anticlines, faults, coal seam thickness, and coal seam dip angle. Applying this prediction method to other coal mines is the same, except that the influencing factor coefficients involved in the prediction need to be determined based on the monitoring data of the predicted mine, which can be used for predicting the gas content of the mine.

The proposed algorithm for forecasting coal seam gas content, as presented in this article, relies upon the quantitative analysis of the equivalent layer partitioning and the grouped influencing factors pertinent to coal seam gas content. This approach remains unhindered by the progression of fieldwork or construction activities. The mining gas prediction software (V1.0), intelligently devised upon this predictive methodology, autonomously computes and delineates the contour mapping of gas content across the entire coal seam. This facility significantly aids on-site personnel in taking preemptive and judicious gas prevention measures, thereby contributing to the safe and efficient operation of coal mines.

2. The Equivalent Layers of Coal Seam Gas Emission

The theory and prediction methodology of the gas emission equivalent layer has undergone rigorous testing and optimization for over a decade. This predictive approach has advanced from manual execution to intelligent augmentation, bolstered by its dynamic correction algorithm [23,24,25,26,27,28,29,30,31]. It consistently maintains a monthly prediction accuracy of over 90% for gas emissions emanating from mining faces.

The equivalent layer theory and its corresponding prediction methodologies encompass five fundamental components: the equivalent lithology, the diagram for the division of equivalent layers, the basic emission quantity of gases within the equivalent layers, the grouping of factors influencing gas emissions, and the algorithmic prediction for the quantity of gas emissions within the equivalent layers. This framework ensures a systematic and comprehensive approach to the analysis and prediction of gas emissions.

2.1. Equivalent Lithology

Equivalent lithology pertains to the diverse lithologies of the roof and floor that are encompassed within the influence sphere of coal seams and their respective mining operations. These diverse lithologies are deemed equivalent to a single lithological type based on a predetermined set of rules. This determination is founded on the comprehensive analysis of rock core data derived from exploration boreholes that are specifically targeted at coal-bearing strata. The equivalence is established longitudinally along the borehole’s trajectory.

The primary purpose of equivalent lithology is to ascertain the varying degrees of influence that the roof and floor lithologies exert on gas emissions. Typically, the classification system for equivalent lithology encompasses six distinct categories: fine sandstone, medium-fine sandstone, medium sandstone, gritstone, clay rock, and thick mudstone. It is noteworthy that the extent of the roof and floor that is impacted by mining and excavation activities varies, and as a result, the types of equivalent lithology that are encountered also differ.

2.2. The Division of Equivalent Layer

The division of equivalent layers represents the horizontal equivalence between the lithologies of adjacent boreholes on mining engineering drawings. During this process, it is essential to consider not only the hydraulic sorting of the lithologies but also to account for the magnitude of the fundamental gas emissions from the equivalent layers. Contrary to the vertical equivalent behavior, horizontal equivalent refers to the consolidation of lithology between adjacent boreholes into a single unit when the equivalent lithology is identical or into multiple units when the equivalent lithologies differ. Consequently, all adjacent and identical equivalent lithologies are amalgamated into a region, which is further categorized into excavation and mining zones.

2.3. The Basic Gas Emission of the Equivalent Layer

The basic gas emission of an equivalent layer, also known as the basic quantity, represents a quantitative evaluation of the typical gas emission from a specified equivalent layer. This parameter is solely dependent on the inherent rock properties of the coal seam and its immediate roof and floor strata. The determination of the basic quantity relies on a rigorous statistical analysis of historical monitoring data gathered from mining operations in production areas where gas emission is minimally or not at all influenced by external factors.

It is important to note that the equivalent range of mining and excavation activities can vary for the top and bottom plates, resulting in distinct basic quantities for each.

2.4. The Influence Factor Group of Gas Emission

The influence factor group pertaining to gas emission exerts a noteworthy influence on the volume of gas emissions during mining operations. This group encompasses all discernible, recognized, and quantifiable human and natural elements that are collectively designated as such. Each mining statistic comprises 17 distinct categories, with each category further subdivided into 46 individual items, as detailed in

Table 1. Factors and sub items affecting gas emission during excavation.

Factors	Sub Items	Factors	Sub Items	Factors	Sub Items	Factors	Sub Items
Excavation technology	Blasting excavation	Coal seam thickness (m)	<a	Dip angle of coal seam (°)	<a	Left and right goaf (m)	≤10
	Comprehensive excavation		a~b		a~b		11~25
	Other		>b		>b		26~40
Upper and lower goafs	Overlying goaf	Gas drainage	Drainage	Production interruption (Month)	1~2	Gas gradient m3/100 m	≤h
	Underlying goaf		Adjacent alley ≤ 30 m		3~6		>h
	Layered goaf		Water gushing		≥7
Number of small fault layers	1~2	Fold ratio	<2	Synclinal zoning	Synclinal axis	Anticlinal zoning	Anticlinal axis
	3~4		2~3		Middle syncline		Middle part of anticline
	≥5, reverse fault		>3		Synclinal margin		Anticlinal margin
Basin zoning	Bottom of Basin	Large partition	Middle part of major fault	Adjacent layer influence	Gasification adjacent layer	Abnormal area	Enriched gas zone
	Central Basin		Edge of major faults		Multiple gas adjacent layers
	Basin margin		Rich gas fault		Strong gas adjacent layer	other factors	Unknown item

.

The quantification of factors influencing gas emission relies on a rigorous statistical analysis of historical monitoring data. In mining production, it is common to encounter multiple, simultaneously interacting influencing factors. As such, their quantification process necessitates separate assessment, adjustment, and eventual fixation in the form of influence coefficients.

The intensity of the impact of a given influencing factor on the type of equivalent effect layer and its associated mining gas emission varies, and its corresponding influence coefficient must be determined specifically for the equivalent effect layer and mining type involved.

The quantified coefficients of the influencing factors are integrated with the basic quantities and subsequently entered into a database for utilization by gas prediction software (V1.0).

2.5. Prediction Algorithm for Gas Emission from Equivalent Layers

Equivalent layer gas emissions can be categorized into two main types: mining and excavation. The predictive valuation encompasses not just the gas emissions originating from the coal seam but also all additional emissions stemming from both human and natural factors. The total equivalent layer gas emission is equivalent to the sum of the basic amount and the influence amount. The influence amount, specifically, pertains to factors that transcend the boundaries of the equivalent layer. During excavation, the additional gas emissions are quantified through an impact coefficient in the calculation.

In the equation, the influence amount signifies the extra emissions arising from excavation, which are represented by an influence coefficient supplementary to the equivalent layer (refer to Table 1 or

Table 2. Factors and sub items affecting gas emission during mining.

Factors	Sub Items	Factors	Sub Items	Factors	Sub Items	Factors	Sub Items
Mining technology	Ordinary mining	Coal seam thickness (m)	<a	Dip angle of coal seam (°)	<a	Left and right goaf (≤40 m)	1~2 year
	Fully mechanized mining		a~b		a~b		3~5 year
	Fully mechanized sublevel caving		>b		>b		>5 year
Upper and lower goafs	Overlying goaf	Gas drainage	Drainage	Production interruption (Month)	1~2	Gas gradient (m3/100 m)	≤h
	Underlying goaf		Old Lane		3~6		>h
	Layered goaf		Water gushing		≥7
Number of small faults	1~2	Fold ratio	<2	Synclinal zoning	Synclinal axis	Anticlinal zoning	Anticlinal axis
	3~4		2~3		Middle syncline		Middle part of anticline
	≥5, Reverse fault		≥3		Synclinal margin		Anticlinal margin
Basin zoning	Bottom of Basin	Major fault zoning	Middle part of major fault	Adjacent layer influence	Gasification adjacent layer	Abnormal area	Gas enrichment zone
	Central Basin		Edge of major faults		Multiple gas adjacent layers
	Basin margin		Rich gas fault		Strong gas adjacent layer	Other factors	Unknown item

).

The gas emission (Q_p, in units of m³/min) can be determined using Equation (1):

$$Q_p=k_f(Q_j+Q_z)\ast \prod k_{ij}$$

(1)

In the context provided, k_f represents the surplus coefficient, Q_j (measured in m³/min) denotes the basic quantity of an equivalent layer, Q_z (also measured in m³/min) signifies the incremental gas volume with respect to depth, and ∏k_ij is the cumulative effect of influencing factor coefficients.

$$Q_z=q_t\ast (H-h)/100$$

(2)

where q_t is the gas gradient of an equivalent layer, m³/(min·100m), H denotes excavation depth, m, h denotes Initial depth.

3. The Gas Content of the Equivalent Layer

3.1. The Basic Definition

In accordance with the definition of gas content, the gas content within the equivalent layer can be considered as the volume of gas contained in the equivalent coal and rock mass per unit mass. This encompasses two primary aspects: the fundamental gas content of the equivalent layer and the influence of geological factors on the gas content of the equivalent layer. Specifically, the fundamental gas content of the equivalent layer represents the baseline volume of gas present in the original coal and rock state, uninfluenced by geological structural factors. Additionally, the influence of geological factors on the gas content of the equivalent layer denotes the incremental value of gas content that arises due to the extent of geological structural impact within the equivalent layer.

3.2. The Basic Gas Content of the Equivalent Layer

The fundamental gas content within the equivalent layer encompasses both the fundamental gas content inherent in the rock mass and the fundamental gas content present in the coal body, specifically:

$$Q_{jh}=Q_{yj}+Q_{mj}$$

(3)

where Q_jh is the basic gas content of the equivalent layer, m³/t, Q_yj denotes the basic gas content of the rock mass, m³/t, Q_mj denotes the basic gas content of the coal body, m³/t.

Given the occurrence of gas reservoirs within rock formations, external gas primarily accumulates in a free state within medium to large pores (referred to as effective pores), which are conducive to gas flow. This is attributed to the impact of small pore water film or condensation blockage. Consequently, the amount of residual gas can be deemed negligible, encompassing the following scenarios:

$$Q_{yj}\approx q_{yy}$$

(4)

Coal seams are gas-producing layers, and gas is not affected by pore water film or condensation blockage. It can be stored in both adsorbed and free states in all pores. Therefore:

$$Q_{mj}=q_{my}+q_{mc}$$

(5)

where $q_{my}$ denotes the emission amount of basic gas content in equivalent layer coal seams, m³/t, $q_{mc}$ deotes the residual amount of basic gas content in equivalent layer coal seams, m³/t.

Substitute Equations (4) and (5) into Formula (3), and the calculation formula for the basic gas content of the equivalent layer is:

$${\mathrm{Q}}_{\mathrm{j}\mathrm{h}}=q_{my}+q_{mc}+q_{yy}$$

(6)

Let $q_{yy}$ + $q_{my}$ = $q_{dj}$. Where $q_{dj}$ denotes equivalent layer basic gas emission, m³/t, hence, the Equation (6) can be expressed as follows:

$${\mathrm{Q}}_{\mathrm{j}\mathrm{h}}=q_{dj}+q_{mc}$$

(7)

3.3. Discussion on Algorithm for Calculating the Basic Gas Content of Equivalent Coal Seams

There exist six distinct categories of equivalent layers, with the prediction of gas emissions during excavation encompassing both surrounding rock and coal seam gas. It is not necessary to consider the influence of surrounding rock gas in isolation, whereas the prediction of coal seam gas content ought to strive to mitigate the impact of surrounding rock gas. Based on Formulas (3) and (5), it can be definitively stated that:

$$Q_{mj}=Q_{jh}-Q_{yj}$$

(8)

$$Q_{mj}\approx Q_{jh}-q_{yy}$$

(9)

(I) If the surrounding rock is clay or limestone with strong sealing properties, it can be considered that:

$$q_{yy}=0$$

(10)

From Equations (5), (9) and (10), we can obtain:

$$Q_{mj}=Q_{jh}=q_{my}+q_{mc}=q_{dj}+q_{mc}$$

(11)

In the region where the adjacent rock comprises clay, the permeation capability of clay slurry into the coal mass, under hydraulic action during coal formation, surpasses that of mortar. This enhanced permeation facilitates an increase in the resistance against gas diffusion and flow within the coal, effectively augmenting its sealing properties. Consequently, coal seams and strata with clay-rich adjacent rocks exhibit higher gas concentrations compared to those with sandstone-dominated surroundings. Furthermore, clay layers of a certain thickness demonstrate a commendable sealing efficacy against gas diffusion, with this efficacy intensifying in proportion to the clay’s thickness.

(II) In the scenario where the adjacent rock is composed of fine sandstone characterized by low porosity, the effective porosity of this fine (silty) sandstone, while slightly higher than coal, possesses an exceedingly underdeveloped system of small pores, hindering effective gas storage. Consequently, the fundamental gas content within fine sandstone is substantially lower than that of coal. Hence, the emission component of the fundamental gas content in fine sandstone can be disregarded, stated as follows:

$$q_{yy}\approx 0$$

(12)

From Equations (5), (9) and (12), we can obtain:

$$Q_{mj}=Q_{jh}=q_{my}+q_{mc}=q_{dj}+q_{mc}$$

(13)

(III) In the case where the surrounding rock comprises medium-fine and medium-coarse sandstone with significant porosity, the gas emission emanating from its gas content cannot be overlooked. To ascertain the fundamental gas content of the equivalent seam, it is imperative to subtract the emission emanating from the pertinent sandstone.

From Equations (6) and (9), we can obtain:

$$Q_{mj}\approx (q_{yy}+q_{my}+q_{mc})-q_{yy}=q_{my}+q_{mc}$$

(14)

This is equivalent to the formula for the area where the surrounding rock is fine sandstone, therefore:

$$Q_{mj}\approx q_{my}+q_{mc}=q_{dj}+q_{mc}$$

(15)

In actuality, upon the computation of the fundamental gas content within the corresponding seam, and the subsequent deduction of the emissions emanating from medium-fine and medium-coarse sandstone, the resulting figure aligns precisely with the fundamental gas content of the corresponding seam of fine sandstone. This underscores the fact that, irrespective of the thickness of the sandstone, the baseline gas emissions of the equivalent coal seam layer can be deemed equivalent to those of the fine sandstone layer. Although this value is of an approximate nature, the predictive outcomes are deemed satisfactory in fulfilling the precision criteria for practical application.

Thus, the area equivalent to predicting coal seam gas content can be categorized into sandstone and clay zones. By eliminating the impact of medium-coarse sandstone within the sandstone zone, the fundamental gas emission and its influencing factors are incorporated into the calculations using fine sandstone that has an insignificant gas content. Since the surrounding rock in the clay zone has zero gas content and is solely influenced by the thickness of the clay layer, the fundamental gas emission and its influencing factors of clay rock and thick mudstone continue to be utilized for the calculations.

The fundamental gas content of coal seams across different lithologies can be calculated using the same methodology, which is:

$$Q_{mj}\approx q_{dj}+q_{mc}$$

(16)

Formula (16) serves as a fundamental indicator of the correlation between coal seam gas content and the equivalent layer gas emission. This relationship constitutes a pivotal foundation upon which an equivalent algorithm for predicting deep coal seam gas content can be formulated.

4. Equivalent Algorithm for Predicting Coal Seam Gas Content

At the equivalent layer, the gas content within the coal seam is equivalent to the cumulative sum of the coal seam’s fundamental gas content and the influence factor pertaining to the coal seam’s gas content. The fundamental gas content of the coal seam, in turn, comprises both the baseline gas emission emanating from the coal seam and the residual gas content that remains within the coal seam. This relationship can be expressed as follows:

$$Q_{mh}=Q_{jh}+Q_{my}=q_{dj}+q_{mc}+Q_{my}$$

(17)

where Q_mh denotes equivalent layer coal seam gas content, Q_my denotes the influence of coal seam gas content.

4.1. Determination of Residual Gas Content in Coal Seams

The residual gas content within coal seams is intimately linked to the state of gas adsorption and the volatile matter content, both of which are primarily influenced by the extent of coal metamorphism. The determination of this residual gas content can be achieved through a combination of on-site sampling and measurement procedures, along with an assessment of the volatile matter content of the coal, as detailed in

Table 3. The residual gas content ($q_{mc}$) in coal seams.

Volatile %	<10	10~20	20~30	30~40	>40
Residual gas content (m3/t)	5–15	2.5~7	2~3	1.2~1.5	1.1~1.6

.

The residual gas content within coal seams of identical quality and comparable volatile matter situated in the same mining area can be considered approximately constant, with the assumption that geological structure and other factors have negligible influence. This is due to the fact that adsorbed gas resides within the coal body’s small pores and is thus seldom impacted by structural stress.

4.2. Calculation of the Influence of Coal Seam Gas Content

The extent of influence on coal seam gas content is determined by the surrounding rock and geological structure, which adds value to the gas content. The primary target of this influence is the free gas within the coal seam, and the degree of impact is indicated by the magnitude of the influence coefficient. Its calculation formula still uses formula $K_{mh}=\prod k_{ij}$, where K_mh is the influence coefficient of equivalent layer coal seam gas content.

It should be noted that:

(1)

The gas content prediction solution relies on numerical values derived from the raw coal state, thus, it only accounts for the impact of natural factors, including the surrounding rock of the coal seam and geological structures. It does not take into consideration the effects of adjacent layers or mining activities.

(2)

The measurement of coal seam gas content is expressed in units of m³/t. Given that the sole measurement of gas emission during mining is also conducted in m³/t, the influence coefficients utilized within the prediction algorithm are inherently shared with those employed for predicting gas emission in the mining face.

4.3. Calculation Formula for Gas Content in Equivalent Layers of Coal Seams

The outcomes of the algorithmic analysis pertaining to the residual gas content and the influence quantity of basic gas content within the aforementioned coal seam have been incorporated into Equation (17), thereby establishing the fundamental calculation formula for predicting the equivalent coal seam gas content, which can be formulated as:

$$Q_{mh}=k_f{K}_{mh}{q}_{dj}+q_{mc}$$

or

$$Q_{mh}=k_f{q}_{dj}\ast \prod k_{ij}+q_{mc}$$

(18)

As the equivalent calculation for predicting gas content necessitates the utilization of pertinent parameters aimed at forecasting gas emissions during the extraction of equivalent layers, in the aforementioned equation, it is essential to:

$$q_{dj}=T_{min}\times (Q_{cj}+Q_{cz})/A_c$$

(19)

where $T_{min}$ = 1440 × 30, min, the number 1440 is the minutes of one day, the number 30 is the monthly average days, $Q_{cj}$ denotes the average absolute gas emission from an equivalent layer during mining, m³/min, $Q_{cz}$ is gas increment of equivalent layer in a certain depth of mining, m³/(min·100m), $A_c$ denotes the average monthly production of coal seam mining face, t.

From Equations (18) and (19), we can obtain:

$$Q_{mh}=k_f\left\{T_{min}\right(Q_{cj}+Q_{cz})/A_c\}\times \prod k_{ij}+q_{mc}$$

(20)

where $Q_{cz}$ = $q_{ct}\times$($H_c$ − $h_c$)/100 (m³/min), where $q_{ct}$ denotes the gas gradient of equivalent layer extraction, m³/min.100 m, $H_c$ denotes the mining depth, m; $h_c$ denotes the starting depth of mining, m.

Equation (20) represents the corresponding algorithm utilized for forecasting the gas content within coal seams. Within this formulation, all parameters, excluding the residual gas content specific to the coal seam, can leverage the pre-existing database of the equivalent layer.

Due to the division of equivalent layers and the establishment of their respective databases, primarily centered on the distribution patterns of exploration boreholes, the geological context surrounding coal seam occurrence, and the historical documentation of gas emissions during mining operations, these endeavors are not constrained by advancements in mining engineering. Consequently, the prediction of gas content within deep coal seams, grounded on the theory of equivalent layers, transcends any limitations imposed by the pace of mining engineering progress.

The prediction outcomes pertaining to the gas content within the corresponding strata of deep coal seams have been consolidated to ascertain the distribution pattern of gas content within these coal seams.

5. Example of Coal Seam Gas Content Prediction

The prediction of coal seam gas content is undertaken utilizing intelligent auxiliary software (V1.0) specifically designed for mine gas forecasting. This software incorporates Equation (20) and necessitates the preliminary setting of fundamental parameters pertaining to the coal seam in order to accurately carry out the prediction process.

5.1. Basic Parameter Settings

The Kailuan Group’s mine engages in the extraction of coal seams with a thickness ranging from 1.5 to 4.0 m and a dip angle between 10 and 30 degrees. The primary type of coal being mined is coking coal, where Coal Seam 12 exhibits a volatile content of approximately 21%, an average residual gas content of 2.7 m³/t, an average monthly output of 52,000 tons from the stope face, and an anticipated initial depth for deep gas content at −600 m.

Based on the predictive influence coefficient pertaining to gas emission within Coal Seam 12, a comprehensive assessment of the deep gas content prediction influence coefficient is presented in

Table 4. Prediction influence coefficient of deep gas content in Coal Seam 12.

Influence Factor	Factor Subitem	Fine Sand	Medium Fine Sandstone	Medium Sandstone	Coarse Sand	Clay	Thick Mud
Equivalent layer	Basic quantity m3/min	0.55	0.55	0.55	0.55	0.80	1.10
Coal seam thickness	<2 m	0.95	0.95	0.95	0.95	0.95	0.95
	2~4 m	1	1	1	1	1	1
	>4 m	1.05	1.05	1.05	1.05	1.05	1.05
Dip angle of coal seam	<15	1.05	1.05	1.05	1.05	1.05	1.05
	15~25	1	1	1	1	1	1
	>25	0.95	0.95	0.95	0.95	0.95	0.95
Gas gradient	≤600 m	0	0	0	0	0	0
	>600	0.2	0.2	0.2	0.2	0.2	0.25
Synclinal zoning	Synclinal axis	0.7	0.7	0.7	0.7	0.7	0.7
	Middle syncline	0.8	0.8	0.8	0.8	0.8	0.8
	Synclinal margin	0.9	0.9	0.9	0.9	0.9	0.9
Anticlinal zoning	Anticlinal axis	0.7	0.7	0.7	0.7	0.7	0.7
	Middle part of anticline	0.8	0.8	0.8	0.8	0.8	0.8
	Anticlinal margin	0.9	0.9	0.9	0.9	0.9	0.9
Basin zoning	Bottom of Basin	1.6	1.6	1.6	1.6	1.6	1.6
	Central Basin	1.5	1.5	1.5	1.5	1.5	1.5
	basin margin	1.6	1.6	1.6	1.6	1.6	1.6
Major fault zoning	Middle part of fault	1.2	1.2	1.2	1.2	1.2	1.1
	Fault edge	1.1	1.1	1.1	1.1	1.1	1.05
	Rich fault	1.25	1.25	1.25	1.25	1.25	1.25
Number of small fault layers	1~2	1.05	1.05	1.05	1.05	1.05	1.05
	3~4	1.1	1.1	1.1	1.1	1.1	1.1
	≥5	1.2	1.2	1.2	1.2	1.2	1.15
	Reverse fault	1.2	1.2	1.2	1.2	1.2	1.3
Abnormal area	Gas enrichment zone	1.3	1.3	1.3	1.3	1.3	1.5

Note: The equivalent sandstone zone adopts the parameters of fine sandstone.

. Furthermore, the surplus coefficient k_f utilized for the calculation of coal seam gas content has been designated as 1.1.

5.2. Predicting Gas Content

Firstly, we employ the computational capabilities of the intelligent auxiliary software (V1.0) specifically designed for mine gas prediction. Within the context of the mining engineering’s coordinate grid diagram, a square grid measuring 100 m by 100 m is utilized to determine the gas content value of the coal seam. Subsequently, the software’s contour mapping functionality is leveraged to interpolate and delineate the contour of the coal seam’s gas content using the obtained data points (Figure 1). Finally, this contour is seamlessly translated and integrated into the CAD mining engineering plan, as depicted in Figure 2.

5.3. Predictive Testing

The accuracy of the prediction results is judged by comparing the gas content values predicted by the gas geological method [32] and equivalent algorithm with the gas content values measured in the mining face. The gas geological method is actually a comprehensive analysis of the geological structure, gas, and structural coal conditions of coal seams, mining areas, working faces, or tunnels. It identifies their differences and connections in time and space and determines the gas emission rate at each location based on a certain combination of quantitative indicators.

The inspection site is selected from the 6121 fully mechanized mining face of the 12th coal seam in a certain mine, as indicated by the two dashed boxes in Figure 3 and Figure 4. The equivalent layer in this area is thick mudstone with an average depth of 850 m. The residual gas volume of the coal seam is calculated to be 2.7 m³/t.

The actual gas emission monitoring values and the gas emission amounts obtained from two locations using the prediction method and gas geological method proposed in this article are shown in

Table 5. Statistics of average gas content at 6121 fully mechanized mining faces.

Forecast Date	Gas Monitoring Report (m3/t)	Gas Geological Method Prediction (m3/t)	Equivalent Algorithm Prediction (m3/t)
April 2023	4.4	4.3	5.0
May 2023	5.0	4.3	5.4

.

By comparing the prediction results of the method proposed in this article, the calculation results of the gas geological method, and the monitoring data of gas emission, it was found that:

(1) Although the gas content estimations generated by the gas geological method remained relatively consistent in April, they failed to accurately reflect the actual fluctuations in gas content that occurred in May.

(2) The gas content estimated through the equivalent algorithm exhibits commendable accuracy in mirroring the actual fluctuations in gas content; however, the numerical outcomes are consistently elevated, ranging between 0.4 to 0.6 m³/t. The principal explanation for this discrepancy lies in the proximity of the working face to adjacent goaf areas, resulting in a degree of pre-discharge of coal seam gas. Nevertheless, it is imperative to note that the equivalent algorithm’s predictions do not account for the multifaceted influence of mining parameters and thus yield values that approximate the virgin state of the coal prior to extraction.

Test results indicate that, after excluding the potential interference from mining factors, the predicted coal seam gas content, as calculated by the equivalent algorithm, aligns closely with the actual coal seam gas content. While a degree of error is present, it falls within the tolerable limits as determined by field standards.

6. Conclusions

(1) The equivalent algorithm for predicting coal seam gas content is based on the correlation between the basic gas emission rate of the equivalent mining layer and the coal seam gas content. The prediction method fully considers the differences in the development degree of coal seam and surrounding rock pores and their gas occurrence, and the prediction results are sufficiently close to the original coal gas content.

(2) This method divides the equivalent layer into two categories: sandstone zone and clay zone. The sandstone zone shares the relevant parameters of fine sandstone, while the clay zone is still divided into clay rock and thick mudstone values. The influencing factor options only consider natural factors such as coal seam occurrence and geological structure. The residual gas content is determined using the existing measurement parameters or recognized experimental parameters of the coal seam.

(3) Based on the prediction method proposed in this article, intelligent auxiliary software for mine gas forecasting has been developed, which can easily obtain the contour map of gas content in the entire coal seam, providing useful assistance for coal mine safety production.

(4) Compared with the gas geological method, the method proposed in this article can automatically correct the changes in actual gas content monitoring values, and the accuracy of prediction results is very good. It is not only theoretically reasonable but also has high practical value.

(5) As long as the exploration data of the mine is sufficient and the prediction area is not limited by the progress of the tunnel engineering, the prediction method in this article can more reasonably represent the distribution characteristics of deep coal seam gas content, and effectively guide the formulation of medium and long-term gas prevention and control plans for the mine.