A Phase-Controlled Poststack Seismic Inversion Method and System Based on Geological Body Unit Parameter Update

Abstract

The present invention discloses a phase-controlled post-stack seismic inversion method and system based on updating geological body unit parameters, comprising: establishing a three-dimensional sedimentary phase plane distribution map based on seismic data, well logging data and regional geological data, and generating a two-dimensional profile background model using horizon data; extracting geological body unit parameters by scanning sedimentary phase interpretation data as initial model parameters; then combining a hybrid L1 and L2 norm minimization method with dynamic geological body unit parameter continuity constraints to construct an inversion objective function; using a hierarchical grid sampling method and a random hill climbing algorithm to iteratively update the geological body unit parameters until the objective function reaches a preset threshold, thereby obtaining optimal geological body unit parameters and generating a three-dimensional inversion result with high vertical resolution and lateral continuity. The advantages of the present invention are: improving the resolution and geological consistency of the inversion results, enhancing computational efficiency and model stability, and providing an effective technical means for fine characterization of reservoirs under complex geological conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to a phase-controlled post-stack seismic inversion method and system based on geological body unit parameter updating. Background Art

[0002] Post-stack seismic inversion, a key tool in oil and gas exploration, aims to extract wave impedance information from post-stack seismic data, providing a quantitative basis for reservoir characterization, thickness prediction, and reservoir description. However, traditional post-stack seismic inversion methods are primarily limited by the bandwidth and sampling interval of seismic data, and their inversion resolution typically struggles to reach less than 10 meters. This results in inaccurate and low-resolution inversion results for the prediction of geological units such as thin-bedded channel sandbodies.

[0003] At present, there are two main approaches to achieve high-resolution post-stack inversion: on the one hand, geostatistical methods (such as random inversion method based on sequential Gaussian simulation) are used to break through the limitations of sampling interval and frequency band range. Although this method can improve the inversion resolution, the lateral continuity during the inversion process is poor and the computational efficiency is low. On the other hand, the use of external high-frequency information, such as introducing logging data for broadband constrained inversion, can improve the resolution problem to a certain extent, but its effect is limited in areas where the number of well control is insufficient.

[0004] Furthermore, in sedimentary environments such as rivers, deltas, and deep seas, reservoirs often contain geological units with unique shapes and sedimentary characteristics, such as channels, estuary bars, levees, and various shale types. In recent years, the integration of geological unit theory with post-stack seismic inversion has gained increasing attention. This approach represents geological units as discrete objects, constructs an initial wave impedance model using well logging interpretation and sedimentary facies analysis, and then, combined with geological unit parameter updating techniques, iteratively inverts the geometric shape and physical properties of the geological units. This improves vertical resolution while enhancing the lateral continuity and geological consistency of the inversion results.

[0005] Existing technologies, such as the post-stack adaptive broadband constrained impedance inversion method disclosed in Chinese invention patent CN202011175057, have improved inversion accuracy to a certain extent. However, because the adaptive damping factor is calculated separately for each seismic trace, the calculation is complex and the lateral continuity of the inversion results is insufficient. Therefore, there is an urgent need for a new method that can not only improve the inversion resolution, but also ensure the geological consistency and lateral continuity of the inversion results, while taking into account the computational efficiency. Summary of the Invention

[0006] In view of the defects of the prior art, the present invention provides a phase-controlled post-stack seismic inversion method and system based on geological body unit parameter updating.

[0007] In order to achieve the above object of the invention, the technical solution adopted by the present invention is as follows:

[0008] A phase-controlled post-stack seismic inversion method based on updating geological unit parameters includes the following steps:

[0009] a) establishing a three-dimensional sedimentary facies plane distribution map based on seismic data, well logging data calibration results and regional geological data, wherein the three-dimensional sedimentary facies plane distribution map is used to reflect the reservoir characteristics and sedimentary facies and superposition patterns of geological bodies in the region;

[0010] b) Based on the horizon data and well logging data, an interpolation algorithm is used to generate a background model for each two-dimensional profile. This background model can reflect the ups and downs of the seismic horizon and the gradual change of impedance from top to bottom;

[0011] c) scanning the sedimentary facies interpretation result data and extracting geological body unit parameters from all two-dimensional sections as initial model parameters, wherein the geological body unit parameters include: geological body type t; transverse coordinates x and y, where x represents the line number of the two-dimensional section where the geological body is located, and y is the track number of the midpoint of the geological body; vertical time coordinate z; width w; thickness h; and wave impedance AI, which is expressed as the product of velocity and density;

[0012] d) Based on the hybrid L1 and L2 norm minimization method and combined with the continuity constraint of dynamic geological units, the inversion objective function is constructed, and its mathematical expression is:

[0013]

[0014] Where s is the seismic data; W is the Toeplitz matrix composed of wavelets; D is the first-order difference matrix; M is the natural logarithmic impedance I is a two-dimensional wave impedance matrix); d k is the impedance response operator of the kth geological unit; h k is the logging response operator of the kth geological unit; Δ k is the continuity coefficient between the kth geological unit and the adjacent unit parameters, and λ is the deviation threshold of geological unit parameters, p k It refers to the parameters of the k-th geological unit; α, β, γ are weight coefficients;

[0015] e) updating the geological body unit parameters within a preset parameter update range according to the layered grid sampling method, wherein the parameter update range includes: the positive and negative intervals of the position coordinate y; the time window range of the vertical coordinate t; the positive and negative intervals of the width w; and the update range of the thickness h;

[0016] The inversion objective function is iteratively optimized using a hill climbing algorithm. When the updated objective function value decreases compared to the original value, the updated geological unit parameters are saved until the objective function value reaches a preset threshold.

[0017] f) Repeat step (e) for each 2D section to finally generate a 3D inversion result.

[0018] Furthermore, the three-dimensional sedimentary facies plane distribution map established in step a) is based on well-seismic calibration, seismic layer tracing, reservoir reflection characteristic analysis and seismic attribute analysis to determine the sedimentary facies interpretation and superposition pattern of the geological body.

[0019] Furthermore, the layered grid sampling method includes: first using a larger step size for uniform sampling within the update range of geological unit parameters to obtain the preliminary optimal parameters, and then using a smaller step size for fine uniform sampling within the neighborhood of the optimal parameters to improve calculation efficiency and parameter update accuracy.

[0020] Furthermore, the step e) includes the following sub-steps:

[0021] 1) Perform a preliminary update of geological unit parameters according to the layered grid sampling method within the preset parameter update range;

[0022] 2) Using the initially updated geological unit parameters, a two-dimensional wave impedance model is constructed, synthetic seismic records are obtained through convolution forward modeling, and the initial inversion objective function value is calculated;

[0023] 3) Using random hill climbing algorithm to iteratively optimize the geological unit parameters, and calculate the value of the inversion objective function after each update;

[0024] 4) Comparing the objective function values before and after each iterative update, when the updated objective function value is lower than the value before the update, saving the updated geological unit parameters;

[0025] 5) Repeat steps 3) and 4) until the inversion objective function value reaches a preset stop threshold, thereby obtaining the optimal geological unit parameters.

[0026] Furthermore, the dynamic geological body unit continuity constraint dynamically allocates weights according to the degree of deviation of geological body unit parameters. When the degree of deviation of geological body parameters is low, the L2 norm is mainly used to reduce the weight of the continuity coefficient; when the degree of deviation of geological body parameters is high, the weight of the continuity coefficient is increased, and a continuity coefficient weight threshold is set to avoid overfitting.

[0027] Furthermore, the random hill climbing algorithm compares the changes in the objective function value before and after the geological unit parameter is updated, saves the updated parameter when the objective function value decreases, and continuously iterates until the objective function value meets the preset stop condition.

[0028] The present invention also discloses a phase-controlled post-stack seismic inversion system based on geological body unit parameter updating. The system can be used to implement the above-mentioned phase-controlled post-stack seismic inversion method, specifically comprising:

[0029] The 3D sedimentary facies plane distribution module uses seismic data, well logging data and regional geological data to establish a 3D sedimentary facies plane distribution map reflecting reservoir characteristics and geological body superposition patterns;

[0030] The background model generation module uses interpolation algorithms to generate background models for each two-dimensional section based on the layer data and well logging data. This model can reflect the seismic layer fluctuations and impedance gradient characteristics.

[0031] The geological unit parameter extraction module is used to scan the sedimentary facies interpretation result data and extract the geological unit parameters from each two-dimensional section as the initial model parameters;

[0032] Inversion objective function construction module, which constructs the inversion objective function based on the hybrid L1 and L2 norm minimization method and combined with the continuity constraint of dynamic geological units;

[0033] The geological unit parameter update module uses a layered grid sampling method to preliminarily update the geological unit parameters within the preset parameter update range, and uses a random hill climbing algorithm to iteratively optimize the inversion objective function. When the objective function value decreases after the update, the updated parameters are saved until the objective function value reaches the preset threshold;

[0034] The three-dimensional inversion result generation module performs cyclic inversion on the geological unit parameters inverted by each two-dimensional section and finally generates a three-dimensional inversion result.

[0035] The present invention also discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned phase-controlled post-stack seismic inversion method is implemented.

[0036] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned phase-controlled post-stack seismic inversion method is implemented.

[0037] Compared with the prior art, the advantages of the present invention are:

[0038] This method uses a two-dimensional inversion method based on updating geobody unit parameters. It iteratively updates geobody unit parameters directly within a two-dimensional profile. This results in better lateral continuity of geobody units during the inversion process, a clearer vertical spatial structure, and a more intuitive reflection of reservoir characteristics under complex geological conditions. Compared to traditional single-channel or random inversion methods, this method effectively overcomes the problems of poor lateral continuity and low vertical resolution in the inversion results.

[0039] At the same time, a layered grid sampling method is used to update the parameters of the geounits, greatly improving the computational efficiency of parameter optimization. A hybrid L1 and L2 norm minimization method fully combines the advantages of both norms: the L2 norm effectively suppresses high-frequency noise, maintains model continuity, and ensures stable convergence, while the L1 norm leverages the sparsity of thin-layer responses in well logging data to enhance vertical resolution. Furthermore, the introduction of continuity constraints on the geounit parameters makes the distribution of geounits in three-dimensional space more continuous, more consistent with actual geological distribution patterns.

[0040] In summary, the present invention not only improves the resolution and geological consistency of the inversion results, but also significantly enhances the computational efficiency and model stability, providing an effective technical means for the fine characterization of reservoirs under complex geological conditions, and has high application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flow chart of a phase-controlled post-stack seismic inversion method provided by an embodiment of the present invention;

[0042] Figure 2 is a schematic diagram of generating a background model provided by an embodiment of the present invention;

[0043] Figure 3 It is a schematic cross-sectional view of a filled geological body unit provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0045] like Figure 1 As shown, this application proposes a phase-controlled post-stack seismic inversion method based on geological unit parameter update, including:

[0046] Step 1. Based on the calibration results of seismic data and well logging data, a 3D sedimentary facies plane distribution map is established, including:

[0047] 11. Based on regional geological data, combined with reservoir characteristics and four-property relationship analysis in the study area, a geological body superposition model is established.

[0048] 12. Conduct geological sedimentary facies interpretation and overlay pattern analysis for typical wells and backbone section wells.

[0049] 13. Well seismic calibration, tracking seismic horizons, and analyzing reservoir seismic reflection characteristics.

[0050] 14. Identify seismic boundaries, analyze seismic attributes, identify reservoir distribution characteristics, and complete three-dimensional sedimentary facies planar distribution research.

[0051] Step 2. Generate a background model for each 2D profile using an interpolation algorithm based on the layer data and well logging data.

[0052] Step 3. Scan all sedimentary facies interpretation result data and generate all geological unit parameters in all 2D sections as initial model parameters including:

[0053] The geological body type t is the result of sedimentary facies interpretation and can be represented by different numbers.

[0054] The horizontal line number coordinates x and y, where x is the line number of the two-dimensional section where the geological unit is located; y is the line number of the midpoint of the geological unit.

[0055] The vertical time coordinate z is the time at the midpoint of the geological unit.

[0056] The width w of the geological unit is the number of channels covered by the geological unit.

[0057] The thickness h of the geological unit is the thickness of the geological unit in the time domain, and different initial values are set according to different geological unit types.

[0058] The wave impedance AI of the geological body unit is the longitudinal wave impedance of the geological body unit, that is, the product of velocity and density. Different initial values are set according to different geological body types.

[0059] Step 4. Based on the hybrid L1 and 2 norm minimization method, combined with the continuity constraint of the dynamic geological unit, the inversion objective function is established, including:

[0060]

[0061] Wherein, the s refers to seismic data;

[0062] The d k It refers to the impedance response operator of the k-th geological unit;

[0063] The h k It refers to the logging response operator passed by the k-th geological unit;

[0064] The α, β, and γ are weight coefficients;

[0065] The Δk It refers to the continuity coefficient of the kth geological unit, which represents the degree of deviation between the geological parameters of the current section and the geological parameters corresponding to the adjacent sections:

[0066] in:

[0067] p k Refers to the parameters of the kth geological unit;

[0068] p k-1 Refers to the parameters of the k-1th geological unit;

[0069] λ refers to the deviation threshold of geological unit parameters.

[0070] The WDM is a synthetic seismic record of a geological body model, where:

[0071] W is the Toepliz matrix constructed from the wavelets:

[0072]

[0073] D is defined by the first-order difference matrix:

[0074]

[0075] M refers to the natural logarithmic impedance:

[0076]

[0077] Where I is the two-dimensional wave impedance matrix.

[0078] The above-mentioned dynamic continuity constraint refers to the dynamic allocation of weight coefficients according to the degree of deviation of geological body unit parameters, including: reducing the weight of the continuity coefficient when the degree of deviation of geological body parameters is low, and taking the L2 norm as the dominant factor; conversely, increasing the weight of the continuity coefficient when the degree of deviation of geological body parameters is high; and it is necessary to set a continuity coefficient weight threshold to avoid overfitting.

[0079] Step 5. Update the geological unit parameters according to the hierarchical grid sampling method and use the random hill climbing algorithm to iteratively invert the inversion objective function. Finally, the optimal geological unit parameters are obtained, including:

[0080] 51. Set the parameter update range of all geological units in the 2D section based on the seismic layer data and well logging data to ensure that the position, thickness and impedance of the geological units are consistent with the actual data, including:

[0081] The update range of the position coordinate y is the positive and negative interval of the initial coordinates of the geological body, and is generally set according to the size of the geological body.

[0082] The update range of the vertical coordinate t is a time window along the seismic horizon, and the size of the time window is generally set according to the thickness of the stratum.

[0083] The update range of width w is the positive and negative interval of the initial width of the geological body, and is generally set according to the size of the geological body.

[0084] The update range of thickness h is the average thickness of geological bodies interpreted by well logging.

[0085] 52. A 2D wave impedance model is constructed based on the initial geological unit parameters, seismic horizons and 2D profile background model. Synthetic seismic records are obtained by convolution forward modeling and the initial objective function value is calculated.

[0086] The above-mentioned two-dimensional wave impedance model is constructed by directly filling all geological bodies in the section in the background model according to the geological body unit parameters. The geological body morphology is a symmetrical downcut river channel geological body, and the top interface of the geological body is parallel to the seismic layer.

[0087] 53. Use the layered grid sampling method to update the geological unit parameters and calculate the updated objective function value.

[0088] 54. Compare the changes in the objective function values before and after the update. When the objective function value decreases, save the updated geological unit parameters as the optimal geological unit parameters and return to step 53; otherwise, directly return to step 53.

[0089] 55. When the objective function value reaches the threshold, the iteration stops and the optimal geological unit parameters are output.

[0090] The hierarchical grid sampling method mentioned above refers to first updating the geological unit parameters using uniform sampling with a large step size within the parameter update range. After the iteration is completed, the unit parameters are updated using uniform sampling with a small step size from the neighborhood of the current optimal parameter until the grid threshold is reached to improve computational efficiency.

[0091] Step 6. Circularly invert the two-dimensional profile to finally generate a three-dimensional inversion result.

[0092] Figure 2 An example of a background model in step 2 is shown. An interpolation method is used to generate an impedance model that gradually changes from top to bottom, and the change trend is consistent with the undulating shape of the seismic layer.

[0093] Figure 3 The figure shows a two-dimensional impedance profile generated by filling the geobody elements in the background model using the geobody element parameters in step 3, while keeping the top of the geobody parallel to the seismic horizon.

[0094] In yet another embodiment of the present invention, a phase-controlled post-stack seismic inversion system based on geological volume unit parameter updating is provided. The system can be used to implement the above-mentioned phase-controlled post-stack seismic inversion method, specifically comprising:

[0095] The 3D sedimentary facies plane distribution module uses seismic data, well logging data and regional geological data to establish a 3D sedimentary facies plane distribution map reflecting reservoir characteristics and geological body superposition patterns;

[0096] The background model generation module uses interpolation algorithms to generate background models for each two-dimensional section based on the layer data and well logging data. This model can reflect the seismic layer fluctuations and impedance gradient characteristics.

[0097] The geological unit parameter extraction module is used to scan the sedimentary facies interpretation result data and extract the geological unit parameters from each two-dimensional section as the initial model parameters;

[0098] Inversion objective function construction module, which constructs the inversion objective function based on the hybrid L1 and L2 norm minimization method and combined with the continuity constraint of dynamic geological units;

[0099] The geological unit parameter update module uses a layered grid sampling method to preliminarily update the geological unit parameters within the preset parameter update range, and uses a random hill climbing algorithm to iteratively optimize the inversion objective function. When the objective function value decreases after the update, the updated parameters are saved until the objective function value reaches the preset threshold;

[0100] The three-dimensional inversion result generation module performs cyclic inversion on the geological unit parameters inverted by each two-dimensional section and finally generates a three-dimensional inversion result.

[0101] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory, wherein the memory is configured to store a computer program, wherein the computer program includes program instructions, and the processor is configured to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be another general-purpose processor, 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, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically for loading and executing one or more instructions to implement a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for operating a phase-controlled post-stack seismic inversion method.

[0102] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.

[0103] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the phase-controlled post-stack seismic inversion method in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor.

[0104] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0108] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A phase-controlled post-stack seismic inversion method based on updating geological unit parameters, characterized in that: The following steps are involved: a) establishing a three-dimensional sedimentary facies plane distribution map based on seismic data, well logging data calibration results and regional geological data, wherein the three-dimensional sedimentary facies plane distribution map is used to reflect the reservoir characteristics and sedimentary facies and superposition patterns of geological bodies in the region; b) Based on the horizon data and well logging data, an interpolation algorithm is used to generate a background model for each two-dimensional profile. This background model can reflect the ups and downs of the seismic horizon and the gradual change of impedance from top to bottom; c) scanning the sedimentary facies interpretation result data and extracting geological body unit parameters from all two-dimensional sections as initial model parameters, wherein the geological body unit parameters include: geological body type t; transverse coordinates x and y, where x represents the line number of the two-dimensional section where the geological body is located, and y is the track number of the midpoint of the geological body; vertical time coordinate z; width w; thickness h; and wave impedance AI, which is expressed as the product of velocity and density; d) Based on the hybrid L1 and L2 norm minimization method and combined with the continuity constraint of dynamic geological units, the inversion objective function is constructed, and its mathematical expression is: Where s is the seismic data; W is the Toeplitz matrix composed of wavelets; D is the first-order difference matrix; M is the natural logarithmic impedance I is a two-dimensional wave impedance matrix); d k is the impedance response operator of the kth geological unit; h k is the logging response operator of the kth geological unit; Δ k is the continuity coefficient between the kth geological unit and the adjacent unit parameters, and λ is the deviation threshold of geological unit parameters, p k It refers to the parameters of the k-th geological unit; α, β, γ are weight coefficients; e) updating the geological body unit parameters within a preset parameter update range according to the layered grid sampling method, wherein the parameter update range includes: the positive and negative intervals of the position coordinate y; the time window range of the vertical coordinate t; the positive and negative intervals of the width w; and the update range of the thickness h; The random hill climbing algorithm is used to iteratively optimize the inversion objective function. When the updated objective function value decreases compared with the original value, the updated geological unit parameters are saved until the objective function value reaches the preset threshold. f) Repeat step (e) for each 2D section to finally generate a 3D inversion result.

2. The phase-controlled post-stack seismic inversion method according to claim 1, characterized in that: The three-dimensional sedimentary facies plane distribution map established in step a) is based on well-seismic calibration, seismic horizon tracing, reservoir reflection characteristic analysis and seismic attribute analysis to determine the sedimentary facies interpretation and superposition pattern of the geological body.

3. The phase-controlled post-stack seismic inversion method according to claim 1, characterized in that: The hierarchical grid sampling method includes: first using a larger step size to perform uniform sampling within the update range of geological unit parameters to obtain preliminary optimal parameters, and then using a smaller step size to perform fine uniform sampling within the neighborhood of the optimal parameters to improve calculation efficiency and parameter update accuracy.

4. The phase-controlled post-stack seismic inversion method according to claim 1, characterized in that: The step e) comprises the following sub-steps: 1) Perform a preliminary update of geological unit parameters according to the layered grid sampling method within the preset parameter update range; 2) Using the initially updated geological unit parameters, a two-dimensional wave impedance model is constructed, synthetic seismic records are obtained through convolution forward modeling, and the initial inversion objective function value is calculated; 3) Using random hill climbing algorithm to iteratively optimize the geological unit parameters, and calculate the value of the inversion objective function after each update; 4) Comparing the objective function values before and after each iterative update, when the updated objective function value is lower than the value before the update, saving the updated geological unit parameters; 5) Repeat steps 3) and 4) until the inversion objective function value reaches a preset stop threshold, thereby obtaining the optimal geological unit parameters.

5. The phase-controlled post-stack seismic inversion method according to claim 1, characterized in that: The dynamic geological unit continuity constraint dynamically allocates weights according to the degree of deviation of geological unit parameters. When the degree of deviation of geological unit parameters is low, the L2 norm is mainly used to reduce the weight of the continuity coefficient. When the deviation of geological parameters is high, the weight of the continuity coefficient is increased, and a threshold of the continuity coefficient weight is set to avoid overfitting.

6. The phase-controlled post-stack seismic inversion method according to claim 1, characterized in that: The random hill climbing algorithm compares the changes in the objective function value before and after the geological unit parameter is updated, saves the updated parameters when the objective function value decreases, and continuously iterates until the objective function value meets the preset stop condition.

7. A phase-controlled post-stack seismic inversion system based on geological unit parameter updating, characterized by: The system can be used to implement the phase-controlled post-stack seismic inversion method according to any one of claims 1 to 6, specifically comprising: The 3D sedimentary facies plane distribution module uses seismic data, well logging data and regional geological data to establish a 3D sedimentary facies plane distribution map reflecting reservoir characteristics and geological body superposition patterns; The background model generation module uses interpolation algorithms to generate background models for each two-dimensional section based on the layer data and well logging data. This model can reflect the seismic layer fluctuations and impedance gradient characteristics. The geological unit parameter extraction module is used to scan the sedimentary facies interpretation result data and extract the geological unit parameters from each two-dimensional section as the initial model parameters; Inversion objective function construction module, which constructs the inversion objective function based on the hybrid L1 and L2 norm minimization method and combined with the continuity constraint of dynamic geological units; The geological unit parameter update module uses a layered grid sampling method to preliminarily update the geological unit parameters within the preset parameter update range, and uses a random hill climbing algorithm to iteratively optimize the inversion objective function. When the objective function value decreases after the update, the updated parameters are saved until the objective function value reaches the preset threshold; The three-dimensional inversion result generation module performs cyclic inversion on the geological unit parameters inverted by each two-dimensional section and finally generates a three-dimensional inversion result.

8. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the phase-controlled post-stack seismic inversion method according to one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the phase-controlled post-stack seismic inversion method according to one of claims 1 to 6 is implemented.

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