Quantitative coal-seam coal-quality prediction method and system based on seismic AVO attribute

Abstract

A quantitative coal-seam coal-quality prediction method and system based on a seismic AVO attribute. A physical model for self-consistent rock in coal rock is established, which model is based on the coal quality characteristics of a coal seam, and on the basis of the model, an elasticity parameter and seismic AVO attribute of the coal seam are calculated; mathematical relational expressions between the seismic AVO attribute and coal rock components, and rationality evaluation indicators of the mathematical relational expressions are constructed; and a method for using the seismic AVO attribute to perform quantitative prediction on coal rock components of an underground in-situ coal seam is further established. In the method, a physical model for rock is used to perform simulation analysis, such that a certain level of universality is achieved, and the method is easily popularized, and fills in the blanks in the prediction of the coal quality occurrence of the underground coal seam.

Description

Coal seam quality quantitative prediction method and system based on seismic AVO attributes Technical Field

[0001] The present invention relates to the technical field of coal resource and coal quality prediction, and in particular to a method and system for quantitatively predicting coal seam coal quality based on seismic AVO attributes. Background Art

[0002] The industrial composition of coal rock refers to its composition, which includes water (water contained in the rock), ash (minerals contained in the rock), volatile matter, and fixed carbon. The sum of volatile matter and fixed carbon is referred to as the organic matter content, while water and ash are referred to as the inorganic matter. This classification method quantitatively divides the chemical composition of coal rock and accurately determines the proportions of organic and inorganic components. Understanding the types and occurrence characteristics of coal rock minerals allows for coal quality evaluation, which is crucial for coal selectivity, clean and efficient utilization, environmental pollution prevention, and industrial application analysis. Currently, analysis of coal industrial composition is primarily performed through coal seam sampling and laboratory instrument testing to obtain relevant parameters. This is labor-intensive, inefficient, and costly. Amplitude versus offset (AVO) has proven to be highly effective in high-precision 3D seismic exploration. Changes in rock physical properties control the intensity of the reflected wave and the characteristics of the AVO response. Currently, conventional AVO analysis methods extract amplitudes from seismic data and relate the variation of amplitude with offset to rock properties through two attributes: intercept and slope. However, in the coal industry, using AVO analysis to predict the occurrence of coal quality in underground coal seams is still a new approach.

[0003] Summary of the Invention

[0004] To solve the above problems, an embodiment of the present invention provides a method for quantitatively predicting coal seam coal quality based on seismic AVO attributes, the method comprising: obtaining coal rock physical parameters of the target area coal seam; the coal rock parameters comprising at least one of the following: density, porosity, mineral content, industrial composition, and longitudinal and transverse wave velocities; establishing a self-compatible rock physics model of the coal rock; determining the relationship between each coal rock component and the coal rock elastic parameters based on the coal rock self-compatible rock physics 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 a relationship formula between the seismic AVO attributes and the coal rock components based on the relationship between the 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 formula between the seismic AVO attributes and the coal rock components.

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

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

[0007] Among them, V A represents the volume fraction of coal ash; K and μ represent the bulk modulus and shear modulus of coal ash, respectively; and They represent the equivalent bulk modulus and shear modulus of the coal rock self-compatible model respectively; P and Q are polarization factors.

[0008] Optionally, determining the relationship between each coal rock component and the coal rock elastic parameters based on the coal rock self-compatible rock physics model includes: changing the values ​​of the coal rock components in the coal rock self-compatible rock physics model based on a single variable method to obtain the relationship between each coal rock component and the coal rock elastic parameters; the values ​​of the coal rock components include the proportion of coal rock organic matter components, the organic matter content of coal rock, and the ash content of coal rock, and the coal rock elastic parameters include longitudinal wave velocity and shear wave velocity.

[0009] Optionally, the calculation of the seismic AVO attributes of the coal seam in the target area obtains the relationship between the seismic AVO attributes and the coal rock elastic parameters, and determines the relationship formula between the seismic AVO attributes and the coal rock components according to the relationship between the various coal rock components and the coal rock elastic parameters and the relationship between the seismic AVO attributes and the coal rock elastic parameters, including: calculating the seismic AVO attributes based on the Shuey approximation to obtain the relationship between the seismic AVO attributes and the coal rock elastic parameters; the seismic AVO attributes include intercept attributes and gradient attributes; determining the relationship formula between the seismic AVO attributes and the coal rock components according to the relationship between the various coal rock components and the coal rock elastic parameters and the relationship between the seismic AVO attributes and the coal rock elastic parameters; and using the relationship formula between the measured seismic AVO attributes and the measured coal rock components as an evaluation index to determine the optimal relationship formula between the seismic AVO attributes and the coal rock components among the relationship formulas between the seismic AVO attributes and the coal rock components.

[0010] Optionally, the relationship between the optimal seismic AVO attribute and coal rock composition is as follows:

[0011] P=0.0042V A -0.4216, or V O =100-φ-V A

[0012] Among them, V O V is the volume fraction of organic matter in coal rock, in %; A is the volume fraction of coal ash, in %; φ is the porosity of coal, in %; P is the AVO intercept attribute of coal.

[0013] Optionally, the coal rock components of the coal seam in the target area are quantitatively predicted based on the optimal relationship between the seismic AVO attributes and the coal rock components, including: acquiring three-dimensional seismic data, and performing seismic AVO attribute inversion calculation based on the three-dimensional seismic data to obtain seismic AVO attributes; and calculating the content distribution of coal rock ash components and the content distribution of coal rock organic matter components in the coal seam in the target area based on the optimal relationship between the seismic AVO attributes and the coal rock components and the seismic AVO attributes.

[0014] Optionally, the method further includes: analyzing the types, occurrence characteristics and laws of the coal rock mineral components based on the content distribution of the coal rock ash components and the content distribution of the coal rock organic matter components, and conducting coal quality and coal resource evaluation. An embodiment of the present invention provides a coal seam coal quality quantitative prediction system based on seismic AVO attributes, the system comprising: an acquisition module for acquiring coal rock physical parameters of a target area coal seam; the coal rock parameters comprising at least one of the following: density, porosity, mineral content, industrial composition, and longitudinal and transverse wave velocities; a model establishment module for establishing a self-compatible rock physical model of coal rock; a first relationship determination module for determining the relationship between each coal rock component and the coal rock elastic parameters based on the coal rock self-compatible rock physical 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 rock elastic parameters, and determining a relationship formula between the seismic AVO attributes and the coal rock components based on the relationship between the 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 a component analysis module for quantitatively predicting the coal rock components of the target area coal seam based on the optimal relationship formula between the seismic AVO attributes and the coal rock components.

[0015] Optionally, the model building module is specifically used to: construct a self-compatible rock physics model of coal rock based on a self-compatible approximate model of N-phase mixture; in the self-compatible rock physics model of coal rock, organic matter is used as an infinite background medium, and ash is used to replace organic matter.

[0016] The embodiment of the present invention provides a method and system for quantitatively predicting coal seam coal quality based on seismic AVO attributes, establishes a self-compatible rock physics model of coal rock with coal seam coal quality characteristics, and calculates the elastic parameters and seismic AVO attributes of the coal seam based on the model; constructs a mathematical relationship between seismic AVO attributes and coal rock components and its rationality evaluation index, and further establishes a method for quantitatively predicting coal rock components in underground in-situ coal seams using seismic AVO attributes; the method uses a rock physics model for simulation analysis, has certain universality, and is easy to promote, filling the gap in the prediction of coal quality occurrence conditions in underground coal seams. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0018] FIG1 is a diagram of a coal rock equivalent component model provided by an embodiment of the present invention;

[0019] FIG2 is a flow chart of a method for quantitatively predicting coal seam quality based on seismic AVO attributes according to an embodiment of the present invention;

[0020] FIG3 is a schematic diagram of reflection and transmission of seismic waves propagating in underground strata according to an embodiment of the present invention;

[0021] FIG4 is a diagram showing the relationship between the organic matter content of coal and the AVO intercept attribute provided by an embodiment of the present invention;

[0022] FIG5 is a graph showing the relationship between coal ash content and AVO intercept attributes according to an embodiment of the present invention;

[0023] FIG6 is a diagram showing the relationship between the organic matter content of coal and the AVO gradient attribute provided by an embodiment of the present invention;

[0024] FIG7 is a diagram showing the relationship between coal ash content and AVO gradient properties according to an embodiment of the present invention;

[0025] FIG8 is a distribution diagram of the coal seam AVO intercept P and gradient G attributes provided by an embodiment of the present invention;

[0026] FIG9 is a predicted distribution diagram of coal seam ash content provided by an embodiment of the present invention;

[0027] FIG10 is a predicted distribution diagram of organic matter content in coal seams and coal rocks provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the above-mentioned objects, features and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0029] AVO technology is an important technique that uses the simplified Zoeppritz equation and the principle that the reflection coefficient varies with the angle of incidence to analyze the variation of amplitude with offset on prestack gathers. This allows for the determination of rock elastic parameters, identification of lithology, and detection of fluids. Shuey's (1985) simplified equation (1) is commonly used in AVO analysis, clearly showing how the reflection coefficient varies with the angle of incidence.

[0030] in

[0031] σ* and Δσ are the average and difference of the Poisson's ratio of the media on both sides of the reflection interface, that is,

[0032] Δσ=σ2-σ1

[0033] According to the relationship between Poisson's ratio and rock physical parameters, Poisson's ratio σ and longitudinal velocity ratio V are derived. p / V s The relationship between Right now

[0034] Formula (1) can be further transformed and written as formula (2) G=A0R0+Δσ / (1-σ * ) 2

[0035] From formula (2), we can see that the amplitude of the reflected longitudinal wave Rp(θ) generated on the elastic interface is related to sin 2 θ is linearly related. Among them, the AVO intercept attribute P is the longitudinal wave reflection coefficient at vertical incidence, and the slope (gradient) attribute G is a term related to the longitudinal and transverse wave velocities and density of the rock. At the same time, under the condition that the wave impedance of the media on both sides of the interface remains unchanged, the Poisson's ratio difference Δσ has a great influence on the change of the reflection amplitude with the incident angle. The larger Δσ is, the greater the change of the amplitude with the incident angle is. The Poisson's ratio is related to the longitudinal and transverse velocity ratio V. p / V s Poisson's ratio or the ratio of the longitudinal and transverse velocities V p / V sIt is a physical constant whose 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 within a certain range are relatively stable or vary little. The P-wave and S-wave velocity ratios (Poisson's ratio), and density of coal seams are closely related to the composition of the coal seams. The AVO attributes of coal seams are also closely correlated with the composition of the coal seams. This provides a theoretical and methodological foundation for the quantitative prediction of coal seam composition using seismic AVO attributes.

[0036] Based on the above situation, an embodiment of the present invention proposes an in-situ non-destructive quantitative prediction technology for underground coal seams, which realizes the large-scale regional quantitative prediction and evaluation of in-situ underground coal seam coal quality characteristics using three-dimensional seismic data and seismic attributes, laying the foundation for efficient and accurate evaluation of coal resource quality.

[0037] The embodiment of the present invention provides a method for quantitatively predicting coal seam coal quality based on seismic AVO attributes, the main ideas of which are as follows: (1) coal rock rock physics testing required for the coal rock seismic rock physics model; (2) establishment of a coal rock self-compatible rock physics model and calculation of coal rock elastic parameters; (3) calculation of seismic AVO attributes based on the coal rock self-compatible rock physics model; (4) mathematical relationship and characteristic analysis between seismic AVO attribute parameters and coal rock components based on the coal rock self-compatible model; (5) prediction of underground in-situ coal seam coal rock components based on seismic AVO attribute data.

[0038] Among them, the above (1) is the basis for quantitative prediction of coal seam quality based on seismic AVO attributes. According to the occurrence characteristics and conditions of coal seams in the target area, such as coal seam occurrence, roof and floor lithology, structure, coal quality abnormality areas, etc., coal rock sample locations are selected for collection, and basic coal rock physical parameters such as density, porosity, mineral composition, industrial composition, and longitudinal and transverse wave velocities are tested indoors to obtain the actual local coal rock physical parameters in the target area.

[0039] The above (2) is based on the known parameters of coal rock such as porosity, pore aspect ratio, bulk modulus and shear modulus of organic matter, and bulk modulus and shear modulus of ash, to establish a self-compatible rock physics model that conforms to the characteristics of coal rock, and further obtain the theoretically simulated basic elastic parameters such as coal rock density, longitudinal wave velocity, and shear wave velocity.

[0040] The above (3) is the basic elastic parameter simulated by the model (2) above. The seismic AVO attributes P and G simulated based on the coal-rock self-compatibility model are calculated using the Shuey seismic AVO approximation.

[0041] The above (4) mathematical relationship and characteristic analysis between the seismic AVO attribute parameters and coal rock components based on the self-compatible model of coal rock are the core of the quantitative prediction and evaluation method of coal seam coal quality based on seismic AVO attributes. The optimal functional relationship between AVO attributes and coal rock components is determined by comparing the statistical mathematical relationship between the different contents of different coal rock components and the corresponding seismic AVO attributes, combined with the AVO attribute calculation based on the measured elastic parameters.

[0042] The above (5) is based on the functional relationship established in (4). By using the AVO attribute data body of three-dimensional seismic inversion, the quantitative distribution map of the coal rock components of the underground in-situ coal seam can be calculated, and then a complete set of non-destructive quantitative regional prediction and evaluation methods for underground coal seam coal quality based on seismic AVO attributes can be formed.

[0043] The embodiments of the present invention have the following advantages:

[0044] 1. Traditional coal rock component analysis primarily involves drilling or taking coal rock samples underground and sending them to the laboratory for testing to obtain coal rock components, physical properties, and physical parameters such as density, porosity, mineral composition, industrial composition, and longitudinal and transverse wave velocities. To fully understand the characteristics and patterns of coal quality distribution in underground coal seams, a large number of samples must be taken at different locations, which is very limited, labor-intensive, and expensive. The coal rock and coal quality prediction method invented by this method, based on the necessary test data, can directly invert the quantitative distribution of coal rock components in underground coal seams using 3D seismic data, eliminating the need for extensive coring and testing. This allows for non-destructive, contactless, and quantitative prediction, improving analysis efficiency and significantly saving costs.

[0045] 2. The traditional method uses drilling and downhole coring tests to obtain scattered data, and uses geostatistical methods to interpolate and extrapolate the point-based measured data. Without the constraints of the coal seam's own characteristic attribute parameters, the interpolated and extrapolated coal quality information has a certain degree of subjective blindness and uncontrollability. These shortcomings often lead to large errors in the coal rock composition, physical properties, and physical elastic parameters calculated by this method. The present invention uses the known data from these rock sample tests to construct a self-compatible rock physics model of coal rock suitable for the characteristics of the coal seam, and combines it with seismic AVO attribute data for regional quantitative prediction. Since seismic data has the characteristics of a high-density grid in the horizontal direction, the extrapolation accuracy of using seismic data is significantly higher than the scattered data results using only coal sample tests. It is a more efficient and effective method for accurately predicting coal seam coal rock composition.

[0046] 3. This embodiment of the present invention establishes a self-compatible rock physics model of coal seams and coal quality characteristics, and uses this model to calculate coal seam elastic parameters and seismic AVO attributes. This rock physics model for simulation and analysis has a certain degree of universality and is easily applicable.

[0047] 4. The embodiment of the present invention constructs a mathematical relationship between seismic AVO attributes and coal rock components, as well as its rationality evaluation index, and further establishes a method for quantitatively predicting coal rock components in underground in-situ coal seams using seismic AVO attributes. This is a brand-new coal rock component prediction method.

[0048] Coal is a mixture of organic and inorganic matter (minerals). Its physical structure is roughly composed of three parts: organic matter, mineral (ash) components, and pores (containing water and air). The coal rock equivalent component model is shown in Figure 1.

[0049] The present invention provides a method for directly and quantitatively predicting the composition of underground coal seams in situ using seismic AVO attributes based on a coal seam rock physics model. The method primarily uses three-dimensional seismic data to invert coal seam AVO attributes, constructs a relationship between AVO attributes and coal rock components, and thus directly and quantitatively obtains the distribution characteristics of underground coal seam coal rock components. The method proposes the measured underground coal seam physical parameters required for the coal rock seismic rock physics model, establishes a self-compatible rock physics model of coal seams suitable for coal seam coal quality characteristics, and analyzes and calculates coal rock elastic parameters. Using seismic AVO theory, the method calculates seismic AVO attributes simulated based on the self-compatible rock physics model. A mathematical relationship between the seismic AVO attribute parameters based on the self-compatible model and coal rock components is established and analyzed for its rationality. Finally, a new and complete method for quantitatively predicting the composition of underground coal seams in situ using three-dimensional seismic data based on a coal rock rock physics model is proposed.

[0050] FIG2 is a flow chart of a method for quantitatively predicting coal seam quality based on seismic AVO attributes according to an embodiment of the present invention. The method includes the following steps:

[0051] S202: Obtaining coal rock physical parameters of the target area coal seam, which may include density, porosity, mineral content, industrial composition, and longitudinal and transverse wave velocities.

[0052] Sampling is carried out in the target area coal seam, and sampling is carried out evenly at different locations of the coal seam. The sampled coal samples are then tested indoors to obtain various coal and rock parameters.

[0053] S204, establishing a coal rock self-compatible rock physics model.

[0054] Specifically, a self-compatible rock physics model of coal can be constructed based on a self-compatible approximate model of an N-phase mixture. In this self-compatible rock physics model, organic matter serves as an infinite background medium, and ash is used to replace organic matter. For example, the expression of the self-compatible rock physics model of coal is as follows:

[0055] Among them, v Arepresents the volume fraction of coal ash; K and μ represent the bulk modulus and shear modulus of coal ash, respectively; and They represent the equivalent bulk modulus and shear modulus of the coal rock self-compatible model respectively; P and Q are polarization factors.

[0056] S206: Determine the relationship between each coal rock component and the coal rock elastic parameters based on the above-mentioned coal rock self-compatible rock physics model.

[0057] For example, the values ​​of coal rock components in a self-compatible rock physics model are varied based on a single variable method to obtain relationships between each coal rock component and its elastic parameters. The coal rock component values ​​include the organic matter fraction, organic matter content, and ash content of the coal rock, and the elastic parameters include the longitudinal wave velocity and the shear wave velocity.

[0058] S208, calculating the seismic AVO attributes of the coal seam in the target area to obtain the relationship between the seismic AVO attributes and the elastic parameters of the coal rock, and determining the relationship between the seismic AVO attributes and the coal rock components based on the relationship between the above-mentioned coal rock components and the elastic parameters and the relationship between the seismic AVO attributes and the elastic parameters of the coal rock.

[0059] Specifically, the seismic AVO attributes can be calculated based on the Shuey approximation to obtain the relationship between the seismic AVO attributes and the elastic parameters of the coal rock; the seismic AVO attributes include intercept attributes and gradient attributes; secondly, the relationship between the seismic AVO attributes and the coal rock components is determined according to the relationship 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; then, the relationship between the measured seismic AVO attributes and the measured coal rock components is used as an evaluation index to determine the optimal relationship between the seismic AVO attributes and the coal rock components.

[0060] Alternatively, the relationship between the optimal seismic AVO attributes and coal rock components is as follows:

[0061] P=0.0042V A -0.4216, or V O =100-φ-V A

[0062] Among them, V O V is the volume fraction of organic matter in coal rock, in %; A is the volume fraction of coal ash, in %; φ is the porosity of coal, in %; P is the AVO intercept attribute of coal;

[0063] S210: Quantitatively predict the coal rock composition of the target area coal seam based on the optimal relationship between the seismic AVO attribute and the coal rock composition.

[0064] Three-dimensional seismic data can be acquired and seismic AVO attributes can be inverted and calculated based on the 3D seismic data to obtain seismic AVO attributes. Then, based on the optimal relationship between the seismic AVO attributes and coal rock components and the seismic AVO attributes, the distribution of coal rock ash components and the distribution of coal rock organic matter components in the target coal seam can be calculated. For example, a distribution map of the coal rock ash components and a distribution map of the coal rock organic matter components in the coal seam can be output.

[0065] Furthermore, based on the content distribution of coal ash components and the content distribution of coal organic matter components, the types, occurrence characteristics and laws of coal mineral components can be analyzed, and coal quality and coal resources can be evaluated, providing a basic basis for economic and technical analysis of coal selectivity, clean and efficient utilization, prevention of environmental pollution and industrial use.

[0066] The embodiment of the present invention provides a method for quantitatively predicting coal seam coal quality based on seismic AVO attributes, establishes a self-compatible rock physics model of coal rock with coal seam coal quality characteristics, and calculates the elastic parameters and seismic AVO attributes of the coal seam based on the model; constructs a mathematical relationship between seismic AVO attributes and coal rock components and its rationality evaluation index, and further establishes a method for quantitatively predicting coal rock components in underground in-situ coal seams using seismic AVO attributes; the method uses a rock physics model for simulation analysis, has certain universality, and is easy to promote, filling the gap in the prediction of coal quality occurrence conditions in underground coal seams.

[0067] The above-mentioned method for quantitatively predicting the quality of underground in-situ coal seams using three-dimensional seismic data based on a coal rock physical model may include the following steps:

[0068] 1. Carry out coal rock composition, physical properties, physical testing analysis required for the seismic rock physics model of coal seams in the target area, and obtain the physical coal rock parameters of underground coal seams.

[0069] 1) Coal Sampling Principles for the Target Area Coal Seam: Sampling should be conducted evenly across the coal seam to ensure freshness, such as at the mining face and in core drilling. Complete coal samples without obvious cracks should be selected. Complete sampling point information should be recorded underground, along with basic information such as the coal seam's occurrence, structure, and texture. In this example, 20 coal rocks were processed and subjected to petrophysical testing.

[0070] 2) Coal rock density and porosity testing

[0071] A high-precision electronic scale was used to weigh the coal sample, M, and a high-precision vernier caliper was used to measure the coal sample length, L, and diameter, D. The volume density, ρ, of the rock sample at room temperature and pressure (20°C, standard atmospheric pressure) was calculated using Equation (3). The test results are shown in Table 1. The density in the study area ranged from 1.41 to 1.60 g / cm³, with minimal variation and an average density of 1.49 g / cm³.

[0072] Coal rock geometry is primarily divided into two categories: the matrix, comprising inorganic and organic matter, and the pores, which are filled with a fluid. Coal porosity is calculated using dry coal rock bulk density and true density, as described in Part 4 of the national standard (GB / T 23561.4-2009). Table 1 shows the porosity test results for various coal samples.

[0073] The porosity in the study area ranges from 7.27 to 15.84%, with an average porosity of 11.64%.

[0074] Table 1

[0075] 3) Quantitative testing of coal rock minerals

[0076] X-ray diffraction (XRD) analysis was used to quantitatively analyze whole-rock sample powder and clay content. The whole-rock sample powder quantitative analysis determined the proportions of quartz, calcite, and other minerals within the entire coal sample. The clay quantitative analysis determined the proportions of clay minerals such as illite. Table 2 shows the mineral content of various coal samples, demonstrating that the types and proportions of minerals vary among samples from different locations within the same coal seam.

[0077] Table 2

[0078] 4) Analysis of industrial composition of coal and rock

[0079] According to the requirements of GB / T212-2008, Industrial Analysis of Coal, a fully automatic industrial analyzer was used for analysis, and the results are shown in Table 3. The inorganic fraction has a low moisture content, with ash being the main component, and will be considered primarily in the subsequent analysis.

[0080] Table 3

[0081] 5) Coal rock ultrasonic velocity test

[0082] A triaxial rock mechanics testing system was used to measure the P-wave and S-wave velocities of the coal samples. The shear wave velocity ranged from 1.11 to 1.42 km / s, with an arithmetic mean of 1.22 km / s. The measured longitudinal wave velocity ranged from 2.12 to 2.62 km / s, with an arithmetic mean of 2.29 km / s. The results of the coal rock ultrasonic testing are shown in Table 4.

[0083] Table 4

[0084] 2. Establish a self-compatible rock physics model of coal rock suitable for coal seam and coal quality characteristics

[0085] The analysis method for the elastic parameters of the coal rock matrix (elastic parameters such as bulk modulus, shear modulus, longitudinal wave velocity, shear wave velocity, etc.) is often obtained by summarizing the well logging data and rock physics test data of the study area to obtain an empirical formula, and then using the empirical formula for calculation. This method of analysis by empirical formula has the disadvantages of large limitations, strong differences, and large amount of calculation. These disadvantages often lead to large errors in the elastic parameters of the coal rock calculated by the empirical formula method. In order to accurately and effectively predict the shear wave velocity of the coal rock, the present invention analyzes the effect of organic matter / ash content on the elastic parameters of the coal rock such as density, longitudinal and shear wave velocities, bulk modulus, shear modulus, etc. by establishing a coal rock self-compatible rock physics model suitable for the characteristics of the coal rock.

[0086] A self-compatible rock physics model utilizes one of the components as an infinite background medium, replacing it with an equivalent medium to achieve elastic interaction between the components. Coal rock has two main structural components: a matrix skeleton and a pore system. The skeleton is composed of ash and organic matter, both of which significantly influence the elastic parameters of the coal rock. During modeling, a self-compatible rock physics model for coal rock was constructed based on the self-compatible approximate model for N-phase mixtures proposed by Berryman (1995). Coal rock has a high organic matter content and a relatively low ash content, and the two interact in a linear relationship of growth and decline. The self-compatible rock physics model for coal rock established in this paper proposes that the organic matter in the coal rock serves as an infinite background medium, replacing the organic matter with ash. Multiple iterations are employed during the solution process.

[0087] Among them, V A represents the volume fraction of coal ash; K and μ represent the bulk modulus and shear modulus of coal ash, respectively; and They 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.

[0088] The shear modulus of organic matter in coal is half its bulk modulus, with the bulk modulus of organic matter being 5 GPa and the shear modulus being 2.5 GPa (Shitrit, 2016). The Voigt-Reuss-Hill average modulus formula was used to calculate the elastic modulus of the ash in the coal sample, yielding a bulk modulus of 8.998 GPa and a shear modulus of 4.965 GPa.

[0089] The equivalent bulk modulus and shear modulus of the coal sample are obtained using the coal rock self-compatibility approximate model (4), and the longitudinal and transverse wave velocities of the coal rock are obtained using formula (5), and compared with the measured longitudinal and transverse wave velocities of the coal rock:

[0090] Among them, V P 、V S Indicates the longitudinal and shear wave velocities in km / s.

[0091] Table 5 shows the comparison results of the calculated and measured P- and S-wave velocities of the coal rock self-compatible rock physics model.

[0092] Table 5

[0093] The difference between the modeled and measured P-wave velocities fluctuates between 0.01 and 0.26, while the difference between S-wave velocities fluctuates between 0.01 and 0.08. These small fluctuations demonstrate the feasibility of the self-compatible rock physics model for coal rock. Furthermore, the correlation coefficient between the predicted and measured S-wave velocities is greater than that between the P-wave velocities. This is because, when calculating using the laws of seismic wave propagation, the calculation of S-wave velocities is only related to the shear modulus of the coal rock skeleton, while the calculation of P-wave velocities requires both bulk and shear moduli.

[0094] 3. Analysis of basic elastic parameters of coal rock required for calculating AVO properties based on the self-compatible rock physics model of coal rock

[0095] To understand the relationship between coal rock composition and AVO properties, it's necessary to establish the relationship between coal rock composition and P- and S-wave velocities. To understand the impact of coal rock composition on P- and S-wave velocities, a single-variable approach was used to determine the relationship between coal rock composition and P- and S-wave velocities by varying the organic matter content variable within the self-compatible rock physics model. Therefore, during model development, the organic matter volume fraction was set to 50%, 55%, 60%, 65%, 70%, 75%, 80%, and 85%, respectively. The porosity of 11.64% measured from the coal samples in the target area was used as the porosity for the model.

[0096] 1) Coal rock density calculation formula under different proportion parameters of coal rock organic matter components (6)

[0097] in, It represents the density of coal rock with different component ratios; ρ O It represents the density of organic matter. The specific implementation case uses the average organic matter density of 20 coal samples; ρ A It represents the density of ash. The specific ash density used in the implementation case is the average ash density of 20 coal samples.

[0098] Calculation formula for organic matter density of coal rock sample (7)

[0099] Among them, ρ o It represents the density of organic matter in g / cm 3 ;M o It represents the mass of organic matter in g; V o It represents the volume of organic matter in cm 3 ;M ad The mass fraction of volatile matter, expressed in %; M cd It represents the mass fraction of fixed carbon in %; ρ c It represents the bulk density of coal rock in g / cm 3 ; V A represents the volume fraction of ash in the coal sample, in %; φ represents the porosity of the coal rock.

[0100] The ash volume fraction refers to the sum of the volume contents of various minerals in coal rock, which is calculated using formula (8).

[0101] Among them, m i It represents the mass fraction of the i-th mineral in the total minerals, in %, and can be calculated using the aforementioned coal rock mineral quantitative test results. A It represents the mass fraction of ash in %; ρ i It represents the density of the i-th mineral in g / cm 3 The values ​​that can be cited are those in the rock physics handbook, see Table 6 for the mineral density values ​​in coal rocks.

[0102] Table 6 Coal rock ash density is calculated using formula (9),

[0103] 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.

[0104] Table 7

[0105] The calculated results for the organic matter density of the coal samples in the table show that the density of organic matter in the coal varies between 1.51 and 1.71 g / cm³, with an average organic matter density of 1.61 g / cm³, which was used as the organic matter density during the establishment of the self-compatible rock physics model of the coal. The ash density ranged from 2.00 to 2.92 g / cm³, with an average ash density of 2.32 g / cm³, which was used as the ash density during the establishment of the self-compatible rock physics model of the coal.

[0106] 2) Analysis of organic matter content and coal rock elastic parameters

[0107] A self-compatible rock physics model of coal rock was established using the aforementioned basic parameters. Elastic parameters were calculated for coal rock with varying organic matter volume fractions of 50%, 55%, 60%, 65%, 70%, 75%, 80%, and 85%. The model calculations yielded the corresponding elastic moduli (bulk modulus and shear modulus), longitudinal and shear wave velocities, and coal rock density, as shown in Table 8. These elastic parameter predictions for different organic matter composition ratios are based on the self-compatible model.

[0108] Table 8

[0109] From this table, we can see that there is a linear relationship between organic matter content and coal rock density. As the organic matter content increases, the coal rock density decreases. The relationship between coal rock density and organic matter content conforms to the linear equation (10), showing a strict linear negative correlation. C =-0.63V O +2.04 (10)

[0110] 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. As the organic matter content increases, the longitudinal and shear wave velocities of the coal rock skeleton gradually decrease, indicating a negative correlation between the organic matter content and the longitudinal and shear wave velocities of the coal rock skeleton. The elastic modulus (bulk modulus and shear modulus) of the coal rock skeleton changes rapidly with increasing organic matter content, while the longitudinal and shear wave velocities change more slowly with increasing organic matter content. This suggests that the organic matter content has a greater impact on the elastic modulus of the coal rock skeleton than on the longitudinal and shear wave velocities.

[0111] 3) Analysis of ash content and coal rock elastic parameters

[0112] Coal rock is composed of water, ash, and organic matter. Therefore, the ash volume fraction can be calculated using the subtraction method: Ash volume fraction = 100% - porosity - organic matter volume fraction. This shows that the organic matter content and ash content in coal rock are inversely correlated. Calculations can be used to determine the ash content for the eight models described above, as shown in Table 9, for the ash content of the self-compatible coal rock models with different component ratios.

[0113] Table 9

[0114] Combining Tables 8 and 9, we can see that there is a linear relationship between ash content and coal rock density. As ash content increases, coal rock density increases, showing a strict positive correlation. Because the organic matter content and ash content in coal rock are inversely correlated, the bulk modulus and shear modulus of the coal rock skeleton gradually increase with increasing ash content. In other words, the volume fraction of ash and the bulk and shear moduli of the coal rock skeleton are positively correlated. As ash content increases, the longitudinal and shear wave velocities of the coal rock skeleton also gradually increase. In other words, there is a positive correlation between ash content and the longitudinal and shear wave velocities of the coal rock skeleton. Similarly, the elastic modulus (bulk modulus and shear modulus) of the coal rock skeleton changes rapidly with increasing ash content, while the longitudinal and shear wave velocities change more slowly with increasing organic matter content. This also shows that the ash content has a greater impact on the elastic modulus of the coal rock skeleton than on the longitudinal and shear wave velocities. In summary, the coal rock components have a linear relationship with the elastic parameters such as the elastic modulus and velocity of the matrix skeleton of the coal rock body. At the same time, its impact on the elastic modulus of the coal rock skeleton is greater than its impact on the longitudinal and shear wave velocities.

[0115] 4. Establish the mathematical relationship between seismic AVO attribute parameters and coal rock components based on the coal rock self-compatibility model, and the rationality and optimal solution evaluation analysis method

[0116] 1) Elastic parameters of the coal-rock self-compatibility model for seismic AVO attribute analysis

[0117] A single-interface double-layer geophysical model is established to perform AVO attribute analysis. The obtained data on the P-wave and S-wave velocities and density of the double-layer medium are used to solve the reflection coefficient using the Zoeppritz equation or its approximate equation. Figure 3 shows a schematic diagram of the reflection and transmission of seismic waves propagating in underground strata. Among them, θ1 and φ1 are the reflection angles of P-wave and S-wave; θ2 and φ2 are the transmission angles of P-wave and S-wave; ρ1 and ρ2 are the densities of the media above and below the reflection interface; V P1 、V P2 is the longitudinal wave velocity of the media above and below the reflection interface; VS1 、V S2 is the shear wave velocity of the media above and below the reflecting interface.

[0118] Here, the Shuey approximation is used to calculate the AVO attributes, thereby obtaining the relationship between the P-wave velocity, S-wave velocity and the AVO attributes, and then exploring the relationship between the coal rock components and the AVO attributes.

[0119] The coal seam roof in this implementation case is primarily mudstone. During modeling, the coal seam roof was set to 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. The lower layer represents the coal seam, and calculations were performed using the P- and S-wave velocities and density calculated using the coal rock self-compatibility model. The model parameters are established as shown in Table 10: Elastic Parameters of the Coal Rock Self-Compatibility Model for Different Organic Matter Ratios.

[0120] Table 10

[0121] At the same time, the density and longitudinal and shear wave velocities of 20 coal samples obtained from the case study were used as parameters for Medium 2 to perform AVO property analysis, obtaining AVO property values ​​based on actual data. The relationship between AVO property values ​​calculated from actual data and organic matter / ash content can be used to verify and evaluate the rationality of the relationship between AVO properties and organic matter / ash content derived from the coal rock self-compatibility model. Thus, the data for Medium 2 was set to actual data, and the parameters were as shown in Table 11, a table of measured elastic parameters for coal samples.

[0122] Table 11

[0123] 2) Analysis of the relationship between AVO attributes and coal rock components

[0124] The above elastic parameters and Shuey approximation are used to calculate the AVO properties of single-interface double-layer media, and the intercept and gradient attribute values ​​are obtained respectively. The AVO attribute values ​​obtained based on the self-compatible model of coal rock with different organic matter component ratios can be found in Table 12, which shows the seismic AVO intercept P and gradient G attribute table calculated based on the self-compatible model of coal rock with different organic matter component ratios. The AVO attribute values ​​calculated based on the measured coal rock elastic parameters can be found in Table 13, which shows the seismic AVO intercept P and gradient G attribute calculated based on the measured coal rock data.

[0125] Table 12

[0126] Table 13

[0127] The organic matter content and ash content of the coal rock are correlated with the intercept and gradient attributes obtained. The relationship diagram between the organic matter content of the coal rock and the AVO intercept attribute is shown in Figure 4, the relationship diagram between the ash content of the coal rock and the AVO intercept attribute is shown in Figure 5, the relationship diagram between the organic matter content of the coal rock and the AVO gradient attribute is shown in Figure 6, and the relationship diagram between the ash content of the coal rock and the AVO gradient attribute is shown in Figure 7. The linear relationship expressions of the organic matter content and ash content of the coal rock and the intercept and gradient attributes obtained based on the simulated data of the coal rock self-compatibility model and the measured data of the coal samples are obtained respectively. Among them, the relationship expression based on the measured data of the coal samples is used as the evaluation index.

[0128] In the above functional relationship, the correlation between the linear relationship between the organic matter content and ash content of coal rock and the AVO intercept attribute P is 0.998, and the correlation with the AVO gradient attribute G is 0.911. Therefore, the functional relationship with the AVO intercept attribute P is taken as the optimal solution, and the optimal relationship is further selected from the linear relationship fitted by the measured data as the evaluation index. Among them, the correlation of the linear relationship of the AVO attribute calculated by the measured data of the coal sample, the correlation between the linear relationship of the organic matter content of the coal rock sample and the attribute P is 0.723, the correlation between the linear relationship of the ash content and the attribute P is 0.871, the correlation between the linear relationship of the ash content and the attribute P is 0.724, and the correlation between the linear relationship of the ash content and the attribute G is 0.82. Therefore, the linear relationship between the ash content and the attribute P is selected as the optimal relationship between the AVO attributes and the coal rock components (10):

[0129] P=0.0042V A -0.4216, or V O =100-φ-V A (11)

[0130] Among them, V O V is the volume fraction of organic matter in coal rock, in %; A is the volume fraction of coal ash, in %; φ is the porosity of coal, in %; P is the AVO intercept attribute of coal.

[0131] Therefore, a mathematical relationship between seismic AVO attribute parameters and coal rock components based on the coal rock self-compatibility model and its optimal evaluation method were established.

[0132] 5. Carry out quantitative prediction of coal rock components in underground in-situ coal seams based on seismic AVO attribute data.

[0133] 1) After the 3D seismic data is processed to preserve its fidelity, a pre-stack seismic data volume is obtained;

[0134] 2) AVO attribute inversion calculations can then be performed to obtain seismic AVO intercept attribute P and gradient attribute G data volumes, track the target coal seam top interface, and extract coal seam AVO attribute slices, as shown in Figure 8, which shows the coal seam AVO intercept P and gradient G attribute distribution maps. The left figure is the coal seam intercept P slice map, and the right figure is the coal seam gradient G slice map.

[0135] 3) Based on the established mathematical relationship (10) between the seismic AVO attribute parameters and the coal rock components based on the coal rock self-compatibility model, the target area coal seam intercept attribute P data body is used for calculation to obtain the content distribution map of the underground in-situ coal seam coal rock ash components. Figure 9 shows the predicted distribution map of the coal seam coal rock ash content. Further calculation using the relationship (11) between the coal rock components can obtain the distribution map of the coal seam coal rock organic matter component content. Figure 10 shows the predicted distribution map of the coal seam coal rock organic matter content. This can then be used to analyze the types and occurrence characteristics and regularities of coal rock mineral components, evaluate coal quality and coal resources, and provide a basis for economic and technical analysis of coal selectivity, clean and efficient utilization, prevention of environmental pollution, and industrial use.

[0136] An embodiment of the present invention provides a coal seam quality quantitative prediction system based on seismic AVO attributes, the system comprising:

[0137] An acquisition module is used to acquire coal rock physical parameters of the coal seam in the target area; the coal rock parameters include at least one of the following: density, porosity, mineral content, industrial composition, and longitudinal and shear wave velocities;

[0138] Model building module, used to build coal rock self-compatible rock physics model;

[0139] The first relationship determination module is used to determine the relationship between each coal rock component and the coal rock elastic parameter based on the coal rock self-compatible rock physics model;

[0140] The second relationship determination module is used to calculate the seismic AVO attributes of the coal seam in the target area to obtain the relationship between the seismic AVO attributes and the elastic parameters of the coal rock, and to determine the relationship between the seismic AVO attributes 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 attributes and the coal rock elastic parameters;

[0141] The component analysis module is used to quantitatively predict the coal rock components of the target area coal seam based on the relationship between the optimal seismic AVO attributes and coal rock components.

[0142] Optionally, the model building module is specifically configured to: construct a self-compatible rock physics model of coal rock based on a self-compatible approximate model of an N-phase mixture; in the self-compatible rock physics model of coal rock, organic matter is used as an infinite background medium, and ash is used to replace organic matter. Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing a control device via a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes of the above-described method embodiments, wherein the storage medium can be a memory, a magnetic disk, an optical disk, etc. In this document, relational terms such as first and second, etc., are used solely to distinguish one entity or operation from another, and do not necessarily require or imply any actual relationship or order between these entities or operations. Furthermore, the terms "comprise," "include," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a list of elements includes not only those elements but also other elements not explicitly listed, or also includes elements inherent to such process, method, article, or apparatus. Without further constraints, an element defined by the phrase "comprises a..." does not preclude the existence of additional identical elements in the process, method, article or apparatus that includes the element.

[0143] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one 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 present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for quantitatively predicting coal seam quality based on seismic AVO attributes, characterized in that: The method comprises: Obtaining coal rock physical parameters of the coal seam in the target area; the coal rock 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 of coal and rock; Determine the relationship between each coal rock component and the coal rock elastic parameter according to the coal rock self-compatible rock physics model; Calculating the seismic AVO attributes of the coal seam in the target area to obtain the relationship between the seismic AVO attributes and the elastic parameters of the coal rock, and determining the relationship between the seismic AVO attributes and the coal rock components according to the relationship between each coal rock component and the coal rock elastic parameter and the relationship between the seismic AVO attributes and the coal rock elastic parameter; According to the optimal relationship between the seismic AVO attributes and the coal rock components, the coal rock components of the target area coal seam are quantitatively predicted.

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

3. The method according to claim 2, characterized in that The expression of the coal-rock self-compatible rock physics model is as follows: Among them, V A represents the volume fraction of coal ash; K and μ represent the bulk modulus and shear modulus of coal ash, respectively; and They represent the equivalent bulk modulus and shear modulus of the coal-rock self-compatible model respectively; P and Q are polarization factors.

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

5. The method according to claim 1, characterized in that The calculating 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 parameter, and determining the relationship between the seismic AVO attribute and the coal rock component according to 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, include: Calculating seismic AVO attributes based on Shuey approximation to obtain the relationship between the seismic AVO attributes and the coal rock elastic parameters; the seismic AVO attributes include intercept attributes and gradient attributes; Determine a relationship between the seismic AVO attribute and the coal rock component according to 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; The relationship between the measured seismic AVO attributes and the measured coal rock components is used as an evaluation index to determine the optimal relationship between the seismic AVO attributes and the coal rock components among the relationship between the seismic AVO attributes and the coal rock components.

6. The method according to claim 5, characterized in that The relationship between the optimal seismic AVO attributes and coal rock components is as follows: P=0.0042V A -0.4216, or V O =100-φ-V A Among them, V O V is the volume fraction of organic matter in coal rock, in %; A is the volume fraction of coal rock ash, in %; φ is the porosity of coal rock, in %; P is the AVO intercept attribute of coal rock.

7. The method according to claim 1, characterized in that The method of quantitatively predicting the coal rock components of the target area coal seam according to the optimal relationship between the seismic AVO attributes and the coal rock components includes: Acquiring three-dimensional seismic data, and performing seismic AVO attribute inversion calculation based on the three-dimensional seismic data to obtain seismic AVO attributes; According to the optimal relationship between the seismic AVO attributes and coal rock components and the seismic AVO attributes, 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 comprises: According to the content distribution of the coal rock ash components and the content distribution of the coal rock organic matter components, the types, occurrence characteristics and laws of the coal rock mineral components are analyzed, and the coal quality and coal resources are evaluated.

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

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

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