Prediction Method and Device for Dual-Porosity Structure Parameters of Deep Coalbed Methane Reservoirs

Abstract

The present application provides a method and device for predicting dual-porosity structure parameters of deep coalbed methane reservoirs, relating to the technical field of coalbed methane reservoir development, and solving the technical problem of relatively low accuracy of the prediction results of the pores in deep coalbed methane reservoirs. The method includes: establishing a dual-porosity coal rock physical model of a coalbed methane reservoir based on the initial organic matter content, clay content, and brittle mineral content by using an equivalent medium theory model; calculating the matrix porosity, fracture porosity, and fracture aspect ratio according to the observed longitudinal wave and transverse wave velocities in well logging data, and using the matrix porosity, fracture porosity, and fracture aspect ratio beside the well as the prior information of the pore parameters of the dual-porosity coal rock physical model of the coalbed methane reservoir; based on the specified three-dimensional seismic horizon data and the prior information of the pore parameters, performing prestack seismic inversion by using the prestack Bayesian inversion method through the dual-porosity coal rock physical model of the coalbed methane reservoir to obtain the prediction results of the dual-porosity reservoir of the coalbed methane reservoir.

Description

Technical Field

[0001] This application relates to the technical field of deep coalbed methane reservoir development, and in particular, to a method and device for predicting dual-porosity 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 engineering sweet spots in deep coalbed methane reservoirs, an important reference for evaluating drainage and gas production in deep coalbed methane reservoirs, and of great significance for the exploration and development of deep coalbed methane reservoirs. However, the pore structure of coalbed methane reservoirs is complex, with multiple pore types. In particular, it is difficult to predict 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-porosity structure composed of fractures and matrix pores. At present, the existing pore prediction methods for coalbed methane reservoirs mainly originate from a single equivalent pore hypothesis and do not consider the influence of the dual-porosity characteristics of coalbed methane reservoirs, resulting in low accuracy of the prediction results of the pores in coalbed methane reservoirs by the existing methods. Summary of the Invention

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

[0004] In a first aspect, this application provides a method for predicting dual-porosity structure parameters of deep coalbed methane reservoirs driven by a petrophysical model, the method including:

[0005] Obtain coal seam mineral data, regional sedimentary background data, and logging data corresponding to a deep coalbed methane reservoir, and perform simplification processing on the mineral components of the coalbed 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;

[0006] Based on the organic matter data, determine the organic matter content by using the resistivity porosity overlap method, 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 logging data;

[0007] Based on the initial clay content, the organic matter content, and the brittle mineral content, establish a dual-porosity coal petrophysical model for the coalbed methane reservoir by using the equivalent medium theory model;

[0008] Calculate the matrix porosity, fracture porosity, and fracture aspect ratio of the coalbed methane reservoir adjacent to the well according to the longitudinal and transverse wave velocities observed in the logging data, and use the matrix porosity, fracture porosity, and fracture aspect ratio of the coalbed methane reservoir adjacent to the well as the prior information of the pore parameters of the dual-porosity coal rock physical model of the deep coalbed methane reservoir;

[0009] Based on the specified three-dimensional seismic horizon data and the prior information of the pore parameters, perform prestack seismic Bayesian inversion using the prestack Bayesian inversion method through the dual-porosity coal rock physical model of the deep coalbed methane reservoir 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.

[0010] In a possible implementation, establishing the dual-porosity coal rock physical model of the coalbed methane reservoir based on the initial clay content, organic matter content, and brittle mineral content using the equivalent medium theory model includes:

[0011] Mix organic matter, clay, and brittle minerals according to the initial clay content, organic matter content, and brittle mineral content using the Voigt-Ruess-Hill boundary theory model to obtain the elastic parameters of the coal matrix;

[0012] Construct the dual-porosity characteristics of the deep coalbed methane reservoir. Based on the elastic parameters of the coal matrix, add the dual pores corresponding to the dual-porosity characteristics of the coalbed methane reservoir using the KT model. Based on the dual-porosity characteristics of the coalbed methane reservoir after adding the dual pores, simulate the pore fluid bulk modulus under the condition of water saturation using the equal-stress averaging formula, and add fluid to the dry coal rock dry skeleton of the coalbed methane reservoir using the Gassmann equation to simulate the influence result of the pore fluid, and obtain the initial rock physical model;

[0013] Perform linearization processing on the initial rock physical model to obtain the dual-porosity coal rock physical model of the coalbed methane reservoir.

[0014] In a possible implementation, the elastic parameters of the coal matrix are obtained through the following formula:

[0015] ;

[0016] where, is the elastic modulus of the i th brittle mineral, N is the total number of brittle minerals, is the volume fraction of the i th material mineral, MVRH is the elastic parameter of the coal matrix, MV and MR are intermediate conversion parameters;

[0017] 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: , where is the total porosity, is the porosity of the matrix pores, and the matrix pores are randomly distributed pores in the coal matrix, is the fracture porosity, and the fracture porosity is randomly arranged;

[0018] The addition of the dual porosity corresponding to the dual-porosity characteristics of the coalbed methane reservoir by using the KT model includes adding the dual porosity to the dual-porosity characteristics of the coalbed methane reservoir through the following formula by using the KT model:

[0019] , where ;

[0020] and are the bulk modulus and shear modulus of the coal rock matrix respectively, Ki is the bulk modulus of the i th type of pore, is the volume fraction of the pores containing, and are parameters related to the pore shape, K is the bulk modulus, G is the shear modulus, N is the total number of pores;

[0021] The simulation of the pore fluid bulk modulus under the water saturation condition based on the dual-porosity characteristics of the coalbed methane reservoir after adding the dual porosity includes simulating the pore fluid bulk modulus under the water saturation condition through the following formula based on the dual-porosity characteristics of the coalbed methane reservoir after adding the dual porosity:

[0022] ;

[0023] where is the equivalent bulk modulus of the pore fluid, is the liquid bulk modulus, is the air bulk modulus, Sw is the water saturation;

[0024] The addition of fluid to the dry coal rock dry skeleton of the coalbed methane reservoir by using the Gassmann equation includes adding fluid to the dry coal rock dry skeleton of the coalbed methane reservoir through the following equation:

[0025] ;

[0026] where and are the porosities of matrix pores and fractures respectively, is the bulk modulus of rock minerals, is the bulk modulus of dry rock skeleton, is the bulk modulus of fluid, Kd is the bulk modulus of dry coal-rock skeleton;

[0027] The forward relationship of the dual-porosity coal-rock physical model for deep coalbed methane reservoirs is:

[0028] ;

[0029] where, x is the petrophysical parameter to be inverted, e is the random error, m is the elastic parameter, is the corresponding relationship of the petrophysical model; By performing a first-order Taylor approximation on the forward relationship at the point, we get:

[0030] ;

[0031] where, D is the linear forward operator of the petrophysical model, x is the petrophysical parameter to be inverted, m is the elastic parameter, is the corresponding relationship of the petrophysical model, x0 is the point selected on different independent variables.

[0032] In a possible implementation, calculating the matrix porosity and fracture aspect ratio of the coalbed methane reservoir beside the well according to the logging observed longitudinal wave velocity and logging observed transverse wave velocity in the logging data includes:

[0033] Solving the inversion data through the Bayesian theory model to obtain the posterior probability of the target parameter with respect to the observed data;

[0034] Using the Gaussian mixture model to describe the prior distribution of physical properties to determine the prior information of the mixture Gaussian distribution;

[0035] Based on the posterior probability and the prior information, determining the probability distribution function;

[0036] Calculating the matrix porosity and fracture aspect ratio of the coalbed methane reservoir beside the well through the probability distribution function according to the logging observed longitudinal wave velocity and logging observed transverse wave velocity in the logging data to obtain the constructed inversion objective function.

[0037] In a possible implementation, the posterior probability of the target parameter with respect to the observed data is:

[0038] ;

[0039] Among them, is the posterior probability, is the likelihood probability, is the prior probability;

[0040] The prior information is:

[0041] ;

[0042] Among them, is the prior probability, represents the k-th Gaussian distribution, is the expected value of the k-th Gaussian distribution, is the covariance of the k-th Gaussian distribution, is the weight coefficient of the k-th Gaussian distribution, C is the total number of Gaussian distributions, r represents the prior;

[0043] The inversion objective function is:

[0044] ;

[0045] Among them, asp is the aspect ratio of reservoir fractures, and are respectively the observed longitudinal wave velocity and the observed shear wave velocity in the logging data, and are respectively the predicted longitudinal wave velocity and the predicted shear wave velocity of the model.

[0046] In a possible implementation, based on the specified three-dimensional seismic horizon data and the prior information of the pore parameters, through the dual-porosity coal rock physics model of the deep coalbed methane reservoir, using the prestack Bayesian inversion method to perform prestack seismic Bayesian inversion, obtaining the dual-porosity structure data of the coalbed methane reservoir, and taking 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, including:

[0047] Extracting the prestack seismic angle gathers based on the specified three-dimensional seismic horizon data to obtain the relationship between the reflection wave time and amplitude at each point under different incident angles;

[0048] Obtaining the time information of the reflection wave of the target coal seam at each point based on the logging data and the seismic data corresponding to the specified three-dimensional seismic horizon data, and extracting the reflection wave information of the target coal seam by opening a time window along the coal seam;

[0049] Obtaining the coal composition information and the water saturation information through the interpolation result of the logging data;

[0050] Construct a Bayesian inversion objective function for seismic data based on the time information, the reflected wave information, the coal composition information, the water saturation information, and the relationship between the reflected wave time and amplitude;

[0051] Bring the data of the same common angle gather into the Bayesian inversion objective function for inversion respectively to obtain the dual-porosity rock physical parameters of the coalbed methane reservoir at a spatial position, and use the dual-porosity rock physical parameters of the coalbed methane reservoir as the dual-porosity structure data of the coalbed methane reservoir.

[0052] In a possible implementation, the Bayesian inversion objective function is:

[0053] ;

[0054] where, is the specific gravity factor of the i-th Gaussian distribution, d is the seismic data, m is the inversion parameter; f is the mapping relationship, is the expectation, is the covariance matrix, w is the seismic wavelet.

[0055] In a second aspect, the present application provides a device for predicting the dual-porosity structure parameters of a deep coalbed methane reservoir driven by a rock physical model, including:

[0056] An acquisition module, configured to acquire the coal seam mineral data, the regional sedimentary background data, and the logging data corresponding to the deep coalbed methane reservoir, and perform a simplification process on the mineral components of the coalbed 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;

[0057] A determination module, configured to determine the initial clay content based on the clay data by using the resistivity extreme value method, determine the organic matter content based on the organic matter data by using the resistivity-porosity overlapping method, 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 overlapping method are both determined by the logging data;

[0058] A building module, configured to build a dual-porosity coal rock physical model of the deep coalbed methane reservoir based on the initial clay content, the organic matter content, and the brittle mineral content by using the equivalent medium theory model;

[0059] A calculation module, configured to calculate the matrix porosity, fracture porosity, and fracture aspect ratio of the coalbed methane reservoir beside the well according to the longitudinal and transverse wave velocities of the well logging observations in the well logging data, and use the matrix porosity, fracture porosity, and fracture aspect ratio of the coalbed methane reservoir beside the well as the prior information of the pore parameters of the dual-porosity coal petrophysical model of the deep coalbed methane reservoir;

[0060] An inversion module, configured to perform pre-stack seismic Bayesian inversion through the dual-porosity coal petrophysical model of the deep coalbed methane reservoir based on the specified three-dimensional seismic horizon data and the prior information of the pore parameters, 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.

[0061] In a third aspect, the present application further provides an electronic device, including a memory and a processor. A computer program that can run on the processor is stored in the memory. When the processor executes the computer program, the method described in the first aspect above is implemented.

[0062] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and run by the processor, the computer-executable instructions cause the processor to run the method described in the first aspect above.

[0063] The present application brings the following beneficial effects:

[0064] A method and device for predicting double-porosity structure parameters of deep coalbed methane reservoirs provided by this application can obtain coal seam mineral data, regional sedimentary background data, and logging data corresponding to deep coalbed methane reservoirs, simplify the mineral components of the coalbed methane reservoirs based on 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 using the resistivity extreme value method based on the clay data, determine the organic matter content using the resistivity-porosity overlapping 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. Among them, the resistivity corresponding to the resistivity extreme value method and the porosity corresponding to the resistivity-porosity overlapping method are both determined through the logging data. Based on the initial clay content, the organic matter content, and the brittle mineral content, an equivalent medium theory model is used to establish a double-porosity coal rock physical model for the coalbed methane reservoir. Calculate the matrix porosity, fracture porosity, and fracture aspect ratio of the coalbed methane reservoir near the well according to the observed longitudinal and transverse wave velocities in the 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 double-porosity coal rock physical model of the deep coalbed methane reservoir. Based on the specified three-dimensional seismic horizon data and the prior information of the pore parameters, through the double-porosity coal rock physical model of the deep coalbed methane reservoir, pre-stack Bayesian inversion processing is carried out using the pre-stack Bayesian inversion method to obtain the double-porosity structure data of the coalbed methane reservoir, and the double-porosity structure data of the coalbed methane reservoir is used as the prediction result of the double-porosity structure parameters of the coalbed methane reservoir. In this solution, based on the pore structure characteristics of the coal rock in the coalbed methane reservoir, a double-porosity coalbed methane reservoir rock physical model is established, the prior information of Bayesian inversion is determined in combination with logging data, and then the double-porosity structure parameters of the coalbed methane reservoir are inverted in combination with pre-stack seismic data, which can fully combine the actual geological characteristics of the coalbed methane reservoir rock, deeply restore the pore structure characteristics of the coalbed methane, and obtain a complete elastic matrix in combination with well-seismic data, so as to obtain the accurate double-porosity structure characteristics of the coalbed methane reservoir. By constructing a rock physical model using the material composition and pore structure characteristics of the coalbed methane reservoir and linearizing it using the Taylor series, the inversion efficiency is higher, and it meets the reservoir prediction analysis of conventional logging and seismic data, can predict the double-porosity structure parameters of the coalbed methane reservoir, and thus improve the prediction accuracy of the coalbed methane reservoir.

[0065] To make the above objects, features, and advantages of this application more obvious and understandable, the following specifically gives preferred embodiments and detailed descriptions in conjunction with the accompanying drawings as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] To more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings required for the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0067] Figure 1 Schematic flow chart of the prediction method for the dual-porosity structure parameters of deep coalbed methane reservoirs driven by a rock physics model provided by an embodiment of the present application;

[0068] Figure 2 Schematic flow chart of the modeling process of the dual-porosity rock physics model for deep coalbed methane reservoirs provided by an embodiment of the present application;

[0069] Figure 3 Schematic diagram of the linearization result of the crack density corresponding to the deep coalbed methane reservoir provided by an embodiment of the present application;

[0070] Figure 4 Another schematic diagram of the linearization result of the crack density corresponding to the deep coalbed methane reservoir provided by an embodiment of the present application;

[0071] Figure 5 Schematic diagram of the linearization result of the pore porosity corresponding to the deep coalbed methane reservoir provided by an embodiment of the present application;

[0072] Figure 6 Another schematic diagram of the linearization result of the pore porosity corresponding to the deep coalbed methane reservoir provided by an embodiment of the present application;

[0073] Figure 7 Schematic diagram of the linearization result of a model asp corresponding to the deep coalbed methane reservoir provided by an embodiment of the present application;

[0074] Figure 8 Another schematic diagram of the linearization result of a model asp corresponding to the deep coalbed methane reservoir provided by an embodiment of the present application;

[0075] Figure 9 Inversion diagram of the mixed Gaussian distribution of well logging data provided by an embodiment of the present application;

[0076] Figure 10 Another schematic flow chart of the prediction method for the dual-porosity structure parameters of deep coalbed methane reservoirs driven by a rock physics model provided by an embodiment of the present application;

[0077] Figure 11 Schematic diagram of the matrix porosity in the prediction effect of the reservoir dual-porosity structure;

[0078] Figure 12 It is a schematic diagram of fracture density in the prediction effect of the dual-porosity structure of the reservoir;

[0079] Figure 13 It is a schematic diagram of the fracture aspect ratio in the prediction effect of the dual-porosity structure of the reservoir;

[0080] Figure 14 It is a schematic structural diagram of a device for predicting dual-porosity structure parameters of deep coalbed methane reservoirs driven by a rock physics model provided by an embodiment of the present application;

[0081] Figure 15 It shows a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0082] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0083] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes other steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0084] In the prior art, to address the lack of a systematic evaluation method for the supercritical CO2 fracturability 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 a supercritical CO2 fracturability evaluation system for deep coalbed methane reservoirs can be established. In addition, at present, brittle characterization can also be carried out through various brittle representation methods. The Xu-White model is selected to conduct rock physics modeling on the target layer and supplement the logging data. However, there are certain differences between the dual-porosity structure characteristics of coalbed methane reservoirs and the Xu-White model. There is still a lack of a prediction method for the dual-porosity structure of coalbed methane reservoirs. At present, the dual-porosity characteristic evaluation system of coalbed methane reservoirs is not yet complete, and the accuracy of the prediction results of the existing methods for the pores of coalbed methane reservoirs is relatively low.

[0085] Based on this, the embodiments of the present application provide a method and device for predicting the dual-porosity structure parameters of deep coalbed methane reservoirs, which can solve the technical problem of low accuracy of the prediction results of the pores in deep coalbed methane reservoirs.

[0086] The embodiments of the present invention will be further introduced below with reference to the accompanying drawings.

[0087] Figure 1 It is a schematic flow chart of a method for predicting the dual-porosity structure parameters of deep coalbed methane reservoirs driven by a rock physics model provided by the embodiments of the present application. As Figure 1 shown, the method includes:

[0088] Step S110, obtain the coal seam mineral data, regional sedimentary background data, and logging data corresponding to the deep coalbed methane reservoir, and simplify the mineral components of the coalbed 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.

[0089] As a possible implementation manner, according to the regional sedimentary background and relevant data of deep coal seam minerals, the mineral components of the deep coalbed methane reservoir are simplified into organic matter, clay, and brittle minerals.

[0090] Step S120, 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 determine the remaining part of the mineral components of the coalbed methane reservoir except for the initial clay content and the organic matter content as the brittle mineral content.

[0091] Among them, the resistivity corresponding to the resistivity extreme value method and the porosity corresponding to the resistivity porosity overlap method are both determined through logging data. Exemplarily, the initial clay content is obtained by using the resistivity extreme value method; the organic matter content is obtained by using the resistivity porosity overlap method; among them, the resistivity and porosity are from logging data. The remaining part is regarded as brittle minerals.

[0092] Step S130, establish a dual-porosity coal rock physics model for the deep coalbed methane reservoir based on the initial clay content, organic matter content, and brittle mineral content using the equivalent medium theory model.

[0093] In an alternative implementation manner, as Figure 2 shown, the coalbed methane reservoir has dual-porosity characteristics. According to the appendix Figure 2 , based on the equivalent medium theory, a dual-porosity coal rock physics model of the coalbed methane reservoir is established to obtain a dual-porosity coal rock physics model of the coalbed methane reservoir. As an example, step S130 may specifically include the following steps:

[0094] Mix organic matter, clay, and brittle minerals according to the initial clay content, organic matter content, and brittle mineral content using the Voigt-Ruess-Hill boundary theory model to obtain the elastic parameters of the coal matrix;

[0095] Construct the dual-porosity characteristics of the coalbed methane reservoir. Based on the elastic parameters of the coal matrix, add the dual porosity corresponding to the dual-porosity characteristics of the coalbed methane reservoir using the KT model. Based on the dual-porosity characteristics of the coalbed methane reservoir after adding the dual porosity, use the equal-stress averaging formula to simulate the pore fluid bulk modulus under the condition of water saturation, and use the Gassmann equation to add fluid to the dry coal rock dry skeleton of the coalbed methane reservoir to simulate the influence result of the pore fluid, and obtain the initial rock physics model;

[0096] Linearly process the initial rock physics model to obtain the dual-porosity coal rock physics model of the coalbed methane reservoir.

[0097] As an alternative implementation, first mix organic matter, clay, and brittle minerals 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 through the following formula:

[0098] (1);

[0099] Wherein, is the elastic modulus of the i th brittle mineral, N is the total number of brittle minerals, is the volume fraction of the i th mineral substance (there are three components in total: organic matter, clay, and brittle minerals), MVRH is the elastic parameter of the coal matrix, MV and MR are intermediate conversion parameters;

[0100] Then, construct the dual-porosity characteristics of the coalbed methane reservoir, and add dual porosity to the elastic parameters of the coal matrix obtained by mixing the above organic matter, clay, and brittle minerals 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), wherein, is the total porosity, is the porosity of the matrix pores, and the matrix pores are randomly distributed pores in the coal matrix, is the fracture porosity, and the fracture porosity is randomly arranged.

[0101] After that, the KT model is used to add double porosity to the coalbed methane reservoir. The double porosity includes using the KT model to add double porosity to the coalbed methane reservoir through the following formula, that is, using the KT model 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 aspect ratio of the fractures is unknown and needs to be obtained through inversion:

[0102] (3), where, (4);

[0103] and are the bulk modulus and shear modulus of the coal rock matrix respectively, Ki is the i th bulk modulus of the pores, is the volume fraction of the pores containing (there are 2 types in total, namely matrix pores and fractures), and are parameters related to the pore shape, K is the bulk modulus, G is the shear modulus, N is the total number of pores;

[0104] Then, the isostress averaging formula (WOOD formula) is used to simulate the bulk modulus of the pore fluid under a certain water saturation. For example, based on the double porosity characteristics of the coalbed methane reservoir after adding double porosity, the WOOD formula is used to simulate the bulk modulus of the pore fluid under the water saturation, including simulating the bulk modulus of the pore fluid under the water saturation through the following formula based on the double porosity characteristics of the coalbed methane reservoir after adding double porosity:

[0105] (5);

[0106] where, is the equivalent bulk modulus of the pore fluid, is the liquid bulk modulus (can be equivalent to water, taking 2.2 GPa), is the air bulk modulus (can be equivalent to air, taking 0.14 MPa), Sw is the water saturation;

[0107] After that, in order to simulate the influence of the pore fluid, the Gassmann equation is used to add fluid to the dry coal rock dry skeleton, that is, using the Gassmann equation to add fluid to the dry coal rock dry skeleton of the coalbed methane reservoir includes adding fluid to the dry coal rock dry skeleton of the coalbed methane reservoir through the following equation:

[0108] (6);

[0109] where, and are the porosities of matrix pores and fractures respectively, is the bulk modulus of rock minerals, is the bulk modulus of dry rock skeleton, is the bulk modulus of fluid, Kd is the bulk modulus of dry coal-rock skeleton;

[0110] In practical applications, the rock physics model has strong non-linear characteristics and will fall into local optimal solutions during the inversion process, thus generating errors. Therefore, it is necessary to linearize the model. The forward relationship of the dual-porosity coal-rock physics model for coalbed methane reservoirs is:

[0111] (7);

[0112] Among them, x is the rock physics parameter to be inverted, e is the random error, m is the elastic parameter, is the corresponding relationship of the rock physics model. Then, to obtain the linear forward model, the first-order Taylor approximation is performed on the above formula at the point x0, that is, by performing the first-order Taylor approximation on the forward relationship at the point to obtain:

[0113] (8);

[0114] Among them, D is the linear forward operator of the rock physics model, x is the rock physics parameter to be inverted, m is the elastic parameter, is the corresponding relationship of the rock physics model, x0 is the point selected on different independent variables.

[0115] (9)

[0116] Among them, ∂ is the symbol of partial derivative. The effect of model linearization is as Figures 3 - 8 shown.

[0117] Step S140, calculate the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir beside the well according to the observed longitudinal and transverse wave velocities in the logging data, and use the matrix porosity, fracture porosity and fracture aspect ratio of the coalbed methane reservoir beside the well as the prior information of pore parameters of the dual-porosity coal-rock physics model for deep coalbed methane reservoirs.

[0118] In practical applications, since the fracture aspect ratio is difficult to obtain directly from the observed data, the matrix pores and fracture aspect ratio beside the well are estimated according to the observed longitudinal and transverse wave velocities in the logging data, so as to provide prior information of pore parameters for subsequent inversion, that is, the prior distribution of model parameters obtained from the logging data.

[0119] 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:

[0120] 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.

[0121] 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.

[0122] 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:

[0123] (11);

[0124] 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.

[0125] 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 Figure 9 shown.

[0126] 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:

[0127] (12);

[0128] (13);

[0129] The final inversion result of the target parameter is the weighted mean of the posterior expectation :

[0130] (14);

[0131] where (15).

[0132] Then, the inversion objective function is constructed based on well logging data. The inversion objective function constructed from well logging data is: ; where asp is the aspect ratio of fractures in the reservoir, and are the observed compressional wave velocity and the observed shear wave velocity in well logging data (obtained from well logging data) respectively, and are the predicted compressional wave velocity and shear wave velocity of the model respectively, obtained from the model, and are the compressional and shear wave velocities in the vertical direction. The grid search method is used to solve the above formula (14), and the result at the minimum of the objective function is taken as the inversion result.

[0133] Step S150, perform prestack seismic Bayesian inversion through the dual-porosity coal rock physics model of coalbed methane reservoirs based on the specified three-dimensional seismic horizon data and the prior information of pore parameters using the prestack Bayesian inversion method, 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.

[0134] In the process of model-driven prestack seismic Bayesian inversion, based on the three-dimensional seismic horizon interpretation result, extract the reflection wave waveform of the coalbed methane reservoir, and combine the rock physics model of the coalbed methane reservoir with the prestack Bayesian inversion method to obtain the complete dual-porosity structure information of the coal seam.

[0135] As an alternative implementation, in step S150, perform prestack seismic Bayesian inversion through the dual-porosity coal rock physics model of coalbed methane reservoirs based on the specified three-dimensional seismic horizon data and the prior information of pore parameters using the prestack Bayesian inversion method, 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, which may specifically include the following steps:

[0136] Extract the pre-stack seismic angle gather based on the specified 3D seismic horizon data to obtain the relationship between the reflection wave time and amplitude at each point under different incident angles; obtain the time information of the reflection wave of the target coal seam at each point based on the logging data and the seismic data corresponding to the specified 3D seismic horizon data, and extract the reflection wave information of the target coal seam by opening a time window along the coal seam; obtain the coal composition information and water saturation information through the interpolation results of the logging data; construct the Bayesian inversion objective function of the seismic 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; bring the same trace angle gather data into the Bayesian inversion objective function for inversion respectively to obtain the dual-porosity rock physical parameters of the coalbed methane reservoir at a spatial position, and use the dual-porosity rock physical parameters of the coalbed methane reservoir as the dual-porosity structure data of the coalbed methane reservoir.

[0137] Exemplarily, the pre-stack seismic angle gather can be extracted first to obtain the reflection wave time-amplitude relationship at each point under different incident angles. Then, the time information of the reflection wave of the target coal seam at each point can be obtained by combining the seismic data and the logging data. And the reflection wave information of the target coal seam can be extracted by opening a time window along the coal seam. After that, the coal composition information and water saturation information can be obtained through the interpolation results of the logging data. The prediction process of the reservoir dual-porosity structure is as Figure 10 shown.

[0138] To simulate the seismic response characteristics of the underground coal seam, the Aki-Richards equation is used to calculate the P-wave reflectivity at different incident angles:

[0139] (18);

[0140] where

[0141] (19);

[0142] and are the P-wave and S-wave velocities respectively, is the density, is the incident angle, and the subscripts 1 and 2 are the upper and lower layer interfaces respectively.

[0143] Then, construct the inversion objective function of the seismic data. The Bayesian inversion objective function constructed from the seismic data is:

[0144] (17);

[0145] where is the specific gravity factor of the i-th Gaussian distribution, d is the seismic data, m is the inversion parameter; f is the mapping relationship, is the expectation, is the covariance matrix, w is the seismic wavelet, which is generally represented by the Ricker wavelet and has a fixed functional relationship. The prior Gaussian mixture distribution comes from the logging data analysis results in the second step. Combining the inversion objective function constructed from the seismic data, prestack seismic inversion is carried out. The parameter calculation process in the objective function is the same as the above formulas (10) to (13).

[0146] Finally, the data of the same common azimuth gather are respectively substituted into the above formula for inversion to obtain the dual-porosity rock physical parameters of the coalbed methane reservoir at a spatial position. The prediction effect of the reservoir dual-porosity structure is as Figures 11 - 13 shown.

[0147] In the embodiments of the present application, based on the pore structure characteristics of the coal rock in the coalbed methane reservoir, a rock physical model of the dual-porosity coalbed methane reservoir is established, the prior information of Bayesian inversion is determined by combining logging data, and then the dual-porosity structure parameters of the coalbed methane reservoir are inverted by combining prestack seismic data. It can fully combine the actual geological characteristics of the coalbed methane reservoir, adopt the material composition and pore structure characteristics of the coalbed methane reservoir to construct a rock physical model, and use the Taylor series for linearization, making the inversion efficiency higher, and meeting the conventional reservoir prediction analysis of logging and seismic data. It can predict the dual-porosity structure parameters of the coalbed methane reservoir, thereby improving the prediction accuracy of the coalbed methane reservoir. Moreover, rock physical modeling is a bridge between the microscopic structure and macroscopic properties of rocks, and can quantitatively describe the influence of rock physical parameters such as pore structure on the elastic characteristics of rocks. The method of rock physical modeling can deeply restore the pore structure characteristics of coalbed methane, and combine well-seismic data to obtain a complete elastic matrix, so as to obtain the accurate dual-porosity structure characteristics of the coalbed methane reservoir. The pore structure in coal is complex and can be regarded as a dual-porosity medium composed of matrix pores and fractures. The rock physical model is used to describe the dual-porosity characteristics of the deep coalbed methane reservoir, and then drive the prestack AVO inversion of the coalbed methane reservoir to finally obtain the distribution of the dual-porosity structure of the coalbed methane reservoir.

[0148] Figure 14 A schematic structural diagram of a device for predicting dual-porosity structure parameters of a deep coalbed methane reservoir driven by a rock physical model is provided. As Figure 14 shown, the device 700 for predicting dual-porosity structure parameters of a coalbed methane reservoir driven by a rock physical model includes:

[0149] An acquisition module 701, configured to acquire coal seam mineral data, regional sedimentary background data, and logging data corresponding to a deep coalbed methane reservoir, and perform a simplification process on the mineral components of the coalbed 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;

[0150] A determination module 702 is configured to determine an initial clay content by using a resistivity extreme value method based on the clay data, determine an organic matter content by using a resistivity porosity overlap method based on the organic matter data, and determine the remaining portion of the mineral components in the deep coalbed methane reservoir other than the initial clay content and the organic matter content as a 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 logging data;

[0151] An establishment module 703 is configured to establish a dual-porosity coal petrophysical model of a deep coalbed methane reservoir by using an equivalent medium theory model based on the initial clay content, the organic matter content, and the brittle mineral content;

[0152] A calculation module 704 is configured to calculate a matrix porosity, a fracture porosity, and a fracture aspect ratio of a coalbed methane reservoir adjacent to a well according to the observed longitudinal wave and transverse wave velocities in the logging data, and use the matrix porosity, the fracture porosity, and the fracture aspect ratio of the coalbed methane reservoir adjacent to the well as prior information on porosity parameters of the dual-porosity coal petrophysical model of the deep coalbed methane reservoir;

[0153] An inversion module 705 is configured to perform prestack seismic Bayesian inversion by using a prestack Bayesian inversion method through the dual-porosity coal petrophysical model of the deep coalbed methane reservoir based on specified three-dimensional seismic horizon data and the prior information on porosity parameters, so as to obtain dual-porosity structure data of the coalbed methane reservoir, and use the dual-porosity structure data of the coalbed methane reservoir as a prediction result of dual-porosity structure parameters of the coalbed methane reservoir.

[0154] The device for predicting dual-porosity structure parameters of a deep coalbed methane reservoir driven by a petrophysical model provided in an embodiment of the present application has the same technical features as the method for predicting dual-porosity structure parameters of a deep coalbed methane reservoir driven by a petrophysical model provided in the foregoing embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0155] An electronic device provided in an embodiment of the present application, as Figure 15 shown, the electronic device 800 includes a processor 802 and a memory 801. A computer program that can run on the processor is stored in the memory. When the processor executes the computer program, the steps of the method provided in the foregoing embodiment are implemented.

[0156] Referring to Figure 15 , the electronic device further includes: a bus 803 and a communication interface 804. The processor 802, the communication interface 804, and the memory 801 are connected through the bus 803; the processor 802 is configured to execute an executable module stored in the memory 801, such as a computer program.

[0157] Among them, the memory 801 may include a high-speed random access memory (Random Access Memory, RAM for short), and may also include 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 804 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0158] The bus 803 can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 8 only a bidirectional arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0159] Among them, the memory 801 is used to store programs. After receiving an execution instruction, the processor 802 executes the program. The method executed by the device defined by the process disclosed in any embodiment of the present application can be applied to or implemented by the processor 802.

[0160] 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 integrated logic circuit of the hardware in the processor 802 or the instructions in the form of software. The above-mentioned processor 802 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by a combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 801, and the processor 802 reads the information in the memory 801 and combines its hardware to complete the steps of the above method.

[0161] Corresponding to the above-mentioned method for predicting the dual-porosity structure parameters of deep coalbed methane reservoirs driven by a petrophysical model, an embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to run the steps of the above-mentioned method for predicting the dual-porosity structure parameters of deep coalbed methane reservoirs driven by a petrophysical model.

[0162] The device for predicting the dual-porosity structure parameters of deep coalbed methane reservoirs driven by a petrophysical model provided in the embodiments of the present application may be specific hardware on a device or software or firmware installed on the device, etc. For the device provided in the embodiments of the present application, the implementation principle and the technical effects produced are the same as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding content in the foregoing method embodiments. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can all refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0163] 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 illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For another 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 couplings or direct couplings or communication connections shown or discussed with each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0164] Again, for example, the flowcharts and block diagrams in the drawings show the possible architectures, functions, and operations of the devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0165] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] In addition, the functional units in the embodiments provided in the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0167] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method for predicting the double-porosity structure parameters of deep coalbed methane reservoirs driven by the petrophysical model described in various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0168] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", "third", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0169] Finally, it should be noted that the above-mentioned embodiments are only specific implementation manners of this application, used to illustrate the technical solutions of this application, rather than limiting it. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed in this application can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to 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 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.

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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