Direct prediction method and device for pore parameters of deep coal bed gas reservoir

Abstract

The invention provides a deep coal bed gas reservoir pore parameter direct prediction method and device, and belongs to the technical field of coal bed development, and the method comprises the steps: obtaining the prior data of pore parameters at a target coal bed gas reservoir position, and the actual observation seismic data; simulating and generating simulated seismic data based on the prior data of the pore parameters and a pre-constructed linear mapping relation between the seismic data and the pore parameters; based on the actually observed seismic data, the simulated seismic data and the prior data of the pore parameters, performing iterative inversion by using a pre-constructed Bayesian inversion objective function to determine a minimum objective function value; and directly determining a target pore parameter based on the simulated seismic data corresponding to the minimum target function value and the mapping relation between the seismic data and the pore parameter. According to the method, through the pre-constructed mapping relation between the seismic data and the pore parameters, inversion is directly performed on the pore parameters, accumulative errors generated by intermediate conversion are reduced, and the prediction accuracy of the pore parameters is greatly improved.

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 a deep coalbed methane reservoir. Background Art

[0002] The pore structure of a deep coalbed methane reservoir is a key parameter for predicting engineering sweet spots of a coalbed methane reservoir and an important reference for evaluating coalbed methane drainage and gas production. Existing methods are mostly indirect inversions based on rock physics models, which require conversion through intermediate parameters to obtain target parameters, resulting in error accumulation and making it difficult to meet the direct characterization requirements of pore structure parameters of deep coalbed methane reservoirs. Summary of the Invention

[0003] In view of this, an object of the present invention is to provide a method and device for directly predicting pore parameters of a deep coalbed methane reservoir 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 includes: Obtaining prior data of pore parameters and actually observed seismic data at the position of a target coalbed methane reservoir; Based on the prior data of pore parameters and a pre-constructed linear mapping relationship between seismic data and pore parameters, simulating and generating simulated seismic data; Based on the actually observed seismic data, the simulated seismic data, and the prior data of pore parameters, using a pre-constructed Bayesian inversion objective function for iterative inversion to determine the minimum objective function value; Based on the simulated seismic data corresponding to the minimum objective function value and the linear mapping relationship between seismic data and pore parameters, directly determining the target pore parameters.

[0005] Optionally, the construction process of the linear mapping relationship between seismic data and pore parameters includes: Obtaining elastic parameters of multiple mineral components, where the multiple mineral components include organic matter, clay, and brittle minerals; Based on the elastic parameters of multiple mineral components, constructing a rock physics model of a deep coalbed methane reservoir; Based on the rock physics model, predicting the longitudinal wave reflection coefficient; According to the predicted longitudinal wave reflection coefficient, determining the linear mapping relationship between the longitudinal wave reflection coefficient and pore parameters; Based on the linear mapping relationship between the longitudinal wave reflection coefficient and pore parameters, and seismic wavelet data, constructing the linear mapping relationship between seismic data and pore parameters:

[0006] Wherein, , is the pore parameter; , and are water saturation, porosity, and pore aspect ratio, respectively; is seismic data; is a Toeplitz matrix composed of seismic wavelets; is the linear mapping relationship between the P-wave reflection coefficient and pore parameters; represents random error.

[0007] Optionally, predicting the P-wave reflection coefficient based on a rock physics model includes: Obtaining the volume fraction, density, and elastic parameters of the rock physics model for each mineral component; the elastic parameters include bulk modulus and shear modulus; Determining the equivalent density of the rock physics model based on the volume fraction and density of each mineral component;

[0008] where is the density of the th mineral component; is the total number of mineral components; is the volume fraction of the th component of the saturated coal rock; Predicting the P-wave and S-wave velocities of seismic waves based on the elastic parameters and equivalent density of the rock physics model:

[0009] where is the predicted P-wave velocity; is the predicted S-wave velocity; is the equivalent density of the saturated coal rock; is the bulk modulus of the rock physics model; is the shear modulus of the rock physics model; Predicting the P-wave reflection coefficient of the rock physics model based on the predicted P-wave and S-wave velocities and the equivalent density of the saturated coal rock through the following formula:

[0010] where , and are the average values of the P-wave and S-wave velocities and density on both sides of the coal seam interface; , , are the differences in the P-wave and S-wave velocities and density on both sides of the coal seam interface; is the S-wave to P-wave velocity ratio.

[0011] Optionally, based on the prior data of pore parameters and the linear mapping relationship between the pre-constructed seismic data and the pore parameters, simulating and generating simulated seismic data includes: Constructing a linear forward operator of the P-wave reflection coefficient and the pore parameters based on the prior data of the pore parameters: Based on the linear forward operator, linearizing the mapping relationship between the pre-constructed seismic data and the pore parameters to simulate and generate linearized simulated seismic data:

[0012] Wherein, , is the pore parameter; , and are the water saturation, porosity, and pore aspect ratio respectively; is the linearized simulated seismic data; is the Toeplitz matrix composed of seismic wavelets; is the approximate point of the target parameter of a section of Taylor wire; is a constant; is a random error.

[0013] Optionally, the linear forward operator is obtained by using the linear method of Taylor expansion:

[0014] Wherein, , is the pore parameter; , and are the water saturation, porosity, and pore aspect ratio respectively; is the P-wave reflection coefficient.

[0015] Optionally, the Bayesian inversion objective function is:

[0016] Wherein, is the actual seismic 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 parameter; is the covariance of the prior data of the pore parameter; is the number of Gaussian distributions of the prior data; is the th weight factor of the Gaussian distribution.

[0017] Optionally, the prior data of the pore parameter includes the pore aspect ratio, and the determination process of the pore aspect ratio is: Obtain the actual observed P-wave and S-wave velocities of the earthquake and the P-wave and S-wave velocities predicted by the rock physics model; Based on the actual observed P-wave and S-wave velocities of the earthquake and the P-wave and S-wave velocities predicted by the rock physics model, use Bayesian inversion to obtain:

[0018] Wherein: 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 physical rock model respectively.

[0019] In a second aspect, there is provided a device for directly predicting pore parameters of a deep coalbed methane reservoir, the device comprising: An acquisition unit for acquiring prior data of pore parameters and actual observed seismic data at the position of the target coalbed methane reservoir; A generation unit for simulating and generating simulated seismic data based on the prior data of pore parameters and a pre-constructed linear mapping relationship between seismic data and pore parameters; An inversion unit for performing iterative inversion using a pre-constructed Bayesian inversion objective function based on the actual observed seismic data, the simulated seismic data, and the prior data of pore parameters to determine the minimum objective function value; A determination unit for 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 seismic data and pore parameters.

[0020] In a third aspect, there is provided an electronic device, 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 through the communication bus; The memory is used for storing a computer program; When the processor executes the program stored in the memory, it implements the method steps of any one of the first aspect.

[0021] In a fourth aspect, there is provided a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, it implements the method steps of any one of the first aspect.

[0022] A method and device for directly predicting pore parameters of a deep coalbed methane reservoir provided by an embodiment of the present invention obtain prior data of pore parameters at the position of a target coalbed methane reservoir and actually observed seismic data; based on the prior data of pore parameters and a pre-constructed linear mapping relationship between seismic data and pore parameters, simulated seismic data is generated by simulation; based on the actually observed seismic data, the simulated seismic data, and the prior data of pore parameters, iterative inversion is performed using a pre-constructed Bayesian inversion objective function to determine the minimum objective function value; based on the simulated seismic data corresponding to the minimum objective function value and the mapping relationship between seismic data and pore parameters, the target pore parameters are directly determined. By using the pre-constructed linear mapping relationship between seismic data and pore parameters, the present invention can directly invert the pore parameters. After determining the minimum objective function value, the optimal target pore parameters can be directly obtained through this linear mapping relationship, reducing the cumulative error caused by intermediate conversion and greatly improving the prediction accuracy of pore parameters.

[0023] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, provides a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 Shows a flowchart of a method for directly predicting pore parameters of a deep coalbed methane reservoir provided by an embodiment of the present invention; Figure 2 Shows a schematic diagram of the construction process of a rock physics model of a deep coalbed methane reservoir provided by an embodiment of the present invention; FIG. 3(a) shows the forward modeling result of the non-linear reflection coefficient; FIG. 3(b) shows the forward modeling result of the linear reflection coefficient constrained by the rock physics model; FIG. 4(a) shows the forward modeling result of the non-linear reflection coefficient with a 3% random error; FIG. 4(b) shows the forward modeling result of the linear reflection coefficient constrained by the rock physics model with a 3% random error; Figure 5 Shows a schematic diagram of the process of using Bayesian inversion in an embodiment of the present invention; FIG. 6(a) shows the inversion profile of the porosity in an embodiment of the present invention; Figure 6(b) shows the inversion profile of the water saturation Sw of the embodiment of the present invention; Figure 6(c) shows the inversion profile of the pore aspect ratio Asp of the embodiment of the present invention; Figure 7 shows a schematic structural diagram of a device for directly predicting pore parameters of a deep coalbed methane reservoir provided by the embodiment of the present invention; Figure 8 shows a schematic structural diagram of an electronic device provided by the embodiment of the present invention. Detailed implementation manners

[0026] To make the objectives, 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 with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Usually, the components of the embodiments of the present invention described and illustrated 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 accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0027] Considering that most of the existing methods are indirect inversions based on rock physics models, and intermediate parameter conversion is required to obtain the target parameters, which will lead to error accumulation and it is difficult to meet the direct characterization requirements of the pore structure parameters of deep coalbed methane reservoirs.

[0028] Based on this, the embodiments of the present invention provide a method and a device for directly predicting pore parameters of a deep coalbed methane reservoir, which will be described below through embodiments.

[0029] The embodiments of the present invention provide a method for directly predicting pore parameters of a deep coalbed methane reservoir, as Figure 1 shown, the method includes the following steps: Step S101: Obtain prior data of pore parameters and actually observed seismic data at the position of the target coalbed methane reservoir.

[0030] In this step, the pore parameters include porosity, water saturation, and pore aspect ratio. Among them, the prior data of porosity and water saturation can be directly obtained according to well logging data, while the pore aspect ratio cannot be directly obtained according to well logging data.

[0031] In a feasible implementation manner, the pore aspect ratio can be obtained by Bayesian inversion, and the specific process is as follows: Step S101A: Obtain the actual observed P-wave and S-wave velocities of the earthquake and the P-wave and S-wave velocities predicted by the rock physics model.

[0032] Step S101B: Based on the actual observed P-wave and S-wave velocities of the earthquake and the P-wave and S-wave velocities predicted by the rock physics model, use Bayesian inversion to obtain: (1); Where: is the aspect ratio of pores 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 physical rock model respectively.

[0033] Step S102: Based on the prior data of pore parameters and the pre-constructed linear mapping relationship between seismic data and pore parameters, simulate and generate simulated seismic data.

[0034] In the embodiment of the present invention, the construction process of the linear mapping relationship between seismic data and pore parameters includes: The first step: Obtain the elastic parameters of various mineral components. In this step, according to the regional sedimentary background and the relevant data of deep coal seam minerals, the mineral components of the deep coal seam gas reservoir are simplified into organic matter, clay and brittle minerals; the resistivity extreme value method is used to obtain the initial clay content; the resistivity porosity overlap method is used to obtain the organic matter content; among them, the resistivity and porosity come from well logging data. The rest is regarded as brittle minerals.

[0035] The second step: Based on the elastic parameters of various mineral components, construct a rock physics model of the deep coal seam gas reservoir.

[0036] As Figure 2 shown, the specific construction process of this rock physics model is as follows: Step S102A: The construction of the coal matrix is the basis of rock physics modeling. The coal matrix is regarded as a mixture composed of organic matter, clay and brittle minerals. The volume modulus and shear modulus of the coal matrix are obtained by VRH (VRH refers to estimating the effective elastic modulus of materials by combining the predicted values of the Voigt and Reus models): (2); (3); Where, is the volume modulus of the th mineral component; is the shear modulus of the th mineral component; is the Voigt upper bound of the equivalent bulk modulus of N mineral components; is the Voigt upper bound of the equivalent shear modulus of N mineral components; is the Ruess lower bound of the equivalent bulk modulus of N mineral components; is the Ruess lower bound of the equivalent shear modulus of N mineral components; is the Hill average value of the equivalent bulk modulus of N mineral components; is the Hill average value of the equivalent shear modulus of N mineral components; is the volume fraction of the

[0037] Step S102B: Using the effective medium theory (NIA), add the pore medium to the coal matrix, and the volume and shear moduli of the dry coal-rock skeleton containing the pore medium can be expressed as: , (4); , (5); where 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 the Poisson's ratio, e is the fracture density, and its expression is: (6); where is the porosity; is the pore aspect ratio.

[0038] Step S102C: Assume that the pore fluid is a mixed fluid of water and gas, and use the WOOD model to calculate the bulk modulus of the pore fluid. Among them, 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 mixed fluid based on the volume fractions of each phase fluid and their respective bulk moduli: (7); where is the equivalent bulk modulus of the pore fluid; is the liquid bulk modulus, set to 2.25 GPa; is the air bulk modulus, set to 0.016 GPa, is the water saturation.

[0039] The Gassmann equation is used to add pore fluid to the dry coal rock dry skeleton to obtain the bulk modulus and shear modulus of the rock physical model containing pore fluid: (8); (9); Among them, the meanings of each parameter are the same as those of the above-mentioned same parameters and will not be elaborated here.

[0040] Step 3: Predict the P-wave reflection coefficient based on the rock physical model.

[0041] In a feasible implementation, predicting the P-wave reflection coefficient based on the rock physical model includes: Obtain the volume fraction, density, and elastic parameters of the rock physical model of each mineral component; the elastic parameters include the bulk modulus and the shear modulus.

[0042] Determine the equivalent density of the rock physical model based on the volume fraction and density of each mineral component; (10); Among them, is the density of the th mineral component; is the total number of mineral components; is the volume fraction of the th component of the saturated coal rock; Predict the P-wave and S-wave velocities of the seismic waves based on the elastic parameters and equivalent density of the rock physical model: (11); (12); Among them, is the predicted P-wave velocity; is the predicted S-wave velocity; is the equivalent density of the saturated coal rock; is the bulk modulus of the rock physical model; is the shear modulus of the rock physical model; Predict the P-wave reflection coefficient constrained by the rock physical model based on the predicted P-wave and S-wave velocities and the equivalent density of the saturated coal rock through the following formula: (13); Among them, , and are the average values of the P-wave and S-wave velocities and density on both sides of the coal seam interface; , , are the differences in the P-wave and S-wave velocities and density on both sides of the coal seam interface; is the shear wave to compressional wave velocity ratio.

[0043] Step 4: Determine the linear mapping relationship between the compressional wave reflection coefficient and the pore parameters according to the predicted compressional wave reflection coefficient.

[0044] Step 5: Based on the linear mapping relationship between the compressional wave reflection coefficient and the pore parameters, and the seismic wavelet data, construct the linear mapping relationship between the seismic data and the pore parameters: (14); where , is the pore parameter; , and are the water saturation, porosity and pore aspect ratio respectively; is the seismic data; is the Toeplitz matrix composed of seismic wavelets; is the mapping relationship between the compressional wave reflection coefficient and the pore parameters; represents the random error.

[0045] The mapping relationship in the above formula is a non - linear mapping relationship, which will fall into a local optimal solution during the inversion process, thus generating errors. Therefore, linearization processing is required. To directly invert the target pore parameters of the reservoir from seismic data, a seismic petrophysical forward operator from pore parameters to seismic data needs to be established.

[0046] Specifically, based on the prior data of pore parameters and the pre - constructed linearized mapping relationship between seismic data and pore parameters, the simulated seismic data is generated, including: Step A: Construct a linear forward operator of the compressional wave reflection coefficient and the pore parameters based on the prior data of pore parameters; In an example, the linear forward operator is obtained by using the linear method of Taylor expansion: (15); where , is the pore parameter; , and are the water saturation, porosity and pore aspect ratio respectively; is the compressional wave reflection coefficient.

[0047] Based on the above - obtained compressional wave reflection coefficient, substituting it into formula 15, the specific expression is: (16); (17); (18); Among them, V p, V s, and ρ The relationship with the reflectivity level can be approximately expressed as (Gholami, 2015): (19); (20); (21); Finally, we can obtain: (22); (23); (24).

[0048] As shown in Figure 3(a), it is the forward modeling result of the nonlinear reflection coefficient; Figure 3(b) is the forward modeling result of the linear reflection coefficient constrained by the rock physics model. It can be seen that for different degrees of gradual change models, compared with the nonlinear reflection coefficient obtained by forward modeling based on the conventional reflection coefficient formula, the forward modeling result of the linear reflection coefficient formula constrained by the rock physics model is closer to the true model.

[0049] As shown in Figure 4(a), it is the forward modeling result of the nonlinear reflection coefficient obtained by forward modeling based on the conventional reflection coefficient formula with 3% random error; Figure 4(b) is the forward modeling result of the linear reflection coefficient constrained by the rock physics model with 3% random error.

[0050] Among them, the abscissas of Figure 3(a), Figure 3(b), Figure 4(a), and Figure 4(b) are all the normalized P-wave reflection coefficients after normalization processing, and the ordinates are time.

[0051] Step B: Based on the linear forward operator, linearize the mapping relationship between the pre-constructed seismic data and pore parameters to simulate and generate linearized simulated seismic data: (25); Among them, , is the pore parameter; , and are the water saturation, porosity, and pore aspect ratio respectively; is the linearized simulated seismic data; is the Toeplitz matrix composed of seismic wavelets; is the approximate point of the target parameter of a section of Taylor wire; is a constant; is the random error.

[0052] Based on the finally obtained forward operator above, Equation (25) can be further transformed into: (26); For each parameter in the equation, refer to the same parameters above and will not be elaborated here.

[0053] Step S103: Based on the actually observed seismic data, simulated seismic data, and prior data of pore parameters, use the pre-constructed Bayesian inversion objective function for iterative inversion to determine the minimum objective function value.

[0054] In this step, first construct a Bayesian framework. Under the Bayesian framework, solving the inverse problem is equivalent to estimating the maximum a posteriori probability of the inversion target pore parameters given the known seismic observation data. The posterior probability of the inversion target parameter with respect to the observation data is: (27); Where, represents the posterior probability density function of the parameter to be inverted b under the condition of known prior information m .

[0055] is the likelihood function, which reflects the matching degree between the observation data and the parameter to be inverted.

[0056] is the prior distribution of the parameter to be inverted b when there is no observation data m .

[0057] In seismic inversion under the Bayesian framework, appropriate selection of prior data can improve the stability and accuracy of the inversion results. Considering the differences in the reservoir lithofacies to be inverted, the traditional Gaussian distribution function is difficult to effectively describe the prior distribution. In the embodiments of the present invention, a Gaussian mixture model is used to describe the prior distribution of the inversion target parameters.

[0058] (28); Where, , and are the mean, covariance, and weight of the i-th Gaussian distribution respectively; C is the number of Gaussian distributions. According to the logging data and the inversion results of the logging data, the Gaussian mixture model of the inversion target parameters is calculated through the expectation maximization algorithm.

[0059] In the embodiments of the present invention, the Bayesian inversion objective function is: (29); Where, is the posterior expectation; is the actual seismic 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 parameter; is the covariance of the prior data of the pore parameter; is the number of Gaussian distributions of the prior data; is the weight factor of the \(i\)-th Gaussian distribution.

[0060] In a feasible embodiment, as Figure 5 shown, a flowchart of the iterative inversion is shown. 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.

[0061] In Figure 5 , the prior distribution is obtained by using well logging data; the linear reflection coefficient is predicted by using a rock physics model, and the linearized simulated seismic data is generated based on the linear reflection coefficient; the prestack seismic data is used as the actual seismic data.

[0062] The three data are respectively input into the Bayesian inversion objective function for prestack AVO (Amplitude Variation with Offset) Bayesian inversion.

[0063] The posterior expectation is calculated during each iterative inversion, and it is determined whether the error of the posterior expectation is less than a preset threshold. If it is less, the iterative inversion is stopped and the final inversion result of the pore parameter is output; otherwise, the iterative inversion continues.

[0064] Step S104: Directly determine the target pore parameter 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 parameter.

[0065] In this step, after the minimum objective function value is determined, the corresponding simulated seismic data can be determined, and the target pore parameter can be directly determined based on Equation (25) or (26) above, and the target pore parameter is determined as the optimal prediction result.

[0066] As shown in Figures 6(a), 6(b), and 6(c), the seismic inversion result profiles along the transverse line Xline = 0 - 400 are respectively given. Figure 6(a) is the inversion profile of porosity; Figure 6(b) is the inversion profile of water saturation Sw; Figure 6(c) is the inversion profile of pore aspect ratio Asp. This figure reveals the spatial distribution characteristics of the pore parameters of the target coal seam. The internal continuity of the reservoir is relatively good, and there are obvious variation characteristics in the porosity, pore aspect ratio, and water saturation of the deep coalbed methane reservoir in space. The black solid line in the figure marks the logging positions and inversion results of the target parameters.

[0067] 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 value of the objective function, the optimal target pore parameters can be directly obtained through this mapping relationship, reducing the cumulative error caused by intermediate conversions and greatly improving the prediction accuracy of pore parameters.

[0068] However, the prior art (such as patent CN119882093A) belongs to indirect inversion. After obtaining the minimum value of the objective function, it is still necessary to use the parameters of the rock physics model for multiple intermediate conversions to finally determine the pore parameters. Each conversion will generate a certain error, resulting in error accumulation and low inversion accuracy.

[0069] Based on the same inventive concept, a device for directly predicting the pore parameters of a deep coalbed methane reservoir is provided, as Figure 7 shown. The device includes: An acquisition unit 701, configured to acquire prior data of pore parameters and actually observed seismic data at the position of the target coalbed methane reservoir; A generation unit 702, configured to simulate and generate simulated seismic data based on the prior data of pore parameters and the pre - constructed linear mapping relationship between seismic data and pore parameters; An inversion unit 703, configured to perform iterative inversion using the pre - constructed Bayesian inversion objective function based on the actually observed seismic data, simulated seismic data, and prior data of pore parameters to determine the minimum value of the objective function; A determination unit 704, configured to directly determine the target pore parameters based on the simulated seismic data corresponding to the minimum value of the objective function and the linear mapping relationship between seismic data and pore parameters.

[0070] Based on the same technical concept, an embodiment of the present invention also provides an electronic device, as Figure 8 shown, including a processor 801, a communication interface 802, a memory 803, and a communication bus 804. Among them, the processor 801, the communication interface 802, and the memory 803 communicate with each other through the communication bus 804.

[0071] A memory 803 for storing a computer program; A processor 801, when executing the program stored on the memory 803, implements the steps of the method for directly predicting pore parameters of deep coalbed methane reservoirs.

[0072] The communication bus mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0073] The communication interface is used for communication between the above electronic device and other devices.

[0074] The memory may include a Random Access Memory (RAM), or may also include a Non-Volatile Memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located far from the aforementioned processor.

[0075] The above-mentioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processing (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0076] The computer program product for directly predicting pore parameters of deep coalbed methane reservoirs provided by the embodiments of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the method described in the previous method embodiments. For specific implementation, reference can be made to the method embodiments, which will not be elaborated here.

[0077] The device for directly predicting the pore parameters of deep coalbed methane reservoirs provided by the embodiments of the present invention can be specific hardware on the device, or software or firmware installed on the device, etc. The device provided by the embodiments of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference can 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 foregoing described systems, devices, and units can all refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0078] In the embodiments provided by the present invention, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are only 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 displayed or discussed couplings or direct couplings or communication connections to 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.

[0079] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or 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.

[0080] In addition, each functional unit in the embodiments provided by the present invention 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.

[0081] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0082] It should be noted that like reference numerals and letters denote like items in the following figures, and thus, once an item is defined in one figure, it will not be necessary to further define and explain it in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only for descriptive distinction and should not be construed as indicating or implying relative importance.

[0083] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present invention, used to illustrate the technical solutions of the present invention, rather than to limit it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements for some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. All should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A direct prediction method for pore parameters of deep coalbed methane reservoirs, characterized in that The method includes: Obtaining prior data of pore parameters and actually observed seismic data at the target coalbed methane reservoir location; Based on the prior data of the pore parameters and a pre - constructed linear mapping relationship between seismic data and pore parameters, simulating and generating simulated seismic data; Based on the actually observed seismic data, the simulated seismic data, and the prior data of the pore parameters, using a pre - constructed Bayesian inversion objective function for iterative inversion to determine the minimum objective function value; 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, directly determining the target pore parameters.

2. The method according to claim 1, wherein The construction process of the linear mapping relationship between seismic data and pore parameters includes: Obtaining elastic parameters of multiple mineral components, where the multiple mineral components include organic matter, clay, and brittle minerals; Based on the elastic parameters of the multiple mineral components, constructing a rock physics model of the deep coalbed methane reservoir; Based on the rock physics model, predicting the P - wave reflection coefficient; According to the predicted P - wave reflection coefficient, determining the linear mapping relationship between the P - wave reflection coefficient and the pore parameters; Based on the linear mapping relationship between the P - wave reflection coefficient and the pore parameters, and seismic wavelet data, constructing the linear mapping relationship between seismic data and pore parameters: Among them, , is the pore parameter; , and are the water saturation, porosity, and pore aspect ratio, respectively; is the seismic data; is the Toeplitz matrix composed of seismic wavelets; is the linear mapping relationship between the P-wave reflection coefficient and the pore parameter; represents the random error.

3. The method according to claim 2, wherein The predicting the P - wave reflection coefficient based on the rock physics model includes: Obtaining the volume fraction, density of each mineral component, and the elastic parameters of the rock physics model; the elastic parameters include bulk modulus and shear modulus; Based on the volume fraction and density of each mineral component, determining the equivalent density of the rock physics model; Among them, is the density of the th mineral component; is the total number of mineral components; is the volume fraction of the th component of the saturated coal rock; Based on the elastic parameters and equivalent density of the rock physics model, predicting the P - wave and S - wave velocities of the seismic waves; wherein, is the predicted P-wave velocity; is the predicted S-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 P - wave and S - wave velocities and the equivalent density of the saturated coal rock, predicting the P - wave reflection coefficient of the rock physics model through the following formula: Among them, , and are the average values of the longitudinal and transverse wave velocities and density on both sides of the coal seam interface; , , are the differences in the longitudinal and transverse wave velocities and density on both sides of the coal seam interface; is the transverse wave to longitudinal wave velocity ratio.

4. The method according to claim 2, wherein The simulating and generating simulated seismic data based on the prior data of the pore parameters and a pre - constructed linear mapping relationship between seismic data and pore parameters includes: Based on the prior data of the pore parameters, constructing a linear forward operator between the P - wave reflection coefficient and the pore parameters; Based on the linear forward operator, performing linearization processing on the pre - constructed mapping relationship between seismic data and pore parameters to simulate and generate linearized simulated seismic data: Among them, , is the pore parameter; , and are the water saturation, porosity, and pore aspect ratio, respectively; is the linearized simulated seismic data; is the Toeplitz matrix composed of seismic wavelets; is the approximate point of the target parameter of a section of Taylor wire; is a constant; is the 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: Among them, , is the pore parameter; , and are the water saturation, porosity and pore aspect ratio respectively; is the P-wave reflection coefficient.

6. The method according to claim 1, wherein The Bayesian inversion objective function is: Among them, is the posterior expectation; is the actual seismic 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 parameter; is the covariance of the prior data of the pore parameter; is the number of Gaussian distributions of the prior data; is the weight factor of the -th Gaussian distribution.

7. The method according to claim 1, characterized in that, The prior data of the pore parameters includes the pore aspect ratio, and the determination process of the pore aspect ratio is: Obtaining the actually observed P - wave and S - wave velocities of the seismic waves and the P - wave and S - wave velocities predicted by the rock physics model; Based on the actually observed P - wave and S - wave velocities of the seismic waves and the P - wave and S - wave velocities predicted by the rock physics model, obtaining through Bayesian inversion: Wherein: is the pore aspect ratio to be inverted; and are respectively the measured P-wave and S-wave velocities, obtained from well logging data; and are respectively the P-wave and S-wave velocities predicted by the physical rock model.

8. A direct prediction device for pore parameters of deep coalbed methane reservoirs, characterized in that, The device includes: An acquisition unit for obtaining prior data of pore parameters and actually observed seismic data at the target coalbed methane reservoir location; A generation unit for simulating and generating simulated seismic data based on the prior data of the pore parameters and a pre - constructed linear mapping relationship between seismic data and pore parameters; An inversion unit, configured to perform iterative inversion using a pre-constructed Bayesian inversion objective function based on actually observed seismic data, simulated seismic data, and prior data of pore parameters, so as to determine a minimum objective function value; A determination unit, configured to directly determine target pore parameters based on the simulated seismic data corresponding to the minimum objective function value and the 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. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus; The memory is used to store a computer program; When the processor is configured to execute the program stored on the memory, it implements the method steps described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the method steps described in any one of claims 1-7.

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