A method and device for determining the horizontal stress difference ratio of a deep coalbed methane reservoir

Abstract

The present invention provides a method and device for determining the horizontal stress difference ratio of a deep coalbed methane reservoir, which relates to the technical field of deep coalbed methane exploration, and includes: obtaining well logging data and azimuth seismic observation data of the deep coalbed methane reservoir, constructing an initial reservoir parameter model of the deep coalbed methane reservoir based on the well logging data, constructing an objective functional for inverting the reservoir parameters of the deep coalbed methane reservoir based on the azimuth seismic observation data and the azimuth seismic reflection coefficient equation, and solving the objective functional under the constraint of the initial model based on the generalized linear inversion theory and the iterative reweighted least squares joint algorithm to obtain an objective reservoir parameter model, and then calculating the horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir according to the objective model. This method realizes the accurate estimation of the horizontal stress difference ratio of the deep coalbed methane reservoir under the constraint of an accurate initial model, and alleviates the technical problem of low accuracy existing in the existing horizontal stress difference estimation method.

Description

Technical Field

[0001] The present invention relates to the technical field of deep coalbed methane exploration, and in particular to a method and device for determining the differential horizontal stress ratio of a deep coalbed methane reservoir. Background Art

[0002] With the continuous deepening of the exploration and development of deep coalbed methane, it is becoming increasingly important to accurately depict the distribution of engineering sweet spots in deep coalbed methane. Deep coalbed methane reservoirs are characterized by very low porosity and very low permeability, and fracturing is required to form large-scale production. In-situ stress is an important factor determining the shape, orientation, and extension direction of fractures, and the differential horizontal stress ratio (DHSR) is an important parameter characterizing the in-situ stress of the reservoir. Accurately predicting the DHSR of deep coalbed methane reservoirs can significantly improve the coalbed methane production efficiency.

[0003] In the prior art, the method for determining the DHSR of deep coalbed methane reservoirs is to estimate the DHSR of the formation using the reservoir parameters and anisotropic parameters obtained by inversion. However, the current method is a direct inversion based on the assumption of a transversely isotropic medium. Since the transversely isotropic medium does not conform to the actual structure of coal seams, the accuracy of the DHSR determined by the existing method is relatively low. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and device for determining the differential horizontal stress ratio of a deep coalbed methane reservoir, so as to alleviate the technical problem of relatively low accuracy existing in the existing horizontal stress difference estimation method.

[0005] In a first aspect, the present invention provides a method for determining the differential horizontal stress ratio of a deep coalbed methane reservoir, including: obtaining the azimuth seismic observation data and logging data of the deep coalbed methane reservoir; wherein, the logging data includes: the organic matter content, clay content, brittle mineral content, pore content, and bulk density of multiple sampling points; the brittle minerals include: calcite, quartz, and pyrite; determining an initial reservoir parameter model of the deep coalbed methane reservoir based on the logging data; wherein, the reservoir parameters include: acoustic impedance, anisotropic shear modulus, horizontal longitudinal wave phase velocity along the fracture strike, azimuth anisotropy gradient, and relative fracture density; constructing a target functional for inverting the reservoir parameters of the deep coalbed methane reservoir under the Bayesian inversion framework based on the azimuth seismic observation data and the azimuth seismic reflection coefficient equation; solving the target functional based on the generalized linear inversion theory and the iterative reweighted least squares joint algorithm under the constraint of the initial reservoir parameter model to obtain a target model of the reservoir parameters of the deep coalbed methane reservoir; calculating the differential horizontal stress ratio of each sampling point in the deep coalbed methane reservoir based on the target model of the reservoir parameters.

[0006] Optionally, an initial reservoir parameter model of a deep coalbed methane reservoir is determined based on logging data, including: processing the brittle mineral content of a target sampling point by using the Voigt-Ruess-Hill model to obtain a first equivalent elastic stiffness matrix of the brittle mineral mixture at the target sampling point; wherein the target sampling point represents any one of a plurality of sampling points; processing the organic matter content and clay content of the target sampling point by using the Backus average model to obtain a second equivalent elastic stiffness matrix of the layered mixture composed of organic matter and clay at the target sampling point; processing the second equivalent elastic stiffness matrix by using the Bond transformation model to obtain a third equivalent elastic stiffness matrix of the rotated layered mixture composed of organic matter and clay; processing the first equivalent elastic stiffness matrix, the third equivalent elastic stiffness matrix and the pore content of the target sampling point based on the anisotropic differential equivalent medium model to obtain a fourth equivalent elastic stiffness matrix of the coal matrix with pores at the target sampling point; using a linear slip model to superimpose preset vertical fracture parameters onto the fourth equivalent elastic stiffness matrix to obtain an equivalent elastic stiffness matrix at the target sampling point; calculating the initial reservoir parameters of the target sampling point based on the equivalent elastic stiffness matrix and bulk density at the target sampling point; constructing an initial reservoir parameter model of the deep coalbed methane reservoir based on the initial reservoir parameters of a plurality of sampling points in the deep coalbed methane reservoir.

[0007] Optionally, based on azimuthal seismic observation data and an azimuthal seismic reflection coefficient equation, a target functional for inverting the reservoir parameters of a deep coalbed methane reservoir is constructed under the Bayesian inversion framework, including: extracting a seismic wavelet from the azimuthal seismic observation data; determining an expression for forward modeling seismic data based on the seismic wavelet and the azimuthal seismic reflection coefficient equation; under the Bayesian inversion framework, with the premise that the prior distribution of the reservoir parameters follows a Cauchy distribution and the maximization of the posterior distribution of the reservoir parameters as the goal, constructing a target functional for inverting the reservoir parameters of a deep coalbed methane reservoir based on the azimuthal seismic observation data and the expression for forward modeling seismic data.

[0008] Optionally, under the constraint of the initial reservoir parameter model, the target functional is solved based on the generalized linear inversion theory and the iterative reweighted least squares joint algorithm to obtain a target model of the reservoir parameters of the deep coalbed methane reservoir, including: performing a first-order Taylor expansion of the target functional at the initial reservoir parameter model based on the generalized linear inversion theory to obtain an objective function of the reservoir parameters; calculating the partial derivative of the objective function with respect to the perturbation amount of the reservoir parameters to obtain a partial derivative function; determining an initial solution expression for the perturbation amount of the reservoir parameters when the partial derivative function is equal to 0; simplifying the initial solution expression based on the iterative reweighted least squares joint algorithm to obtain a target solution expression for the perturbation amount of the reservoir parameters; iteratively updating the reservoir parameters based on the initial reservoir parameter model and the target solution expression for the perturbation amount of the reservoir parameters until a preset iteration end condition is satisfied to obtain a target model of the reservoir parameters.

[0009] Optionally, the objective functional of the reservoir parameters for inverting the deep coalbed methane reservoir is expressed as: ; where represents the azimuthal seismic observation data, represents the expression of the forward modeled seismic data, , represents the seismic wavelet, represents the azimuthal seismic reflection coefficient equation, represents the matrix composed of reservoir parameters, represents the covariance matrix of the noise, represents the total number of the sampling points, i represents the i th sampling point, is a 5×5 covariance matrix, D is a 5×5N adjustment matrix.

[0010] Optionally, the objective solution expression of the reservoir parameter perturbation amount is: ; where represents the reservoir parameter perturbation amount, represents the variance of the azimuthal seismic observation data noise.

[0011] Optionally, the formula for the horizontal stress difference ratio is: ; where represents the horizontal stress difference ratio, A represents the acoustic impedance, B represents the anisotropic shear modulus, C represents the horizontal longitudinal wave phase velocity along the fracture strike, D represents the azimuthal anisotropy gradient, E represents the relative fracture density, , .

[0012] In a second aspect, the present invention provides an apparatus for determining the horizontal stress difference ratio of a deep coalbed methane reservoir, comprising: an acquisition module configured to acquire azimuth seismic observation data and logging data of the deep coalbed methane reservoir; wherein the logging data includes: organic matter content, clay content, brittle mineral content, pore content, and bulk density of a plurality of sampling points; the brittle minerals include: calcite, quartz, and pyrite; a determination module configured to determine an initial model of reservoir parameters of the deep coalbed methane reservoir based on the logging data; wherein the reservoir parameters include: acoustic impedance, anisotropic shear modulus, horizontal longitudinal wave phase velocity along the fracture strike, azimuthal anisotropy gradient, and relative fracture density; a construction module configured to construct a target functional for inverting the reservoir parameters of the deep coalbed methane reservoir under a Bayesian inversion framework based on the azimuth seismic observation data and the azimuth seismic reflection coefficient equation; a solution module configured to solve the target functional based on an iterative reweighted least squares joint algorithm under the constraint of the initial model of reservoir parameters to obtain a target model of reservoir parameters of the deep coalbed methane reservoir; and a calculation module configured to calculate the horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir based on the target model of reservoir parameters.

[0013] In a third aspect, the present invention provides an electronic device, comprising a memory and a processor, where the memory stores a computer program that can run on the processor, and when the processor executes the computer program, it implements the method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir according to any one of the foregoing embodiments.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, and when the computer instructions are executed by a processor, the method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir according to any one of the foregoing embodiments is implemented.

[0015] The present invention provides a method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir. After acquiring the azimuth seismic observation data and logging data of the deep coalbed methane reservoir, first, an initial model of reservoir parameters of the deep coalbed methane reservoir is constructed based on the logging data, then a target functional for inverting the reservoir parameters of the deep coalbed methane reservoir is constructed based on the azimuth seismic observation data and the azimuth seismic reflection coefficient equation. Next, under the constraint of the initial model, the target functional is solved based on the generalized linear inversion theory and the iterative reweighted least squares joint algorithm to obtain a target model of reservoir parameters, and then the horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir is calculated according to the target model. This method constructs an initial model of reservoir parameters that can accurately characterize the anisotropic characteristics of the reservoir based on the organic matter content, clay content, brittle mineral content, pore content, and bulk density of a plurality of sampling points in the deep coalbed methane reservoir. Furthermore, under the constraint of an accurate initial model, the accurate estimation of the horizontal stress difference ratio of the deep coalbed methane reservoir is realized, alleviating the technical problem of low accuracy existing in the existing horizontal stress difference estimation methods. Description of the Drawings

[0016] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of a method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir provided by an embodiment of the present invention;

[0018] Figure 2 It is a schematic diagram of coal in a deep coalbed methane reservoir;

[0019] Figure 3 It is a flowchart of a method for physical modeling of coal rock in a deep coalbed methane reservoir based on the equivalent medium theory provided by an embodiment of the present invention;

[0020] Figure 4 It is a diagram of an initial reservoir parameter model for constructing a deep coalbed methane reservoir based on logging data;

[0021] Figure 5 It is a schematic diagram of a horizontal stress difference ratio inversion result profile obtained by applying the method provided by an embodiment of the present invention;

[0022] Figure 6 It is a functional module diagram of a device for determining the horizontal stress difference ratio of a deep coalbed methane reservoir provided by an embodiment of the present invention;

[0023] Figure 7 It is a schematic diagram of an electronic device provided by an embodiment of the present invention. Detailed Embodiments

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated in the drawings here can be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0026] The following will, in conjunction with the accompanying drawings, elaborate on some embodiments of the present invention. Without conflict, the following embodiments and the features in the embodiments may be combined with each other.

[0027] Embodiment 1

[0028] Figure 1 The following is a flowchart of a method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir provided by an embodiment of the present invention. As Figure 1 shown, the method specifically includes the following steps:

[0029] Step S102: Obtain the azimuth seismic observation data and well logging data of the deep coalbed methane reservoir.

[0030] To determine the horizontal stress difference ratio of the deep coalbed methane reservoir, the data sources required in the embodiments of the present invention are the azimuth seismic observation data and well logging data of the deep coalbed methane reservoir. The azimuth seismic observation data is data collected through a professional data acquisition system by conducting seismic exploration in the area where the deep coalbed methane reservoir is located, or data obtained through a seismic monitoring network; the well logging data is directly or indirectly calculated based on the well logging curves of the deep coalbed methane reservoir. The well logging curves are data curves used in petroleum exploration to record the physical properties of underground rocks. It reflects the characteristics of underground rock formations by measuring various physical properties of underground rocks, such as resistivity, acoustic propagation time, natural gamma radiation, etc.

[0031] In the embodiments of the present invention, the well logging data includes: the organic matter content, clay content, brittle mineral content, pore content, and bulk density of multiple sampling points; the brittle minerals include: calcite, quartz, and pyrite.

[0032] The bulk density of each sampling point in the deep coalbed methane reservoir is directly obtained from the density curve in the well logging curve. It is known that the coal in the deep coalbed methane reservoir is mainly composed of organic matter, clay, and brittle minerals, with organic matter being the main component, and the content is generally between 55% and 95%. The organic matter content at each sampling point can be obtained through the natural gamma curve. The calculation formula for the organic matter content is: TOC = 0.265 × U - 0.119, where U represents the uranium U value of the radioactive element, and TOC represents the organic matter content.

[0033] Based on the shale content curve, the shale content (i.e., clay content) of each sampling point in the deep coalbed methane reservoir can be calculated, in %. Specifically, first, calculate the shale index using the following formula: SH = (GR - GR min ) / (GR max - GR min ), where: SH represents the shale index, GR represents the natural gamma well logging curve value of the deep coalbed methane reservoir; GR min represents the natural gamma curve value of pure sandstone, GRmax Indicates the natural gamma ray curve value of pure shale. Next, use the following formula to calculate the shale content, V clay =(2 GCUR*SH -1) / (2 GCUR -1), where: V clay represents the shale content, GCUR represents the Hilchie index, which is an empirical coefficient related to the formation. The value of GCUR depends on the geological age of the formation. Usually, the value is 2.0 in old formations and 3.7 in new formations (such as Tertiary formations).

[0034] In the embodiments of the present invention, both the organic matter content and the shale content are volume contents. It is known that the sum of the volume contents of organic matter, clay, and brittle minerals is 1. Therefore, 1 - organic matter content - clay content = brittle mineral content. In the embodiments of the present invention, the brittle minerals in the deep coalbed methane reservoir include: calcite, quartz, and pyrite, and the relative contents of the three brittle minerals, calcite, quartz, and pyrite, are obtained from empirical data. By default, it is 4:2:1 in the embodiments of the present invention. Therefore, after determining the brittle mineral content, according to the above ratio, the calcite content, quartz content, and pyrite content at each sampling point in the deep coalbed methane reservoir can be determined respectively.

[0035] In the embodiments of the present invention, the equivalent porosity is used to characterize the pore content of the coal in the deep coalbed methane reservoir, and the porosity obtained from the resistivity curve is used as the equivalent porosity. The specific process of obtaining the porosity from the resistivity curve is as follows: When R s >R d , ; When R s <R d , ; where, Porosity represents porosity, R d represents the deep lateral resistivity, R s represents the shallow lateral resistivity, R w represents the formation water resistivity, R wf represents the formation free water resistivity, s represents the cementation coefficient, and R mf represents the mud filtrate resistivity.

[0036] Step S104, determine the initial model of the reservoir parameters of the deep coalbed methane reservoir based on well logging data.

[0037] Figure 2 is a schematic diagram of the coal in the deep coalbed methane reservoir. In the embodiments of the present invention, the deep coalbed methane reservoir is equivalent to an orthotropic medium with vertical fractures in a VTI background. Therefore, the present invention pre-constructs a physical model of coal rock in the deep coalbed methane reservoir based on the equivalent medium theory according to Figure 3 , and constructs an anisotropic rock physical model of the deep coalbed methane reservoir. Through Figure 3It can be known that the rock physics model is related to the organic matter content, clay content, brittle mineral content, and pore content. Therefore, after obtaining the organic matter content, clay content, brittle mineral content, and pore content at multiple sampling points in the deep coalbed methane reservoir, substituting the organic matter content, clay content, brittle mineral content, and pore content of each sample into the rock physics model respectively, the equivalent elastic stiffness matrix at each sampling point can be obtained. Further, in combination with the bulk density at each sampling point, the initial reservoir parameters at each sampling point can be calculated respectively, and then the initial model of the reservoir parameters of the deep coalbed methane reservoir can be constructed. Among them, the reservoir parameters include: acoustic impedance, anisotropic shear modulus, horizontal P-wave phase velocity along the fracture strike, azimuthal anisotropy gradient, and relative fracture density.

[0038] Step S106: Based on the azimuthal seismic observation data and the azimuthal seismic reflection coefficient equation, construct a target functional for inverting the reservoir parameters of the deep coalbed methane reservoir under the Bayesian inversion framework.

[0039] It is known that the azimuthal seismic reflection coefficient equation is a PP-wave reflection coefficient equation expressed based on reservoir parameters, and forward model the seismic data , represents the seismic wavelet, represents the azimuthal seismic reflection coefficient equation, represents the matrix composed of reservoir parameters, A represents the acoustic impedance, B represents the anisotropic shear modulus, C represents the horizontal P-wave phase velocity along the fracture strike, D represents the azimuthal anisotropy gradient, and E represents the relative fracture density. And, it is known that the following relationship exists between the azimuthal seismic observation data and the forward modeled seismic data: , where represents the azimuthal seismic observation data, represents the forward modeled seismic data, n represents the azimuthal seismic observation data noise.

[0040] Therefore, based on the relationship between the azimuthal seismic observation data and the forward modeled seismic data, under the Bayesian inversion framework, assuming that the likelihood function follows a Gaussian distribution and the prior distribution of the reservoir parameters follows a Cauchy distribution, according to the maximum a posteriori probability estimation, the target functional for inverting the reservoir parameters of the deep coalbed methane reservoir can be constructed.

[0041] Step S108: Under the constraint of the initial reservoir parameter model, solve the target functional based on the generalized linear inversion theory and the iterative reweighted least squares joint algorithm to obtain the target model of the reservoir parameters of the deep coalbed methane reservoir.

[0042] Since the objective functional is a complex non - linear expression with respect to the reservoir parameters to be inverted, considering factors such as the computational amount, convergence rate, and computational accuracy, in the embodiments of the present invention, when solving the objective functional, first, the initial model of the reservoir parameters that has been obtained is used as a constraint. According to the generalized linear inversion theory, the non - linear objective functional is linearized through the Taylor series expansion, and then the iterative re - weighted least - squares joint algorithm is used for solution to obtain the target model of the reservoir parameters, that is, the reservoir parameters at each sampling point in the deep coal - bed methane reservoir are predicted.

[0043] Step S110, calculate the horizontal stress difference ratio at each sampling point in the deep coal - bed methane reservoir based on the target model of the reservoir parameters.

[0044] In the embodiments of the present invention, the formula for the horizontal stress difference ratio is: ; where DHSR represents the horizontal stress difference ratio, A represents the acoustic impedance, B represents the anisotropic shear modulus, C represents the horizontal longitudinal wave phase velocity along the fracture strike, D represents the azimuthal anisotropy gradient, E represents the relative fracture density, , . Substitute the reservoir parameters of any sampling point in the deep coal - bed methane reservoir into the formula of DHSR, and the horizontal stress difference ratio of this sampling point can be obtained.

[0045] The embodiments of the present invention provide a method for determining the horizontal stress difference ratio of a deep coal - bed methane reservoir. After obtaining the azimuthal seismic observation data and logging data of the deep coal - bed methane reservoir, first, an initial model of the reservoir parameters of the deep coal - bed methane reservoir is constructed based on the logging data, then an objective functional for inverting the reservoir parameters of the deep coal - bed methane reservoir is constructed based on the azimuthal seismic observation data and the azimuthal seismic reflection coefficient equation. Next, under the constraint of the initial model, the objective functional is solved based on the generalized linear inversion theory and the iterative re - weighted least - squares joint algorithm to obtain the target model of the reservoir parameters, and then the horizontal stress difference ratio at each sampling point in the deep coal - bed methane reservoir is calculated according to the target model. This method constructs an initial model of the reservoir parameters that can accurately characterize the anisotropic characteristics of the reservoir based on the organic matter content, clay content, brittle mineral content, pore content, and bulk density of multiple sampling points in the deep coal - bed methane reservoir. Then, under the constraint of the accurate initial model, the accurate estimation of the horizontal stress difference ratio of the deep coal - bed methane reservoir is realized, alleviating the technical problem of low accuracy existing in the existing horizontal stress difference estimation methods.

[0046] In an optional implementation manner, in step S104 above, determining the initial model of the reservoir parameters of the deep coal - bed methane reservoir based on the logging data specifically includes the following steps:

[0047] Step S1041: Process the brittle mineral content of the target sampling point using the Voigt-Ruess-Hill model to obtain the first equivalent elastic stiffness matrix of the brittle mineral mixture at the target sampling point. Here, the target sampling point represents any one of multiple sampling points.

[0048] Specifically, substitute the calcite content, quartz content, and pyrite content of the target sampling point into the Voigt-Ruess-Hill model to simulate the mixing of calcite, quartz, and pyrite at the target sampling point, and then the first equivalent elastic stiffness matrix of the brittle mineral mixture at the target sampling point can be obtained.

[0049] Step S1042: Process the organic matter content and clay content of the target sampling point using the Backus average model to obtain the second equivalent elastic stiffness matrix of the layered mixture composed of organic matter and clay at the target sampling point.

[0050] Similarly to the previous step, substitute the organic matter content and clay content into the Backus average model to simulate the mixing of organic matter and clay at the target sampling point, and thus the second equivalent elastic stiffness matrix of the layered mixture at the target sampling point can be obtained.

[0051] Step S1043: Process the second equivalent elastic stiffness matrix using the Bond transformation model to obtain the third equivalent elastic stiffness matrix of the rotated layered mixture composed of organic matter and clay.

[0052] That is, apply the Bond transformation theory to rotate the solid block composed of organic matter and clay. The third equivalent elastic stiffness matrix is equivalent to the equivalent flexible matrix elastic stiffness matrix simulating the coal bedding.

[0053] Step S1044: Based on the anisotropic differential equivalent medium model, process the first equivalent elastic stiffness matrix, the third equivalent elastic stiffness matrix, and the pore content of the target sampling point to obtain the fourth equivalent elastic stiffness matrix of the pore-containing coal matrix at the target sampling point.

[0054] Specifically, first use the anisotropic differential equivalent medium model to process the first equivalent elastic stiffness matrix and the third equivalent elastic stiffness matrix, which is equivalent to adding brittle minerals to the layered flexible matrix to obtain the coal matrix. Then, apply the anisotropic differential equivalent medium model to add equivalent pores to the above coal matrix to obtain the pore-containing coal matrix. By calculating the above relevant data using the anisotropic differential equivalent medium model, the fourth equivalent elastic stiffness matrix of the pore-containing coal matrix at the target sampling point can be obtained.

[0055] Step S1045: Use the linear slip model to superimpose the preset vertical fracture parameters onto the fourth equivalent elastic stiffness matrix to obtain the equivalent elastic stiffness matrix at the target sampling point.

[0056] Optionally, the linear sliding model selects the Schoenberg transform model. Using this model, the preset vertical fracture parameters can be superimposed on the fourth equivalent elastic stiffness matrix, so as to add vertical fractures to the deep porous coal matrix, and then to the equivalent elastic stiffness matrix at the target sampling point in the deep coalbed methane reservoir.

[0057] Based on the description above, the establishment of the anisotropic rock physics model for the deep coalbed methane reservoir includes the calculation of the equivalent elastic stiffness matrix of the brittle mineral mixture, the calculation of the equivalent elastic stiffness matrix of the organic matter and clay laminated mixture, the calculation of the equivalent elastic stiffness matrix of the coal matrix, the calculation of the equivalent elastic stiffness matrix of the porous coal matrix, and the calculation of the equivalent elastic stiffness matrix of the deep coalbed methane reservoir. By substituting the logging data at the actual sampling point, the corresponding rock physics model can be constructed.

[0058] Step S1046: Calculate the initial reservoir parameters of the target sampling point based on the equivalent elastic stiffness matrix and bulk density at the target sampling point.

[0059] Step S1047: Construct an initial model of the reservoir parameters of the deep coalbed methane reservoir based on the initial reservoir parameters of multiple sampling points in the deep coalbed methane reservoir.

[0060] According to the stiffness coefficients in the equivalent elastic stiffness matrix at the target sampling point and the bulk density of the target sampling point, the initial reservoir parameters of the target sampling point can be obtained, that is, the initial values of the five reservoir parameters at this sampling point. Using the initial reservoir parameters of all sampling points, an initial model of the reservoir parameters can be constructed.

[0061] The calculation formulas for the above five reservoir parameters are as follows: ; ; ; ; , where A represents acoustic impedance, B represents anisotropic shear modulus, C represents the horizontal P-wave phase velocity along the fracture strike, D represents azimuthal anisotropy gradient, E represents relative fracture density, represents bulk density, represents the element in the i-th row and j-th column of the equivalent elastic stiffness matrix, represents the density-normalized elastic stiffness coefficient, , , , , , , , , , represents Figure 2The longitudinal wave anisotropy parameter in the symmetry plane of [x2, x3], denote Figure 2 the longitudinal wave anisotropy parameter in the symmetry plane of [x1, x3], denote Figure 2 the shear wave anisotropy parameter in the symmetry plane of [x2, x3], denote Figure 2 the shear wave anisotropy parameter in the symmetry plane of [x1, x3].

[0062] In an alternative embodiment, in step S106, based on the azimuthal seismic observation data and the azimuthal seismic reflection coefficient equation, in the Bayesian inversion framework, a reservoir parameter objective functional for inverting the deep coalbed methane reservoir is constructed, which specifically includes the following steps:

[0063] Step S1061, extract the seismic wavelet from the azimuthal seismic observation data.

[0064] Step S1062, determine the expression of the forward modeled seismic data based on the seismic wavelet and the azimuthal seismic reflection coefficient equation.

[0065] Extracting the seismic wavelet W from the azimuthal seismic observation data is a well-known technique in the art and will not be elaborated here. In the embodiment of the present invention, the azimuthal seismic reflection coefficient equation is expressed as , where denotes the incident angle, denotes the azimuth angle. According to this equation, it can be seen that the reservoir parameters A, B, and C are independent of the azimuthal seismic data, and the reservoir parameters D and E are related to the azimuthal seismic data. The expression of the forward modeled seismic data is: .

[0066] Step S1063, in the Bayesian inversion framework, on the premise that the prior distribution of the reservoir parameters follows a Cauchy distribution, with the maximization of the posterior distribution of the reservoir parameters as the goal, based on the azimuthal seismic observation data and the expression of the forward modeled seismic data, construct a reservoir parameter objective functional for inverting the deep coalbed methane reservoir.

[0067] It is known that the Bayesian theory is: Assume that a random sample of size n is observed, denoted as to infer the population and find the point estimate of the population parameter , where is the parameter of the population distribution . Using the Bayesian formula: , where denotes the prior estimate of the parameter , denotes the likelihood function of this parameter , Represents the marginal probability density function of the data, which can be simply understood as the integral over all value cases . Finally, it can be obtained that when observing such a sample, the posterior distribution of the population parameter is .

[0068] Under the Bayesian framework, the posterior probability distribution of the parameters to be inverted in the AVAZ inversion (specifically the reservoir parameters in the embodiments of the present invention) can be expressed as the joint distribution of the prior probability and the likelihood function : (Equation 1), where is the prior distribution assumed to be satisfied by m, is the probability distribution of obtaining the observed data under this model, is the distribution of the observed data.

[0069] Assume that the noise of the seismic data follows a Gaussian distribution and is independent. The distribution of the azimuthal seismic observation data noise n can be written in the following form (Equation 2), where is a preset constant, represents the covariance matrix of the noise, represents the azimuthal seismic observation data noise.

[0070] It is known that there is the following relationship between the azimuthal seismic observation data and the forward modeled seismic data: (Equation 3). Substituting Equation 3 into Equation 2, the likelihood function can be obtained: (Equation 4).

[0071] Based on Equation 1, the expression form of the posterior distribution of the parameters to be inverted can be obtained: (Equation 5). Assume that the prior distribution of the parameters to be inverted follows a Cauchy distribution, that is (Equation 6), where represents the covariance matrix of the reservoir parameters, that is, a fifth-order covariance matrix, which characterizes the statistical correlation between the five reservoir parameters, D represents a 5×5N adjustment matrix, , such that contains the statistical correlation between the reservoir parameters. Equation 5 can be rewritten as (Equation 7). According to the maximum a posteriori probability estimation, the objective functional for inverting the reservoir parameters of the deep coalbed methane reservoir can be constructed.

[0072] In the embodiments of the present invention, the objective functional for inverting the reservoir parameters of the deep coalbed methane reservoir is expressed as: (Equation 8); where represents azimuthal seismic observation data, represents the expression of forward modeled seismic data, , represents the seismic wavelet, represents the azimuthal seismic reflection coefficient equation, represents the matrix composed of reservoir parameters, represents the covariance matrix of noise, represents the total number of sampling points, i represents the i -th sampling point, is a 5×5 covariance matrix, D is a 5×5N adjustment matrix for adjusting the dimension of the covariance matrix and establishing the statistical correlation between reservoir parameters. Obviously, the smaller the function value of the objective functional, the larger.

[0073] In an alternative embodiment, in step S108 above, under the constraint of the initial model of reservoir parameters, the objective functional is solved based on the generalized linear inversion theory and the iterative reweighted least squares joint algorithm to obtain the target model of reservoir parameters for deep coalbed methane reservoirs, which specifically includes the following steps:

[0074] Step S1081, perform a first-order Taylor expansion of the objective functional at the initial model of reservoir parameters based on the generalized linear inversion theory to obtain the objective function of reservoir parameters.

[0075] Since the objective functional is a non-linear problem, therefore, in the embodiment of the present invention, based on the generalized linear inversion theory, first at perform a first-order Taylor expansion, and we can get: (Equation 9), where represents the initial model of reservoir parameters, represents the forward modeled seismic data determined based on , represents the perturbation of reservoir parameters.

[0076] Assume that the random noise in the seismic data is independent of each other, that is, the covariance matrix of noise can be simplified to a diagonal matrix, written as , where I is an N×N identity matrix and N represents the total number of sampling points. Then the above Equation 8 can be expressed as: ; multiply both sides of the equation by to get: ; substitute Equation 9 into Equation 8 and replace m in the regularization Cauchy prior term with , then we can obtain the objective function of reservoir parameters: (Equation 10).

[0077] Step S1082: Calculate the partial derivative of the objective function with respect to the reservoir parameter perturbation amount to obtain the partial derivative function.

[0078] Equation 10 is the objective function of the reservoir parameters. From the description above, it can be seen that the purpose of the embodiments of the present invention is to find the minimum value of the objective function. Because of the non-linearity of the objective function, it is necessary to take the derivative to construct an iterative form to achieve inversion.

[0079] Therefore, after obtaining the expression of the objective function, calculate the partial derivative of the objective function with respect to the reservoir parameter perturbation amount to obtain the following partial derivative function: (Equation 11).

[0080] Step S1083: When the partial derivative function is equal to 0, determine the initial solution expression of the reservoir parameter perturbation amount.

[0081] Let the partial derivative function be equal to 0, and the initial solution expression of the reservoir parameter perturbation amount can be obtained: (Equation 12).

[0082] Step S1084: Simplify the initial solution expression based on the iterative reweighted least squares joint algorithm to obtain the target solution expression of the reservoir parameter perturbation amount.

[0083] In the embodiments of the present invention, let , , then the reservoir parameter perturbation amount can be expressed by the following formula .

[0084] Since there is a perturbation amount on both sides of the formula, the above equation is solved using iterative reweighted least squares, and the initial perturbation amount = 0. Since this method has a fast convergence rate, and during the iterative calculation, the ( ) in the regularization Cauchy prior term is a double iteration. To reduce the calculation, generally only one iteration is performed when calculating in the regularization. Through this simplification method, the target solution expression of the reservoir parameter perturbation amount is: (Equation 13); where represents the reservoir parameter perturbation amount, and represents the variance of the azimuth seismic observation data noise.

[0085] Step S1085: Iteratively update the reservoir parameters based on the initial model of the reservoir parameters and the target solution expression of the reservoir parameter perturbation amount until the preset iteration end condition is met, and obtain the target model of the reservoir parameters.

[0086] After the initial model of the reservoir parameters has been determined Based on substitute into the target solution expression of , then the solution of can be obtained. Furthermore, based on perform the first iterative update on , where represents the preset iteration step size, which takes a constant value. Optionally, takes the value of 1. Next, substitute into the target solution expression of to solve . And so on, based on perform multiple iterative updates on until the preset iteration end condition is satisfied, and take the obtained in the last iteration as the reservoir parameter target model. The embodiments of the present invention do not specifically limit the iteration end condition, which can be that the iteration reaches a specified number of times, or meets the preset accuracy requirement.

[0087] In the embodiments of the present invention, when solving , in the target solution expression, given , then there is (Formula 14), is the first-order partial derivative of the P-wave reflection coefficient with respect to the five attribute parameters. Therefore, for a determined incident angle and azimuth , the first-order partial derivative of with respect to the five attribute parameters can be expressed by the following formula:

[0088] Among them, represents the wavelet matrix when the incident angle is and the azimuth is . , , , , respectively represent the first-order partial derivatives of the reflection coefficient with respect to acoustic impedance, anisotropic shear modulus, horizontal P-wave phase velocity along the fracture strike, azimuthal anisotropy gradient, and relative fracture density. The specific expressions are:

[0089] , ,

[0090] ,

[0091] ,

[0092] 。 When the inversion involves incidence angles and azimuth angles, the partial derivative matrix of the P-wave reflection coefficient in the above formula (15) is extended to matrix.

[0093] After solving the target model of reservoir parameters, the horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir can be calculated using the formula

[0094] Verify the rationality of the inversion method of the present invention:

[0095] Select a deep coalbed methane reservoir on the eastern margin of the Ordos Basin to test the method for determining the horizontal stress difference ratio of the deep coalbed methane reservoir of the present invention. The target layer of the study area has continuous horizontal bedding and is accompanied by a large number of nearly vertical and vertical fractures. Therefore, this deep coalbed methane reservoir can be regarded as an orthotropic medium. Based on well logging data, an initial model of reservoir parameters of the deep coalbed methane reservoir is constructed as Figure 4 shown. The azimuth angles of the seismic data are 45°, 90°, 135° and 180° respectively. The near, medium and far pre-stack average incidence angles corresponding to each azimuth angle are 5°, 15° and 25° respectively. The method for determining the horizontal stress difference ratio of the deep coalbed methane reservoir is used to predict the horizontal stress difference ratio of the target layer. The inversion results are as Figure 5 shown, and the true value of the well logging curve at the position of the black curve well in the figure is shown by high-cut filtering. From Figure 5 it can be seen that the inversion results of the target layer have good vertical difference and horizontal continuity. The horizontal stress difference ratio of the target layer is relatively low, which is consistent with the geological prior information of the work area.

[0096] In summary, the embodiment of the present invention provides a method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir. The method pre-constructs an anisotropic rock physics model of the deep coalbed methane reservoir, proposes to use the standard deviation of the Gaussian distribution to describe the degree of oriented arrangement of organic matter and clay, characterize the influence of coal bedding on anisotropy (because the Bond transformation is based on the Gaussian distribution), and considers the influence of vertical fractures on the anisotropy of the deep coalbed methane reservoir. In the Bayesian inversion framework, considering the constraints of the initial model of reservoir parameters, a joint algorithm of generalized linear inversion and iteratively reweighted least squares is used to solve the non-linear objective functional, realizing the accurate estimation of the horizontal stress difference ratio of the deep coalbed methane reservoir and improving the accuracy of the inversion results.

[0097] Embodiment 2

[0098] ​The embodiment of the present invention also provides a device for determining the horizontal stress difference ratio of a deep coalbed methane reservoir. This device is mainly used to execute the method for determining the horizontal stress difference ratio of the deep coalbed methane reservoir provided in the first embodiment above. The following is a specific introduction to the device for determining the horizontal stress difference ratio of the deep coalbed methane reservoir provided by the embodiment of the present invention.

[0099] Figure 6 FIG. is a functional module diagram of a device for determining the horizontal stress difference ratio of a deep coalbed methane reservoir provided by an embodiment of the present invention. As Figure 6 shown, the device mainly includes: an acquisition module 10, a determination module 20, a construction module 30, a solution module 40, and a calculation module 50, where:

[0100] The acquisition module 10 is used to acquire the azimuth seismic observation data and well logging data of the deep coalbed methane reservoir; among them, the well logging data includes: the organic matter content, clay content, brittle mineral content, pore content, and bulk density of multiple sampling points; the brittle minerals include: calcite, quartz, and pyrite.

[0101] The determination module 20 is used to determine the initial reservoir parameter model of the deep coalbed methane reservoir based on the well logging data; among them, the reservoir parameters include: acoustic impedance, anisotropic shear modulus, horizontal longitudinal wave phase velocity along the fracture strike, azimuthal anisotropy gradient, and relative fracture density.

[0102] The construction module 30 is used to construct a target functional for inverting the reservoir parameters of the deep coalbed methane reservoir under the Bayesian inversion framework based on the azimuth seismic observation data and the azimuth seismic reflection coefficient equation.

[0103] The solution module 40 is used to solve the target functional based on the generalized linear inversion theory and the iterative reweighted least squares joint algorithm under the constraint of the initial reservoir parameter model to obtain the target model of the reservoir parameters of the deep coalbed methane reservoir.

[0104] The calculation module 50 is used to calculate the horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir based on the target model of the reservoir parameters.

[0105] An embodiment of the present invention provides an apparatus for determining the horizontal stress difference ratio of a deep coalbed methane reservoir. After obtaining the azimuth seismic observation data and well logging data of the deep coalbed methane reservoir, the apparatus first constructs an initial reservoir parameter model of the deep coalbed methane reservoir based on the well logging data, and then constructs a target functional for inverting the reservoir parameters of the deep coalbed methane reservoir based on the azimuth seismic observation data and the azimuth seismic reflection coefficient equation. Next, under the constraint of the initial model, the target functional is solved based on the generalized linear inversion theory and the iterative reweighted least squares joint algorithm to obtain a target model of the reservoir parameters, and then the horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir is calculated according to the target model. The apparatus constructs an initial reservoir parameter model that can accurately characterize the anisotropic characteristics of the reservoir based on the organic matter content, clay content, brittle mineral content, pore content, and bulk density of multiple sampling points in the deep coalbed methane reservoir. Furthermore, under the constraint of an accurate initial model, the accurate estimation of the horizontal stress difference ratio of the deep coalbed methane reservoir is realized, alleviating the technical problem of low accuracy existing in the existing horizontal stress difference estimation method.

[0106] Optionally, the determination module 20 is specifically configured to:

[0107] Process the brittle mineral content of the target sampling point by using the Voigt-Ruess-Hill model to obtain a first equivalent elastic stiffness matrix of the brittle mineral mixture at the target sampling point; wherein, the target sampling point represents any one of the multiple sampling points.

[0108] Process the organic matter content and clay content of the target sampling point by using the Backus average model to obtain a second equivalent elastic stiffness matrix of the layered mixture composed of organic matter and clay at the target sampling point.

[0109] Process the second equivalent elastic stiffness matrix by using the Bond transformation model to obtain a third equivalent elastic stiffness matrix of the rotated layered mixture composed of organic matter and clay.

[0110] Process the first equivalent elastic stiffness matrix, the third equivalent elastic stiffness matrix, and the pore content of the target sampling point based on the anisotropic differential equivalent medium model to obtain a fourth equivalent elastic stiffness matrix of the pore-containing coal matrix at the target sampling point.

[0111] Superimpose the preset vertical fracture parameters on the fourth equivalent elastic stiffness matrix by using the linear slip model to obtain an equivalent elastic stiffness matrix at the target sampling point.

[0112] Calculate the initial reservoir parameters of the target sampling point based on the equivalent elastic stiffness matrix and bulk density at the target sampling point.

[0113] Construct an initial reservoir parameter model for the deep coalbed methane reservoir based on the initial reservoir parameters at multiple sampling points in the deep coalbed methane reservoir.

[0114] Optionally, the construction module 30 is specifically configured to:

[0115] Extract seismic wavelets from azimuth seismic observation data.

[0116] Based on the seismic wavelets and the azimuth seismic reflection coefficient equation, determine the expression of the forward modeled seismic data.

[0117] Under the Bayesian inversion framework, with the premise that the prior distribution of reservoir parameters follows a Cauchy distribution and the maximization of the posterior distribution of reservoir parameters as the goal, construct a target functional for inverting the reservoir parameters of the deep coalbed methane reservoir based on the azimuth seismic observation data and the expression of the forward modeled seismic data.

[0118] Optionally, the solution module is specifically configured to:

[0119] Perform a first-order Taylor expansion of the target functional at the initial reservoir parameter model based on the generalized linear inversion theory to obtain the objective function of the reservoir parameters.

[0120] Calculate the partial derivative of the objective function with respect to the perturbation of the reservoir parameters to obtain the partial derivative function.

[0121] When the partial derivative function is equal to 0, determine the initial solution expression of the perturbation of the reservoir parameters.

[0122] Simplify the initial solution expression based on the iterative reweighted least squares joint algorithm to obtain the target solution expression of the perturbation of the reservoir parameters.

[0123] Iteratively update the reservoir parameters based on the initial reservoir parameter model and the target solution expression of the perturbation of the reservoir parameters until the preset iteration end condition is satisfied, and obtain the target model of the reservoir parameters.

[0124] Optionally, the target functional for inverting the reservoir parameters of the deep coalbed methane reservoir is expressed as: ; where represents the azimuth seismic observation data, represents the expression of the forward modeled seismic data, , represents the seismic wavelets, represents the azimuth seismic reflection coefficient equation, represents the matrix composed of reservoir parameters, represents the covariance matrix of the noise, represents the total number of sampling points, i represents the i th sampling point, is a 5×5 covariance matrix,D is a 5×5N adjustment matrix.

[0125] Optionally, the target solution expression for the reservoir parameter perturbation amount is: ; where represents the reservoir parameter perturbation amount, represents the variance of the azimuthal seismic observation data noise.

[0126] Optionally, the formula for the horizontal stress difference ratio is: ;

[0127] where represents the horizontal stress difference ratio, A represents the acoustic impedance, B represents the anisotropic shear modulus, C represents the horizontal P-wave phase velocity along the fracture strike, D represents the azimuthal anisotropy gradient, E represents the relative fracture density, , .

[0128] Embodiment III

[0129] Referring to Figure 7 , an embodiment of the present invention provides an electronic device, which includes: a processor 60, a memory 61, a bus 62, and a communication interface 63. The processor 60, the communication interface 63, and the memory 61 are connected through the bus 62; the processor 60 is configured to execute an executable module stored in the memory 61, such as a computer program.

[0130] Among them, the memory 61 may include a high-speed random access memory (RAM, Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 63 (which can be wired or wireless), a communication connection is established between this system network element and at least one other network element, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0131] The bus 62 may be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 7 only a bidirectional arrow is used in

[0132] but it does not mean that there is only one bus or one type of bus.

[0133] The processor 60 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 60 or the instructions in the form of software. The above-mentioned processor 60 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 61, and the processor 60 reads the information in the memory 61 and combines its hardware to complete the steps of the above method.

[0134] A computer program product of a method and device for determining the horizontal stress difference ratio of a deep coalbed methane reservoir provided by an embodiment of the present invention includes a computer-readable storage medium storing non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the method in the foregoing method embodiments. For specific implementation, reference can be made to the method embodiments and will not be elaborated herein.

[0135] In addition, in each embodiment of the present invention, the various functional units may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0136] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0137] It should be noted that: similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0138] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed during use. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0139] In addition, the terms "horizontal", "vertical", "hanging", etc. do not mean that the components are required to be absolutely horizontal or hanging, but can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but can be slightly inclined.

[0140] In the description of the present invention, it should also be noted that unless otherwise clearly specified and limited, the terms "set", "install", "connect", "couple" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the internal communication of two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir, characterized in that: include: Obtain azimuthal seismic observation data and well logging data of deep coalbed methane reservoirs; well logging data include: organic matter content, clay content, brittle mineral content, pore content and bulk density of multiple sampling points; brittle minerals include: calcite, quartz and pyrite; The Voigt-Ruess-Hill model is used to process the brittle mineral content of the target sampling point to obtain the first equivalent elastic stiffness matrix of the brittle mineral mixture at the target sampling point; wherein the target sampling point represents any sampling point among the multiple sampling points; The organic matter content and clay content of the target sampling point were processed using the Backus average model to obtain the second equivalent elastic stiffness matrix of the layered mixture composed of organic matter and clay at the target sampling point. The second equivalent elastic stiffness matrix is ​​processed by using the Bond transformation model to obtain the third equivalent elastic stiffness matrix of the layered mixture composed of organic matter and clay after rotation. Based on the anisotropic differential equivalent medium model, the first equivalent elastic stiffness matrix, the third equivalent elastic stiffness matrix and the pore content of the target sampling point are processed to obtain the fourth equivalent elastic stiffness matrix of the porous coal matrix at the target sampling point; The preset vertical crack parameters are superimposed on the fourth equivalent elastic stiffness matrix using a linear sliding model to obtain the equivalent elastic stiffness matrix at the target sampling point; Based on the equivalent elastic stiffness matrix and volume density at the target sampling point, the initial reservoir parameters of the target sampling point are calculated; Based on the initial reservoir parameters of multiple sampling points in the deep coalbed methane reservoir, an initial reservoir parameter model of the deep coalbed methane reservoir is constructed; wherein the reservoir parameters include: acoustic impedance, anisotropic shear modulus, horizontal P-wave phase velocity along the fracture direction, azimuthal anisotropy gradient and relative fracture density; Based on azimuthal seismic observation data and azimuthal seismic reflection coefficient equation, a target functional for inverting reservoir parameters of deep coalbed methane reservoirs is constructed in the Bayesian inversion framework. Under the constraints of the initial model of reservoir parameters, the target functional is solved based on the generalized linear inversion theory and iterative reweighted least squares joint algorithm to obtain the reservoir parameter target model of deep coalbed methane reservoirs. The horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir is calculated based on the reservoir parameter target model.

2. The method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir according to claim 1, characterized in that: Based on the azimuthal seismic observation data and the azimuthal seismic reflection coefficient equation, a reservoir parameter target functional for inverting deep coalbed methane reservoirs is constructed under the Bayesian inversion framework, including: extracting seismic wavelets from the azimuth seismic observation data; Based on the seismic wavelet and the azimuthal seismic reflection coefficient equation, determining an expression for forward modeling seismic data; In the Bayesian inversion framework, with the premise that the prior distribution of the reservoir parameters obeys the Cauchy distribution and the goal of maximizing the posterior distribution of the reservoir parameters, a reservoir parameter target functional for inverting deep coalbed methane reservoirs is constructed based on the expressions of the azimuthal seismic observation data and the forward simulation seismic data.

3. The method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir according to claim 2, characterized in that: Under the constraints of the initial model of reservoir parameters, the target functional is solved based on the generalized linear inversion theory and iterative reweighted least squares joint algorithm to obtain the reservoir parameter target model of the deep coalbed methane reservoir, including: Based on the generalized linear inversion theory, a first-order Taylor expansion is performed on the target functional at the initial model of the reservoir parameters to obtain the target function of the reservoir parameters; Calculating the partial derivative of the objective function with respect to the reservoir parameter disturbance amount to obtain a partial derivative function; When the partial derivative function is equal to 0, determining an initial solution expression for the reservoir parameter disturbance amount; Simplifying the initial solution expression based on an iterative reweighted least squares joint algorithm to obtain a target solution expression for the reservoir parameter disturbance; The reservoir parameters are iteratively updated based on the reservoir parameter initial model and the target solution expression of the reservoir parameter disturbance amount until a preset iteration termination condition is met, thereby obtaining the reservoir parameter target model.

4. The method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir according to claim 3, characterized in that: The target functional of the reservoir parameters used to invert the deep coalbed methane reservoir is expressed as: ； in, represents the azimuth seismic observation data, The expression representing the forward modeling seismic data is, , represents the seismic wavelet, represents the azimuthal seismic reflection coefficient equation, represents the matrix composed of reservoir parameters, represents the covariance matrix of the noise, represents the total number of sampling points, i Indicates i sampling points, is a 5×5 covariance matrix, D It is a 5×5N adjustment matrix.

5. The method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir according to claim 4, characterized in that: The target solution expression of the reservoir parameter disturbance is: ;in, represents the reservoir parameter disturbance, Represents the variance of the noise in the azimuth seismic observation data.

6. The method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir according to claim 1, characterized in that: The formula for the horizontal stress difference ratio is: ; in, represents the horizontal stress difference ratio, A represents the acoustic impedance, B represents the anisotropic shear modulus, C represents the horizontal longitudinal wave phase velocity along the crack direction, D represents the azimuthal anisotropy gradient, and E represents the relative crack density. , .

7. A device for determining horizontal stress difference ratio of deep coalbed methane reservoir, characterized in that: include: The acquisition module is used to obtain the azimuthal seismic observation data and well logging data of deep coalbed methane reservoirs; the well logging data includes: organic matter content, clay content, brittle mineral content, pore content and bulk density of multiple sampling points; brittle minerals include: calcite, quartz and pyrite; A determination module is used to process the brittle mineral content of the target sampling point using the Voigt-Ruess-Hill model to obtain a first equivalent elastic stiffness matrix of the brittle mineral mixture at the target sampling point; wherein the target sampling point represents any sampling point among the multiple sampling points; The organic matter content and clay content of the target sampling point were processed using the Backus average model to obtain the second equivalent elastic stiffness matrix of the layered mixture composed of organic matter and clay at the target sampling point. The second equivalent elastic stiffness matrix is ​​processed by using the Bond transformation model to obtain the third equivalent elastic stiffness matrix of the layered mixture composed of organic matter and clay after rotation. Based on the anisotropic differential equivalent medium model, the first equivalent elastic stiffness matrix, the third equivalent elastic stiffness matrix and the pore content of the target sampling point are processed to obtain the fourth equivalent elastic stiffness matrix of the porous coal matrix at the target sampling point; The preset vertical crack parameters are superimposed on the fourth equivalent elastic stiffness matrix using a linear sliding model to obtain the equivalent elastic stiffness matrix at the target sampling point; Based on the equivalent elastic stiffness matrix and volume density at the target sampling point, the initial reservoir parameters of the target sampling point are calculated; Based on the initial reservoir parameters of multiple sampling points in the deep coalbed methane reservoir, an initial reservoir parameter model of the deep coalbed methane reservoir is constructed; wherein the reservoir parameters include: acoustic impedance, anisotropic shear modulus, horizontal P-wave phase velocity along the fracture direction, azimuthal anisotropy gradient and relative fracture density; A construction module for constructing a reservoir parameter target functional for inverting deep coalbed methane reservoirs based on azimuthal seismic observation data and azimuthal seismic reflection coefficient equations in a Bayesian inversion framework; A solution module is used to solve the target functional under the constraints of the initial model of reservoir parameters based on the generalized linear inversion theory and iterative reweighted least squares joint algorithm to obtain the reservoir parameter target model of the deep coalbed methane reservoir; The calculation module is used to calculate the horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir based on the reservoir parameter target model.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the method for determining the horizontal stress difference ratio of the deep coalbed methane reservoir described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir according to any one of claims 1 to 6.

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