A method and device for directly predicting pore parameters of deep coalbed methane reservoirs

Abstract

The present application provides a method and device for directly predicting pore parameters of deep coalbed methane reservoirs, which belongs to the field of coalbed development technology, including obtaining prior data of pore parameters at the target coalbed methane reservoir location and actual observed seismic data; simulating and generating simulated seismic data based on the prior data of pore parameters and the linear mapping relationship between pre-constructed seismic data and pore parameters; performing iterative inversion using a pre-constructed Bayesian inversion objective function based on the actual observed seismic data, simulated seismic data and prior data of pore parameters to determine the minimum objective function value; and directly determining the target pore parameters based on the simulated seismic data corresponding to the minimum objective function value and the mapping relationship between seismic data and pore parameters. The present invention directly inverts the pore parameters through the mapping relationship between pre-constructed seismic data and pore parameters, reducing the cumulative error generated by intermediate conversion and greatly improving the prediction accuracy of the pore parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of coal seam development, and in particular to a method and device for directly predicting pore parameters of deep coal seam gas reservoirs. Background Art

[0002] The pore structure of deep coalbed methane reservoirs is a key parameter for predicting engineering sweet spots in coalbed methane reservoirs and an important reference for evaluating drainage and gas recovery. Existing methods, most of which rely on indirect inversion of rock physics models, require intermediate parameter conversion to obtain the target parameters, which leads to error accumulation and makes it difficult to directly characterize the pore structure parameters of deep coalbed methane reservoirs. Summary of the Invention

[0003] In view of this, the object of the present invention is to provide a method and device for directly predicting the pore parameters of deep coalbed methane reservoirs, so as to improve the accuracy of pore parameter prediction.

[0004] In a first aspect, a method for directly predicting pore parameters of a deep coalbed methane reservoir is provided, the method comprising:

[0005] Obtaining prior data on pore parameters and actual observed seismic data at the target coalbed methane reservoir location;

[0006] Based on the prior data of pore parameters and the linear mapping relationship between pre-constructed seismic data and pore parameters, simulated seismic data are generated;

[0007] Based on the actual observed seismic data, simulated seismic data and prior data of pore parameters, an iterative inversion is performed using a pre-constructed Bayesian inversion objective function to determine the minimum objective function value;

[0008] The target pore parameters are directly determined based on the simulated seismic data corresponding to the minimum objective function value and the linear mapping relationship between the seismic data and the pore parameters.

[0009] Optionally, the process of constructing the linear mapping relationship between seismic data and pore parameters includes:

[0010] Obtain elastic parameters of various mineral components, including organic matter, clay, and brittle minerals;

[0011] Constructing a rock physics model of deep coalbed methane reservoirs based on the elastic parameters of various mineral components;

[0012] Predicting the P-wave reflection coefficient based on rock physics models;

[0013] Determine the linear mapping relationship between the longitudinal wave reflection coefficient and the pore parameters according to the predicted longitudinal wave reflection coefficient;

[0014] Based on the linear mapping relationship between the longitudinal wave reflection coefficient and the pore parameters, the linear mapping relationship between seismic data and pore parameters is constructed using seismic wavelet data:

[0015]

[0016] in, , is the pore parameter; , and are water saturation, porosity, and pore aspect ratio, respectively; For earthquake data; The Toeplitz matrix constructed for the seismic wavelet; is the linear mapping relationship between the longitudinal wave reflection coefficient and the pore parameters; Represents random error.

[0017] Optionally, predicting the P-wave reflection coefficient based on the rock physics model includes:

[0018] Obtain the volume fraction and density of each mineral component, as well as the elastic parameters of the rock physics model; the elastic parameters include bulk modulus and shear modulus;

[0019] Determine the equivalent density of the rock physics model based on the volume fraction and density of each mineral component;

[0020]

[0021] in, For the The density of each mineral component; is the total amount of mineral components; The first Volume fraction of each component;

[0022] Prediction of seismic P- and S-wave velocities based on elastic parameters and equivalent density of rock physics models:

[0023]

[0024] in, is the predicted longitudinal wave velocity; To predict the shear wave velocity; is the equivalent density of saturated coal rock; is the bulk modulus of the rock physics model; is the shear modulus of the rock physics model;

[0025] Based on the predicted P-wave and S-wave velocities and the equivalent density of saturated coal rock, the P-wave reflection coefficient of the rock physics model is predicted by the following formula:

[0026]

[0027] in, 、 and is the average value of the longitudinal and shear wave velocities and densities on both sides of the coal seam interface; 、 、 is the difference in P-wave and S-wave velocity and density on both sides of the coal seam interface; is the ratio of shear wave velocity to longitudinal wave velocity.

[0028] Optionally, simulating and generating simulated seismic data based on prior data of pore parameters and a pre-constructed linear mapping relationship between seismic data and pore parameters includes:

[0029] Based on the prior data of pore parameters, a linear forward operator of the longitudinal wave reflection coefficient and pore parameters is constructed:

[0030] Based on the linear forward operator, the mapping relationship between the pre-constructed seismic data and the pore parameters is linearized to simulate and generate linearized simulated seismic data:

[0031]

[0032] in, , is the pore parameter; , and are water saturation, porosity, and pore aspect ratio, respectively; is the linearized simulated seismic data; The Toeplitz matrix constructed for the seismic wavelet; is the target parameter approximation point of the first-order Taylor approximation; is a constant; is a random error.

[0033] Alternatively, the linear forward operator is obtained using the linear method of Taylor expansion:

[0034]

[0035] in, , is the pore parameter; , and are water saturation, porosity, and pore aspect ratio, respectively; is the longitudinal wave reflection coefficient.

[0036] Optionally, the Bayesian inversion objective function is:

[0037]

[0038] in, is the actual earthquake data; is the linearized simulated seismic data; is the pore parameter to be predicted; is the prior expectation of the prior data of the pore parameters; is the covariance of the prior data of the pore parameters; is the number of Gaussian distributions of the prior data; For the The weight factor of the Gaussian distribution.

[0039] Optionally, the prior data of the pore parameters include a pore aspect ratio, and the pore aspect ratio is determined as follows:

[0040] Obtain the actual observed seismic P- and S-wave velocities and the P- and S-wave velocities predicted by the rock physics model;

[0041] Based on the actual observed seismic P- and S-wave velocities and the P- and S-wave velocities predicted by the rock physics model, the following is obtained using Bayesian inversion:

[0042]

[0043] in: is the pore aspect ratio to be inverted; and are the measured P-wave and S-wave velocities, respectively, obtained from well logging data; and are the P-wave and S-wave velocities predicted by the rock physics model, respectively.

[0044] In a second aspect, a device for directly predicting pore parameters of deep coalbed methane reservoirs is provided, the device comprising:

[0045] An acquisition unit, used to acquire prior data of pore parameters at a target coalbed methane reservoir location and actual observed seismic data;

[0046] A generation unit, for simulating and generating simulated seismic data based on prior data of pore parameters and a pre-constructed linear mapping relationship between seismic data and pore parameters;

[0047] An inversion unit is used to perform iterative inversion based on actual observed seismic data, simulated seismic data, and prior data of pore parameters using a pre-built Bayesian inversion objective function to determine the minimum objective function value;

[0048] The determination unit is used to directly determine the target pore parameters based on the simulated seismic data corresponding to the minimum objective function value and the linear mapping relationship between the seismic data and the pore parameters.

[0049] According to a third aspect, an electronic device is provided, comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus.

[0050] The memory is used to store computer programs;

[0051] The processor is configured to implement any of the method steps described in the first aspect when executing the program stored in the memory.

[0052] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any method step described in the first aspect is implemented.

[0053] The embodiment of the present invention provides a method and device for directly predicting pore parameters of deep coalbed methane reservoirs, which obtains prior data of pore parameters and actual observed seismic data at the target coalbed methane reservoir location; simulates and generates simulated seismic data based on the prior data of pore parameters and a pre-constructed linear mapping relationship between seismic data and pore parameters; performs iterative inversion using a pre-constructed Bayesian inversion objective function based on the actual observed seismic data, simulated seismic data, and prior data of pore parameters to determine the minimum objective function value; and directly determines the target pore parameters based on the simulated seismic data corresponding to the minimum objective function value and the mapping relationship between seismic data and pore parameters. The present invention can directly invert the pore parameters through the pre-constructed linear mapping relationship between seismic data and pore parameters. After determining the minimum objective function value, the optimal target pore parameters can be directly obtained through the linear mapping relationship, reducing the cumulative error generated by intermediate conversion and greatly improving the prediction accuracy of the pore parameters.

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without making any creative efforts.

[0056] Figure 1 A flow chart of a method for directly predicting pore parameters of deep coalbed methane reservoirs provided by an embodiment of the present invention is shown;

[0057] Figure 2A schematic diagram of the process of constructing a rock physics model of a deep coalbed methane reservoir provided by an embodiment of the present invention is shown;

[0058] Figure 3(a) shows the forward modeling results of the nonlinear reflection coefficient;

[0059] Figure 3(b) shows the forward modeling results of the linear reflection coefficient based on the constraints of the rock physics model;

[0060] Figure 4 (a) shows the forward modeling results of the nonlinear reflection coefficient with a 3% random error;

[0061] Figure 4(b) shows the forward modeling results of the linear reflection coefficient based on the constraints of the rock physics model with a random error of 3%.

[0062] Figure 5 A schematic diagram of a process of using Bayesian inversion in an embodiment of the present invention is shown;

[0063] FIG6( a ) shows an inversion cross-section of porosity according to an embodiment of the present invention;

[0064] FIG6( b ) shows an inversion cross-section of water saturation Sw according to an embodiment of the present invention;

[0065] FIG6( c ) shows an inversion cross-sectional view of the pore aspect ratio Asp according to an embodiment of the present invention;

[0066] Figure 7 A schematic structural diagram of a device for directly predicting pore parameters of deep coalbed methane reservoirs provided by an embodiment of the present invention is shown;

[0067] Figure 8 A schematic structural diagram of an electronic device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0069] Considering that most existing methods are indirect inversion based on rock physics models, target parameters need to be obtained through intermediate parameter conversion, which will lead to error accumulation and make it difficult to meet the needs of direct characterization of pore structure parameters in deep coalbed methane reservoirs.

[0070] Based on this, an embodiment of the present invention provides a method and device for directly predicting pore parameters of deep coalbed methane reservoirs, which will be described below through examples.

[0071] The embodiment of the present invention provides a method for directly predicting pore parameters of deep coalbed methane reservoirs. Figure 1 As shown, the method includes the following steps:

[0072] Step S101: Acquire priori data of pore parameters at the target coalbed methane reservoir location and actually observed seismic data.

[0073] In this step, the pore parameters include porosity, water saturation, and pore aspect ratio. The prior data of porosity and water saturation can be directly obtained from well logging data, while the pore aspect ratio cannot be directly obtained from well logging data.

[0074] In one feasible embodiment, the pore aspect ratio can be obtained by Bayesian inversion, and the specific process is as follows:

[0075] Step S101A: Obtain the actually observed seismic P-wave and S-wave velocities and the P-wave and S-wave velocities predicted by the rock physics model.

[0076] Step S101B: Based on the actually observed seismic P-wave and S-wave velocities and the P-wave and S-wave velocities predicted by the rock physics model, Bayesian inversion is used to obtain:

[0077] (1);

[0078] in: is the pore aspect ratio to be inverted; and are the measured P-wave and S-wave velocities, respectively, obtained from well logging data; and are the P-wave and S-wave velocities predicted by the rock physics model, respectively.

[0079] Step S102: generating simulated seismic data based on the priori data of pore parameters and the pre-constructed linear mapping relationship between seismic data and pore parameters.

[0080] In an embodiment of the present invention, the process of constructing the linear mapping relationship between seismic data and pore parameters includes:

[0081] Step 1: Obtain the elastic parameters of various mineral components.

[0082] In this step, based on the regional sedimentary background and deep coal seam mineral data, the deep coalbed methane reservoir mineral composition was simplified to organic matter, clay, and brittle minerals. The initial clay content was calculated using the resistivity extreme value method, and the organic matter content was calculated using the resistivity-porosity overlap method. Resistivity and porosity were obtained from well logging data. The remaining components were considered brittle minerals.

[0083] Step 2: Construct a rock physics model of the deep coalbed methane reservoir based on the elastic parameters of multiple mineral components.

[0084] like Figure 2 As shown in Figure 2, the specific construction process of the rock physics model is as follows:

[0085] Step S102A: The construction of the coal matrix is ​​the foundation of rock physics modeling. The coal matrix is ​​considered to be a mixture of organic matter, clay, and brittle minerals. The bulk modulus and shear modulus of the coal matrix are averaged using VRH (VRH refers to estimating the effective elastic modulus of a material by combining the predictions of the Voigt and Reus models):

[0086] (2);

[0087] (3);

[0088] in, For the Bulk modulus of the mineral components; For the Shear modulus of the mineral components; is the Voigt upper limit of the equivalent bulk modulus of N mineral components; is the Voigt upper limit of the equivalent shear modulus of N mineral components; is the Ruess lower limit of the equivalent bulk modulus of N mineral components; is the Ruess lower limit of the equivalent shear modulus of N mineral components; is the Hill average of the equivalent bulk moduli of N mineral components; is the Hill average of the equivalent shear modulus of N mineral components; For the Volume fraction of the substance mineral; N=3.

[0089] Step S102B: Using the effective medium theory (NIA), the porous medium is added to the coal matrix, and the volume and shear moduli of the dry coal rock skeleton containing the porous medium can be expressed as:

[0090] , (4);

[0091] , (5);

[0092] in, K 0 and G 0 are the bulk modulus and shear modulus of the coal matrix, respectively; is the bulk modulus of the dry coal rock skeleton; is the shear modulus of the dry coal rock skeleton; ν 0 is Poisson's ratio, e is the crack density, and its expression is:

[0093] (6);

[0094] in, is the porosity; is the pore aspect ratio.

[0095] Step S102C: Assuming the pore fluid is a mixture of water and gas, the WOOD model is used to calculate the bulk modulus of the pore fluid. The WOOD model is a method for calculating the effective bulk modulus of a multiphase fluid mixture. It estimates the effective bulk modulus of the mixture based on the volume fraction and bulk modulus of each phase:

[0096] (7);

[0097] in, is the equivalent bulk modulus of the pore fluid; is the liquid bulk modulus, set to 2.25 GPa; is the bulk modulus of air, set to 0.016 GPa, is the water saturation.

[0098] The Gassmann equation is used to add pore fluid to the dry coal rock skeleton to obtain the bulk modulus and shear modulus of the rock physics model containing pore fluid:

[0099] (8);

[0100] (9);

[0101] The meaning of each parameter is the same as that of the above parameters, and will not be repeated here.

[0102] Step 3: Predict the P-wave reflection coefficient based on the rock physics model.

[0103] In one feasible embodiment, predicting the P-wave reflection coefficient based on the rock physics model includes:

[0104] Obtain the volume fraction and density of each mineral component, as well as the elastic parameters of the rock physics model; the elastic parameters include bulk modulus and shear modulus.

[0105] Determine the equivalent density of the rock physics model based on the volume fraction and density of each mineral component;

[0106] (10);

[0107] in, For the The density of each mineral component; is the total amount of mineral components; The first Volume fraction of each component;

[0108] Prediction of seismic P- and S-wave velocities based on elastic parameters and equivalent density of rock physics models:

[0109] (11); (12);

[0110] in, is the predicted longitudinal wave velocity; To predict the shear wave velocity; is the equivalent density of saturated coal rock; is the bulk modulus of the rock physics model; is the shear modulus of the rock physics model;

[0111] Based on the predicted P-wave and S-wave velocities and the equivalent density of saturated coal rock, the P-wave reflection coefficient constrained by the rock physics model is predicted by the following formula:

[0112] (13);

[0113] in, 、 and is the average value of the longitudinal and shear wave velocities and densities on both sides of the coal seam interface; 、 、 is the difference in P-wave and S-wave velocity and density on both sides of the coal seam interface; is the ratio of shear wave velocity to longitudinal wave velocity.

[0114] Step 4: Determine the linear mapping relationship between the longitudinal wave reflection coefficient and the pore parameters based on the predicted longitudinal wave reflection coefficient.

[0115] Step 5: Based on the linear mapping relationship between the longitudinal wave reflection coefficient and the pore parameters, and the seismic wavelet data, a linear mapping relationship between the seismic data and the pore parameters is constructed:

[0116] (14);

[0117] in, , is the pore parameter; , and are water saturation, porosity, and pore aspect ratio, respectively; For earthquake data; The Toeplitz matrix constructed for the seismic wavelet; is the mapping relationship between the longitudinal wave reflection coefficient and the pore parameters; Represents random error.

[0118] The mapping relationship in the above equation is nonlinear, and during the inversion process, it can fall into local optimal solutions, leading to errors. Therefore, linearization is necessary. To directly invert the target reservoir pore parameters from seismic data, a seismic rock physics forward modeling operator must be established from pore parameters to seismic data.

[0119] Specifically, based on the prior data of pore parameters and the linearized mapping relationship between pre-constructed seismic data and pore parameters, the simulated seismic data are generated by simulating:

[0120] Step A: Construct a linear forward operator of the P-wave reflection coefficient and pore parameters based on the prior data of pore parameters;

[0121] In one example, the linear forward operator is obtained using the linear method of Taylor expansion:

[0122] (15);

[0123] in, , is the pore parameter; , and are water saturation, porosity, and pore aspect ratio, respectively; is the longitudinal wave reflection coefficient.

[0124] Based on the longitudinal wave reflection coefficient obtained above, substituting it into formula 15, the specific expression is:

[0125] (16);

[0126] (17);

[0127] (18);

[0128] in, V p、 V s and ρThe relationship with reflectivity level can be approximately expressed as (Gholami, 2015):

[0129] (19);

[0130] (20);

[0131] (twenty one);

[0132] Finally, we can get:

[0133] (twenty two);

[0134] (twenty three);

[0135] (twenty four).

[0136] As shown in Figure 3 (a), the forward modeling results of the nonlinear reflection coefficient are shown; Figure 3 (b) shows the forward modeling results of the linear reflection coefficient based on the constraints of the rock physics model. It can be seen that for models with different degrees of gradual change, the forward modeling results of the linear reflection coefficient formula based on the constraints of the rock physics model are closer to the real model than the nonlinear reflection coefficient obtained by forward modeling based on the conventional reflection coefficient formula.

[0137] As shown in Figure 4 (a), the nonlinear reflection coefficient forward modeling result obtained based on the conventional reflection coefficient formula with a random error of 3% is shown; Figure 4 (b) is the linear reflection coefficient forward modeling result based on the rock physics model constraint with a random error of 3%.

[0138] Among them, the horizontal axes of Figures 3(a), 3(b), 4(a) and 4(b) are the normalized longitudinal wave reflection coefficients after normalization, and the vertical axes are time.

[0139] Step B: Based on the linear forward operator, the mapping relationship between the pre-constructed seismic data and the pore parameters is linearized to simulate and generate linearized simulated seismic data:

[0140] (25);

[0141] in, , is the pore parameter; , and are water saturation, porosity, and pore aspect ratio, respectively; is the linearized simulated seismic data; The Toeplitz matrix constructed for the seismic wavelet; is the target parameter approximation point of the first-order Taylor approximation; is a constant; is a random error.

[0142] Based on the forward operator finally obtained above, Equation 25 can be further converted into:

[0143] (26);

[0144] The parameters in the formula refer to the same parameters as above and will not be repeated here.

[0145] Step S103: Based on the actually observed seismic data, the simulated seismic data and the prior data of the pore parameters, an iterative inversion is performed using a pre-constructed Bayesian inversion objective function to determine the minimum objective function value.

[0146] In this step, we first construct a Bayesian framework. Under this Bayesian framework, solving the inverse problem is equivalent to estimating the maximum posterior probability of the inversion target pore parameters given the known seismic observation data. The posterior probability of the inversion target parameters with respect to the observation data is:

[0147] (27);

[0148] in, Represents known prior information b In the case of m The posterior probability density function of .

[0149] is the likelihood function, which reflects the matching degree between the observed data and the parameters to be inverted.

[0150] No observation data b When the inversion parameters m The prior distribution of .

[0151] Appropriate selection of prior data in seismic inversion within a Bayesian framework can improve the stability and accuracy of inversion results. Given the differences in lithofacies within the reservoirs to be inverted, the traditional Gaussian distribution function struggles to effectively describe the prior distribution. In this embodiment of the present invention, a Gaussian mixture model is employed to describe the prior distribution of the inversion target parameters.

[0152] (28);

[0153] in, , and are the expectation, covariance and weight of the i-th Gaussian distribution respectively; C is the number of Gaussian distributions. Based on the well logging data and the inversion results of the well logging data, the Gaussian mixture model of the inversion target parameters is calculated using the expectation maximization algorithm.

[0154] In an embodiment of the present invention, the Bayesian inversion objective function is:

[0155] (29);

[0156] in, is the posterior expectation; is the actual earthquake data; is the linearized simulated seismic data; is the pore parameter to be predicted; is the prior expectation of the prior data of the pore parameters; is the covariance of the prior data of the pore parameters; is the number of Gaussian distributions of the prior data; For the The weight factor of the Gaussian distribution.

[0157] In one possible implementation, Figure 5 , which shows a flow chart of iterative inversion. In the embodiment of the present invention, a simulated annealing algorithm can be used to optimize the Bayesian inversion objective function to find the minimum objective function value.

[0158] exist Figure 5 In this method, the prior distribution is obtained using logging data; the linear reflection coefficient is predicted using a rock physics model, and linearized simulated seismic data is generated based on the linear reflection coefficient; and the pre-stack seismic data is used as the actual seismic data.

[0159] The three data are input into the Bayesian inversion objective function respectively to perform prestack AVO (Amplitude Variation with Offset) Bayesian inversion.

[0160] Calculate the posterior expectation at each iterative inversion , and judge whether the error of the posterior expectation is less than the preset threshold. If it is less than, stop the iteration and output the final pore parameter inversion result, otherwise continue the iteration.

[0161] Step S104: directly determining the target pore parameters based on the simulated seismic data corresponding to the minimum objective function value and the linear mapping relationship between the seismic data and the pore parameters.

[0162] In this step, after determining the minimum objective function value After that, the corresponding simulated seismic data can be determined. Based on the above formula 25 or 26, the target pore parameters can be directly determined and determined as the optimal prediction results.

[0163] Figures 6(a), 6(b), and 6(c) show the seismic inversion results along the horizontal line Xline = 0 to 400, respectively. Figure 6(a) shows the inversion profile of porosity; Figure 6(b) shows the inversion profile of water saturation Sw; and Figure 6(c) shows the inversion profile of pore aspect ratio Asp. These figures reveal the spatial distribution characteristics of the target coal seam pore parameters. The reservoir has good internal continuity, and the porosity, pore aspect ratio, and water saturation of deep coalbed methane reservoirs exhibit significant spatial variation. The solid black lines in the figures mark the well logging locations and inversion results of the target parameters.

[0164] Through the above embodiments, it can be understood that the present invention can directly invert the pore parameters through the pre-constructed mapping relationship between seismic data and pore parameters. After determining the minimum objective function value, the optimal target pore parameters can be directly obtained through the mapping relationship, which reduces the cumulative error caused by the intermediate conversion and greatly improves the prediction accuracy of the pore parameters.

[0165] However, existing technologies (such as patent CN119882093A) use indirect inversion. After obtaining the minimum objective function value, multiple intermediate conversions using the parameters of the rock physics model are required to finally determine the pore parameters. Each conversion will produce a certain error, resulting in error accumulation and low inversion accuracy.

[0166] Based on the same inventive concept, a device for directly predicting pore parameters of deep coalbed methane reservoirs is provided, such as Figure 7 As shown, the device includes:

[0167] An acquisition unit 701 is used to acquire prior data of pore parameters at a target coalbed methane reservoir location and actual observed seismic data;

[0168] A generating unit 702 is configured to simulate and generate simulated seismic data based on prior data of pore parameters and a pre-constructed linear mapping relationship between seismic data and pore parameters;

[0169] An inversion unit 703 is configured to perform iterative inversion based on the actual observed seismic data, the simulated seismic data, and the prior data of the pore parameters using a pre-constructed Bayesian inversion objective function to determine a minimum objective function value;

[0170] The determination unit 704 is configured to directly determine the target pore parameters based on the simulated seismic data corresponding to the minimum objective function value and the mapping linear relationship between the seismic data and the pore parameters.

[0171] Based on the same technical concept, an embodiment of the present invention further provides an electronic device, such as Figure 8As shown, it includes a processor 801 , a communication interface 802 , a memory 803 and a communication bus 804 , wherein the processor 801 , the communication interface 802 and the memory 803 communicate with each other via the communication bus 804 .

[0172] Memory 803, used for storing computer programs;

[0173] The processor 801 is configured to implement the steps of the method for directly predicting pore parameters of deep coalbed methane reservoirs when executing the program stored in the memory 803 .

[0174] The communication bus mentioned in the electronic devices mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This communication bus can be divided into address buses, data buses, control buses, etc. For ease of illustration, only a single thick line is used in the figure, but this does not mean that there is only one bus or only one type of bus.

[0175] The communication interface is used for communication between the above electronic device and other devices.

[0176] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage. Alternatively, the memory may be at least one storage device located away from the processor.

[0177] The above-mentioned processor 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 gate or transistor logic devices, and discrete hardware components.

[0178] The computer program product for directly predicting the pore parameters of deep coalbed methane reservoirs provided in an embodiment of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment and will not be repeated here.

[0179] The device for directly predicting the pore parameters of deep coalbed methane reservoirs provided in the embodiment of the present invention can be specific hardware on the device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in the embodiment of the present invention are the same as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding content 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.

[0180] In the embodiments provided by the present invention, 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 merely a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, the indirect coupling or communication connection of the device or unit may be electrical, mechanical or other forms.

[0181] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0182] In addition, each functional unit in the embodiment provided by the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0183] If the functions are implemented as 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 the present invention, or the portion that contributes to the prior art, or a portion of the 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 can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0184] 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.

[0185] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. However, such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. They should all be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for directly predicting pore parameters of deep coalbed methane reservoirs, characterized in that: The method comprises: Obtaining prior data on pore parameters and actual observed seismic data at the target coalbed methane reservoir location; Based on the linear mapping relationship between the longitudinal wave reflection coefficient and the pore parameters, the linear mapping relationship between the seismic data and the pore parameters is constructed as well as the seismic wavelet data; Based on the prior data of the pore parameters, a linear forward operator of the longitudinal wave reflection coefficient and the pore parameters is constructed: Based on the linear forward operator, a mapping relationship between pre-constructed seismic data and pore parameters is linearized to simulate and generate linearized simulated seismic data; Based on the actual observed seismic data, simulated seismic data and prior data of pore parameters, an iterative inversion is performed using a pre-constructed Bayesian inversion objective function to determine the minimum objective function value; The target pore parameters are directly determined based on the simulated seismic data corresponding to the minimum objective function value and the linear mapping relationship between the seismic data and the pore parameters.

2. The method according to claim 1, characterized in that The process of constructing the linear mapping relationship between the longitudinal wave reflection coefficient and the pore parameters includes: Obtaining elastic parameters of multiple mineral components, the multiple mineral components including organic matter, clay, and brittle minerals; constructing a rock physics model of a deep coalbed methane reservoir based on the elastic parameters of the plurality of mineral components; predicting a longitudinal wave reflection coefficient based on the rock physics model; Determining a linear mapping relationship between a longitudinal wave reflection coefficient and a pore parameter according to the predicted longitudinal wave reflection coefficient; Linear mapping relationship between seismic data and pore parameters: in, , is the pore parameter; , and are water saturation, porosity, and pore aspect ratio, respectively; For earthquake data; The Toeplitz matrix constructed for the seismic wavelet; is the linear mapping relationship between the longitudinal wave reflection coefficient and the pore parameters; Represents random error.

3. The method according to claim 2, characterized in that The predicting of the longitudinal wave reflection coefficient based on the rock physics model includes: Obtaining the volume fraction and density of each mineral component, as well as the elastic parameters of the rock physics model; the elastic parameters include bulk modulus and shear modulus; Determining an equivalent density of a rock physics model based on the volume fraction and density of each mineral component; in, For the The density of each mineral component; is the total amount of mineral components; The first Volume fraction of each component; Predict seismic P- and S-wave velocities based on the elastic parameters and equivalent density of the rock physics model: in, is the predicted longitudinal wave velocity; To predict the shear wave velocity; is the equivalent density of saturated coal rock; is the bulk modulus of the rock physics model; is the shear modulus of the rock physics model; Based on the predicted longitudinal and shear wave velocities and the equivalent density of saturated coal rock, the longitudinal wave reflection coefficient of the rock physics model is predicted by the following formula: in, 、 and is the average value of the longitudinal and shear wave velocities and densities on both sides of the coal seam interface; 、 、 is the difference in P-wave and S-wave velocity and density on both sides of the coal seam interface; is the ratio of shear wave velocity to longitudinal wave velocity.

4. The method according to claim 2, characterized in that The linearized simulated seismic data can be expressed as: in, , is the pore parameter; , and are water saturation, porosity, and pore aspect ratio, respectively; is the linearized simulated seismic data; is the Toeplitz matrix constructed for the seismic wavelet; is the target parameter approximation point of the first-order Taylor approximation; is a linear forward operator; is a constant; is a random error.

5. The method according to claim 4, characterized in that The linear forward operator is obtained by using the linear method of Taylor expansion: in, , is the pore parameter; , and are water saturation, porosity, and pore aspect ratio, respectively; is the longitudinal wave reflection coefficient.

6. The method according to claim 1, characterized in that The Bayesian inversion objective function is: in, is the posterior expectation; is the actual earthquake data; is the linearized simulated seismic data; is the pore parameter; is the prior expectation of the prior data of the pore parameters; is the covariance of the prior data of the pore parameters; is the number of Gaussian distributions of the prior data; For the The weight factor of the Gaussian distribution.

7. The method according to claim 1, characterized in that The priori data of the pore parameters include the pore aspect ratio, and the process of determining the pore aspect ratio is as follows: Obtain the actual observed seismic P- and S-wave velocities and the P- and S-wave velocities predicted by the rock physics model; Based on the actual observed seismic P- and S-wave velocities and the P- and S-wave velocities predicted by the rock physics model, the following is obtained using Bayesian inversion: in: is the pore aspect ratio; and are the measured P-wave and S-wave velocities, respectively, obtained from well logging data; and are the P-wave and S-wave velocities predicted by the rock physics model, respectively.

8. A device for directly predicting pore parameters of deep coalbed methane reservoirs using the method for directly predicting pore parameters of deep coalbed methane reservoirs according to any one of claims 1 to 7, characterized in that: The device comprises: An acquisition unit, used to acquire prior data of pore parameters at a target coalbed methane reservoir location and actual observed seismic data; A generating unit, configured to simulate and generate simulated seismic data based on the prior data of the pore parameters and a pre-constructed linear mapping relationship between the seismic data and the pore parameters; An inversion unit is used to perform iterative inversion based on actual observed seismic data, simulated seismic data, and prior data of pore parameters using a pre-built Bayesian inversion objective function to determine the minimum objective function value; A determination unit is used to directly determine the target pore parameters based on the simulated seismic data corresponding to the minimum objective function value and a mapping relationship between the seismic data and the pore parameters.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; The memory is used to store computer programs; The processor is configured to implement the method steps described in any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps according to any one of claims 1 to 7 are implemented.

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