Method for describing geological sweet spots of deep coal bed gas

Abstract

A deep coal bed gas geological dessert description method relates to the field of coal bed gas exploration and evaluation, and comprises the following steps: performing coal bed inversion by well-seismic combination to obtain coal bed distribution burial depth and thickness characteristics; coal seam structure characteristics are analyzed through coring, and coal seam dirt band distribution rules are explained through logging; logging and coal core analysis data are counted, multiple regression modeling is carried out, a coal seam ash content calculation model is established, and coal seam ash content characteristics are solved; performing correlation analysis on the gas content of the coal core and the logging response value, and establishing a multiple regression analysis content prediction model; identifying lithology by an intersection method, and distinguishing lithology characteristics of a top plate and a bottom plate; and analyzing a screening principle of a deep coal seam evaluation potential favorable area by combining characteristics such as burial depth, and forming a deep coal rock gas dessert evaluation standard. The method fills the blank that no method for quantitatively describing the geological sweet spot of the deep coal bed gas exists in China, provides a reference scheme for formulating a reasonable development scheme and a technical decision, and is of great significance to later exploration and development of the deep coal bed gas.

Description

Technical Field

[0001] This invention relates to the field of coalbed methane exploration and evaluation technology, and in particular to a method for describing geological sweet spots in deep coalbed methane formations. Background Technology

[0002] With the continuous development of the global economy, energy supply has become increasingly strained, leading to a growing demand for new energy sources. Deep coalbed methane (CBM) is a newly emerging clean energy source in recent years, belonging to the important strategic energy development area, and possessing broad development potential and market prospects. Although CBM geological resources are abundant, the structure of deep coal seams is extremely complex, requiring highly advanced extraction technologies, and involving significant upfront investment and high profit risks. Currently, most CBM development is in shallow to medium-depth layers, and exploration and development of deep CBM beyond 3000 meters is still in its initial stage. There is an urgent need to conduct development potential assessments and pilot tests for deep CBM in the Sulige area, and to actively explore both theoretical research and production practice.

[0003] Generally speaking, descriptions of geological sweet spots are qualitative, roughly determining their location. Furthermore, there is almost no research on deep coalbed methane, and a lack of quantitative methods for definitively delineating the location of geological sweet spots is also present. Summary of the Invention

[0004] Currently, coalbed methane development is mostly concentrated in shallow and medium-depth formations, while theoretical research on deep coalbed methane formations (below 3000 meters) is still in its early stages, and there is a lack of quantitative methods to describe coalbed methane "sweet spots." This invention provides a method for describing geological sweet spots in deep coalbed methane formations. This invention summarizes a set of principles for screening geological sweet spots in deep coalbed methane formations and preliminarily establishes evaluation criteria for these sweet spots, achieving the goal of more accurately evaluating geological sweet spots in deep coalbed methane formations.

[0005] The technical solution adopted by this invention to solve the technical problem is as follows:

[0006] This invention provides a method for describing geological sweet spots in deep coalbed methane, which specifically includes the following steps:

[0007] Step 1: The electrical characteristics of the coal seam are revealed through rock physics analysis. The coal seam is inverted using a combination of well and seismic analysis to obtain the characteristics of the coal seam distribution depth and thickness.

[0008] Step 2: Core sampling to analyze the structural characteristics of the coal seam, and well logging to interpret the distribution pattern of interbedded rock in the coal seam;

[0009] Step 3: Integrate statistical logging data and coal core analysis data, analyze the correlation between each logging response value and coal seam ash content, perform unified multivariate regression modeling, establish a calculation model for coal seam ash content, and solve for the characteristics of coal seam ash content.

[0010] Step 4: Conduct correlation analysis on the gas content of coal cores and well logging response values, and establish a gas content prediction model based on logarithmic transformation and multiple regression analysis.

[0011] Step 5: Identify lithology using the intersection method and distinguish the lithological characteristics of the top and bottom plates;

[0012] Step Six: Combining burial depth, thickness, coal seam structure, interbedded gangue layers, ash content, gas content, and roof lithology characteristics, comprehensively analyze the screening principles for potential favorable areas in deep coal seams, formulate evaluation criteria for sweet spots in deep coal and rock gas, and determine potential favorable areas.

[0013] In a preferred embodiment, in step one, rock physical analysis reveals that the coal seam exhibits electrical characteristics of low gamma, high resistivity, low density, and high time difference.

[0014] As a preferred embodiment, in step one, based on well and seismic data, the system uses spatially variable wavelet and global optimization algorithm to start from the well location, extrapolates the seismic data, finds the optimal solution through the global optimization algorithm, and uses the combination of synthetic records and seismic profiles to perform coal seam inversion prediction, thereby obtaining high-resolution characteristics of coal seam distribution depth and thickness.

[0015] As a preferred embodiment, in step two, core analysis is performed on typical wells in the preferred block to macroscopically analyze the structural characteristics of the coal seam. Combined with the macroscopic understanding of the core samples, well logging interpretation is performed by utilizing the different sensitivities of interbedded rock layers to logging curves, and the structure of the coal seam and the distribution law of interbedded rock layers are analyzed.

[0016] As a preferred embodiment, a coal seam with an ash volume percentage greater than 40% is considered to be an interbedded gangue layer.

[0017] In a preferred embodiment, in step three, the calculation formula for the coal seam ash content calculation model is as follows:

[0018] A ad =28.9908*DEN-0.0509*AC+0.05*GR-6.2199

[0019] FC ad =-1.0095*A ad +87.292

[0020] V ad = -0.9753*(A ad +FC ad +97.164

[0021] Among them, A ad FC represents the ash content of coal seams. ad V represents the fixed carbon content. adThe values ​​indicate the volatile matter content of coal; DEN indicates the logging compensation density; AC indicates the logging compensation sonic wave; and GR indicates the logging natural gamma.

[0022] As a preferred embodiment, the coal seam ash content calculation model is improved by setting a threshold value for clay content. The calculation formula of the improved coal seam ash content calculation model is as follows:

[0023] A ad =30.9034*DEN-0.0997*AC+10.3371(V sh <30%)

[0024] Among them, A ad Indicates coal seam ash content; DEN indicates logging compensation density; AC indicates logging compensation sonic logging; V sh Indicates the mud content.

[0025] In a preferred embodiment, in step four, the following functions are selected: lgDEN (logistic logging compensation density), lgAC (logistic logging compensation acoustic wave), and lgA (logistic logging coal seam ash content). ad The logarithmic function of the ratio of fixed carbon content to volatile matter content in coal (lgFC) ad / V ad The correlation between the gas content and the coal core content was analyzed.

[0026] In a preferred embodiment, in step four, the mathematical expression of the content prediction model based on logarithmic transformation and multiple regression analysis is:

[0027]

[0028] In the formula, V gas A represents the gas content of the coal core. ad This indicates the ash content of the coal seam, DEN indicates the logging compensation density, AC indicates the logging compensation sonic logging, and FC indicates the ash content of the coal seam. ad V represents the fixed carbon content. ad This indicates the volatile matter content of coal.

[0029] As a preferred embodiment, in step five, based on the different identification effects of different lithologies reflected in the cross-plots, natural gamma-compensated acoustic waves, natural gamma-compensated density, and natural gamma-resistivity are used for cross-plotting to identify the lithology. Then, combined with the sedimentary environment and the thickness parameters of the top and bottom plates, the combination of lithological characteristics of the top and bottom plates is distinguished.

[0030] In a preferred embodiment, the evaluation criteria for the deep coalbed methane sweet spot in step six are as follows:

[0031] The evaluation criteria for Class I sweet spot areas are: net coal thickness > 6m, burial depth ≤ 3500m, roof lithology of coal ash and coal slime, net coal ash content < 25%, and net coal gas content > 12m³. 3 / t, with 0 to 1 interbedded gangue layers; the evaluation criteria for Class II sweet spots are: net coal thickness of 4m to 6m, burial depth ≤ 3500m, roof lithology of coal ash and coal slime, net coal ash content of 25% to 35%, and net coal gas content of 8m 3 / t~12m 3 / t, with 1-3 interbedded gangue layers; the evaluation criteria for Class III sweet spots are: net coal thickness < 4m, burial depth > 3500m, roof lithology of coal sand, net coal ash content > 35%, and net coal gas content < 8m 3 / t, the number of interbedded coal layers is >3.

[0032] The beneficial effects of this invention are:

[0033] This invention provides a method for describing geological sweet spots in deep coalbed methane. By analyzing key parameters of geological sweet spots in deep coalbed methane, a set of principles for screening geological sweet spots in deep coalbed methane were determined, and a preliminary evaluation standard for deep coalbed methane sweet spots was established. This fills the gap in China where there is no quantitative method for describing geological sweet spots in deep coalbed methane, and provides a reference scheme for formulating reasonable development plans and technical decisions. It is of great significance for the later exploration and development of deep coalbed methane. Attached Figure Description

[0034] Figure 1 This is a flowchart of a method for describing geological sweet spots in deep coalbed methane provided by the present invention.

[0035] Figure 2 This is a cross-sectional view of a deep coal seam connecting wells in an embodiment of the present invention.

[0036] Figure 3 This is an inversion profile of a deep coal seam in an embodiment of the present invention.

[0037] Figure 4 This is a cross-plot of logging response characteristics (natural gamma-compensated acoustic waves) for different lithologies in an embodiment of the present invention.

[0038] Figure 5 This is a cross-plot of logging response characteristics (natural gamma-compensated density) for different lithologies in an embodiment of the present invention.

[0039] Figure 6 This is a cross-plot of logging response characteristics (natural gamma-resistivity) for different lithologies in an embodiment of the present invention.

[0040] Figure 7 This is a schematic diagram of the lithological assemblage (Type I coal slime assemblage) of the deep coal-rock gas roof in an embodiment of the present invention.

[0041] Figure 8 This is a schematic diagram of the lithological combination (Type II coal ash combination) of the deep coal-rock gas roof in an embodiment of the present invention.

[0042] Figure 9 This is a schematic diagram of the lithological combination (Type III coal sand combination) of the deep coal-rock gas roof in an embodiment of the present invention. Detailed Implementation

[0043] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. The illustrative examples and descriptions of the present invention are used to explain the present invention, but are not intended to limit the present invention.

[0044] See Figure 1 The present invention provides a method for describing geological sweet spots in deep coalbed methane, the specific implementation process of which is as follows:

[0045] Step 1: Combining well and seismic analysis, inversion is performed on the coal seam to obtain the characteristics of its distribution depth and thickness; the specific operation steps are as follows:

[0046] First, rock physics analysis revealed that the coal seam exhibits electrical characteristics of low gamma, high resistivity, low density, and high time difference. Then, based on well and seismic data, coal seam inversion was performed using space-variable wavelet and model-based global optimization algorithms. Specifically, the inversion method starts from the well location, extrapolates seismic data, finds the optimal solution through a global optimization algorithm, and uses a combination of synthetic records and seismic profiles to predict the coal seam inversion, obtaining high-resolution characteristics of the coal seam distribution, burial depth, and thickness.

[0047] The deep coal seam interconnection profile in this invention is as follows: Figure 2 (The horizontal axis represents impedance (Ω), and the vertical axis represents depth (m)). The coal seam exhibits electrical characteristics of low gamma, high resistivity, low density, and high time lag. The darkest area in the figure (between the two horizontal lines) is the coal seam.

[0048] The deep coal seam inversion profile in this invention is as follows: Figure 3 (The horizontal axis represents time (ms), and the vertical axis represents depth (m)). Ordinary sparse pulse inversion has limited resolution, while the model-based inversion method used in this invention primarily utilizes well and seismic data, employing spatially varied wavelets and a global optimization algorithm to invert coal seams. This inversion method starts from the well location, extrapolates using seismic data, and finally finds the optimal solution through a global optimization algorithm, thereby obtaining the inversion prediction results and precisely characterizing the patterns of coal seam thickness and depth. Specifically, the relationship between coal seam thickness and depth is as follows: the boundary value between the coal seam and sandstone / mudstone in terms of P-wave impedance is 6.5 × 10⁻⁶. 6 The predicted value is kg / m·m / s and its results are in good agreement with the actual coal seam being drilled.

[0049] Step 2: Core sampling to analyze the structural characteristics of the coal seam, and well logging to interpret the distribution patterns of interbedded rock. The specific operational steps are as follows:

[0050] Typical wells in the selected blocks were used as core wells for coring analysis. The structural characteristics of the coal seam were analyzed macroscopically. Based on the current domestic and international consensus that a coal seam ash volume percentage greater than 40% is the result of interbedded gangue layers, and combined with the macroscopic understanding of the core samples, the different sensitivities of interbedded gangue layers to logging curves such as natural gamma, compensated density, and compensated sonic logging were used to interpret the logging data and analyze the structure of the coal seam and the distribution pattern of interbedded gangue layers.

[0051] Step 3: Perform unified multivariate regression modeling to establish a calculation model for coal seam ash content, and then solve for the characteristics of coal seam ash content; the specific operation steps are as follows:

[0052] By combining statistical logging data and coal core analysis data, the correlation between each logging response value and coal seam ash content is analyzed, and a unified multivariate regression model is performed to establish a coal seam ash content calculation model.

[0053] The specific calculation formulas for the established coal seam ash content calculation model are as follows:

[0054] A ad =28.9908*DEN-0.0509*AC+0.05*GR-6.2199

[0055] FC ad =-1.0095*A ad +87.292

[0056] V ad = -0.9753*(A ad +FC ad +97.164

[0057] Among them, A ad FC represents the ash content of coal seams. ad V represents the fixed carbon content. ad The values ​​indicate the volatile matter content of coal; DEN indicates the logging compensation density; AC indicates the logging compensation sonic wave; and GR indicates the logging natural gamma.

[0058] Since coal seams do not exist as pure coal and often contain interbedded gangue, resulting in a high clay content, the calculated coal seam ash content deviates significantly from the actual content. Therefore, an improvement is made by setting a threshold value for clay content. The specific calculation formula for the improved coal seam ash content calculation model is as follows:

[0059] A ad =30.9034*DEN-0.0997*AC+10.3371(V sh <30%)

[0060] Among them, A ad Indicates coal seam ash content; DEN indicates logging compensation density; AC indicates logging compensation sonic logging; V sh Indicates the mud content.

[0061] Step 4: Conduct correlation analysis between coal core gas content and well logging response values, and establish a content prediction model based on logarithmic transformation and multiple regression analysis; the specific operation steps are as follows:

[0062] Coal core gas content is a crucial parameter for calculating coal gas reserves and predicting production capacity, serving as the geological basis for coal gas exploration and development. This paper establishes a multivariate regression analysis model for gas content prediction based on logarithmic transformation by analyzing the correlation between coal core gas content and well logging response values. Specifically, the model can utilize the logarithmic function of compensated density (lgDEN), the logarithmic function of compensated sonic logging (lgAC), or the logarithmic function of coal seam ash content (lgA). ad The logarithmic function of the ratio of fixed carbon content to volatile matter content in coal (lgFC) ad / V ad The correlation between the content of gas in coal cores and the content of gas in coal cores was analyzed.

[0063] In summary, the mathematical expression of the gas content prediction model based on logarithmic transformation and multiple regression analysis for deep coal cores in the Sulige area established in this invention is as follows:

[0064]

[0065] In the formula, V gas A represents the gas content of the coal core. ad This indicates the ash content of the coal seam, DEN indicates the logging compensation density, AC indicates the logging compensation sonic logging, and FC indicates the ash content of the coal seam. ad V represents the fixed carbon content. ad This indicates the volatile matter content of coal.

[0066] Step 5: Identify lithology using the intersection method to distinguish the lithological characteristics of the top and bottom plates; the specific operational steps are as follows:

[0067] Based on the core data, it was found that different lithologies had different identification effects in the cross-plot. Natural gamma-compensated acoustic waves, natural gamma-compensated density, and natural gamma-resistivity were used for cross-plotting to identify the lithology. Then, combined with parameters such as sedimentary environment and top and bottom plate thickness, the combination of top and bottom plate lithological characteristics was effectively distinguished.

[0068] The cross-plot of logging response characteristics of different lithologies in this invention is shown below. Figures 4-6As shown. By cross-refracting natural gamma-compensated acoustic waves, natural gamma-compensated density, and natural gamma-resistivity, different lithologies can be distinguished in the cross-refractory chart due to variations in their identification effectiveness. For example, as... Figure 4 As shown, through the convergence of natural gamma-compensated sound waves, carbonate rocks exhibit lower sound wave characteristics, while coal rocks exhibit higher sound wave characteristics; for example... Figure 5 As shown, through the intersection of natural gamma-compensated density, coal and rock have lower compensated densities, while sandstone and mudstone have higher densities; for example... Figure 6 As shown, through the intersection of natural gamma and resistivity, the resistivity of coal and rock is the highest.

[0069] The schematic diagram of the deep coal and gas roof lithology combination in this invention is shown below. Figures 7-9 As shown in Table 1, detailed information on the lithological assemblage of the deep coalbed methane roof is presented. Based on parameters such as sedimentary environment and roof and floor thickness, a schematic diagram of the reservoir-seal combination is derived. Table 1 clearly illustrates several reservoir-seal combination patterns and characteristics of deep coalbed methane.

[0070] Table 1. Several reservoir-capsule combinations and characteristics of deep coalbed methane.

[0071]

[0072]

[0073] Step Six: Combining burial depth, thickness, coal seam structure, interbedded gangue layers, ash content, gas content, and roof lithology characteristics, comprehensively analyze the screening principles for potential favorable areas in deep coal seams, formulate evaluation criteria for sweet spots in deep coal and rock gas, and determine potential favorable areas.

[0074] This invention summarizes the sweet spot evaluation criteria for deep coalbed methane in the Sulige area by combining characteristics such as net coal thickness, burial depth, roof lithology, net coal ash content, net coal gas content, number of interbedded gangue layers, and coal seam structure, as shown in Table 2, thereby identifying potentially favorable development blocks.

[0075] Table 2. Sweet Spot Evaluation Criteria for Deep Coalbed Methane in the Sulige Area

[0076]

[0077] Specifically, the evaluation criteria for Class I sweet spot areas are: net coal thickness > 6m, burial depth ≤ 3500m, roof lithology of coal ash and coal slime, net coal ash content < 25%, and net coal gas content > 12m³. 3 / t, with 0 to 1 interbedded gangue layers; the evaluation criteria for Class II sweet spots are: net coal thickness of 4m to 6m, burial depth ≤ 3500m, roof lithology of coal ash and coal slime, net coal ash content of 25% to 35%, and net coal gas content of 8m 3 / t~12m3 / t, with 1-3 interbedded gangue layers; the evaluation criteria for Class III sweet spots are: net coal thickness < 4m, burial depth > 3500m, roof lithology of coal sand, net coal ash content > 35%, and net coal gas content < 8m 3 / t, the number of interbedded coal layers is >3.

[0078] This invention provides a method for describing the geological sweet spot of deep coalbed methane. It adopts the approach of evaluating several key parameters of deep coalbed methane one by one, forming a quantitative evaluation standard for deep coalbed methane sweet spots, which provides a reference for formulating reasonable development plans and technical decisions, thereby facilitating the research and development of deep coalbed methane.

[0079] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for describing geological sweet spots in deep coalbed methane, characterized in that, Includes the following steps: Step 1: The electrical characteristics of the coal seam are revealed through rock physics analysis. The coal seam is inverted using a combination of well and seismic analysis to obtain the characteristics of the coal seam distribution depth and thickness. Step 2: Core sampling to analyze the structural characteristics of the coal seam, and well logging to interpret the distribution pattern of interbedded rock in the coal seam; Step 3: Integrate statistical logging data and coal core analysis data, analyze the correlation between each logging response value and coal seam ash content, perform unified multivariate regression modeling, establish a calculation model for coal seam ash content, and solve for the characteristics of coal seam ash content. Step 4: Conduct correlation analysis on the gas content of coal cores and well logging response values, and establish a gas content prediction model based on logarithmic transformation and multiple regression analysis. Step 5: Identify lithology using the intersection method and distinguish the lithological characteristics of the top and bottom plates; Step Six: Combining burial depth, thickness, coal seam structure, interbedded gangue layers, ash content, gas content, and roof lithology characteristics, comprehensively analyze the screening principles for potential favorable areas in deep coal seams, formulate evaluation criteria for sweet spots in deep coal and rock gas, and determine potential favorable areas.

2. The method for describing geological sweet spots in deep coalbed methane according to claim 1, characterized in that, In step one, based on well and seismic data, the system uses spatially variable wavelet and global optimization algorithm to start from the well location, extrapolate the seismic data, find the optimal solution through global optimization algorithm, and use synthetic records combined with seismic profiles to perform coal seam inversion prediction, thereby obtaining high-resolution characteristics of coal seam distribution, burial depth and thickness.

3. The method for describing deep coalbed methane geological sweet spots according to claim 1, characterized in that, In step two, core analysis is performed on typical wells in the selected blocks to macroscopically analyze the structural characteristics of the coal seam. Combined with the macroscopic understanding of the core samples, well logging interpretation is performed by utilizing the different sensitivities of interbedded rock layers to logging curves, and the structure of the coal seam and the distribution pattern of interbedded rock layers are analyzed.

4. The method for describing deep coalbed methane geological sweet spots according to claim 3, characterized in that, A coal seam with an ash volume percentage greater than 40% is considered to have interbedded gangue layers.

5. The method for describing deep coalbed methane geological sweet spots according to claim 1, characterized in that, In step three, the calculation formula for the coal seam ash content calculation model is as follows: WHOSE ad =28.9908*DEN-0.0509*AC+0.05*GR-6.2199 FC ad =-1.0095*A ad +87.292 In ad =-0.9753*(A ad +FC ad )+97.164 Among them, A ad FC represents the ash content of coal seams. ad V represents the fixed carbon content. ad The values ​​indicate the volatile matter content of coal; DEN indicates the logging compensation density; AC indicates the logging compensation sonic wave; and GR indicates the logging natural gamma.

6. The method for describing deep coalbed methane geological sweet spots according to claim 5, characterized in that, The coal seam ash content calculation model was improved by setting a threshold value for clay content. The calculation formula for the improved coal seam ash content calculation model is as follows: WHOSE ad =30.9034*DEN-0.0997*AC+10.3371(V sh <30%) Among them, A ad Indicates coal seam ash content; DEN indicates logging compensation density; AC indicates logging compensation sonic logging; V sh Indicates the mud content.

7. The method for describing deep coalbed methane geological sweet spots according to claim 1, characterized in that, In step four, the following functions are selected: logarithmic function for compensated density lgDEN, logarithmic function for compensated acoustic wave lgAC, and logarithmic function for coal seam ash content lgA. ad The logarithmic function of the ratio of fixed carbon content to volatile matter content in coal (lgFC) ad / V ad The correlation between the gas content and the coal core content was analyzed.

8. The method for describing deep coalbed methane geological sweet spots according to claim 1, characterized in that, In step four, the mathematical expression of the content prediction model based on logarithmic transformation and multiple regression analysis is: In the formula, V gas A represents the gas content of the coal core. ad This indicates the ash content of the coal seam, DEN indicates the logging compensation density, AC indicates the logging compensation sonic logging, and FC indicates the ash content of the coal seam. ad V represents the fixed carbon content. ad This indicates the volatile matter content of coal.

9. The method for describing geological sweet spots in deep coalbed methane according to claim 1, characterized in that, In step five, based on the core data, it is shown that different lithologies have different identification effects in the cross-plot. Natural gamma-compensated acoustic waves, natural gamma-compensated density, and natural gamma-resistivity are used for cross-plotting to identify the lithology. Then, combined with the sedimentary environment and the thickness parameters of the top and bottom plates, the combination of lithological characteristics of the top and bottom plates is distinguished.

10. The method for describing geological sweet spots in deep coalbed methane according to claim 1, characterized in that, In step six, the evaluation criteria for the sweet spot of deep coal and rock gas are as follows: The evaluation criteria for Class I sweet spot areas are: net coal thickness > 6m, burial depth ≤ 3500m, roof lithology of coal ash and coal slime, net coal ash content < 25%, and net coal gas content > 12m³. 3 / t, with 0 to 1 interbedded gangue layers; the evaluation criteria for Class II sweet spots are: net coal thickness of 4m to 6m, burial depth ≤ 3500m, roof lithology of coal ash and coal slime, net coal ash content of 25% to 35%, and net coal gas content of 8m 3 / t~12m 3 / t, with 1-3 interbedded gangue layers; the evaluation criteria for Class III sweet spots are: net coal thickness < 4m, burial depth > 3500m, roof lithology of coal sand, net coal ash content > 35%, and net coal gas content < 8m 3 / t, the number of interbedded coal layers is >3.

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