Deep coal bed gas reservoir double-pore structure parameter prediction method and device

Abstract

The invention provides a deep coalbed methane reservoir double-pore structure parameter prediction method and device, relates to the technical field of coalbed methane reservoir development, and solves the technical problem of low precision of a prediction result of pores of a deep coalbed methane reservoir. The method comprises the steps that based on the initial organic matter content, the clay content and the brittle mineral content, an equivalent medium theoretical model is used for establishing a coal bed gas reservoir double-pore coal rock physical model; calculating matrix porosity, fracture porosity and fracture aspect ratio according to logging observation longitudinal wave and transverse wave velocities in the logging data, and taking well-side matrix porosity, fracture porosity and fracture aspect ratio as pore parameter prior information of the coal bed gas reservoir double-pore coal rock physical model; and based on the specified three-dimensional seismic horizon data and the pore parameter prior information, pre-stack seismic inversion is carried out by using a pre-stack Bayesian inversion method through the coal bed gas reservoir double-pore coal rock physical model, and a coal bed gas reservoir double-pore reservoir prediction result is obtained.

Description

Technical Field

[0001] The present application relates to the technical field of deep coalbed methane reservoir development, and in particular to a method and device for predicting dual-pore structure parameters of deep coalbed methane reservoirs. Background Art

[0002] At present, deep coalbed methane resources are abundant. The pore structure is a key parameter for predicting the sweet spot of deep coalbed methane reservoir engineering. It is an important reference for evaluating the drainage and gas production of deep coalbed methane, and is of great significance to the exploration and development of deep coalbed methane reservoirs. However, the pore structure of coalbed methane reservoirs is complex, and there are many types of pores. In particular, there are certain difficulties in predicting the pore structure of deep coalbed methane reservoirs. The pore structure of coalbed methane reservoirs is complex, and can be mainly divided into a dual-pore structure composed of fractures and matrix pores. At present, the existing coalbed methane reservoir pore prediction methods are mainly derived from a single equivalent pore hypothesis, but do not consider the influence of the dual-pore characteristics of coalbed methane reservoirs, resulting in low accuracy of the existing methods for predicting the pores of coalbed methane reservoirs. Summary of the invention

[0003] The purpose of the present invention is to provide a method and device for predicting dual-porosity structural parameters of deep coalbed methane reservoirs, so as to solve the technical problem of low accuracy of prediction results of pores in deep coalbed methane reservoirs.

[0004] In a first aspect, the present application provides a method for predicting dual-porosity structural parameters of deep coalbed methane reservoirs driven by a rock physics model, the method comprising: Obtaining coal seam mineral data, regional sedimentary background data and well logging data corresponding to the deep coal seam methane reservoir, and simplifying the mineral components of the coal seam methane reservoir according to the coal seam mineral data and the regional sedimentary background data to obtain organic matter data, clay data and brittle mineral data; Determine the organic matter content by using the resistivity-porosity overlap method based on the organic matter data, determine the initial clay content by using the resistivity extreme value method based on the clay data, and determine the remaining part of the mineral components of the coalbed methane reservoir except the initial clay content and the organic matter content as the brittle mineral content; wherein the resistivity corresponding to the resistivity extreme value method and the porosity corresponding to the resistivity-porosity overlap method are both determined by the well logging data; Based on the initial clay content, the organic matter content and the brittle mineral content, a dual-porosity coal rock physical model of a coalbed methane reservoir is established using an equivalent medium theory model; Calculating the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well according to the well logging observation P-wave and S-wave velocities in the well logging data, and using the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well as prior information of pore parameters of the dual-porosity coal rock physical model of the deep coalbed methane reservoir; Based on the specified three-dimensional seismic layer data and the prior information of the pore parameters, pre-stack seismic Bayesian inversion is performed through the dual-porosity coal rock physical model of the deep coalbed methane reservoir using the pre-stack Bayesian inversion method to obtain the dual-porosity structure data of the coalbed methane reservoir, and the dual-porosity structure data of the coalbed methane reservoir is used as the prediction result of the dual-porosity structure parameters of the coalbed methane reservoir.

[0005] In a possible implementation, the dual-porosity coal rock physical model of the coalbed methane reservoir is established based on the initial clay content, the organic matter content and the brittle mineral content using an equivalent medium theory model, including: According to the initial clay content, the organic matter content and the brittle mineral content, the organic matter, clay and brittle minerals are mixed using the Voigt-Ruess-Hill boundary theory model to obtain the elastic parameters of the coal matrix; Constructing the dual porosity characteristics of deep coalbed methane reservoirs, adding the dual porosity corresponding to the dual porosity characteristics of the coalbed methane reservoirs by using the KT model based on the elastic parameters of the coal matrix, simulating the bulk modulus of the pore fluid under water saturation conditions by using the equal stress average formula based on the dual porosity characteristics of the coalbed methane reservoirs after adding the dual porosity, and adding fluid to the dry coal rock dry skeleton of the coalbed methane reservoir by using the Gassmann equation to simulate the influence of the pore fluid, thereby obtaining an initial rock physics model; The initial rock physics model is linearized to obtain a dual-porosity coal rock physics model of a coalbed methane reservoir.

[0006] In a possible implementation, the elastic parameter of the coal matrix is ​​obtained by the following formula: ; in, For the i The elastic modulus of a brittle mineral, N is the total number of brittle minerals, For the i The volume fraction of the mineral substance, MVRH is the elastic parameter of coal matrix, MV and MR is the intermediate conversion parameter; The total pore volume of the coalbed methane reservoir corresponding to the dual porosity characteristics of the deep coalbed methane reservoir includes the following two types of pores: ,in, is the total porosity, is the porosity of matrix pores, which are randomly distributed pores in the coal matrix, is the fracture porosity, and the fracture porosity is randomly arranged; The adding of the dual porosity corresponding to the dual porosity characteristic of the coalbed methane reservoir using the KT model includes adding the dual porosity to the dual porosity characteristic of the coalbed methane reservoir using the KT model through the following formula: ,in, ; and are the bulk modulus and shear modulus of coal rock matrix, Ki For the i The bulk modulus of the pores, is the volume fraction containing pores, and is a parameter related to the pore shape, K is the bulk modulus, G is the shear modulus, N is the total number of pores; The method of simulating the bulk modulus of the pore fluid under water saturation by using the WOOD formula based on the dual porosity characteristics of the coalbed methane reservoir after adding the dual porosity includes simulating the bulk modulus of the pore fluid under water saturation by using the following formula based on the dual porosity characteristics of the coalbed methane reservoir after adding the dual porosity: ; in, is the equivalent bulk modulus of the pore fluid, is the bulk modulus of the liquid, is the bulk modulus of air, Sw is water saturation; The adding of fluid into the dry coal-rock dry skeleton of the coalbed methane reservoir by using the Gassmann equation includes adding fluid into the dry coal-rock dry skeleton of the coalbed methane reservoir by using the following equation: ; in, and are the porosity of matrix pores and fractures, respectively. is the bulk modulus of rock minerals, is the bulk modulus of the dry rock skeleton, is the bulk modulus of the fluid, Kd is the bulk modulus of the dry coal rock skeleton; The forward modeling relationship of the dual-porosity coal rock physics model of the deep coalbed methane reservoir is: ; in, x is the rock physical parameter to be inverted, e is the random error, m is the elastic parameter, is the corresponding relationship of the rock physics model; by applying the forward modeling equation to The first-order Taylor approximation of the point is: ; in, D is the linear forward operator of the rock physics model, x is the rock physical parameter to be inverted, m is the elastic parameter, is the corresponding relationship of the rock physics model, x0 are points selected on different independent variables.

[0007] In a possible implementation, the calculating the matrix porosity and fracture aspect ratio of the coalbed methane reservoir near the well according to the well logging observed longitudinal wave velocity and the well logging observed shear wave velocity in the well logging data includes: The inversion data is solved by using the Bayesian theory model to obtain the posterior probability of the target parameter with respect to the observed data; The prior information of the mixed Gaussian distribution is determined by using the Gaussian mixture model to describe the prior distribution of physical properties; Determining a probability distribution function based on the posterior probability and the prior information; The matrix porosity and fracture aspect ratio of the coalbed methane reservoir near the well are calculated by the probability distribution function according to the well logging observed longitudinal wave velocity and the well logging observed shear wave velocity in the well logging data, and the constructed inversion objective function is obtained.

[0008] In one possible implementation, the posterior probability of the target parameter with respect to the observed data is: ; in, is the posterior probability, is the likelihood probability, is the prior probability; The prior information is: ; in, is the prior probability, represents the kth Gaussian distribution, is the expected value of the kth Gaussian distribution, is the covariance of the kth Gaussian distribution, is the weight coefficient of the kth Gaussian distribution, C is the total number of Gaussian distributions, r Represents a priori; The inversion objective function is: ; in, asp is the reservoir fracture aspect ratio, and are respectively the well logging observed P-wave velocity and the well logging observed S-wave velocity in the well logging data, and are the longitudinal wave velocity and shear wave velocity predicted by the model, respectively.

[0009] In a possible implementation, based on the specified three-dimensional seismic horizon data and the prior information of the pore parameters, the dual-porosity coal rock physical model of the deep coalbed methane reservoir is used to perform prestack seismic Bayesian inversion using a prestack Bayesian inversion method to obtain the dual-porosity structure data of the coalbed methane reservoir, and the dual-porosity structure data of the coalbed methane reservoir is used as the prediction result of the dual-porosity structure parameters of the coalbed methane reservoir, including: Extract pre-stack seismic angle gathers based on the specified 3D seismic layer data to obtain the relationship between the reflection wave time and amplitude under different incident angles at each point; Based on the well logging data and the seismic data corresponding to the designated three-dimensional seismic layer data, the time information of the reflection wave of the target coal seam at each point is obtained, and a time window is opened along the coal seam to extract the reflection wave information of the target coal seam; Obtaining coal composition information and water saturation information through interpolation results of the logging data; Constructing a Bayesian inversion objective function of seismic data based on the time information, the reflection wave information, the coal composition information, the water saturation information, and the relationship between the reflection wave time and amplitude; The same angle gather data are respectively brought into the Bayesian inversion objective function for inversion to obtain the dual-porosity rock physical parameters of the coalbed methane reservoir at a spatial position, and the dual-porosity rock physical parameters of the coalbed methane reservoir are used as the dual-porosity structural data of the coalbed methane reservoir.

[0010] In one possible implementation, the Bayesian inversion objective function is: ; in, is the weight factor of the i-th Gaussian distribution, d For earthquake data, m is the inversion parameter; f For the mapping relationship, For expectations, is the covariance matrix, w is the earthquake wavelet.

[0011] In a second aspect, the present application provides a device for predicting dual-pore structure parameters of deep coalbed methane reservoirs driven by a rock physics model, comprising: An acquisition module is used to acquire coal seam mineral data, regional sedimentary background data and well logging data corresponding to a deep coal seam gas reservoir, and simplify the mineral components of the coal seam gas reservoir according to the coal seam mineral data and the regional sedimentary background data to obtain organic matter data, clay data and brittle mineral data; A determination module, configured to determine the initial clay content by using the resistivity extreme value method based on the clay data, determine the organic matter content by using the resistivity porosity overlap method based on the organic matter data, and determine the remaining part of the mineral components of the coalbed methane reservoir except the initial clay content and the organic matter content as the brittle mineral content; wherein the resistivity corresponding to the resistivity extreme value method and the porosity corresponding to the resistivity porosity overlap method are both determined by the well logging data; Establishing a module for establishing a dual-porosity coal rock physical model of a deep coalbed methane reservoir using an equivalent medium theory model based on the initial clay content, the organic matter content and the brittle mineral content; A calculation module, for calculating the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well according to the well logging observation P-wave and S-wave velocities in the well logging data, and using the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well as the prior information of the pore parameters of the dual-porosity coal rock physical model of the deep coalbed methane reservoir; The inversion module is used to perform prestack seismic Bayesian inversion based on the specified three-dimensional seismic layer data and the prior information of the pore parameters, through the dual-porosity coal rock physical model of the deep coalbed methane reservoir, using the prestack Bayesian inversion method to obtain the dual-porosity structure data of the coalbed methane reservoir, and use the dual-porosity structure data of the coalbed methane reservoir as the prediction result of the dual-porosity structure parameters of the coalbed methane reservoir.

[0012] In a third aspect, the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0013] In a fourth aspect, the present application further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the method described in the first aspect.

[0014] This application brings the following beneficial effects: The present application provides a method and device for predicting dual-porosity structural parameters of a deep coalbed methane reservoir, which can obtain coalbed mineral data, regional sedimentary background data and well logging data corresponding to the deep coalbed methane reservoir, and simplify the mineral components of the coalbed methane reservoir according to the coalbed mineral data and the regional sedimentary background data to obtain organic matter data, clay data and brittle mineral data, determine the initial clay content based on the clay data using the resistivity extreme value method, determine the organic matter content based on the organic matter data using the resistivity porosity overlap method, and remove the initial clay content and the brittle mineral data from the mineral components of the coalbed methane reservoir. The remaining part other than the organic matter content is determined as the brittle mineral content, wherein the resistivity corresponding to the resistivity extreme value method and the porosity corresponding to the resistivity-porosity overlap method are both determined by the well logging data, and a dual-porosity coal rock physical model of the coalbed methane reservoir is established based on the initial clay content, the organic matter content and the brittle mineral content using an equivalent medium theoretical model, and the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well are calculated according to the well logging observed longitudinal and shear wave velocities in the well logging data, and the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well are used as the The pore parameter prior information of the dual-porosity coal rock physical model of the deep coalbed methane reservoir is based on the specified three-dimensional seismic layer data and the pore parameter prior information. The dual-porosity coal rock physical model of the deep coalbed methane reservoir is used to perform pre-stack seismic Bayesian inversion processing using the pre-stack Bayesian inversion method to obtain the dual-porosity structure data of the coalbed methane reservoir, and the dual-porosity structure data of the coalbed methane reservoir is used as the prediction result of the dual-porosity structure parameters of the coalbed methane reservoir. In this scheme, based on the pore structure characteristics of the coal rock of the coalbed methane reservoir, a dual-porosity coalbed methane reservoir rock physical model is established, and the prior information of the Bayesian inversion is determined in combination with the logging data. , and then combined with pre-stack seismic data to invert the dual-pore structure parameters of the coalbed methane reservoir, which can fully combine the actual geological characteristics of the coalbed methane reservoir rock, can deeply restore the pore structure characteristics of the coalbed methane, and combine with well seismic data to obtain a complete elastic matrix, so as to obtain data-accurate dual-pore structure characteristics of the coalbed methane reservoir. By adopting the material composition and pore structure characteristics of the coalbed methane reservoir to construct a rock physics model, and using Taylor series for linearization, the inversion efficiency is higher, and it meets the conventional reservoir prediction analysis of well logging and seismic data, and can predict the dual-pore structure parameters of the coalbed methane reservoir, thereby improving the prediction accuracy of the coalbed methane reservoir.

[0015] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present application 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A schematic flow chart of a method for predicting dual-pore structure parameters of deep coalbed methane reservoirs driven by a rock physics model provided in an embodiment of the present application; Figure 2 A schematic diagram of the modeling process of a dual-porosity rock physics model for a deep coalbed methane reservoir provided in an embodiment of the present application; Figure 3 A schematic diagram of a linearization result of a deep coalbed methane reservoir corresponding to a crack density Crack Density provided in an embodiment of the present application; Figure 4 A schematic diagram of another linearization result of a deep coalbed methane reservoir corresponding to a crack density Crack Density provided in an embodiment of the present application; Figure 5 A schematic diagram of the linearization result of the porosity Pore Porosity corresponding to the deep coalbed methane reservoir provided in the embodiment of the present application; Figure 6 A schematic diagram of another porosity linearization result corresponding to a deep coalbed methane reservoir provided in an embodiment of the present application; Figure 7 A schematic diagram of the linearization results of a deep coalbed methane reservoir corresponding to a model asp provided in an embodiment of the present application; Figure 8 A schematic diagram of the linearization results of a deep coalbed methane reservoir corresponding to another model asp provided in an embodiment of the present application; Fig. 9 A mixed Gaussian distribution inversion diagram of the well logging data provided in the embodiment of the present application; Fig.10 Another schematic diagram of the process of predicting dual-pore structure parameters of deep coalbed methane reservoirs driven by a rock physics model provided in an embodiment of the present application; Fig.11 Schematic diagram of matrix porosity in predicting the effect of dual-porosity structure of reservoir; Fig.12 Schematic diagram of fracture density in predicting the effect of dual porosity structure of reservoir; Fig.13 Schematic diagram of fracture aspect ratio in predicting the effect of dual porosity structure of reservoir; Fig.14A schematic diagram of the structure of a device for predicting dual-pore structure parameters of deep coalbed methane reservoirs driven by a rock physics model provided in an embodiment of the present application; Fig.15 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0019] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0020] In the existing technology, in order to address the lack of systematic evaluation methods for supercritical CO2 fracturing of deep coalbed methane reservoirs, the geological factors and mechanical properties of coalbed methane reservoirs can be used to analyze the occurrence characteristics and influencing factors of coalbed methane reservoirs, and establish a supercritical CO2 fracturing evaluation system for deep coalbed methane reservoirs. In addition, brittleness can be characterized by a variety of brittleness representation methods. The Xu-White model is used to carry out rock physics modeling of the target layer and supplement the logging data. However, there are certain differences between the dual porosity structural characteristics of coalbed methane reservoirs and the Xu-White model. There is still a lack of dual porosity structure prediction methods for coalbed methane reservoirs. At present, the dual porosity characteristic evaluation system of coalbed methane reservoirs is not complete, and the existing methods have low accuracy in predicting the pores of coalbed methane reservoirs.

[0021] Based on this, an embodiment of the present application provides a method and device for predicting dual-pore structure parameters of deep coalbed methane reservoirs. This method can solve the technical problem of low accuracy of prediction results of the pores of deep coalbed methane reservoirs.

[0022] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0023] Figure 1 A schematic diagram of a process flow of a rock physics model driven deep coalbed methane reservoir dual porosity structure parameter prediction method provided in an embodiment of the present application. Figure 1 As shown, the method includes: Step S110, obtaining coal seam mineral data, regional sedimentary background data and logging data corresponding to the deep coal seam methane reservoir, and simplifying the mineral components of the coal seam methane reservoir according to the coal seam mineral data and the regional sedimentary background data to obtain organic matter data, clay data and brittle mineral data.

[0024] As a possible implementation method, based on the regional sedimentary background and deep coal seam mineral related data, the mineral components of deep coalbed methane reservoirs are simplified to organic matter, clay and brittle minerals.

[0025] Step S120, determine the initial clay content using the resistivity extreme value method based on the clay data, determine the organic matter content using the resistivity porosity overlap method based on the organic matter data, and determine the remaining part of the mineral components of the coalbed methane reservoir except the initial clay content and organic matter content as the brittle mineral content.

[0026] Among them, the resistivity corresponding to the resistivity extreme value method and the porosity corresponding to the resistivity-porosity overlap method are both determined by well logging data. For example, the resistivity extreme value method is used to obtain the initial clay content; the resistivity-porosity overlap method is used to obtain the organic matter content; among them, the resistivity and porosity are derived from well logging data. The rest is considered as brittle minerals.

[0027] Step S130, based on the initial clay content, organic matter content and brittle mineral content, an equivalent medium theory model is used to establish a dual-porosity coal rock physical model for a deep coalbed methane reservoir.

[0028] In an optional embodiment, if Figure 2 As shown in the figure, the coalbed methane reservoir has dual porosity characteristics. Figure 2 Based on the equivalent medium theory, the dual-porosity coal rock physical modeling of the coalbed methane reservoir is performed to obtain the dual-porosity coal rock physical model of the coalbed methane reservoir. As an example, step S130 may specifically include the following steps: According to the initial clay content, organic matter content and brittle mineral content, the organic matter, clay and brittle minerals are mixed using the Voigt-Ruess-Hill boundary theory model to obtain the elastic parameters of the coal matrix. Construct the dual porosity characteristics of coalbed methane reservoirs, add the dual porosity corresponding to the dual porosity characteristics of coalbed methane reservoirs using the KT model based on the elastic parameters of coal matrix, simulate the bulk modulus of pore fluid under water saturation conditions using the equal stress average formula based on the dual porosity characteristics of coalbed methane reservoirs after adding dual porosity, and use the Gassmann equation to add fluid to the dry coal rock dry skeleton of the coalbed methane reservoir to simulate the influence of pore fluid and obtain the initial rock physics model; The initial rock physics model is linearized to obtain the dual-porosity coal rock physics model of the coalbed methane reservoir.

[0029] As an optional implementation, the organic matter, clay and brittle minerals are first mixed using the Voigt-Ruess-Hill boundary theory to obtain the elastic parameters of the coal matrix; the elastic parameters of the coal matrix are obtained by the following formula: (1); in, For the i The elastic modulus of a brittle mineral, N is the total number of brittle minerals, For the i The volume fraction of the minerals (there are three components: organic matter, clay and brittle minerals), MVRH is the elastic parameter of coal matrix, MV and MR is the intermediate conversion parameter; Then, the dual porosity characteristics of the coalbed methane reservoir are constructed, and the elastic parameters of the coal matrix obtained by mixing the above organic matter, clay and brittle minerals are added to the dual porosity using the KT (Kuster-Toksoz) model. The total pore volume of the coalbed methane reservoir corresponding to the dual porosity characteristics of the coalbed methane reservoir includes the following two types of pores: (2) where is the total porosity, is the porosity of matrix pores, which are randomly distributed pores in the coal matrix. is the fracture porosity, and the fracture porosity is randomly arranged.

[0030] Then, the KT model is used to add dual porosity to the dual porosity characteristics of the coalbed methane reservoir. The KT model is used to add dual porosity to the dual porosity characteristics of the coalbed methane reservoir through the following formula, that is, the KT model is used to add matrix pores and fractures with a certain aspect ratio. Here, it is assumed that the matrix particle medium is circular and the aspect ratio is equal to 1. The fracture aspect ratio is unknown and needs to be obtained through inversion: (3), where (4); and are the bulk modulus and shear modulus of coal rock matrix, Ki For the i The bulk modulus of the pores, is the volume fraction containing pores (there are two types, matrix pores and cracks), and is a parameter related to the pore shape, K is the bulk modulus, G is the shear modulus, N is the total number of pores; Then, the isostress average formula (WOOD formula) is used to simulate the bulk modulus of the pore fluid under a certain water saturation. For example, based on the dual porosity characteristics of the coalbed methane reservoir after adding dual porosity, the WOOD formula is used to simulate the bulk modulus of the pore fluid under water saturation, including simulating the bulk modulus of the pore fluid under water saturation based on the dual porosity characteristics of the coalbed methane reservoir after adding dual porosity through the following formula: (5); in, is the equivalent bulk modulus of the pore fluid, is the bulk modulus of the liquid (equivalent to water, 2.2 GPa), is the bulk modulus of air (equivalent to air, 0.14MPa), Sw is water saturation; Then, in order to simulate the influence of pore fluid, the Gassmann equation is used to add fluid to the dry coal rock dry skeleton, that is, adding fluid to the dry coal rock dry skeleton of the coalbed methane reservoir using the Gassmann equation includes adding fluid to the dry coal rock dry skeleton of the coalbed methane reservoir through the following equation: (6); in, and are the porosity of matrix pores and fractures, respectively. is the bulk modulus of rock minerals, is the bulk modulus of the dry rock skeleton, is the bulk modulus of the fluid, Kd is the bulk modulus of the dry coal rock skeleton; In practical applications, the rock physics model has strong nonlinear characteristics, and will fall into the local optimal solution during the inversion process, resulting in errors. Therefore, the model needs to be linearized. The forward relationship of the dual-porosity coal rock physics model of the coalbed methane reservoir is: (7); in, x is the rock physical parameter to be inverted, e is the random error, m is the elastic parameter, is the corresponding relationship of the rock physics model. Then, in order to obtain the linear forward model, the first-order Taylor approximation is performed on the above equation at point x0, that is, by The first-order Taylor approximation of the point is: (8); in, D is the linear forward operator of the rock physics model, x is the rock physical parameter to be inverted, m is the elastic parameter, is the corresponding relationship of the rock physics model, x0 are points selected on different independent variables.

[0031] (9) Where ∂ is the sign of the partial derivative. The effect of model linearization is as follows Figure 3-Figure 8 shown.

[0032] Step S140, calculate the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well according to the well logging observation longitudinal wave and shear wave velocities in the well logging data, and use the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well as the prior information of the pore parameters of the dual-porosity coal rock physical model of the deep coalbed methane reservoir.

[0033] In practical applications, since the fracture aspect ratio is difficult to obtain directly through observation data, the matrix porosity and fracture aspect ratio near the well are estimated based on the P-wave and S-wave velocities observed by well logging, thereby providing prior information on pore parameters for subsequent inversion, that is, the prior distribution of model parameters obtained from well logging data.

[0034] As an optional implementation, in step S140, the aspect ratio of matrix pores and fractures near the wellbore is calculated according to the well logging observed P-wave velocity and the well logging observed S-wave velocity in the well logging data, which may specifically include the following steps: The inversion data is solved by the Bayesian theoretical model to obtain the posterior probability of the target parameter with respect to the observed data; the prior information of the mixed Gaussian distribution is determined by using the Gaussian mixture model to describe the prior distribution of physical properties; the probability distribution function is determined based on the posterior probability and the prior information; the aspect ratio of the matrix pores and fractures near the well is calculated through the probability distribution function according to the well logging observation compressional wave velocity and the well logging observation shear wave velocity in the well logging data, and the constructed inversion target function is obtained.

[0035] It should be noted that in Bayesian theory, solving the inversion problem is equivalent to estimating the maximum posterior probability, and the posterior probability of the target parameter with respect to the observed data is: (10); among them, is the posterior probability, is the likelihood probability, is the prior probability.

[0036] Then, the prior information of the mixed Gaussian distribution is obtained. The statistical characteristic distribution of rock formation physical property parameters usually presents multi-peak characteristics, which are difficult to be effectively described by traditional Gaussian distribution or single distribution function. Therefore, the Gaussian mixture model is used to describe the prior distribution of physical properties. The prior information is: (11); in, is the prior probability, represents the kth Gaussian distribution, is the expected value, is the covariance, is the sum weight coefficient, C is the total number of Gaussian distributions, r Represents a priori.

[0037] The maximum expectation algorithm is used to estimate the prior probability distribution of the target parameters, providing prior expectation and covariance for the subsequent Bayesian inversion. The Gaussian mixture distribution of the inversion parameters is calculated as the prior distribution of the Bayesian inversion. The mixed Gaussian distribution of the logging data is shown in Fig. 9 shown.

[0038] After that, the probability distribution function is obtained. Assume that the random error in the forward model conforms to the standard normal distribution , likelihood probability Determined by the linear model, the posterior probability It still conforms to the Gaussian mixture distribution, and its posterior expectation and covariance can be expressed as: (12); (13); The final inversion result of the target parameter is the posterior expectation The weighted mean of : (14); in, (15).

[0039] Then, the inversion objective function based on the logging data is constructed. The inversion objective function constructed by the logging data is: ;in, asp is the reservoir fracture aspect ratio, and are respectively the well logging observation P-wave velocity and well logging observation S-wave velocity (obtained from well logging data), and are the longitudinal wave velocity and shear wave velocity predicted by the model, respectively, and are the longitudinal and shear wave velocities in the vertical direction obtained by the model. The grid search method is used to solve the above equation (14), and the result at the minimum value of the objective function is taken as the inversion result.

[0040] Step S150, based on the specified three-dimensional seismic layer data and pore parameter prior information, pre-stack seismic Bayesian inversion is performed through the coalbed methane reservoir dual-porosity coal rock physical model using the pre-stack Bayesian inversion method to obtain the coalbed methane reservoir dual-porosity structure data, and the coalbed methane reservoir dual-porosity structure data is used as the coalbed methane reservoir dual-porosity structure parameter prediction result.

[0041] In the model-driven prestack seismic Bayesian inversion process, the reflection wave waveform of the coalbed methane reservoir is extracted based on the 3D seismic horizon interpretation results, and the complete dual-porosity structure information of the coal seam is obtained by combining the coalbed methane reservoir rock physics model with the prestack Bayesian inversion method.

[0042] As an optional implementation, in step S150, based on the specified three-dimensional seismic layer data and the prior information of pore parameters, the prestack seismic Bayesian inversion is performed by using the prestack Bayesian inversion method through the dual-porosity coal rock physical model of the coalbed methane reservoir to obtain the dual-porosity structure data of the coalbed methane reservoir, and the dual-porosity structure data of the coalbed methane reservoir is used as the prediction result of the dual-porosity structure parameters of the coalbed methane reservoir, which may specifically include the following steps: Based on the specified three-dimensional seismic layer data, the pre-stack seismic angle gathers are extracted to obtain the relationship between the reflection wave time and amplitude under different incident angles at each point; based on the logging data and the seismic data corresponding to the specified three-dimensional seismic layer data, the time information of the reflection wave of the target coal seam at each point is obtained, and the reflection wave information of the target coal seam is extracted by opening a time window along the coal seam; the coal composition information and water saturation information are obtained through the interpolation results of the logging data; based on the time information, reflection wave information, coal composition information, water saturation information and the relationship between the reflection wave time and amplitude, the Bayesian inversion objective function of the seismic data is constructed; the same angle gather data are respectively brought into the Bayesian inversion objective function for inversion to obtain the dual-porosity rock physical parameters of the coalbed methane reservoir at a spatial position, and the dual-porosity rock physical parameters of the coalbed methane reservoir are used as the dual-porosity structural data of the coalbed methane reservoir.

[0043] For example, we can first extract the pre-stack seismic angle gather to obtain the time-amplitude relationship of the reflected wave at different incident angles at each point. Then, we combine the seismic data and logging data to obtain the time information of the reflected wave of the target coal seam at each point. And extract the reflection wave information of the target coal seam by opening a time window along the coal seam. Then, we can obtain the coal composition information and water saturation information through the interpolation results of the logging data. The reservoir dual-pore structure prediction process is as follows: Fig.10 shown.

[0044] In order to simulate the seismic response characteristics of underground coal seams, the Aki-Richards equation is used to calculate the longitudinal wave reflectivity at different incident angles: (18); in, (19); and are the longitudinal and transverse wave velocities, is the density, is the incident angle, and subscripts 1 and 2 are the upper and lower interfaces, respectively.

[0045] Then, the inversion objective function of seismic data is constructed. The Bayesian inversion objective function constructed by seismic data is: (17); in, is the weight factor of the i-th Gaussian distribution, d For earthquake data, m is the inversion parameter; f For the mapping relationship, For expectations, is the covariance matrix, w is a seismic wavelet, which is generally represented by a Ricker wavelet and has a fixed functional relationship. The prior Gaussian mixture distribution comes from the logging data analysis results in the second step. Combined with the inversion objective function constructed by seismic data, prestack seismic inversion is performed. The parameter calculation process in the objective function is the same as the above equations (10) to (13).

[0046] Finally, the same angle gather data is substituted into the above formula for inversion to obtain the dual-porosity rock physical parameters of the coalbed methane reservoir at a spatial position. Figure 11-13 shown.

[0047] In the embodiment of the present application, on the basis of the pore structure characteristics of coal rock in the coalbed methane reservoir, a rock physics model of a dual-porosity coalbed methane reservoir is established, and the prior information of Bayesian inversion is determined in combination with the well logging data, and then the dual-porosity structure parameters of the coalbed methane reservoir are inverted in combination with the pre-stack seismic data, which can fully combine the actual geological characteristics of the coalbed methane reservoir rock, adopt the material composition and pore structure characteristics of the coalbed methane reservoir, construct a rock physics model, and use Taylor series for linearization, so that the inversion efficiency is higher, and it meets the conventional reservoir prediction analysis of well logging and seismic data, and can predict the dual-porosity structure parameters of the coalbed methane reservoir, thereby improving the prediction accuracy of the coalbed methane reservoir. Moreover, rock physics modeling is a bridge between the microstructure and macroscopic properties of rocks, and can quantitatively describe the influence of rock physics parameters such as pore structure on the elastic characteristics of rocks. The method of rock physics modeling can deeply restore the pore structure characteristics of coalbed methane, and obtain a complete elastic matrix in combination with well seismic data, so as to obtain the dual-porosity structure characteristics of coalbed methane reservoirs with accurate data. The pore structure in coal is complex and can be regarded as a dual-porosity medium composed of matrix pores and fractures. A rock physics model is used to describe the dual-porosity characteristics of deep coalbed methane reservoirs, which in turn drives the prestack AVO inversion of coalbed methane reservoirs and finally obtains the distribution of the dual-porosity structure of coalbed methane reservoirs.

[0048] Fig.14 A schematic diagram of the structure of a device for predicting dual-pore structure parameters of deep coalbed methane reservoirs driven by a rock physics model is provided. Fig.14 As shown, the rock physics model driven coalbed methane reservoir dual porosity structure parameter prediction device 700 includes: The acquisition module 701 is used to acquire coal seam mineral data, regional sedimentary background data and well logging data corresponding to the deep coal seam gas reservoir, and simplify the mineral components of the coal seam gas reservoir according to the coal seam mineral data and the regional sedimentary background data to obtain organic matter data, clay data and brittle mineral data; The determination module 702 is used to determine the initial clay content by using the resistivity extreme value method based on the clay data, determine the organic matter content by using the resistivity porosity overlap method based on the organic matter data, and determine the remaining part of the mineral components of the deep coalbed methane reservoir except the initial clay content and the organic matter content as the brittle mineral content; wherein the resistivity corresponding to the resistivity extreme value method and the porosity corresponding to the resistivity porosity overlap method are both determined by the well logging data; Establishing module 703, for establishing a dual-porosity coal rock physical model of a deep coalbed methane reservoir using an equivalent medium theory model based on the initial clay content, the organic matter content and the brittle mineral content; A calculation module 704 is used to calculate the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well according to the well logging observation P-wave and S-wave velocities in the well logging data, and use the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well as the prior information of the pore parameters of the dual-porosity coal rock physical model of the deep coalbed methane reservoir; Inversion module 705 is used to perform pre-stack seismic Bayesian inversion based on the specified three-dimensional seismic layer data and the prior information of the pore parameters through the dual-porosity coal rock physical model of the deep coalbed methane reservoir by using the pre-stack Bayesian inversion method to obtain the dual-porosity structure data of the coalbed methane reservoir, and use the dual-porosity structure data of the coalbed methane reservoir as the prediction result of the dual-porosity structure parameters of the coalbed methane reservoir.

[0049] The rock physics model driven deep coalbed methane reservoir dual-pore structure parameter prediction device provided in the embodiment of the present application has the same technical features as the rock physics model driven deep coalbed methane reservoir dual-pore structure parameter prediction method provided in the above-mentioned embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0050] An electronic device provided in an embodiment of the present application is Fig.15 As shown, the electronic device 800 includes a processor 802 and a memory 801, wherein the memory stores a computer program that can be run on the processor, and the processor implements the steps of the method provided in the above embodiment when executing the computer program.

[0051] See also Fig.15The electronic device further includes: a bus 803 and a communication interface 804, a processor 802, a communication interface 804 and a memory 801 are connected via the bus 803; the processor 802 is used to execute an executable module stored in the memory 801, such as a computer program.

[0052] The memory 801 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 804 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0053] The bus 803 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 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.

[0054] Among them, the memory 801 is used to store programs, and the processor 802 executes the program after receiving the execution instruction. The method executed by the device defined by the process disclosed in any embodiment of the present application can be applied to the processor 802 or implemented by the processor 802.

[0055] The processor 802 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 802. The above processor 802 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 application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature 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. The storage medium is located in the memory 801, and the processor 802 reads the information in the memory 801 and completes the steps of the above method in combination with its hardware.

[0056] Corresponding to the above-mentioned rock physics model driven deep coalbed methane reservoir dual porosity structure parameter prediction method, an embodiment of the present application also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to execute the steps of the above-mentioned rock physics model driven deep coalbed methane reservoir dual porosity structure parameter prediction method.

[0057] The rock physics model-driven deep coalbed methane reservoir dual-pore structure parameter prediction device provided in the embodiment of the present application can be specific hardware on the device or software or firmware installed on the device. The device provided in the embodiment of the present application, its implementation principle and the technical effect produced are the same as those in the aforementioned method embodiment. For the sake of brief description, the parts not mentioned in the device embodiment can refer to the corresponding contents in the aforementioned method embodiment. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0058] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0059] For another example, the flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the device, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0060] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0061] In addition, each functional unit in the embodiments provided in the present application 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.

[0062] 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 computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the rock physics model-driven deep coalbed methane reservoir dual-porosity structural parameter prediction method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store program codes.

[0063] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0064] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solution of the present application, rather than to limit it. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the aforementioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solution recorded in the aforementioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiment of the present application. They should all be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A rock physics model driven method for predicting dual porosity structural parameters of deep coalbed methane reservoirs, characterized in that: The method comprises: Acquire coal seam mineral data, regional sedimentary background data and well logging data corresponding to the deep coal seam methane reservoir, and simplify the mineral components of the deep coal seam methane reservoir according to the coal seam mineral data and the regional sedimentary background data to obtain organic matter data, clay data and brittle mineral data; Determine the initial clay content by using the resistivity extreme value method based on the clay data, determine the organic matter content by using the resistivity porosity overlap method based on the organic matter data, and determine the remaining part of the mineral components of the deep coalbed methane reservoir except the initial clay content and the organic matter content as the brittle mineral content; wherein the resistivity corresponding to the resistivity extreme value method and the porosity corresponding to the resistivity porosity overlap method are both determined by the well logging data; Based on the initial clay content, the organic matter content and the brittle mineral content, a dual-porosity coal rock physical model of a deep coalbed methane reservoir is established using an equivalent medium theoretical model; Calculating the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well according to the well logging observation P-wave and S-wave velocities in the well logging data, and using the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well as prior information of pore parameters of the dual-porosity coal rock physical model of the deep coalbed methane reservoir; Based on the designated three-dimensional seismic layer data and the prior information of the pore parameters, the dual-porosity coal rock physical model of the deep coalbed methane reservoir is used to perform pre-stack seismic Bayesian inversion processing using the pre-stack Bayesian inversion method to obtain the dual-porosity structure data of the coalbed methane reservoir, and the dual-porosity structure data of the coalbed methane reservoir is used as the prediction result of the dual-porosity structure parameters of the coalbed methane reservoir.

2. The method according to claim 1, characterized in that: The dual-porosity coal rock physical model of deep coalbed methane reservoir is established based on the initial clay content, the organic matter content and the brittle mineral content using an equivalent medium theoretical model, including: According to the initial clay content, the organic matter content and the brittle mineral content, the organic matter, clay and brittle minerals are mixed using the Voigt-Ruess-Hill boundary theory model to obtain the elastic parameters of the coal matrix; Constructing the dual porosity characteristics of deep coalbed methane reservoirs, based on the elastic parameters of the coal matrix, using the KT model to add the dual porosity corresponding to the dual porosity characteristics of the coalbed methane reservoir, based on the dual porosity characteristics of the coalbed methane reservoir after adding the dual porosity, using the equal stress average formula to simulate the bulk modulus of the pore fluid under water saturation conditions, and using the Gassmann equation to add fluid to the dry coal rock dry skeleton of the coalbed methane reservoir to simulate the influence of the pore fluid, and obtain an initial rock physics model; The initial rock physics model is linearized to obtain a dual-porosity coal rock physics model of a coalbed methane reservoir.

3. The method according to claim 2, characterized in that The elastic parameters of the coal matrix are obtained by the following formula: ； in, For the i The elastic modulus of a brittle mineral, N is the total number of brittle minerals, For the i The volume fraction of the mineral substance, MVRH is the elastic parameter of coal matrix, MV and MR is the intermediate conversion parameter; The total pore volume of the coalbed methane reservoir corresponding to the dual porosity characteristics of the deep coalbed methane reservoir includes the following two types of pores: ,in, is the total porosity, is the porosity of matrix pores, which are randomly distributed pores in the coal matrix, is the fracture porosity, and the fracture porosity is randomly arranged; The adding of the dual porosity corresponding to the dual porosity characteristic of the coalbed methane reservoir using the KT model includes adding the dual porosity to the dual porosity characteristic of the coalbed methane reservoir using the KT model through the following formula: ,in, ; and are the bulk modulus and shear modulus of coal rock matrix, Ki For the i The bulk modulus of the pores, is the volume fraction containing pores, and is a parameter related to the pore shape, K is the bulk modulus, G is the shear modulus, N is the total number of pores; The method of simulating the bulk modulus of the pore fluid under water saturation by using the equal stress average formula based on the dual porosity characteristics of the coalbed methane reservoir after adding the dual porosity includes simulating the bulk modulus of the pore fluid under water saturation by using the following formula based on the dual porosity characteristics of the coalbed methane reservoir after adding the dual porosity: ； in, is the equivalent bulk modulus of the pore fluid, is the bulk modulus of the liquid, is the bulk modulus of air, Sw is water saturation; The adding of fluid into the dry coal-rock dry skeleton of the coalbed methane reservoir by using the Gassmann equation includes adding fluid into the dry coal-rock dry skeleton of the coalbed methane reservoir by using the following equation: ； in, and are the porosity of matrix pores and fractures, respectively. is the bulk modulus of rock minerals, is the bulk modulus of the dry rock skeleton, is the bulk modulus of the fluid, Kd is the bulk modulus of the dry coal rock skeleton; The forward modeling relationship of the dual-porosity coal rock physics model of the deep coalbed methane reservoir is: ； in, x is the rock physical parameter to be inverted, e is the random error, m is the elastic parameter, is the corresponding relationship of the rock physics model; by applying the forward modeling equation to The first-order Taylor approximation of the point is: ； in, D is the linear forward operator of the rock physics model, x is the rock physical parameter to be inverted, m is the elastic parameter, is the corresponding relationship of the rock physics model, x0 are points selected on different independent variables.

4. The method according to claim 1, characterized in that: The method of calculating the matrix porosity and fracture aspect ratio of the coalbed methane reservoir near the well according to the well logging observation longitudinal wave velocity and the well logging observation shear wave velocity in the well logging data comprises: The inversion data is solved by the Bayesian theory model to obtain the posterior probability of the target parameter with respect to the observed data; The prior information of the mixed Gaussian distribution is determined by using the Gaussian mixture model to describe the prior distribution of physical properties; Determining a probability distribution function based on the posterior probability and the prior information; The matrix porosity and fracture aspect ratio of the coalbed methane reservoir near the well are calculated by the probability distribution function according to the well logging observed longitudinal wave velocity and the well logging observed shear wave velocity in the well logging data, and the constructed inversion objective function is obtained.

5. The method according to claim 4, characterized in that The posterior probability of the target parameter with respect to the observed data is: ； in, is the posterior probability, is the likelihood probability, is the prior probability; The prior information is: ； in, is the prior probability, represents the kth Gaussian distribution, is the expected value of the kth Gaussian distribution, is the covariance of the kth Gaussian distribution, is the weight coefficient of the kth Gaussian distribution, C is the total number of Gaussian distributions, r Represents a priori; The inversion objective function is: ； in, asp is the reservoir fracture aspect ratio, and are respectively the well logging observed P-wave velocity and the well logging observed S-wave velocity in the well logging data, and are the longitudinal wave velocity and shear wave velocity predicted by the model, respectively.

6. The method according to claim 1, characterized in that Based on the specified three-dimensional seismic horizon data and the prior information of the pore parameters, the dual-porosity coal rock physical model of the deep coalbed methane reservoir is used to perform pre-stack seismic Bayesian inversion processing using the pre-stack Bayesian inversion method to obtain the dual-porosity structure data of the coalbed methane reservoir, and the dual-porosity structure data of the coalbed methane reservoir is used as the prediction result of the dual-porosity structure parameters of the coalbed methane reservoir, including: Extract pre-stack seismic angle gathers based on the specified 3D seismic layer data to obtain the relationship between the reflection wave time and amplitude under different incident angles at each point; Based on the well logging data and the seismic data corresponding to the designated three-dimensional seismic layer data, the time information of the reflection wave of the target coal seam at each point is obtained, and a time window is opened along the coal seam to extract the reflection wave information of the target coal seam; Obtaining coal composition information and water saturation information through interpolation results of the logging data; Constructing a Bayesian inversion objective function of seismic data based on the time information, the reflection wave information, the coal composition information, the water saturation information, and the relationship between the reflection wave time and amplitude; The same angle gather data are respectively brought into the Bayesian inversion objective function for inversion to obtain the dual-porosity rock physical parameters of the coalbed methane reservoir at a spatial position, and the dual-porosity rock physical parameters of the coalbed methane reservoir are used as the dual-porosity structural data of the coalbed methane reservoir.

7. The method according to claim 6, characterized in that The Bayesian inversion objective function is: ； in, is the weight factor of the i-th Gaussian distribution, d For earthquake data, m is the inversion parameter; f For the mapping relationship, For expectations, is the covariance matrix, w is the earthquake wavelet.

8. A rock physics model driven deep coalbed methane reservoir dual porosity structure parameter prediction device, characterized in that: include: An acquisition module is used to acquire coal seam mineral data, regional sedimentary background data and well logging data corresponding to a deep coal seam gas reservoir, and simplify the mineral components of the deep coal seam gas reservoir according to the coal seam mineral data and the regional sedimentary background data to obtain organic matter data, clay data and brittle mineral data; A determination module, for determining the initial clay content by using the resistivity extreme value method based on the clay data, determining the organic matter content by using the resistivity porosity overlap method based on the organic matter data, and determining the remaining part of the mineral components of the deep coalbed methane reservoir except the initial clay content and the organic matter content as the brittle mineral content; wherein the resistivity corresponding to the resistivity extreme value method and the porosity corresponding to the resistivity porosity overlap method are both determined by the well logging data; Establishing a module for establishing a dual-porosity coal rock physical model of a deep coalbed methane reservoir using an equivalent medium theory model based on the initial clay content, the organic matter content and the brittle mineral content; A calculation module, for calculating the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well according to the well logging observation P-wave and S-wave velocities in the well logging data, and using the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir near the well as the prior information of the pore parameters of the dual-porosity coal rock physical model of the deep coalbed methane reservoir; The inversion module is used to perform prestack seismic Bayesian inversion processing based on the specified three-dimensional seismic layer data and the prior information of the pore parameters, through the dual-porosity coal rock physical model of the deep coalbed methane reservoir, using the prestack Bayesian inversion method to obtain the dual-porosity structure data of the coalbed methane reservoir, and use the dual-porosity structure data of the coalbed methane reservoir as the prediction result of the dual-porosity structure parameters of the coalbed methane reservoir.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the method according to any one of claims 1 to 7.

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