Determination method and device for horizontal stress difference ratio of deep coal bed gas reservoir

Abstract

The invention provides a method and device for determining the horizontal stress difference ratio of a deep coalbed methane reservoir, and relates to the technical field of deep coalbed methane exploration, and the method comprises the steps: obtaining the logging data and azimuth seismic observation data of the deep coalbed methane reservoir, building a reservoir parameter initial model of the deep coalbed methane reservoir based on the logging data, and obtaining the horizontal stress difference ratio of the deep coalbed methane reservoir; constructing a target functional for inverting deep coal bed gas reservoir parameters on the basis of azimuth seismic observation data and an azimuth seismic reflection coefficient equation, and solving the target functional on the basis of a generalized linear inversion theory and an iterative reweighted least square joint algorithm under the constraint of the initial model to obtain a reservoir parameter target model; and calculating the horizontal stress difference ratio of each sampling point in the deep coal bed gas reservoir according to the target model. According to the method, under the constraint of the accurate initial model, accurate estimation of the horizontal stress difference ratio of the deep coal bed gas reservoir is achieved, and the technical problem that an existing horizontal stress difference estimation method is low in accuracy is solved.

Description

Technical Field

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

[0002] With the continuous deepening of deep coalbed methane exploration and development, it is increasingly important to accurately characterize the distribution of sweet spots in deep coalbed methane engineering. Deep coalbed methane reservoirs have the characteristics of very low porosity and very low permeability, and fracturing is required to form large-scale exploitation. Geostress is an important factor that determines the shape, orientation and extension direction of generated fractures, and the differential horizontal stress ratio (DHSR) is an important parameter to characterize reservoir geostress. Accurately predicting the differential horizontal stress ratio of deep coalbed methane reservoirs can significantly improve the efficiency of coalbed methane extraction.

[0003] The method for determining the DHSR of deep coalbed methane reservoirs in the prior art is to estimate the DHSR of the formation using the reservoir parameters and anisotropy parameters obtained by inversion. However, the current method is a direct inversion based on the assumption of a transversely isotropic medium. Given that the transversely isotropic medium does not match the actual structure of the coal seam, the DHSR determined using the existing method is less accurate. Summary of the invention

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

[0005] In a first aspect, the present invention provides a method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir, comprising: obtaining azimuthal seismic observation data and logging data of a deep coalbed methane reservoir; wherein the logging data comprises: organic matter content, clay content, brittle mineral content, pore content and bulk density of multiple sampling points; brittle minerals comprise: calcite, quartz and pyrite; determining an initial reservoir parameter model of a deep coalbed methane reservoir based on the logging data; wherein the reservoir parameters comprise: acoustic impedance, anisotropic shear modulus, water content along the direction of the fracture; The target functional of reservoir parameters for inverting deep coalbed methane reservoirs is constructed based on the azimuthal seismic observation data and the azimuthal seismic reflection coefficient equation under 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 the iterative reweighted least squares joint algorithm to obtain the target model of reservoir parameters for 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.

[0006] Optionally, an initial model of reservoir parameters of a deep coalbed methane reservoir is determined based on logging data, including: using the Voigt-Ruess-Hill model to process the brittle mineral content of the target sampling point 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 multiple sampling points; using the Backus average model to process the organic matter content and clay content of the target sampling point to obtain a second equivalent elastic stiffness matrix of a layered mixture composed of organic matter and clay at the target sampling point; using the Bond transformation model to process the second equivalent elastic stiffness matrix to obtain the rotated organic matter and clay content. The third equivalent elastic stiffness matrix of the layered mixture composed of the above-mentioned materials is obtained; 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 fracture parameters are superimposed on the fourth equivalent elastic stiffness matrix using the linear sliding model to obtain the equivalent elastic stiffness matrix at the target sampling point; based on the equivalent elastic stiffness matrix and the 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.

[0007] Optionally, based on the azimuthal seismic observation data and the azimuthal seismic reflection coefficient equation, in the Bayesian inversion framework, a reservoir parameter target functional for inverting deep coalbed methane reservoirs is constructed, including: extracting seismic wavelets from the azimuthal seismic observation data; determining the expression of forward simulation seismic data based on the seismic wavelets and the azimuthal seismic reflection coefficient equation; 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.

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

[0009] Optionally, the reservoir parameter target functional for inversion of deep coalbed methane reservoirs is expressed as: ;in, represents the azimuth earthquake observation data, The expression for 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.

[0010] Optionally, 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.

[0011] Optionally, the horizontal stress difference ratio can be calculated as: ;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. , .

[0012] In a second aspect, the present invention provides a device for determining the horizontal stress difference ratio of a deep coalbed methane reservoir, comprising: an acquisition module for acquiring azimuthal seismic observation data and logging data of a deep coalbed methane reservoir; wherein the logging data comprises: organic matter content, clay content, brittle mineral content, pore content and bulk density of multiple sampling points; brittle minerals comprise: calcite, quartz and pyrite; a determination module for determining an initial model of reservoir parameters of a deep coalbed methane reservoir based on the logging data; wherein the reservoir parameters comprise: acoustic impedance, anisotropic shear modulus, and the direction of the fracture. horizontal P-wave phase velocity, 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 equation in the Bayesian inversion framework; a solution module for solving the target functional based on the iterative reweighted least squares joint algorithm under the constraints of the initial reservoir parameter model to obtain the reservoir parameter target model of the deep coalbed methane reservoir; a calculation module for calculating the horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir based on the reservoir parameter target model.

[0013] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be 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 aforementioned embodiments.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium storing 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 the aforementioned embodiments.

[0015] The present invention provides a method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir. After obtaining the azimuthal seismic observation data and well logging data of the deep coalbed methane reservoir, the method first constructs an initial reservoir parameter model of the deep coalbed methane reservoir based on the well logging data, then constructs a reservoir parameter target functional for inverting the deep coalbed methane reservoir based on the azimuthal seismic observation data and the azimuthal seismic reflection coefficient equation, and then solves the target functional based on the generalized linear inversion theory and the iterative reweighted least squares joint algorithm under the constraint of the initial model to obtain the reservoir parameter target model, and then calculates the horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir according to the target model. The method 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 volume density of multiple sampling points in the deep coalbed methane reservoir, and then realizes accurate estimation of the horizontal stress difference ratio of the deep coalbed methane reservoir under the constraint of the accurate initial model, which alleviates the technical problem of low accuracy in the existing horizontal stress difference estimation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flow chart of a method for determining a horizontal stress difference ratio of a deep coalbed methane reservoir provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of deep coalbed methane reservoir coal; Figure 3 A flow chart of coal rock physical modeling for deep coalbed methane reservoirs based on equivalent medium theory provided in an embodiment of the present invention; Figure 4 To construct an initial model diagram of reservoir parameters for deep coalbed methane reservoirs based on well logging data; Figure 5 A schematic cross-sectional diagram of a horizontal stress difference ratio inversion result obtained by applying the method provided in an embodiment of the present invention; Figure 6 A functional module diagram of a device for determining a horizontal stress difference ratio of a deep coalbed methane reservoir provided by an embodiment of the present invention; Figure 7 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0021] Embodiment 1 Figure 1 A flow chart of a method for determining a horizontal stress difference ratio of a deep coalbed methane reservoir provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method specifically comprises the following steps: Step S102, obtaining azimuthal seismic observation data and well logging data of deep coalbed methane reservoirs.

[0022] To determine the horizontal stress difference ratio of the deep coalbed methane reservoir, the data sources required to be used in the embodiment of the present invention are the azimuthal seismic observation data and well logging data of the deep coalbed methane reservoir. The azimuthal seismic observation data are data collected by a professional data acquisition system through seismic exploration of the area where the deep coalbed methane reservoir is located, or data acquired through a seismic monitoring network; the well logging data are directly or indirectly calculated based on the well logging curve of the deep coalbed methane reservoir. The well logging curve is a data curve used to record the physical properties of underground rocks in petroleum exploration. It reflects the characteristics of underground rock formations by measuring various physical properties of underground rocks, such as resistivity, sound wave propagation time, natural gamma radiation, etc.

[0023] In the embodiment of the present invention, the 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.

[0024] The volume density of each sampling point in the deep coalbed methane reservoir is directly obtained from the density curve in the logging curve. It is known that the coal in the deep coalbed methane reservoir is mainly composed of organic matter, clay and brittle minerals, and the organic matter is the main content, generally 55~95%. The organic matter content at each sampling point can be obtained through the natural gamma curve. The calculation formula of the organic matter content is: TOC=0.265×U-0.119, where U represents the U value of the radioactive element uranium and TOC represents the organic matter content.

[0025] 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 units of %. Specifically, the shale index is first calculated using the following formula: SH = (GR-GR min ) / (GR max -GR min ), where: SH represents the shale index, GR represents the natural gamma logging curve value of deep coalbed methane reservoir; GR min Represents the natural gamma curve value of pure sandstone, GR max represents the natural gamma curve value of pure mudstone. Next, the mud content is calculated using the following formula, V clay =(2 GCUR*SH -1) / (2 GCUR -1), where: Vclay It represents mud content, GCUR represents Hilch index, which is an empirical coefficient related to the stratum. The value of GCUR depends on the geological age of the stratum. It is usually 2.0 in old strata and 3.7 in new strata (such as Tertiary strata).

[0026] In the embodiment of the present invention, the organic matter content and the mud content are both volume contents. It is known that the sum of the volume contents of organic matter, clay and brittle minerals is 1, so 1-organic matter content-clay content=brittle mineral content. In the embodiment 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 of calcite, quartz and pyrite are obtained by summarizing empirical data, and the embodiment of the present invention defaults to 4:2:1. Therefore, after determining the brittle mineral content, the calcite content, quartz content and pyrite content of each sampling point in the deep coalbed methane reservoir can be determined respectively according to the above ratios.

[0027] The embodiment of the present invention uses equivalent porosity to characterize the pore content of coal in deep coalbed methane reservoirs, and the porosity obtained from the resistivity curve is used as the equivalent porosity. The specific process of obtaining porosity from the resistivity curve is: when R s >R d hour, ; When R s <R d hour, ; Porosity represents porosity, R d represents the deep lateral resistivity, R s Represents shallow lateral resistivity, R w Represents the formation water resistivity, R wf represents the free water resistivity of the formation, s represents the cementation coefficient, R mf Represents the resistivity of mud filtrate.

[0028] Step S104, determining an initial model of reservoir parameters of a deep coalbed methane reservoir based on well logging data.

[0029] Figure 2 Schematic diagram of deep coalbed methane reservoir. In the embodiment of the present invention, the deep coalbed methane reservoir is equivalent to an orthotropic medium with vertical fractures in the VTI background. Therefore, the present invention is preliminarily based on Figure 3 Based on the equivalent medium theory, the coal rock physics model of deep coalbed methane reservoir was constructed, and the anisotropic rock physics model of deep coalbed methane reservoir was constructed. Figure 3It can be seen that the rock physics model is a model related to 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 of multiple sampling points in the deep coalbed methane reservoir, the organic matter content, clay content, brittle mineral content and pore content of each sample are substituted into the rock physics model, and the equivalent elastic stiffness matrix at each sampling point can be obtained. Further combined with the volume density of each sampling point, the initial reservoir parameters of each sampling point can be calculated respectively, and then the initial reservoir parameter model of the deep coalbed methane reservoir is constructed. Among them, 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.

[0030] Step S106, based on the azimuthal seismic observation data and the azimuthal seismic reflection coefficient equation, a reservoir parameter target functional for inverting the deep coalbed methane reservoir is constructed under the Bayesian inversion framework.

[0031] The known azimuth seismic reflection coefficient equation is a PP wave reflection coefficient equation based on reservoir parameters. , represents the seismic wavelet, represents the azimuthal seismic reflection coefficient equation, represents a matrix composed of reservoir parameters, A represents acoustic impedance, B represents anisotropic shear modulus, C represents horizontal P-wave phase velocity along the fracture direction, D represents azimuthal anisotropy gradient, and E represents relative fracture density. In addition, the following relationship exists between the known azimuthal seismic observation data and the forward simulation seismic data: ,in, represents the azimuth earthquake observation data, represents the forward modeling seismic data, n Represents the azimuth seismic observation data noise.

[0032] Therefore, based on the relationship between azimuthal seismic observation data and forward simulation seismic data, in the Bayesian inversion framework, the embodiment of the present invention assumes that the likelihood function obeys a Gaussian distribution and the prior distribution of reservoir parameters obeys a Cauchy distribution. According to the maximum a posteriori probability estimation, a target functional for inverting the reservoir parameters of deep coalbed methane reservoirs can be constructed.

[0033] Step S108, under the constraints of the initial reservoir parameter model, 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.

[0034] In view of the fact that the target functional is a complex nonlinear expression of the reservoir parameters to be inverted, taking into account factors such as the amount of calculation, convergence speed and calculation accuracy, when solving the target functional, the embodiment of the present invention first uses the initial model of the reservoir parameters that have been obtained as a constraint, and linearizes the nonlinear target functional through Taylor series expansion according to the generalized linear inversion theory, and then uses the iterative reweighted least squares joint algorithm to solve it, so as to obtain the reservoir parameter target model, that is, to predict the reservoir parameters at each sampling point in the deep coalbed methane reservoir.

[0035] Step S110, calculating the horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir based on the reservoir parameter target model.

[0036] In the embodiment of the present invention, the formula for calculating 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 crack direction, D represents the azimuthal anisotropy gradient, and E represents the relative crack density. , Substituting the reservoir parameters of any sampling point in the deep coalbed methane reservoir into the DHSR formula, the horizontal stress difference ratio of the sampling point can be obtained.

[0037] The embodiment of the present invention provides a method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir. After obtaining the azimuthal seismic observation data and well logging data of the deep coalbed methane reservoir, the method first constructs an initial reservoir parameter model of the deep coalbed methane reservoir based on the well logging data, then constructs a reservoir parameter target functional for inverting the deep coalbed methane reservoir based on the azimuthal seismic observation data and the azimuthal seismic reflection coefficient equation, and then solves the target functional based on the generalized linear inversion theory and the iterative reweighted least squares joint algorithm under the constraint of the initial model to obtain the reservoir parameter target model, and then calculates the horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir according to the target model. The method 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 volume density of multiple sampling points in the deep coalbed methane reservoir, and then realizes accurate estimation of the horizontal stress difference ratio of the deep coalbed methane reservoir under the constraint of the accurate initial model, which alleviates the technical problem of low accuracy in the existing horizontal stress difference estimation method.

[0038] In an optional implementation, the above step S104, determining the initial model of reservoir parameters of the deep coalbed methane reservoir based on the well logging data, specifically includes the following steps: Step S1041, using the Voigt-Ruess-Hill model to process the brittle mineral content of the target sampling point, 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.

[0039] Specifically, the calcite content, quartz content and pyrite content of the target sampling point are substituted into the Voigt-Ruess-Hill model to simulate the mixing of calcite, quartz and pyrite at the target sampling point, and the first equivalent elastic stiffness matrix of the brittle mineral mixture at the target sampling point can be obtained.

[0040] Step S1042: Process the organic matter content and clay content of the target sampling point 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.

[0041] Similar to the previous step, by substituting the organic matter content and clay content into the Backus average model, the mixing of organic matter and clay at the target sampling point can be simulated, thereby obtaining the second equivalent elastic stiffness matrix of the layered mixture at the target sampling point.

[0042] Step S1043: Process the second equivalent elastic stiffness matrix using the Bond transformation model to obtain a third equivalent elastic stiffness matrix of the layered mixture of organic matter and clay after rotation.

[0043] That is, by using 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 seam structure.

[0044] Step S1044, 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 a fourth equivalent elastic stiffness matrix of the porous coal matrix at the target sampling point.

[0045] Specifically, the first equivalent elastic stiffness matrix and the third equivalent elastic stiffness matrix are processed by the anisotropic differential equivalent medium model, which is equivalent to adding brittle minerals to the layered flexible matrix to obtain a coal matrix. Then, the equivalent pores are added to the above coal matrix using the anisotropic differential equivalent medium model to obtain a porous coal matrix. The above related data are calculated using the anisotropic differential equivalent medium model to obtain the fourth equivalent elastic stiffness matrix of the porous coal matrix at the target sampling point.

[0046] Step S1045: Using a linear sliding model, the preset vertical crack parameters are superimposed on the fourth equivalent elastic stiffness matrix to obtain the equivalent elastic stiffness matrix at the target sampling point.

[0047] Optionally, the linear sliding model selects the Schoenberg transformation model, which can be used to superimpose preset vertical fracture parameters into the fourth equivalent elastic stiffness matrix, thereby adding 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.

[0048] Based on the above description, the establishment of anisotropic rock physics model of deep coalbed methane reservoir includes the calculation of equivalent elastic stiffness matrix of brittle mineral mixture, equivalent elastic stiffness matrix of organic matter and clay layered mixture, equivalent elastic stiffness matrix of coal matrix, equivalent elastic stiffness matrix of porous coal matrix and equivalent elastic stiffness matrix of deep coalbed methane reservoir. By substituting the logging data at the actual sampling point, the corresponding rock physics model can be constructed.

[0049] Step S1046, calculating the initial reservoir parameters of the target sampling point based on the equivalent elastic stiffness matrix and volume density at the target sampling point.

[0050] Step S1047, constructing an initial reservoir parameter model of the deep coalbed methane reservoir based on the initial reservoir parameters of multiple sampling points in the deep coalbed methane reservoir.

[0051] According to the stiffness coefficient in the equivalent elastic stiffness matrix at the target sampling point and the volume 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 the sampling point. The initial reservoir parameter model can be constructed using the initial reservoir parameters of all sampling points.

[0052] The calculation formulas of the above five reservoir parameters are as follows: ; ; ; ; , where A represents acoustic impedance, B represents anisotropic shear modulus, C represents horizontal longitudinal wave phase velocity along the crack direction, D represents azimuthal anisotropy gradient, and E represents relative crack density. represents the volume 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, , , , , , , , , , express Figure 2The longitudinal wave anisotropy parameter in the [x2, x3] symmetry plane, express Figure 2 The longitudinal wave anisotropy parameter in the [x1, x3] symmetry plane, express Figure 2 The shear wave anisotropy parameter in the [x2, x3] symmetry plane, express Figure 2 Shear wave anisotropy parameters in the [x1, x3] symmetry plane.

[0053] In an optional implementation, the above step S106, based on the azimuthal seismic observation data and the azimuthal seismic reflection coefficient equation, constructs a reservoir parameter target functional for inverting deep coalbed methane reservoirs under the Bayesian inversion framework, specifically comprising the following steps: Step S1061, extracting seismic wavelets from azimuth seismic observation data.

[0054] Step S1062, based on the seismic wavelet and the azimuthal seismic reflection coefficient equation, determine the expression for forward modeling seismic data.

[0055] Extracting seismic wavelet W from azimuth seismic observation data is a well-known technique in the art and will not be described in detail herein. In the embodiment of the present invention, the azimuth seismic reflection coefficient equation is expressed as: ,in, represents the angle of incidence, Represents the azimuth angle. According to this equation, reservoir parameters A, B and C are independent of azimuth seismic data, while reservoir parameters D and E are related to azimuth seismic data. The expression of forward modeling seismic data is: .

[0056] Step S1063, under the Bayesian inversion framework, with the premise that the prior distribution of reservoir parameters obeys the Cauchy distribution and the goal of maximizing the posterior distribution of reservoir parameters, a reservoir parameter target functional for inverting deep coalbed methane reservoirs is constructed based on the expressions of azimuthal seismic observation data and forward simulation seismic data.

[0057] The known Bayesian theory is: Assume that a random sample of size n is observed, denoted as To infer the population, we need to find the population parameters The point estimate of The overall distribution is Parameters. Using the Bayesian formula: ,in, Indicates the parameters A priori estimate of Indicates that in this parameter The likelihood function of Representing the marginal probability density function of the data, it can be simply understood as In case of value Finally, we can get that when we observe When such a sample is The posterior distribution of .

[0058] The posterior probability distribution of the parameters to be inverted (specifically reservoir parameters in the embodiment of the present invention) in AVAZ inversion under the Bayesian framework It can be expressed as a prior probability With the likelihood function The joint distribution of: (Formula 1), where is the prior distribution of the assumed m, is the probability distribution of observed data under this model, is the distribution of the observed data.

[0059] Assuming that the noise in seismic data follows Gaussian distribution and is independent, the noise in seismic observation data is n The distribution of can be written as follows (Formula 2), where is a preset constant, represents the covariance matrix of the noise, Represents the azimuth seismic observation data noise.

[0060] The following relationship exists between the earthquake observation data with known azimuth and the forward simulation earthquake data: (Formula 3), Substituting Formula 3 into Formula 2, we can get the likelihood function: (Formula 4).

[0061] Based on formula 1, the expression of the posterior distribution of the parameters to be inverted can be obtained: (Formula 5). Assume that the prior distribution of the parameters to be inverted follows the Cauchy distribution, that is (Formula 6), where The covariance matrix of the reservoir parameters, that is, the fifth-order covariance matrix, characterizes the statistical correlation between the five reservoir parameters. D represents a 5×5N adjustment matrix, , so that Contains the statistical correlation between reservoir parameters. Formula 5 can be rewritten as (Formula 7). Based on the maximum a posteriori probability estimation, a target functional for the reservoir parameters used to invert deep coalbed methane reservoirs can be constructed.

[0062] In the embodiment of the present invention, the reservoir parameter target functional for inverting the deep coalbed methane reservoir is expressed as: (Formula 8); where represents the azimuth earthquake observation data, The expression for 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 is a 5×5N adjustment matrix, which is used to adjust the dimension of the covariance matrix and establish the statistical correlation between reservoir parameters. Obviously, the smaller the function value of the target functional, The bigger.

[0063] In an optional implementation, the above step S108, under the constraints of the initial model of reservoir parameters, solves the target functional 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, specifically including the following steps: Step S1081, 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.

[0064] Since the target functional is a nonlinear problem, the embodiment of the present invention firstly performs exist Performing a first-order Taylor expansion at , we can obtain: (Formula 9), where represents the initial model of reservoir parameters, Indicates based on Determined forward modeling seismic data, Represents the disturbance of reservoir parameters.

[0065] Assume that the random noise in the seismic data is independent of each other, that is, the covariance matrix of the noise can be simplified to a diagonal matrix, written as , where I is the N×N identity matrix and N represents the total number of sampling points. Then the above formula 8 can be expressed as: ; Multiply both sides of the equation by Then we get: ; Substitute Formula 9 into Formula 8 and replace m in the regularized Cauchy prior term with , we can get the objective function of reservoir parameters: (Formula 10).

[0066] Step S1082, calculating the partial derivative of the objective function with respect to the reservoir parameter disturbance to obtain the partial derivative function.

[0067] Formula 10 is the objective function of the reservoir parameters, and it can be seen from the above description that the purpose of the embodiment of the present invention is to find the minimum value of the objective function. Due to the nonlinearity of the objective function, it is necessary to construct an iterative form through differentiation to achieve inversion.

[0068] Therefore, after obtaining the expression of the objective function, the partial derivative of the objective function with respect to the reservoir parameter disturbance is calculated to obtain the following partial derivative function: (Formula 11).

[0069] Step S1083, when the partial derivative function is equal to 0, determine the initial solution expression of the reservoir parameter disturbance.

[0070] Setting the partial derivative function equal to 0 gives the reservoir parameter disturbance The initial solution expression for : (Formula 12).

[0071] Step S1084, simplifying the initial solution expression based on the iterative reweighted least squares joint algorithm to obtain the target solution expression of the reservoir parameter disturbance.

[0072] The embodiment of the present invention , , then the reservoir parameter disturbance can be expressed by the following formula .

[0073] Since there are perturbations on both sides of the formula , so the above equation is solved by iterative reweighted least squares, and the initial perturbation =0. Since this method converges quickly and during iterative calculation, the regularized Cauchy prior term ( ) is a double iteration, which is usually calculated in regularization to reduce calculations. Only one iteration is performed when the reservoir parameter disturbance is calculated. Through this simplified method, the target solution expression of the reservoir parameter disturbance is: (Formula 13); where represents the reservoir parameter disturbance, Represents the variance of the noise in the azimuth seismic observation data.

[0074] Step S1085, iteratively updating the reservoir parameters based on the reservoir parameter initial model and the target solution expression of the reservoir parameter disturbance amount until the preset iteration end condition is met to obtain the reservoir parameter target model.

[0075] After the reservoir parameters have been determined, the initial model On the basis of Bring in The target solution expression of , and then according to right Perform the first iteration update and get ,in, Represents the preset iteration step size, which is a constant. Optionally, The value is 1. Next, Bring in The goal is to solve the expression to solve , and so on, according to right Perform multiple iterations until the preset iteration end condition is met, and update the last iteration The embodiment of the present invention does not specifically limit the iteration end condition, which can be when the iteration reaches a specified number of times or when Meet the preset accuracy requirements.

[0076] In the embodiment of the present invention, when solving When the target solution expression is , then (Formula 14), is the first-order partial derivative of the longitudinal wave reflection coefficient with respect to the five attribute parameters. Therefore, for a certain incident angle and azimuth , The first-order partial derivatives of the five attribute parameters can be expressed by the following formula: (Equation 15).

[0077] in, The incident angle is , the azimuth is The wavelet matrix when , , , , They represent the first-order derivatives of the reflection coefficient with respect to the acoustic impedance, anisotropic shear modulus, horizontal longitudinal wave phase velocity along the crack direction, azimuthal anisotropy gradient and relative crack density. The specific expressions are: , , , , . When the inversion contains The incident angle and When the azimuth angle is , the partial derivative matrix of the longitudinal wave reflection coefficient in the above formula 15 is expanded to The matrix of .

[0078] After solving the reservoir parameter target model, use the formula The horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir can be calculated.

[0079] Verify the rationality of the inversion method of the present invention: A deep coalbed methane reservoir in the eastern margin of the Ordos Basin was selected to test the method for determining the horizontal stress difference ratio of the deep coalbed methane reservoir of the present invention. The horizontal bedding of the target layer in the study area is continuous, accompanied by a large number of near-vertical and vertical fractures. Therefore, the deep coalbed methane reservoir can be regarded as an orthotropic medium. The initial reservoir parameter model of the deep coalbed methane reservoir based on the logging data is as follows: Figure 4 As shown. The azimuths of the seismic data are 45°, 90°, 135° and 180° respectively. The three pre-stack average incident angles corresponding to each azimuth are 5°, 15° and 25° respectively. The horizontal stress difference ratio of the target layer is predicted by the determination method of the horizontal stress difference ratio of the deep coalbed methane reservoir. The inversion results are shown in Figure 5 As shown in the figure, the true value of the logging curve at the black curve well position is displayed by high-cut filtering. Figure 5 It can be seen that the inversion results of the target layer have good vertical difference and lateral continuity. The horizontal stress difference of the target layer is relatively low, which is consistent with the geological prior information of the work area.

[0080] In summary, the embodiment of the present invention provides a method for determining the horizontal stress difference ratio of deep coalbed methane reservoirs. The method pre-constructs an anisotropic rock physics model of deep coalbed methane reservoirs, proposes to use the standard deviation of Gaussian distribution to characterize the degree of directional arrangement of organic matter and clay, characterizes the influence of coal bed structure on anisotropy (because Bond transformation is based on Gaussian distribution), and considers the influence of vertical fractures on the anisotropy of deep coalbed methane reservoirs. In the Bayesian inversion framework, the constraints of the initial model of reservoir parameters are considered, and the generalized linear inversion and iterative reweighted least squares joint algorithm are used to solve the nonlinear target functional, so as to achieve accurate estimation of the horizontal stress difference ratio of deep coalbed methane reservoirs and improve the accuracy of the inversion results.

[0081] Embodiment 2 An embodiment of the present invention also provides a device for determining the horizontal stress difference ratio of a deep coalbed methane reservoir. The device is mainly used to execute the method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir provided in the above-mentioned embodiment one. The following is a specific introduction to the device for determining the horizontal stress difference ratio of a deep coalbed methane reservoir provided in the embodiment of the present invention.

[0082] Figure 6 A functional module diagram of a device for determining a horizontal stress difference ratio of a deep coalbed methane reservoir provided by an embodiment of the present invention, such as Figure 6 As 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, wherein: The acquisition module 10 is used to obtain azimuthal seismic observation data and logging data of deep coalbed methane reservoirs; wherein the 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.

[0083] Determination module 20 is used to determine the initial model of reservoir parameters of deep coalbed methane reservoir based on logging data; 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.

[0084] The construction module 30 is used to construct 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.

[0085] The solution module 40 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.

[0086] 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 reservoir parameter target model.

[0087] The embodiment of the present invention provides a device for determining the horizontal stress difference ratio of a deep coalbed methane reservoir. After acquiring the azimuthal seismic observation data and well logging data of the deep coalbed methane reservoir, the device first constructs an initial reservoir parameter model of the deep coalbed methane reservoir based on the well logging data, then constructs a reservoir parameter target functional for inverting the deep coalbed methane reservoir based on the azimuthal seismic observation data and the azimuthal seismic reflection coefficient equation, and then solves the target functional based on the generalized linear inversion theory and the iterative reweighted least squares joint algorithm under the constraint of the initial model to obtain the reservoir parameter target model, and then calculates the horizontal stress difference ratio of each sampling point in the deep coalbed methane reservoir according to the target model. The device 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 volume density of multiple sampling points in the deep coalbed methane reservoir, and then realizes accurate estimation of the horizontal stress difference ratio of the deep coalbed methane reservoir under the constraint of the accurate initial model, which alleviates the technical problem of low accuracy in the existing horizontal stress difference estimation method.

[0088] Optionally, the determination module 20 is specifically configured to: 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 multiple sampling points.

[0089] 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.

[0090] 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 rotated organic matter and clay.

[0091] 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.

[0092] The preset vertical crack parameters are superimposed on the fourth equivalent elastic stiffness matrix using the linear sliding model to obtain the equivalent elastic stiffness matrix at the target sampling point.

[0093] 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.

[0094] 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.

[0095] Optionally, the building module 30 is specifically used for: Extract seismic wavelets from azimuthal seismic observation data.

[0096] Based on the seismic wavelet and azimuthal seismic reflection coefficient equation, the expression for forward modeling seismic data is determined.

[0097] In the Bayesian inversion framework, with the premise that the prior distribution of reservoir parameters obeys the Cauchy distribution and the goal of maximizing the posterior distribution of reservoir parameters, a target functional for inverting reservoir parameters for deep coalbed methane reservoirs is constructed based on the expressions of azimuthal seismic observation data and forward simulation seismic data.

[0098] Optionally, the solution module is specifically used for: Based on the generalized linear inversion theory, the first-order Taylor expansion of the target functional is performed at the initial model of reservoir parameters to obtain the target function of reservoir parameters.

[0099] The partial derivative of the objective function with respect to the reservoir parameter disturbance is calculated to obtain the partial derivative function.

[0100] When the partial derivative function is equal to 0, the initial solution expression of the reservoir parameter disturbance is determined.

[0101] The initial solution expression is simplified based on the iterative reweighted least squares joint algorithm to obtain the target solution expression of the reservoir parameter disturbance.

[0102] 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 the preset iteration end condition is met to obtain the reservoir parameter target model.

[0103] Optionally, the reservoir parameter target functional for inversion of deep coalbed methane reservoirs is expressed as: ;in, represents the azimuth earthquake observation data, The expression for 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.

[0104] Optionally, 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.

[0105] Optionally, 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. , .

[0106] Embodiment 3 See also Figure 7An 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 used to execute an executable module stored in the memory 61, such as a computer program.

[0107] The memory 61 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0108] The bus 62 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 7 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0109] Among them, the memory 61 is used to store programs, and the processor 60 executes the program after receiving the execution instruction. The method executed by the device defined by the process disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0110] The processor 60 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 60. The above processor 60 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field 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. The storage medium is located in the memory 61, and the processor 60 reads the information in the memory 61 and completes the steps of the above method in combination with its hardware.

[0111] A computer program product of a method and apparatus for determining the horizontal stress difference ratio of a deep coalbed methane reservoir provided in an embodiment of the present invention includes a computer-readable storage medium storing non-volatile program codes executable by a processor. The instructions included in the program code can be used to execute the methods in the previous method embodiments. For specific implementations, please refer to the method embodiments, which will not be described in detail here.

[0112] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0113] If the 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 that is executable by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0114] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0115] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0116] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0117] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" 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 or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0118] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, 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: Acquire azimuthal seismic observation data and logging data of deep coalbed methane reservoirs; wherein the logging data include: 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; Determine the 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 P-wave phase velocity along the fracture direction, azimuthal anisotropy gradient and relative fracture density; 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 a Bayesian inversion framework; Under the constraints of the initial model of the reservoir parameters, the target functional is solved based on the generalized linear inversion theory and iterative reweighted least squares joint algorithm to obtain a reservoir parameter target model of the deep coalbed methane reservoir; 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: Determining an initial reservoir parameter model of the deep coalbed methane reservoir based on the well logging data includes: The brittle mineral content of the target sampling point is processed 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 sampling point among the multiple sampling points; The organic matter content and clay content of the target sampling point are processed by using the Backus average model to obtain a second equivalent elastic stiffness matrix of a layered mixture composed of organic matter and clay at the target sampling point; The second equivalent elastic stiffness matrix is ​​processed by using a Bond transformation model to obtain a third equivalent elastic stiffness matrix of a layered mixture of organic matter and clay after rotation; The first equivalent elastic stiffness matrix, the third equivalent elastic stiffness matrix and the pore content of the target sampling point are processed based on an anisotropic differential equivalent medium model to obtain a fourth equivalent elastic stiffness matrix of the porous coal matrix at the target sampling point; Using a linear sliding model, the preset vertical crack parameters are superimposed on the fourth equivalent elastic stiffness matrix to obtain the equivalent elastic stiffness matrix at the target sampling point; Calculating initial reservoir parameters of the target sampling point based on the equivalent elastic stiffness matrix and the volume density at the target sampling point; Based on the initial reservoir parameters of the multiple sampling points in the deep coalbed methane reservoir, an initial reservoir parameter model of the deep coalbed methane reservoir is constructed.

3. 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.

4. The method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir according to claim 1, 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.

5. 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.

6. The method for determining the horizontal stress difference ratio of a deep coalbed methane reservoir according to claim 5, 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.

7. 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. , .

8. A device for determining horizontal stress difference ratio of deep coalbed methane reservoir, characterized in that: include: An acquisition module is used to acquire azimuthal seismic observation data and well logging data of deep coalbed methane reservoirs; wherein the well logging data includes: 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; A determination module is used to determine the 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 P-wave phase velocity along the fracture direction, azimuthal anisotropy gradient and relative fracture density; A construction module is used to construct a reservoir parameter target functional for inverting a deep coalbed methane reservoir under a Bayesian inversion framework based on the azimuthal seismic observation data and the azimuthal seismic reflection coefficient equation; A solution module, used for solving the target functional based on an iterative reweighted least squares joint algorithm under the constraints of the initial model of the reservoir parameters to obtain a reservoir parameter target model of the deep coalbed methane reservoir; A 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.

9. 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 7 is implemented.

10. 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 as described in any one of claims 1 to 7.

Images