Coal seam coal quality quantitative prediction method and system based on seismic avo attribute

Abstract

The application provides a coal seam coal quality quantitative prediction method and system based on seismic AVO attributes, establishes a coal rock self-consistent rock physical model of coal seam coal quality characteristics, and calculates coal seam elastic parameters and seismic AVO attributes based on the model; a mathematical relationship between seismic AVO attributes and coal rock components and a rationality evaluation index thereof are constructed, and a quantitative prediction method for in-situ coal seam coal rock components in the underground by using seismic AVO attributes is further established; the method is simulated and analyzed by using the rock physical model, has certain universality, is easy to popularize, and fills the blank of prediction of the occurrence of underground coal seam coal quality.

Description

Technical Field

[0001] This invention relates to the field of coal resource quality prediction technology, and more specifically, to a method and system for quantitative prediction of coal seam quality based on seismic AVO attributes. Background Technology

[0002] The industrial composition of coal refers to the composition of coal as water (water contained in coal), ash (minerals in coal), volatile matter, and fixed carbon. The sum of volatile matter and fixed carbon is called the organic matter content, while water and ash are called the inorganic matter. This classification method quantitatively divides the chemical composition of coal, and can accurately determine the proportion of organic and inorganic components in coal. It helps to understand the types and occurrence characteristics of coal mineral components, conduct coal quality evaluation, and is of great significance for coal washing, clean and efficient utilization, prevention of environmental pollution, and analysis of industrial applications. Currently, the analysis of the industrial composition of coal mainly involves coal seam sampling and obtaining relevant parameters using indoor instruments, which is labor-intensive, inefficient, and costly.

[0003] The amplitude versus offset (AVO) method has proven highly effective in high-precision 3D seismic exploration, with variations in rock properties controlling both the intensity of reflected waves and the characteristics of the AVO response. Currently, conventional AVO analysis methods extract amplitude from seismic data and correlate amplitude variations with rock properties using intercept and slope attributes. However, in the coal industry, the application of AVO analysis to predict the occurrence of underground coal seams remains a gap. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a method for quantitative prediction of coal seam quality based on seismic AVO attributes. The method includes: acquiring coal-rock physical parameters of a target area coal seam; the coal-rock parameters include at least one of the following: density, porosity, mineral content, industrial composition, and P-wave and S-wave velocities; establishing a self-compatible coal-rock rock physical model; determining the relationship between each coal-rock component and the coal-rock elastic parameters based on the self-compatible coal-rock rock physical model; calculating the seismic AVO attributes of the target area coal seam to obtain the relationship between the seismic AVO attributes and the coal-rock elastic parameters, and determining the relationship between the seismic AVO attributes and the coal-rock components based on the relationships between each coal-rock component and the coal-rock elastic parameters, and the relationship between the seismic AVO attributes and the coal-rock elastic parameters; and quantitatively predicting the coal-rock components of the target area coal seam based on the optimal relationship between the seismic AVO attributes and the coal-rock components.

[0005] Optionally, the establishment of the coal-rock self-compatible rock physics model includes: constructing a coal-rock self-compatible rock physics model based on a self-compatible approximation model of an N-phase mixture; in the coal-rock self-compatible rock physics model, organic matter is used as an infinite background medium, and ash is used to replace organic matter.

[0006] Optionally, the expression for the coal-rock self-compatible rock physics model is as follows:

[0007]

[0008]

[0009] Among them, v A The volume fraction of coal and rock ash is represented by K and μ, respectively, which represent the bulk modulus and shear modulus of coal and rock ash. and represents the equivalent bulk modulus and shear modulus of the coal-rock self-compatible model, respectively; P and Q are polarization factors.

[0010] Optionally, determining the relationship between each coal and rock component and the coal and rock elastic parameters based on the coal and rock self-compatible rock physics model includes: changing the values ​​of the coal and rock components in the coal and rock self-compatible rock physics model based on a single variable method to obtain the relationship between each coal and rock component and the coal and rock elastic parameters; the values ​​of the coal and rock components include the proportion of coal and rock organic matter, the content of coal and rock organic matter, and the content of coal and rock ash; the coal and rock elastic parameters include longitudinal wave velocity and transverse wave velocity.

[0011] Optionally, the step of calculating the seismic AVO attribute of the coal seam in the target area to obtain the relationship between the seismic AVO attribute and the coal-rock elastic parameters, and determining the relationship between the seismic AVO attribute and the coal-rock components based on the relationship between each coal-rock component and the coal-rock elastic parameters, and the relationship between the seismic AVO attribute and the coal-rock elastic parameters, includes: calculating the seismic AVO attribute based on the Shuey approximation to obtain the relationship between the seismic AVO attribute and the coal-rock elastic parameters; the seismic AVO attribute includes intercept attribute and gradient attribute; determining the relationship between the seismic AVO attribute and the coal-rock components based on the relationship between each coal-rock component and the coal-rock elastic parameters, and the relationship between the seismic AVO attribute and the coal-rock elastic parameters; and using the measured relationship between the seismic AVO attribute and the measured relationship between the coal-rock components as an evaluation index to determine the optimal relationship between the seismic AVO attribute and the coal-rock components.

[0012] Optionally, the optimal seismic AVO attribute and the relationship between coal and petrographic composition are as follows:

[0013] P = 0.0042V A -0.4216, that is

[0014]

[0015] Among them, V O V represents the volume fraction of organic matter in coal and rock, expressed as %; A This represents the volume fraction of coal and rock ash, expressed as a percentage (%). P represents the porosity of coal and rock, expressed as a percentage (%); P represents the AVO intercept attribute of coal and rock.

[0016] Optionally, the step of quantitatively predicting the coal composition of the target area coal seam based on the optimal relationship between the seismic AVO attribute and the coal composition includes: acquiring three-dimensional seismic data, and performing seismic AVO attribute inversion calculation based on the three-dimensional seismic data to obtain the seismic AVO attribute; and calculating the content distribution of coal ash components and coal organic matter components of the target area coal seam based on the optimal relationship between the seismic AVO attribute and the coal composition and the seismic AVO attribute.

[0017] Optionally, the method further includes: analyzing the types, occurrence characteristics, and patterns of coal mineral components based on the content distribution of the coal ash components and the content distribution of the coal organic matter components, and conducting coal quality and coal resource evaluation.

[0018] This invention provides a quantitative prediction system for coal seam quality based on seismic AVO attributes. The system includes: an acquisition module for acquiring coal petrographic parameters of a target area coal seam; the coal petrographic parameters include at least one of the following: density, porosity, mineral content, industrial composition, and P-wave and S-wave velocities; a model building module for establishing a self-compatible coal petrographic model; a first relationship determination module for determining the relationship between each coal petrographic component and the coal petrographic elastic parameters based on the self-compatible coal petrographic model; a second relationship determination module for calculating the seismic AVO attributes of the target area coal seam to obtain the relationship between the seismic AVO attributes and the coal petrographic elastic parameters, and determining the relationship between the seismic AVO attributes and the coal petrographic components based on the relationship between each coal petrographic component and the coal petrographic elastic parameters; and a component analysis module for quantitatively predicting the coal petrographic components of the target area coal seam based on the optimal relationship between the seismic AVO attributes and the coal petrographic components.

[0019] Optionally, the model building module is specifically used to: construct a coal-rock self-compatible rock physics model based on a self-compatible approximation model of an N-phase mixture; in the coal-rock self-compatible rock physics model, organic matter is used as an infinite background medium, and ash is used to replace organic matter.

[0020] The present invention provides a method and system for quantitative prediction of coal seam quality based on seismic AVO attributes. It establishes a self-compatible rock physics model of coal seam quality characteristics and calculates the elastic parameters and seismic AVO attributes of the coal seam based on this model. It constructs a mathematical relationship between seismic AVO attributes and coal rock components, along with its rationality evaluation index, and further establishes a method for quantitative prediction of coal rock components in underground in-situ coal seams using seismic AVO attributes. This method utilizes a rock physics model for simulation analysis, possesses certain universality, and is easy to promote, filling the gap in the prediction of underground coal seam quality occurrence. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0022] Figure 1 This is a coal and rock equivalent component model diagram provided in an embodiment of the present invention;

[0023] Figure 2 A flowchart illustrating a quantitative prediction method for coal seam quality based on seismic AVO attributes provided in an embodiment of the present invention;

[0024] Figure 3 A schematic diagram illustrating the reflection and transmission of seismic waves propagating in underground strata, provided for an embodiment of the present invention;

[0025] Figure 4 A graph showing the relationship between coal and rock organic matter content and AVO intercept properties provided in an embodiment of the present invention;

[0026] Figure 5 This is a graph showing the relationship between coal ash content and AVO intercept properties provided in an embodiment of the present invention.

[0027] Figure 6 This is a diagram showing the relationship between coal and rock organic matter content and AVO gradient properties provided in an embodiment of the present invention.

[0028] Figure 7 This is a diagram showing the relationship between coal ash content and AVO gradient properties provided in an embodiment of the present invention.

[0029] Figure 8 This is a distribution map of coal seam AVO intercept P and gradient G attributes provided in an embodiment of the present invention;

[0030] Figure 9 This is a predicted distribution map of coal seam ash content provided in an embodiment of the present invention.

[0031] Figure 10 This is a predicted distribution map of coal seam organic matter content provided for an embodiment of the present invention. Detailed Implementation

[0032] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0033] AVO (Advanced Optical Variable) technology is an important technique that, based on the simplified Zoeppritz equation, utilizes the principle that the reflection coefficient varies with the incident angle to analyze the variation of amplitude with offset on pre-stack gathers, and further obtains the elastic parameters of rocks, identifies lithology, and detects fluids. Currently, the simplified equation (1) of Shuey (1985) is commonly used in AVO analysis, which clearly shows the variation of the reflection coefficient with the incident angle.

[0034]

[0035] in

[0036]

[0037]

[0038]

[0039] σ * Δσ and Δσ are the average and difference values ​​of the Poisson's ratio of the media on both sides of the reflecting interface, respectively.

[0040]

[0041] Based on the relationship between Poisson's ratio and rock physical parameters, the Poisson's ratio σ and the longitudinal-to-lateral velocity ratio V are derived. p / V s Relationship

[0042] Right now

[0043] Equation (1) can be further transformed and written as equation (2).

[0044]

[0045]

[0046] G=A0R0+Δσ / (1-σ * ) 2

[0047] From equation (2), it can be seen that the amplitude R of the reflected longitudinal wave generated on the elastic interface is... p (θ) and sin 2 θ shows a linear relationship. Here, the AVO intercept attribute P is the P-wave reflection coefficient at perpendicular incidence, and the slope (gradient) attribute G is a term related to the P-wave and S-wave velocities and density of the rock. Meanwhile, under the condition that the wave impedance of the media on both sides of the interface remains constant, the Poisson's ratio difference Δσ has a significant impact on the variation of the reflection amplitude with the incident angle; the larger Δσ is, the greater the variation of the amplitude with the incident angle. Furthermore, the relationship between Poisson's ratio and the P-wave / S-wave velocity ratio V... p / V s Closely related. Poisson's ratio, or the ratio of longitudinal to transverse velocity V. p / V s Seismic AVO (Air-Volume Occurrence) is a physical property constant, and its relationship with rock parameters (rock composition, porosity, degree of consolidation, temperature, pressure, fluid type, and pore morphology) is complex and influenced by multiple factors. For underground coal seams, the porosity, degree of consolidation, temperature, pressure, fluid type, and pore morphology of coal and rock are relatively stable or change little within a certain range. The P-wave and S-wave velocity ratio (Poisson's ratio), density, and other properties of the coal seam are closely related to the coal and rock composition. Furthermore, the AVO property of the coal seam is closely correlated with the coal and rock composition. This lays the theoretical and methodological foundation for quantitatively predicting the coal and rock composition of coal seams using seismic AVO properties.

[0048] Based on the above, this invention proposes an in-situ non-destructive quantitative prediction technology for coal seam quality in underground coal seams. This technology enables large-scale regional quantitative prediction and evaluation of the coal quality characteristics of in-situ underground coal seams using three-dimensional seismic data and seismic attributes, laying the foundation for efficient and accurate evaluation of coal resource quality.

[0049] This invention provides a quantitative prediction method for coal seam quality based on seismic AVO attributes. The main ideas are as follows: (1) coal rock physics testing required for the seismic rock physics model of coal and rock; (2) establishing a self-compatible rock physics model of coal and rock and calculating the elastic parameters of coal and rock; (3) calculating the seismic AVO attributes based on the self-compatible rock physics model of coal and rock; (4) mathematical relationship and characteristic analysis between the seismic AVO attribute parameters and coal and rock components based on the self-compatible model of coal and rock; (5) predicting the coal and rock components of underground in-situ coal seams based on seismic AVO attribute data.

[0050] Among them, (1) above is the basis for carrying out quantitative prediction of coal seam quality based on seismic AVO attributes. According to the coal seam occurrence characteristics and conditions in the target area, such as coal seam occurrence, roof and floor lithology, structure, coal quality anomaly areas, etc., coal and rock sample locations are selected for collection, and basic coal and rock petrological parameters such as density, porosity, mineral composition, industrial composition, P-wave and S-wave velocity are tested in the laboratory to obtain the actual coal and rock petrological parameters in the target area.

[0051] The above (2) is based on the known parameters of coal and rock, such as porosity, pore aspect ratio, bulk modulus and shear modulus of organic matter, as well as bulk modulus and shear modulus of ash, to establish a self-compatible rock physics model that conforms to the characteristics of coal and rock, and further obtain the basic elastic parameters such as coal and rock density, longitudinal wave velocity and transverse wave velocity that are theoretically simulated.

[0052] The above (3) are the basic elastic parameters simulated by the above (2) model. Using the Shuey earthquake AVO approximation, the earthquake AVO properties P and G simulated based on the coal-rock self-compatible model are calculated.

[0053] The above (4) mathematical relationship and characteristic analysis between seismic AVO attribute parameters and coal and rock components based on the coal and rock self-compatible model is the core of the quantitative prediction and evaluation method of coal seam quality based on seismic AVO attribute. By comparing the statistical mathematical relationship between different contents of coal and rock components and corresponding seismic AVO attributes, and combining the AVO attribute calculated by measured elastic parameters, the optimal functional relationship between AVO attribute and coal and rock components is determined.

[0054] The above (5) is based on the functional relationship established in (4). Using the AVO attribute data volume of the three-dimensional seismic inversion, the quantitative distribution map of coal and rock components in the underground in-situ coal seam can be calculated, thus forming a complete regional prediction and evaluation method for the non-destructive quantitative coal quality of underground coal seams based on seismic AVO attributes.

[0055] The embodiments of the present invention have the following advantages:

[0056] 1. Traditional coal and rock composition analysis primarily involves drilling or underground coal and rock samples and sending them to a laboratory for testing to obtain coal and rock composition, physical properties, and parameters such as density, porosity, mineral composition, industrial composition, and P-wave and S-wave velocities. To gain a comprehensive understanding of the characteristics and patterns of underground coal seam occurrence, a large number of samples need to be taken from different locations, resulting in significant limitations, a large workload, and high testing costs. The coal and rock composition prediction method invented in this paper, based on necessary test data, can directly invert and quantitatively determine the occurrence of underground coal seam coal and rock components using 3D seismic data. This eliminates the need for extensive core sampling and testing, achieving non-destructive, non-contact, and quantitative prediction, improving analytical efficiency and saving substantial costs.

[0057] 2. Traditional methods use scattered data obtained from borehole and downhole coring tests. Geostatistical methods are then used to interpolate and extrapolate this point-based data for prediction. However, these methods lack constraints from the coal seam's own characteristic properties, and the interpolated and extrapolated coal quality information is subject to subjective bias and uncontrollability. These shortcomings often lead to significant errors in the calculated coal and petrographic components, physical properties, and physical elastic parameters. This invention uses known data from rock sample tests to construct a coal-rock self-compatible rock physics model suitable for the characteristics of the coal seam. Combined with seismic AVO attribute data, regional quantitative predictions are performed. Because seismic data has a high-density grid in the lateral direction, the extrapolation accuracy using seismic data is significantly higher than that of scattered data from coal sample tests alone. This represents a more efficient and effective method for accurately predicting coal seam composition.

[0058] 3. This invention establishes a coal-rock self-compatible rock physics model based on the coal seam's quality characteristics, and calculates the coal seam's elastic parameters and seismic AVO properties based on this model. Using a rock physics model for simulation analysis has a certain degree of universality and is easy to promote.

[0059] 4. The embodiments of this invention construct a mathematical relationship between seismic AVO attributes and coal and rock composition, and its rationality evaluation index. Furthermore, it establishes a method for quantitative prediction of coal and rock composition in underground in-situ coal seams using seismic AVO attributes. This is a completely new method for predicting coal and rock composition.

[0060] Coal is a mixture of organic and inorganic substances (minerals). Its structure generally consists of three parts: organic matter, mineral (ash) components, and pores (containing water and air). A coal rock equivalent component model is shown below. Figure 1 As shown.

[0061] This invention provides a method for direct quantitative regional prediction of in-situ coal and rock composition of underground coal seams using seismic AVO attributes based on a coal seam rock physics model. The method primarily involves inverting the AVO attributes of the coal seam from 3D seismic data, constructing a relationship between these attributes and the coal and rock composition, and thus directly and quantitatively obtaining the distribution characteristics of the underground coal seam's coal and rock composition. It proposes the measured coal and rock parameters required for the seismic rock physics model of the coal seam, establishes a self-compatible rock physics model suitable for the coal seam's quality characteristics, and analyzes and calculates the elastic parameters of the coal and rock. Using seismic AVO theory, it calculates the seismic AVO attributes simulated by the self-compatible rock physics model, establishes the mathematical relationship between the seismic AVO attribute parameters and the coal and rock composition based on the self-compatible model, analyzes its rationality, and finally proposes a novel and complete method for quantitative regional prediction of in-situ coal and rock composition of underground coal seams using 3D seismic data based on a coal and rock physics model.

[0062] Figure 2This is a flowchart illustrating a quantitative prediction method for coal seam quality based on seismic AVO attributes, provided in an embodiment of the present invention. The method includes the following steps:

[0063] S202, Obtain the petrographic parameters of the coal seam in the target area. These parameters may include: density, porosity, mineral content, industrial composition, and P-wave and S-wave velocities.

[0064] Sampling was conducted on the coal seams in the target area, with samples taken uniformly at different locations within the seams. The sampled coal samples were then tested indoors to obtain various coal and petrographic parameters.

[0065] S204, Establish a self-compatible rock physics model for coal and rock.

[0066] Specifically, a coal-rock self-compatible rock physics model can be constructed based on a self-compatible approximation model of an N-phase mixture. In this model, organic matter serves as an infinite background medium, and ash is used to replace it. For example, the expression for the coal-rock self-compatible rock physics model is as follows:

[0067]

[0068]

[0069] Among them, v A The volume fraction of coal and rock ash is represented by K and μ, respectively, which represent the bulk modulus and shear modulus of coal and rock ash. and represents the equivalent bulk modulus and shear modulus of the coal-rock self-compatible model, respectively; P and Q are polarization factors.

[0070] S206, Based on the above coal-rock self-compatible rock physics model, determine the relationship between each coal-rock component and the coal-rock elastic parameters.

[0071] For example, by changing the values ​​of coal and rock components in a self-compatible rock physics model based on a single variable method, the relationship between each coal and rock component and the elastic parameters of the coal and rock is obtained. The values ​​of the aforementioned coal and rock components include the proportion of organic matter, the content of organic matter, and the ash content of the coal and rock, while the aforementioned elastic parameters include longitudinal wave velocity and transverse wave velocity.

[0072] S208, calculate the seismic AVO attribute of the coal seam in the target area to obtain the relationship between the seismic AVO attribute and the coal rock elastic parameters, and determine the relationship between the seismic AVO attribute and the coal rock components based on the above-mentioned relationship between each coal rock component and the coal rock elastic parameters, and the relationship between the seismic AVO attribute and the coal rock elastic parameters.

[0073] Specifically, seismic AVO attributes can be calculated based on the Shuey approximation to obtain the relationship between seismic AVO attributes and coal-rock elastic parameters. The seismic AVO attributes include intercept attributes and gradient attributes. Secondly, based on the relationship between each coal-rock component and the coal-rock elastic parameters, and the relationship between seismic AVO attributes and the coal-rock elastic parameters, the relationship between seismic AVO attributes and coal-rock components is determined. Then, using the measured relationship between seismic AVO attributes and measured coal-rock components as an evaluation index, the optimal relationship between seismic AVO attributes and coal-rock components is determined.

[0074] Alternatively, the optimal relationship between seismic AVO properties and coal petrographic composition is as follows:

[0075] P = 0.0042V A -0.4216, that is

[0076]

[0077] Among them, V O V represents the volume fraction of organic matter in coal and rock, expressed as %; A This represents the volume fraction of coal and rock ash, expressed as a percentage (%). is the porosity of coal and rock, in %; P is the AVO intercept attribute of coal and rock.

[0078] S210, based on the optimal relationship between seismic AVO attributes and coal and rock composition, quantitative prediction of coal and rock composition of coal seams in the target area is carried out.

[0079] This process involves acquiring 3D seismic data and performing seismic AVO attribute inversion calculations based on this data to obtain seismic AVO attributes. Then, based on the optimal relationship between seismic AVO attributes and coal rock components, and using the seismic AVO attributes, the content distribution of coal rock ash components and coal rock organic matter components in the target area's coal seam is calculated. For example, a distribution map of coal rock ash components and a distribution map of coal rock organic matter components in the coal seam can be output.

[0080] Furthermore, based on the content distribution of coal ash and organic matter components, the types, occurrence characteristics, and patterns of coal mineral components can be analyzed, and coal quality and coal resources can be evaluated. This provides a basis for economic and technical analysis on coal washability, clean and efficient utilization, prevention of environmental pollution, and industrial applications.

[0081] The quantitative prediction method for coal seam quality based on seismic AVO attributes provided in this invention establishes a self-compatible rock physics model of coal seam quality characteristics, and calculates the elastic parameters and seismic AVO attributes of the coal seam based on this model. It constructs a mathematical relationship between seismic AVO attributes and coal rock components, along with its rationality evaluation index, and further establishes a method for quantitative prediction of coal rock components in underground in-situ coal seams using seismic AVO attributes. This method utilizes a rock physics model for simulation analysis, has a certain degree of universality, and is easy to promote, filling the gap in the prediction of underground coal seam quality occurrence.

[0082] The above-mentioned method for quantitative prediction of coal quality in in-situ underground coal seams using three-dimensional seismic data based on coal and rock physics models can be implemented in the following steps:

[0083] 1. Conduct seismic rock physics modeling of coal seams in the target area, including analysis of coal and rock composition, physical properties, and physical testing, and obtain parameters of the underground coal seam.

[0084] 1) Coal seam sampling in the target area

[0085] Sampling principles: Samples should be taken evenly from different locations within the coal seam to ensure freshness. For example, samples from the working face and drill cores should be complete and free of obvious cracks. Complete sampling point information should be recorded underground, and basic information such as the coal seam's occurrence, structure, and texture should be labeled. In this example, 20 coal and rock samples were processed and subjected to rock physical testing.

[0086] 2) Coal and rock density and porosity testing

[0087] The coal sample was weighed using a high-precision electronic scale (M), and its length (L) and diameter (D) were measured using high-precision vernier calipers. The bulk density (ρ) of the rock sample under normal temperature and pressure (20℃, standard atmospheric pressure) was calculated using formula (3). The test results are shown in Table 1. The density in the study area mainly ranged from 1.41 to 1.60 g / cm³, with relatively small density variations and an average density of 1.49 g / cm³.

[0088]

[0089] The geometric structure of coal and rock is mainly divided into two categories: the coal and rock matrix skeleton, including inorganic and organic matter, and the coal and rock pores, which are filled with a certain amount of fluid. For coal porosity, the calculation method is based on Part IV of the national standard (GB / T 23561.4-2009): coal and rock porosity calculated using dry coal bulk density and true density. The porosity test results for each coal sample are shown in Table 1. The porosity of the study area ranges from 7.27% to 15.84%, with an average porosity of 11.64%.

[0090] Sample number 1 2 3 4 5 6 7 8 9 10 <![CDATA[Density [g / cm 3 > 1.466 1.48 1.515 1.551 1.58 1.595 1.52 1.411 1.539 1.58 Porosity [%) 10.48 9.94 12.36 11.25 9.89 10.22 7.27 10.55 8.56 14.28 Sample number 11 12 13 14 15 16 17 18 19 20 <![CDATA[Density [g / cm 3 > 1.453 1.452 1.443 1.44 1.545 1.437 1.433 1.45 1.451 1.518 Porosity [%) 13.07 10.09 16.11 15.84 11.01 14.24 13.10 9.18 11.66 13.79

[0091] Table 1

[0092] 3) Quantitative testing of coal and petrological minerals

[0093] X-ray diffraction (XRD) was used to perform quantitative analysis of whole-rock coal powder and clay samples. The quantitative analysis of whole-rock coal powder could determine the proportion of quartz, calcite, and other minerals in the whole coal sample. The quantitative analysis of clay could determine the proportion of clay minerals such as illite. The mineral contents of various coal samples are shown in Table 2, indicating that the types and proportions of minerals vary in coal samples from different locations within the same coal seam.

[0094]

[0095]

[0096] Table 2

[0097] 4) Industrial Composition Analysis of Coal and Rock

[0098] According to the requirements of GB / T212-2008, the standard for "Industrial Analysis of Coal", the analysis was conducted using a fully automated industrial analyzer, and the results are shown in Table 3. The inorganic fraction contained relatively little moisture and was mainly composed of ash; therefore, ash content will be the primary consideration in the subsequent analysis.

[0099]

[0100]

[0101] Table 3

[0102] 5) Ultrasonic velocity testing of coal and rock

[0103] A triaxial rock mechanics testing system was used to obtain the P-wave and S-wave velocities of the coal sample. The shear wave velocity ranged from 1.11 to 1.42 km / s, with an arithmetic mean of 1.22 km / s. The measured P-wave velocity ranged from 2.12 to 2.62 km / s, with an arithmetic mean of 2.29 km / s. The results of the ultrasonic testing of coal and rock are shown in Table 4.

[0104] Sample number 1 2 3 4 5 6 7 8 9 10 <![CDATA[Vs[g / cm 3 ]]]> 1.26 1.18 1.25 1.35 1.36 1.42 1.11 1.20 1.24 1.34 <![CDATA[Vp[g / cm 3 ]]]> 2.28 2.29 2.33 2.40 2.52 2.62 2.22 2.15 2.36 2.33 Sample number 11 12 13 14 15 16 17 18 19 20 <![CDATA[Vs[g / cm 3 ]]]> 1.15 1.16 1.19 1.18 1.11 1.14 1.20 1.24 1.19 1.15 <![CDATA[Vp[g / cm 3 ]]]> 2.12 2.32 2.16 2.23 2.17 2.30 2.19 2.21 2.32 2.31

[0105] Table 4

[0106] 2. Establish a coal-rock self-compatible rock physics model suitable for the coal seam's properties.

[0107] The analysis of elastic parameters of coal and rock matrix (such as bulk modulus, shear modulus, P-wave velocity, and S-wave velocity) often involves summarizing well logging data and rock physical test data from the study area to derive empirical formulas, which are then used for calculation. This method, relying on empirical formulas, has significant limitations, exhibits strong variability, and requires substantial computation. These drawbacks often lead to large errors in the calculated elastic parameters of coal and rock. To accurately and effectively predict the S-wave velocity of coal and rock, this invention establishes a self-compatible rock physical model suitable for the characteristics of coal and rock to analyze the impact of organic matter / ash content on elastic parameters such as coal and rock density, P-wave and S-wave velocities, bulk modulus, and shear modulus.

[0108] A self-compatible rock physics model is a rock physics model that uses one of its components as an infinite background medium and replaces the background medium with an equivalent medium to achieve elastic interaction between the components. For coal and rock, their structure mainly consists of two parts: a matrix framework and a pore system. The framework is composed of ash and organic matter, and these two components have a significant impact on the elastic parameters of coal and rock. In the modeling process, a self-compatible rock physics model for coal and rock is constructed based on the self-compatible approximation model of N-phase mixtures given by Berryman (1995). In coal and rock, the content of organic matter is relatively high, while the content of ash is relatively low, and there is a linear interaction between the two. The self-compatible rock physics model for coal and rock established in this invention proposes that the organic matter in coal and rock is used as an infinite background medium, and that ash is used to replace the organic matter. A multi-iteration method is adopted in the solution process.

[0109]

[0110]

[0111] Among them, V A This represents the volume fraction of coal and rock ash; K and μ represent the bulk modulus and shear modulus of coal and rock ash, respectively. and These represent the equivalent bulk modulus and shear modulus of the coal-rock self-compatible model, respectively; P and Q are polarization factors, which are related to the shape of the ash aggregate in the coal-rock and can be measured by the aspect ratio of the shape.

[0112] The shear modulus of coal and rock organic matter is half of its bulk modulus. The bulk modulus of organic matter is taken as 5 GPa, and the shear modulus as 2.5 GPa (Shitrit, 2016). Using the Voigt-Reuss-Hill average modulus calculation formula, the ash elastic modulus in the coal sample was calculated, and the ash bulk modulus was set as 8.998 GPa and the shear modulus as 4.965 GPa.

[0113] The equivalent bulk modulus and shear modulus of the coal sample were obtained using the coal-rock self-compatible approximation model (4). The longitudinal and transverse wave velocities of the coal were obtained using formula (5) and compared with the measured longitudinal and transverse wave velocities of the coal.

[0114]

[0115]

[0116] Among them, V p V s This indicates the longitudinal and transverse wave velocities, with units of km / s.

[0117] Table 5 shows the comparison results of the calculated and measured longitudinal and transverse wave velocities using the coal-rock self-compatible rock physics model.

[0118] Sample number <![CDATA[V p-self (km / s)]]> <![CDATA[V p-real (km / s)]]> <![CDATA[V s-self (km / s)]]> <![CDATA[V s-real (km / s)]]> 1 2.21 2.28 1.27 1.26 2 2.14 2.25 1.21 1.18 3 2.24 2.33 1.29 1.25 4 2.25 2.40 1.34 1.35 5 2.32 2.52 1.38 1.36 6 2.55 2.62 1.46 1.42 7 2.13 2.22 1.19 1.11 8 2.19 2.15 1.27 1.20 9 2.27 2.36 1.30 1.24 10 2.32 2.33 1.29 1.34 11 2.11 2.18 1.22 1.15 12 2.19 2.20 1.26 1.16 13 2.05 2.19 1.19 1.19 14 2.03 2.23 1.18 1.18 15 2.03 2.17 1.21 1.11 16 2.18 2.27 1.13 1.14 17 2.11 2.19 1.22 1.2 18 2.18 2.21 1.26 1.24 19 2.16 2.26 1.25 1.19 20 2.24 2.19 1.18 1.15

[0119] Table 5

[0120] The difference between the model and measured P-wave velocities fluctuated between 0.01 and 0.26, while the difference between S-wave velocities fluctuated between 0.01 and 0.08. The relatively small fluctuations indicate the feasibility of the self-compatible rock physics model for coal and rock. Furthermore, it can be observed that the correlation coefficient between the predicted and measured S-wave velocities is greater than that between the predicted and measured values. This is because, in the calculation using seismic wave propagation laws, the calculation of S-wave velocity is only related to the shear modulus of the coal and rock skeleton, while the calculation of P-wave velocity requires both bulk modulus and shear modulus.

[0121] 3. Analysis of basic elastic parameters of coal and rock required for calculating AVO properties based on a self-compatible rock physics model of coal and rock.

[0122] To determine the relationship between coal and petrographic components and AVO properties, it is necessary to establish the relationship between coal and petrographic components and P-wave and S-wave velocities. To understand the influence of coal and petrographic components on P-wave and S-wave velocities, a single-variable method is used, changing the organic matter content variable in a self-compatible rock physics model of coal and petrographic components to obtain the relationship between these components and P-wave and S-wave velocities. Therefore, during the model establishment process, the organic matter volume fraction was set to 50%, 55%, 60%, 65%, 70%, 75%, 80%, and 85%. The porosity of 11.64% from the coal and petrographic samples tested in the aforementioned target area was selected as the porosity of the model.

[0123] 1) Formula for calculating coal density under different proportions of coal and rock organic matter components (6)

[0124]

[0125] in, This represents the density of coal and petrified material in different component proportions; ρ OThis represents the density of organic matter; in the specific implementation case, the average organic matter density of 20 coal and rock samples was used; ρ A This represents the density of ash content; in the specific implementation case, the average ash density of 20 coal and rock samples was used.

[0126] Formula for calculating the organic matter density of coal and rock samples (7)

[0127]

[0128] Where, ρ o This represents the density of organic matter, expressed in g / cm³. 3 M o This represents the mass of organic matter, measured in grams (g); V o This represents the volume of organic matter, measured in cm³. 3 M ad The mass fraction of volatile matter is expressed as %; M cd This represents the mass fraction of fixed carbon, expressed in %; ρ c This indicates the bulk density of coal and rock, expressed in g / cm³. 3 V A The volume fraction of ash in the coal sample is expressed as %. This indicates the porosity of coal and rock.

[0129] Ash volume fraction refers to the sum of the volume contents of various minerals in coal and rock, and is calculated using formula (8).

[0130]

[0131] Where, m i This represents the mass fraction of the i-th mineral in the total minerals, expressed as a percentage (%). It can be calculated using the aforementioned quantitative test results for coal and petrological minerals. M A This represents the mass fraction of ash, expressed in %; ρ i This represents the density of the i-th mineral, expressed in g / cm³. 3 The values ​​that can be cited are those in the Rock Physics Handbook, as shown in Table 6, which contains mineral density values ​​in coal and rock.

[0132]

[0133] Table 6

[0134] The ash density of coal and rock is calculated using formula (9).

[0135]

[0136] The above formula was used to calculate the ash volume content, ash density, and organic matter density of 20 coal samples collected in the implementation case. The corresponding results are shown in Table 7.

[0137]

[0138]

[0139] Table 7

[0140] The calculated organic matter density of the coal and rock samples in the table shows that the density of organic matter in the coal and rock varies between 1.51 and 1.71 g / cm³, with an average organic matter density of 1.61 g / cm³. This density is used as the organic matter density in the process of establishing the self-compatible rock physics model of coal and rock. The ash density varies between 2.00 and 2.92 g / cm³, with an average ash density of 2.32 g / cm³. This ash density is also used as the ash density in the process of establishing the self-compatible rock physics model of coal and rock.

[0141] 2) Analysis of Organic Matter Content and Coal Rock Elastic Parameters

[0142] A self-compatible rock physics model for coal and rock was established using the given basic parameters. Elastic parameters of coal and rock were calculated for different proportions of organic matter volume fraction (50%, 55%, 60%, 65%, 70%, 75%, 80%, and 85%). The model calculations yielded the corresponding elastic modulus (bulk modulus, shear modulus), longitudinal and transverse wave velocities, and coal and rock densities, as shown in Table 8, which represents the elastic parameter predictions from the self-compatible coal and rock model for different organic matter component proportions.

[0143]

[0144] Table 8

[0145] As can be seen from this table, there is a linear relationship between organic matter content and coal density. As the organic matter content increases, the coal density decreases. The relationship between coal density and organic matter content conforms to the linear equation (10), showing a strict linear negative correlation.

[0146] ρ C = -0.63V O +2.04 (10)

[0147] As the organic matter content increases, the bulk modulus and shear modulus of the coal-rock skeleton gradually decrease, indicating a negative correlation between the volume fraction of organic matter and the bulk and shear moduli of the coal-rock skeleton. Similarly, the longitudinal wave velocity and transverse wave velocity of the coal-rock skeleton gradually decrease with increasing organic matter content, again showing a negative correlation. The elastic modulus (bulk modulus and shear modulus) of the coal-rock skeleton changes more rapidly with increasing organic matter content, while the longitudinal and transverse wave velocities change more slowly. This suggests that the organic matter content has a greater impact on the elastic modulus of the coal-rock skeleton than on its longitudinal and transverse wave velocities.

[0148] 3) Analysis of ash content and coal rock elastic parameters

[0149] Coal and rock are composed of moisture, ash, and organic matter. Therefore, the volume fraction of ash in coal and rock can be obtained using the difference method, i.e., the composition relationship of coal and rock is: Volume fraction of ash = 100% - porosity - volume fraction of organic matter. It can be seen that the content of organic matter in coal and rock is inversely correlated with the content of ash. The ash content of the above eight models can be obtained through calculation, as shown in Table 9 for the ash content of the self-compatible coal and rock models with different component ratios.

[0150] Model label 1 2 3 4 5 6 7 8 Va(%) 38.36 33.36 28.36 23.36 18.36 13.36 8.36 3.36

[0151] Table 9

[0152] Analysis of Tables 8 and 9 shows a linear relationship between ash content and coal density; as ash content increases, coal density also increases, exhibiting a strict positive correlation. Since organic matter content and ash content in coal are inversely correlated, the bulk modulus and shear modulus of the coal skeleton gradually increase with increasing ash content, meaning the volume fraction of ash is positively correlated with the bulk modulus and shear modulus of the coal skeleton. With increasing ash content, the longitudinal wave velocity and transverse wave velocity of the coal skeleton gradually increase, indicating a positive correlation between ash content and the longitudinal and transverse wave velocities of the coal skeleton. Similarly, the elastic modulus (bulk modulus and shear modulus) of the coal skeleton changes rapidly with increasing ash content, while the longitudinal and transverse wave velocities change more slowly with increasing organic matter content. This also indicates that the ash content has a greater impact on the elastic modulus of the coal and rock skeleton than on the longitudinal and transverse wave velocities. In summary, the coal and rock components have a linear relationship with the elastic modulus and velocity of the matrix skeleton of the coal and rock body, and their impact on the elastic modulus of the coal and rock skeleton is greater than their impact on the longitudinal and transverse wave velocities.

[0153] 4. Establish mathematical relationships between seismic AVO attribute parameters and coal-rock components based on a coal-rock self-compatible model, and develop rationality and optimal solution evaluation and analysis methods.

[0154] 1) Elastic parameters of the coal-rock self-compatible model for seismic AVO attribute analysis

[0155] A single-interface, two-layer geophysical model is established to perform AVO property analysis. The obtained data on P-wave and S-wave velocities and densities of the two-layer medium are used to solve for the reflection coefficient using the Zoeppritz equation or its approximation. Figure 3 This diagram illustrates the reflection and transmission of seismic waves propagating through underground strata. Wherein, θ1, θ2 represents the reflection angle of longitudinal and transverse waves. V represents the transmission angles of the longitudinal and transverse waves; ρ1 and ρ2 are the densities of the media above and below the reflecting interface; V P1 V P2 V represents the longitudinal wave velocity of the media above and below the reflecting interface. S1 V S2 The transverse wave velocity is the velocity of the medium above and below the reflection interface.

[0156] Here, the Shuey approximation is used to calculate the AVO property, thereby obtaining the relationship between the P-wave and S-wave velocities and the AVO property, and further exploring the relationship between coal and rock components and the AVO property.

[0157] In the implementation case, the roof lithology of the coal seam is mainly mudstone. During modeling, the roof of the coal seam was set as mudstone, with an average P-wave velocity of 3.0 km / s and an average S-wave velocity of 1.41 km / s, which were used as parameters for medium 1 in the model for simulation. The lower layer represents the coal seam, and the P-wave and S-wave velocities and density obtained from the coal-rock self-compatible model were used for calculation. Therefore, the model parameters are established as shown in Table 10, which is a table of elastic parameters for different organic matter component ratios in the coal-rock self-compatible model.

[0158] Double-layer medium Longitudinal wave velocity Vp (km / s) Transverse wave velocity Vs (km / s) <![CDATA[Density (g / cm 3 )]]> Medium 1 3 1.41 2.26 Medium 2(1) 2.69 1.54 1.5 Medium 2(2) 2.62 1.51 1.47 Medium 2(3) 2.56 1.48 1.43 Medium 2(4) 2.51 1.44 1.4 Medium 2(5) 2.45 1.42 1.37 Medium 2(6) 2.4 1.39 1.34 Medium 2(7) 2.36 1.36 1.31 Medium 2(8) 2.32 1.34 1.28

[0159] Table 10

[0160] Simultaneously, using the density and P- and S-wave velocities of 20 coal and rock samples obtained from the implementation case test as parameters for medium 2, AVO property analysis was performed to obtain the AVO property values ​​of the actual data. We can use the relationship between the AVO property values ​​calculated from the actual data and the organic matter / ash content to verify and evaluate the rationality of the relationship between the AVO properties and organic matter / ash content obtained from the coal and rock self-compatibility model. Thus, the data for medium 2 was set as the actual data, and the parameters are shown in Table 11, the table of measured elastic parameters of coal and rock samples.

[0161]

[0162]

[0163] Table 11

[0164] 2) Analysis of the relationship between AVO properties and coal petrographic components

[0165] Using the aforementioned elastic parameters and the Shuey approximation, the AVO properties of a single-interface two-layer medium were calculated, and the intercept and gradient property values ​​were obtained respectively. The AVO property values ​​obtained based on the coal-rock self-compatible model with different organic matter component ratios are shown in Table 12, which is a table of seismic AVO intercept P and gradient G properties calculated based on the coal-rock self-compatible model with different organic matter component ratios. The AVO property values ​​calculated based on the measured coal-rock elastic parameters are shown in Table 13, which is a table of seismic AVO intercept P and gradient G properties calculated based on measured coal-rock data.

[0166] Model 1 2 3 4 5 6 7 8 P -0.26 -0.28 -0.30 -0.32 -0.35 -0.37 -0.39 -0.40 G 0.62 0.61 0.70 0.84 0.83 0.90 0.88 1.05

[0167] Table 12

[0168] Actual measurement 1 2 3 4 5 6 7 8 9 10 P -0.35 -0.35 -0.32 -0.30 -0.26 -0.24 -0.35 -0.40 -0.31 -0.30 G 0.86 0.99 0.84 0.67 0.63 0.54 1.07 0.99 0.84 0.65 Actual measurement 11 12 13 14 15 16 17 18 19 20 P -0.38 -0.37 -0.38 -0.37 -0.35 -0.36 -0.38 -0.37 -0.36 -0.35 G 1.06 1.05 1.00 1.02 1.05 1.08 0.99 0.90 1.00 1.01

[0169] Table 13

[0170] Correlation analysis was performed on the organic matter content and ash content of coal and rock with the obtained intercept and gradient attributes, such as... Figure 4 The graph showing the relationship between coal and rock organic matter content and AVO intercept properties is shown below. Figure 5 The graph showing the relationship between coal ash content and AVO intercept properties is shown below. Figure 6 The graph showing the relationship between coal and rock organic matter content and AVO gradient properties is shown below. Figure 7 The graph showing the relationship between coal ash content and AVO gradient properties yields linear relationships between coal organic matter content, ash content, intercept, and gradient properties based on coal self-compatible model simulation data and measured coal sample data, respectively. The relationship based on measured coal sample data is used as an evaluation index.

[0171] In the above functional relationships, the correlation between the linear relationship of coal and rock organic matter content, ash content and AVO intercept attribute P is 0.998, and the correlation with AVO gradient attribute G is 0.911. Therefore, the functional relationship with AVO intercept attribute P is the optimal solution. The linear relationship fitted from the measured data is used as the evaluation index to further select the optimal relationship. Among them, the correlation between the linear relationship of AVO attribute fitted from the measured data of coal samples is 0.723 for coal and rock organic matter content and attribute P, 0.871 for ash content and attribute P, 0.724 for ash content and attribute G, and 0.82 for ash content and attribute G. Therefore, the linear relationship between ash content and attribute P is selected as the optimal relationship between AVO attribute and coal and rock components (10).

[0172] P = 0.0042V A-0.4216, that is

[0173]

[0174] Among them, V O V represents the volume fraction of organic matter in coal and rock, expressed as %; A This represents the volume fraction of coal and rock ash, expressed as a percentage (%). P represents the porosity of coal and rock, expressed as a percentage (%); P represents the AVO intercept attribute of coal and rock.

[0175] Therefore, a mathematical relationship between seismic AVO attribute parameters and coal-rock components based on a coal-rock self-compatible model was established, along with its optimal evaluation method.

[0176] 5. Quantitative prediction of coal and petrographic components in underground in-situ coal seams based on seismic AVO attribute data.

[0177] 1) After fidelity processing, the 3D seismic data is used to obtain the pre-stack seismic data volume;

[0178] 2) Subsequently, AVO attribute inversion calculations can be performed to obtain the seismic AVO intercept attribute P and gradient attribute G data volumes. The target coal seam roof interface can then be tracked, and coal seam AVO attribute slices can be extracted, such as... Figure 8 The diagram shows the distribution of AVO intercept P and gradient G attributes of the coal seam. The left diagram is a slice of coal seam intercept P, and the right diagram is a slice of coal seam gradient G.

[0179] 3) Based on the established mathematical relationship between the seismic AVO attribute parameters and coal and rock components (10) based on the coal and rock self-compatible model, the content distribution map of coal and rock ash components in the underground in-situ coal seam is obtained by using the target area coal seam intercept attribute P data volume. Figure 9 The predicted distribution map of coal seam ash content is shown. Further calculation using the relationship between various components of coal seam (11) yields the distribution map of coal seam organic matter content. Figure 10 The diagram shows the predicted distribution of organic matter content in coal seams. This allows for analysis of the types, occurrence characteristics, and patterns of coal mineral components, evaluation of coal quality and resources, and provides a basis for economic and technical analysis regarding coal washability, clean and efficient utilization, environmental pollution prevention, and industrial applications.

[0180] This invention provides a quantitative prediction system for coal seam quality based on seismic AVO attributes. The system includes:

[0181] The acquisition module is used to acquire the coal and petrological physical parameters of the coal seam in the target area; the coal and petrological parameters include at least one of the following: density, porosity, mineral content, industrial composition, and longitudinal and transverse wave velocities;

[0182] The model building module is used to build a self-compatible rock physics model of coal and rock.

[0183] The first relationship determination module is used to determine the relationship between each coal and rock component and the elastic parameters of coal and rock based on the coal and rock self-compatible rock physics model.

[0184] The second relationship determination module is used to calculate the seismic AVO attribute of the coal seam in the target area to obtain the relationship between the seismic AVO attribute and the coal rock elastic parameters, and to determine the relationship between the seismic AVO attribute and the coal rock components based on the relationship between each coal rock component and the coal rock elastic parameters, and the relationship between the seismic AVO attribute and the coal rock elastic parameters.

[0185] The component analysis module is used to quantitatively predict the coal and rock composition of coal seams in the target area based on the optimal relationship between seismic AVO attributes and coal and rock composition.

[0186] Optionally, the model building module is specifically used for: constructing a coal-rock self-compatible rock physics model based on a self-compatible approximation model of an N-phase mixture; in the coal-rock self-compatible rock physics model, organic matter is used as an infinite background medium, and ash is used to replace organic matter.

[0187] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by computer-controlled devices. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The storage medium can be a memory, a disk, an optical disk, etc.

[0188] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0189] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A quantitative prediction method for coal seam quality based on seismic AVO attributes, characterized in that, The method includes: Obtain the petrographic parameters of the coal seam in the target area; the petrographic parameters include at least one of the following: density, porosity, mineral content, industrial composition, and longitudinal and transverse wave velocities; Establish a self-compatible rock physics model for coal and rock; The relationship between each coal and rock component and the elastic parameters of the coal and rock is determined based on the self-compatible rock physics model of the coal and rock. The seismic AVO attribute of the coal seam in the target area is calculated to obtain the relationship between the seismic AVO attribute and the coal rock elastic parameters. The relationship between the seismic AVO attribute and the coal rock components is determined based on the relationship between each coal rock component and the coal rock elastic parameters, and the relationship between the seismic AVO attribute and the coal rock elastic parameters. Based on the optimal relationship between the earthquake AVO attribute and coal and rock composition, the coal and rock composition of the target area is quantitatively predicted.

2. The method according to claim 1, characterized in that, The establishment of a self-compatible rock physics model for coal and rock includes: A coal-rock self-compatible rock physics model is constructed based on the self-compatible approximation model of N-phase mixtures; in the coal-rock self-compatible rock physics model, organic matter is used as an infinite background medium, and ash is used to replace organic matter.

3. The method according to claim 2, characterized in that, The expression for the coal-rock self-compatible rock physics model is as follows: in, v A This indicates the volume fraction of coal and rock ash. K and μ These represent the bulk modulus and shear modulus of coal and rock ash, respectively. and These represent the equivalent bulk modulus and shear modulus of the coal-rock self-compatible model, respectively. P and Q This is the polarization factor.

4. The method according to claim 1, characterized in that, The determination of the relationship between each coal and rock component and the elastic parameters of the coal and rock based on the self-compatible rock physics model includes: By changing the values ​​of coal and rock components in the self-compatible rock physics model of coal and rock using a single variable method, the relationship between each coal and rock component and the elastic parameters of coal and rock is obtained; the values ​​of the coal and rock components include the proportion of coal and rock organic matter, the content of coal and rock organic matter, and the ash content of coal and rock; the elastic parameters of coal and rock include longitudinal wave velocity and transverse wave velocity.

5. The method according to claim 1, characterized in that, The calculation of the seismic AVO attribute of the coal seam in the target area to obtain the relationship between the seismic AVO attribute and the coal-rock elastic parameters, and the determination of the relationship between the seismic AVO attribute and the coal-rock components based on the relationship between each coal-rock component and the coal-rock elastic parameters, and the relationship between the seismic AVO attribute and the coal-rock elastic parameters, include: Seismic AVO attributes are calculated based on the Shuey approximation to obtain the relationship between the seismic AVO attributes and the elastic parameters of the coal and rock; the seismic AVO attributes include intercept attributes and gradient attributes. The relationship between the seismic AVO attribute and the coal rock component is determined based on the relationship between each coal rock component and the coal rock elastic parameter, and the relationship between the seismic AVO attribute and the coal rock elastic parameter. Using the relationship between measured seismic AVO attributes and measured coal and rock composition as an evaluation index, the optimal relationship between seismic AVO attributes and coal and rock composition is determined.

6. The method according to claim 5, characterized in that, The optimal relationship between seismic AVO properties and coal petrographic composition is as follows: ,Right now in, This represents the volume fraction of organic matter in coal and rock, expressed as % . This represents the volume fraction of coal and rock ash, expressed in % (%). Coal porosity, expressed as % . This represents the AVO intercept attribute for coal and rock.

7. The method according to claim 1, characterized in that, The step of quantitatively predicting the coal and petrographic composition of the target area coal seam based on the optimal relationship between the seismic AVO attribute and coal and petrographic composition includes: Acquire three-dimensional seismic data, and perform seismic AVO attribute inversion calculation based on the three-dimensional seismic data to obtain seismic AVO attributes; Based on the optimal relationship between the seismic AVO attribute and the coal rock composition, and the seismic AVO attribute, the content distribution of coal rock ash components and the content distribution of coal rock organic matter components in the target area coal seam are calculated.

8. The method according to claim 1, characterized in that, The method further includes: Based on the content distribution of coal ash components and the content distribution of coal organic matter components, the types, occurrence characteristics, and patterns of coal mineral components are analyzed, and coal quality and coal resources are evaluated.

9. A quantitative prediction system for coal seam quality based on seismic AVO attributes, characterized in that, The system includes: The acquisition module is used to acquire the petrographic parameters of the coal seam in the target area; the petrographic parameters include at least one of the following: density, porosity, mineral content, industrial composition, and longitudinal and transverse wave velocities; The model building module is used to build a self-compatible rock physics model of coal and rock. The first relationship determination module is used to determine the relationship between each coal and rock component and the coal and rock elastic parameters based on the coal and rock self-compatible rock physics model. The second relationship determination module is used to calculate the seismic AVO attribute of the coal seam in the target area to obtain the relationship between the seismic AVO attribute and the coal rock elastic parameters, and to determine the relationship between the seismic AVO attribute and the coal rock components based on the relationship between each coal rock component and the coal rock elastic parameters, and the relationship between the seismic AVO attribute and the coal rock elastic parameters. The component analysis module is used to quantitatively predict the coal and rock composition of coal seams in the target area based on the optimal relationship between the seismic AVO attribute and the coal and rock composition.

10. The system according to claim 9, characterized in that, The model building module is specifically used for: A coal-rock self-compatible rock physics model is constructed based on the self-compatible approximation model of N-phase mixtures; in the coal-rock self-compatible rock physics model, organic matter is used as an infinite background medium, and ash is used to replace organic matter.

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