Physical property parameter prediction method and device, electronic equipment and storage medium

Abstract

The invention discloses a physical property parameter prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: firstly analyzing the linear relation and nonlinear relation between the physical property parameters such as porosity and shale content and the elastic parameters such as speed, density and Young modulus, selecting two elastic parameters with the best fitting relation with each physical property parameter, and selecting the two elastic parameters with the best fitting relation with each physical property parameter; fitting a relational expression between the physical property parameters and the two elastic parameters; then, a more accurate reflection coefficient formula is adopted, pre-stack elastic parameter inversion is carried out in an effective frequency band, and a higher-precision elastic parameter inversion result is obtained; and finally, according to an elastic parameter and physical property parameter fitting relational expression, realizing high-precision prediction of physical property parameters such as porosity and shale content.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development, and more specifically, to a method, apparatus, electronic device, and storage medium for predicting physical property parameters. Background Technology

[0002] Rock porosity, clay content, and other physical properties are crucial for reservoir characterization. Accurate prediction of these properties allows for more detailed reservoir characterization, supporting oil and gas exploration and development. Conventional methods for predicting porosity and clay content typically employ a single linear fitting approach, resulting in multiple solutions and neglecting the nonlinear relationship between physical properties and elastic parameters. Before predicting physical properties, elastic parameter inversion is necessary. Currently, the widely used method is time-domain inversion. Time-domain seismic data utilizes the entire seismic frequency band, but the low-frequency and high-frequency components of seismic data often have very low signal-to-noise ratios and are considered ineffective. Utilizing this data can negatively impact the resolution and accuracy of the inversion. Summary of the Invention

[0003] The purpose of this invention is to propose a method, device, electronic device and storage medium for predicting physical property parameters, so as to achieve high-precision prediction of physical property parameters such as porosity and clay content.

[0004] To achieve the above objectives, in a first aspect, the present invention proposes a method for predicting physical property parameters, comprising:

[0005] S1: Collect well logging data in the research area to obtain elastic parameters and physical property parameters. The elastic parameters include P-wave velocity, S-wave velocity, density, Young's modulus, shear modulus, and Poisson's ratio, etc. The physical property parameters include porosity, clay content, and saturation.

[0006] S2: For each physical property parameter, the relationship between it and each elastic parameter is calculated using the linear fitting formula, the quadratic function fitting formula, and the exponential function fitting formula, and the degree of fit is calculated.

[0007] S3: For each physical property parameter, select the two elastic parameters that best fit it and the corresponding fitting formula, and further fit the formula between each physical property parameter and the corresponding two elastic parameters;

[0008] S4: Collect superimposed data from multiple angles in the research area, perform well-seismic calibration, extract wavelets, and determine the effective frequency band range of the seismic wavelets;

[0009] S5: Establish low-frequency models of P-wave velocity, S-wave velocity, and density based on well logging data and seismic horizons;

[0010] S6: Based on the low-frequency model, calculate the corresponding forward modeling seismic record;

[0011] S7: Perform bandpass filtering on the superimposed data from multiple angles to obtain seismic data within the effective frequency band;

[0012] S8: Based on the seismic data within the effective frequency band, the forward modeling seismic records, and the low-frequency model, the P-wave velocity, S-wave velocity, and density are obtained by inversion.

[0013] S9: Calculate the remaining elastic parameters based on the longitudinal wave velocity, transverse wave velocity and density obtained from the inversion. The remaining elastic parameters include Young's modulus, shear modulus and Poisson's ratio, etc.

[0014] S10: Based on the relationship between each physical property parameter obtained in S3 and the corresponding two elastic parameters, calculate various physical property parameters using the longitudinal wave velocity, transverse wave velocity, density obtained by inversion in S8 and the remaining elastic parameters obtained in S9.

[0015] Optionally, in step S2,

[0016] The linear fitting formula is: P = a*x + b;

[0017] The quadratic function fitting formula is: P = a*x 2 +b;

[0018] The formula for fitting the exponential function is: P = a * e bx+c ;

[0019] Where P represents a physical property parameter, which is one of porosity, clay content, and saturation; a, b, and c are coefficients to be fitted; and x represents an elastic parameter, which is one of elastic parameters such as longitudinal wave velocity, transverse wave velocity, density, Young's modulus, shear modulus, and Poisson's ratio.

[0020] Optionally, in step S2, the degree of fit is calculated using the following formula:

[0021]

[0022] Where n represents the number of sample points, P i fit P represents the fitting result of the physical property parameters. i These are the actual logging curve values ​​for physical property parameters. is the average value of the true physical property parameters, and C is the degree of fit. The closer the value is to 1, the better the fit.

[0023] Optionally, in step S3, the relationship between each fitted physical property parameter and the corresponding two elastic parameters is as follows:

[0024] P i =pF i+qF j+ m

[0025] Among them, F i F j is one of three fitting formulas: linear fitting formula, quadratic function fitting formula, and exponential function fitting formula, where p, q, and m are fitting coefficients.

[0026] Optionally, step S4 specifically includes:

[0027] Data from near, middle, and far angles of the research area were collected and superimposed. Well-seismic calibration was performed on each data point, three wavelets were extracted, and the upper and lower effective frequency band limits f of the seismic wavelets were determined. u f b ;

[0028] Step S7 specifically includes:

[0029] Bandpass filtering was performed on the superimposed data from near, mid, and far angles to obtain the effective frequency band range [f]. b f u Earthquake data S within ]

[0030] Optionally, in step S6, the corresponding forward-modeled seismic record is calculated according to the following formula:

[0031]

[0032] in,

[0033]

[0034]

[0035] Where i1 is the incident angle, α1 and α2 are the P-wave velocities of the upper and lower media respectively, β1 and β2 are the S-wave velocities of the upper and lower media respectively, ρ1 and ρ2 are the densities of the upper and lower media respectively, W(f) is the seismic wavelet spectrum, f represents the frequency, t represents the time, and S syn This is a forward modeling simulation of earthquake records.

[0036] Optionally, step S8 specifically includes:

[0037] Establish the objective function for the inversion:

[0038] J = ||SS syn ||2+||mm lf ||2,

[0039] Where m represents the P-wave velocity, S-wave velocity, and density to be inverted, m lf The low-frequency model established for step S5;

[0040] Given the tolerance error limit and the maximum number of iterations, a very fast simulated annealing algorithm is used to solve the objective function, and the inversion results of P-wave velocity, S-wave velocity and density are obtained.

[0041] Secondly, the present invention provides an electronic device, the electronic device comprising:

[0042] At least one processor; and,

[0043] A memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the physical property parameter prediction method described in the first aspect.

[0045] Thirdly, the present invention provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to execute the physical property parameter prediction method described in the first aspect.

[0046] Fourthly, the present invention proposes a device for predicting physical property parameters, comprising:

[0047] The parameter acquisition module is used to collect well logging data in the research area and acquire elastic parameters and physical property parameters. The elastic parameters include P-wave velocity, S-wave velocity, density, Young's modulus, shear modulus and Poisson's ratio, etc., and the physical property parameters include porosity, clay content and saturation.

[0048] The parameter fitting degree calculation module is used to calculate the relationship between each physical property parameter and each elastic parameter using linear fitting formula, quadratic function fitting formula and exponential function fitting formula, and to calculate the fitting degree.

[0049] The parameter fitting relationship determination module is used to select the two elastic parameters that best fit each physical property parameter and their corresponding fitting relationship, and further fit the relationship between each physical property parameter and the corresponding two elastic parameters.

[0050] The effective frequency band determination module is used to collect superimposed data from multiple angles in the research area, perform well-seismic calibration, extract wavelets, and determine the effective frequency band range of the seismic wavelets.

[0051] The low-frequency module is used to establish low-frequency models of P-wave velocity, S-wave velocity, and density based on well logging data and seismic horizons.

[0052] The forward modeling module is used to calculate the corresponding forward modeling simulation seismic records based on the low-frequency model.

[0053] The filtering module is used to perform bandpass filtering on the superimposed data from multiple angles to obtain seismic data within the effective frequency band.

[0054] The elastic parameter inversion module is used to invert P-wave velocity, S-wave velocity, and density based on seismic data within the effective frequency band, the forward simulation seismic record, and the low-frequency model.

[0055] The elastic parameter calculation module calculates other elastic parameters based on the inverted longitudinal wave velocity, transverse wave velocity, and density. These other elastic parameters include Young's modulus, shear modulus, and Poisson's ratio, etc.

[0056] The physical property parameter calculation module is used to calculate various physical property parameters based on the relationship between each physical property parameter and the corresponding two elastic parameters, using the inverted longitudinal wave velocity, transverse wave velocity, density, and the remaining elastic parameters.

[0057] The beneficial effects of this invention are as follows:

[0058] The method of this invention first considers the linear and nonlinear relationships between physical properties such as porosity and clay content and elastic parameters such as velocity, density, and Young's modulus. For each physical property, the two elastic parameters with the best fit are selected, and then the relationship between the physical property and these two elastic parameters is fitted. Then, a more accurate reflection coefficient formula is used to perform pre-stack elastic parameter inversion within the effective frequency band, which can obtain higher accuracy elastic parameter inversion results. Finally, based on the fitting relationship between elastic parameters and physical properties, high-precision prediction of physical properties such as porosity and clay content is achieved. Using this invention, high-precision prediction results of physical properties can be obtained from pre-stack seismic data and well logging data, which can more finely characterize high-quality reservoirs and provide support for oil and gas exploration and development.

[0059] The system of the present invention has other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0060] The above and other objects, features and advantages of the present invention will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0061] Figure 1 This is a flowchart illustrating the steps of a method for predicting physical property parameters according to an embodiment of the present invention.

[0062] Figure 2This is seismic data at a certain incident angle in one embodiment of the present invention.

[0063] Figure 3 This is a porosity prediction result in one embodiment of the present invention.

[0064] Figure 4 This is a water saturation prediction result in one embodiment of the present invention. Detailed Implementation

[0065] The invention will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0066] Example 1

[0067] like Figure 1 As shown, this embodiment provides a method for predicting physical property parameters, including:

[0068] S1: Collect well logging data in the research area to obtain elastic parameters and physical property parameters. The elastic parameters include P-wave velocity, S-wave velocity, density, Young's modulus, shear modulus, and Poisson's ratio, etc. The physical property parameters include porosity, clay content, and saturation.

[0069] Specifically, well logging data from the research area were collected, and the relationship between elastic parameters such as P-wave velocity, S-wave velocity, density, Young's modulus, shear modulus, and Poisson's ratio, and physical properties such as porosity, clay content, and saturation was analyzed. Three fitting formulas were considered: linear fitting, quadratic function fitting, and exponential function fitting.

[0070] Linear fitting: P = a*x + b;

[0071] Quadratic function fitting: P = a*x 2 +b;

[0072] Exponential function fitting: P = a * e bx+c ;

[0073] Where P represents one of the physical properties such as porosity, clay content, and saturation; a, b, and c are the coefficients to be fitted; and x represents one of the elastic parameters such as longitudinal wave velocity, transverse wave velocity, density, Young's modulus, shear modulus, and Poisson's ratio.

[0074] S2: For each physical property parameter, the relationship between it and each elastic parameter is calculated using the linear fitting formula, the quadratic function fitting formula, and the exponential function fitting formula, and the degree of fit is calculated.

[0075] Specifically, for each physical property parameter, the relationship between it and each elastic parameter is calculated using the three fitting formulas mentioned above, and the degree of fit is calculated using the following formula.

[0076]

[0077] Where n represents the number of sample points, P i fit P represents the fitting result of the physical property parameters. i These are the actual logging curve values ​​for physical property parameters. is the average value of the true physical property parameters, and C is the degree of fit. The closer the value is to 1, the better the fit.

[0078] S3: For each physical property parameter, select the two elastic parameters that best fit it and the corresponding fitting formula, and further fit the formula between each physical property parameter and the corresponding two elastic parameters;

[0079] Specifically, for each physical property parameter, the fitting relationship between the two elastic parameters that best fit it is selected, i.e., the fitting relationship between the two elastic parameters with the largest C values. Assume that for physical property parameter P... i The two best-fitting elastic parameters are x i x j The corresponding fitting equation is F. i F j Then fit P according to the following formula i With x i x j Relationship:

[0080] P i =pF i +qF j+ m

[0081] Among them, F i F j It is one of the three fitting relationships described in step 1, where p, q, and m are fitting coefficients.

[0082] S4: Collect superimposed data from multiple angles in the research area, perform well-seismic calibration, extract wavelets, and determine the effective frequency band range of the seismic wavelets;

[0083] Specifically, near-field, mid-field, and far-field superimposed data of the research area were collected, and well-seismic calibration was performed. Three wavelets were extracted, and the upper and lower effective frequency band limits f of the seismic wavelets were determined. u f b .

[0084] S5: Establish low-frequency models of P-wave velocity, S-wave velocity, and density based on well logging data and seismic horizons;

[0085] S6: Based on the low-frequency model, calculate the corresponding forward modeling seismic record;

[0086] Specifically, using the P-wave velocity, S-wave velocity, and density from step S5, the corresponding forward-modeled seismic record is calculated according to the following formula:

[0087]

[0088] in,

[0089]

[0090]

[0091] Where i1 is the incident angle, α1 and α2 are the P-wave velocities of the upper and lower media respectively, β1 and β2 are the S-wave velocities of the upper and lower media respectively, ρ1 and ρ2 are the densities of the upper and lower media respectively, W(f) is the seismic wavelet spectrum, f represents the frequency, t represents the time, and S syn This is a forward modeling simulation of earthquake records.

[0092] S7: Perform bandpass filtering on the superimposed data from multiple angles to obtain seismic data within the effective frequency band;

[0093] Specifically, bandpass filtering is performed on the superimposed data from the three angles to obtain the effective frequency band range [f]. b f u Earthquake data S within ]

[0094] S8: Based on the seismic data within the effective frequency band, the forward modeling seismic records, and the low-frequency model, the P-wave velocity, S-wave velocity, and density are obtained by inversion.

[0095] Specifically, the objective function for the inversion is established as J = ||SS|| syn ||2+||mm lf ||2, where m represents the P-wave velocity, S-wave velocity, and density to be inverted, m lf The low-frequency model established for step S5; given the tolerance error limit ε and the maximum number of iterations it. max The P-wave velocity, S-wave velocity, and density inversion results can be obtained by using a very fast simulated annealing algorithm.

[0096] S9: Calculate the remaining elastic parameters based on the longitudinal wave velocity, transverse wave velocity and density obtained from the inversion. The remaining elastic parameters include Young's modulus, shear modulus and Poisson's ratio, etc.

[0097] Specifically, based on the longitudinal wave velocity, transverse wave velocity, and density obtained from the inversion, elastic parameters such as Young's modulus, shear modulus, and Poisson's ratio are calculated.

[0098] S10: Based on the relationship between each physical property parameter obtained in S3 and the corresponding two elastic parameters, calculate various physical property parameters using the longitudinal wave velocity, transverse wave velocity, density obtained by inversion in S8 and the remaining elastic parameters obtained in S9.

[0099] Specifically, based on the relationship between physical property parameters and elastic parameters described in step 3, physical property parameters such as porosity and clay content are calculated from the longitudinal wave velocity, transverse wave velocity, density obtained in step S8 or the elastic parameters such as Young's modulus, shear modulus, and Poisson's ratio calculated in step S9.

[0100] This method first considers the linear and nonlinear relationships between physical property parameters and elastic parameters, and optimizes the best relationship by fitting numerous parameter combinations; it proposes an effective frequency band pre-stack elastic parameter inversion method, which can obtain more accurate elastic parameter inversion results; finally, based on the optimal fitting relationship between elastic parameters and physical property parameters, it achieves high-precision prediction of physical property parameters such as porosity and clay content.

[0101] Example 2

[0102] The implementation process of the method of the present invention will be described below with a specific example.

[0103] Figure 2 This data represents seismic data from a specific incident angle in a real work area. Following the steps of Example 1 above, statistical analysis of the well logging data was first performed, revealing a good fit between P-wave velocity and S-wave velocity and porosity and water saturation. Then, pre-stack inversion was performed to obtain the inversion results for P-wave velocity, S-wave velocity, and density. Finally, porosity and water saturation were calculated based on the fitting relationships. The final predicted porosity and water saturation results are as follows: Figure 3 and Figure 4 As shown, the predicted results and the well logging interpretation results are in good agreement, indicating that the method has high accuracy and confirming its effectiveness.

[0104] Example 3

[0105] This embodiment provides a device for predicting physical property parameters, including:

[0106] The parameter acquisition module is used to collect well logging data in the research area and acquire elastic parameters and physical property parameters. The elastic parameters include P-wave velocity, S-wave velocity, density, Young's modulus, shear modulus and Poisson's ratio, etc., and the physical property parameters include porosity, clay content and saturation.

[0107] The parameter fitting degree calculation module is used to calculate the relationship between each physical property parameter and each elastic parameter using linear fitting formula, quadratic function fitting formula and exponential function fitting formula, and to calculate the fitting degree.

[0108] The parameter fitting relationship determination module is used to select the two elastic parameters that best fit each physical property parameter and their corresponding fitting relationship, and further fit the relationship between each physical property parameter and the corresponding two elastic parameters.

[0109] The effective frequency band determination module is used to collect superimposed data from multiple angles in the research area, perform well-seismic calibration, extract wavelets, and determine the effective frequency band range of the seismic wavelets.

[0110] The low-frequency module is used to establish low-frequency models of P-wave velocity, S-wave velocity, and density based on well logging data and seismic horizons.

[0111] The forward modeling module is used to calculate the corresponding forward modeling simulation seismic records based on the low-frequency model.

[0112] The filtering module is used to perform bandpass filtering on the superimposed data from multiple angles to obtain seismic data within the effective frequency band.

[0113] The elastic parameter inversion module is used to invert P-wave velocity, S-wave velocity, and density based on seismic data within the effective frequency band, the forward simulation seismic record, and the low-frequency model.

[0114] The elastic parameter calculation module calculates other elastic parameters based on the inverted longitudinal wave velocity, transverse wave velocity, and density. These other elastic parameters include Young's modulus, shear modulus, and Poisson's ratio, etc.

[0115] The physical property parameter calculation module is used to calculate various physical property parameters based on the relationship between each physical property parameter and the corresponding two elastic parameters, using the inverted longitudinal wave velocity, transverse wave velocity, density, and the remaining elastic parameters.

[0116] Example 4

[0117] This embodiment provides an electronic device, the electronic device comprising:

[0118] At least one processor; and,

[0119] A memory communicatively connected to the at least one processor; wherein,

[0120] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the physical property parameter prediction method described in Embodiment 1.

[0121] An electronic device according to embodiments of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0122] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0123] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0124] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0125] Example 5

[0126] This embodiment provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to execute the physical property parameter prediction method described in Embodiment 1.

[0127] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0128] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0129] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method of predicting a physical property parameter, characterized by, The method comprises the following steps: S1: collecting well logging data of a research area to obtain elastic parameters and physical parameters, the elastic parameters including P-wave velocity, S-wave velocity, density, Young's modulus, shear modulus and Poisson's ratio, and the physical parameters including porosity, shale content and saturation; S2: for each physical parameter, a linear fitting formula, a quadratic function fitting formula and an exponential function fitting formula are respectively used to calculate the relationship between the physical parameter and each elastic parameter, and the fitting degree of coincidence is calculated; S3: for each physical parameter, two elastic parameters with the best fitting degree of coincidence and the corresponding fitting relationship are selected, and a relationship between each physical parameter and the corresponding two elastic parameters is further fitted; S4: collecting multiple angle stack data of the research area, respectively performing well-seismic calibration and extracting wavelet, and determining an effective frequency band range of the seismic wavelet; S5: establishing a low-frequency model of P-wave velocity, S-wave velocity and density according to the well logging data and seismic horizon; S6: calculating corresponding forward modeling seismic records based on the low-frequency model; S7: respectively performing band-pass filtering on the multiple angle stack data to obtain seismic data in the effective frequency band range; S8: inverting P-wave velocity, S-wave velocity and density based on the seismic data in the effective frequency band range, the forward modeling seismic records and the low-frequency model; S9: calculating the remaining elastic parameters including Young's modulus, shear modulus and Poisson's ratio according to the inverted P-wave velocity, S-wave velocity and density; S10: calculating various physical parameters based on the relationship between each physical parameter and the corresponding two elastic parameters obtained in S3, the inverted P-wave velocity, S-wave velocity and density obtained in S8, and the remaining elastic parameters obtained in S9.

2. The method of predicting a physical property parameter according to claim 1, wherein In step S2, the linear fitting formula is P=a*x+b; The quadratic function fitting formula is: P = a*x 2 +b; The exponential function fitting formula is: P=a*e bx+c ; wherein P represents a physical parameter, which is one of porosity, shale content and saturation, a, b and c are to-be-fitted coefficients, and x represents an elastic parameter, which is one of P-wave velocity, S-wave velocity, density, Young's modulus, shear modulus and Poisson's ratio.

3. The method of predicting a physical property parameter according to claim 2, wherein In step S2, the fitting degree of coincidence is calculated by the following formula: wherein n represents the number of samples, P i fit P represents the fitting result of the physical property parameter, P i is the true value of the physical property parameter, is the average value of the true value of the physical property parameter, and C is the fitting degree of coincidence. The closer the value is to 1, the better the fitting degree of coincidence.

4. The method of predicting a physical property parameter according to claim 3, characterized by, In step S3, the fitted relationship between each physical parameter and the corresponding two elastic parameters is: P i = pF i + qF j+ m where F i , F j is one of three fitting relationships of linear fitting formula, quadratic function fitting formula and exponential function fitting formula, and p, q and m are fitting coefficients.

5. The method of predicting a physical property parameter according to claim 4, wherein Step S4 specifically comprises: The near, middle and far angle stack data of the research area are collected, well-to-seismic calibration is respectively conducted, three wavelets are extracted, and the upper and lower limits f u , b of the effective frequency band of the seismic wavelet are determined. Step S7 specifically comprises: The near, middle and far angle superposition data are respectively band-pass filtered to obtain seismic data S within effective frequency band range [f b , f u ].

6. The method of predicting a physical property parameter according to claim 5, wherein In step S6, the corresponding forward modeling seismic records are calculated according to the following formula: wherein, where i1is the incident angle, a1, a2are the longitudinal wave velocities of the upper and lower media, b1, b2are the transverse wave velocities of the upper and lower media, p1, p2are the densities of the upper and lower media, W(f) is the seismic wavelet spectrum, f represents the frequency, t represents time, S syn is the forward modeling seismic record.

7. The method of predicting a physical property parameter according to claim 6, wherein Step S8 specifically comprises: An objective function of inversion is established: J = ||S - S syn ||2+||m-m lf ||2, where m represents the longitudinal wave velocity, the transverse wave velocity and the density to be inverted, m lf a low frequency model established for step S5; Given a tolerance error limit and a maximum number of iterations, a very fast simulated annealing algorithm is used to solve the objective function to obtain the inversion results of P-wave velocity, S-wave velocity and density.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the physical parameter prediction method of any one of claims 1-7.

9. A non-transitory computer-readable storage medium, comprising: The non-transitory computer readable storage medium stores computer instructions for causing a computer to execute the physical property parameter prediction method of any one of claims 1-7.

10. A physical property parameter prediction apparatus characterized by comprising: Comprise: A parameter acquisition module is configured to collect well logging data of a research area, and to acquire elastic parameters and physical property parameters, wherein the elastic parameters include P-wave velocity, S-wave velocity, density, Young's modulus, shear modulus, and Poisson's ratio, and the physical property parameters include porosity, shale content, and saturation; A parameter fitting degree calculation module is configured to calculate the relationship between each physical property parameter and each elastic parameter by using a linear fitting formula, a quadratic function fitting formula, and an exponential function fitting formula, respectively, and to calculate a fitting coincidence degree; A parameter fitting relationship determination module is configured to select two elastic parameters and corresponding fitting relationship formulas that have the best fitting coincidence degree for each physical property parameter, and to further fit a relationship formula between each physical property parameter and the corresponding two elastic parameters; An effective frequency band determination module is configured to collect multi-angle stack data of a research area, to perform well-seismic calibration and extract wavelets, respectively, and to determine an effective frequency band range of seismic wavelets; A low-frequency module establishment module is configured to establish a low-frequency model of P-wave velocity, S-wave velocity, and density based on well logging data and seismic horizons; A forward modeling module is configured to calculate corresponding forward modeling seismic records based on the low-frequency model; A filtering module is configured to perform band-pass filtering on multi-angle stack data, respectively, to obtain seismic data in the effective frequency band range; An elastic parameter inversion module is configured to invert P-wave velocity, S-wave velocity, and density based on the seismic data in the effective frequency band range, the forward modeling seismic records, and the low-frequency model; An elastic parameter calculation module is configured to calculate remaining elastic parameters including Young's modulus, shear modulus, and Poisson's ratio based on the inverted P-wave velocity, S-wave velocity, and density; A physical property parameter calculation module is configured to calculate various physical property parameters based on the inverted P-wave velocity, S-wave velocity, density, and the remaining elastic parameters, and the relationship formula between each physical property parameter and the corresponding two elastic parameters.

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