Post-stack inversion method and device based on phase-controlled low-frequency modeling

Abstract

The invention relates to the technical field of petroleum geophysical exploration, in particular to a post-stack inversion method and device based on phase-control low-frequency modelling, and the corresponding method comprises the steps: extracting seismic attributes in original seismic data, the seismic attributes being used for representing the geological sedimentary facies of a target work area; according to the seismic attributes and the sand body thickness of the target work area, constraining a geostatistical simulation process of the target work area to generate a geostatistical model of the target work area; wherein the sand body thickness is generated by logging data of the target work area; generating low-frequency impedance data according to the logging data and a geological statistical model; and performing inversion according to the low-frequency impedance data, the original seismic data and the post-stack synthetic seismic record to determine the longitudinal wave impedance of the target work area. According to the invention, inversion is carried out by using the phase-controlled low-frequency model, and an accurate elastic result is successfully obtained. The technical problems that in a traditional method, models are simplified, and complex underground low-frequency information features are difficult to capture are effectively solved.

Description

Technical Field

[0001] This disclosure relates to the field of petroleum geophysical exploration technology, particularly to the field of elastic inversion technology for tight formations in petroleum geophysical exploration, specifically to a post-stack inversion method, apparatus, equipment, storage medium, and computer program based on phase-controlled low-frequency modeling. Background Technology

[0002] With the continuous advancement of oil and gas exploration and development technologies, oilfields are actively promoting research and development of complex oil and gas reservoirs. In this process, three-dimensional elastic property information has become the core foundation for seismic inversion and other quantitative seismic interpretation work. To obtain high-precision three-dimensional elastic property information, elastic inversion technology has become an indispensable tool.

[0003] In existing inversion methods (such as constrained sparse pulse inversion), low-frequency variation trends are a key parameter. However, the only feasible way to obtain this parameter is currently through sampling the target subsurface layer using well data. Typically, technicians use seismic horizons to establish a geological grid framework and extend the elastic information from the wells into space using well interpolation methods. However, this method is relatively reliable in areas with wells and close to them, but for spaces far from wells, the reliability of the elastic information is often difficult to guarantee. Therefore, exploring a new method that can reliably obtain low-frequency variation trends has become an urgent need. Summary of the Invention

[0004] To address the following technical problems in existing technologies, this disclosure provides a post-stack inversion method, apparatus, device, storage medium, and computer program based on phased-array low-frequency modeling. Currently, there is still a lack of effective solutions to the problem of missing low-frequency data in various inversion methods. Existing spatial interpolation methods (such as using seismic horizons to establish a geological grid framework and extending the elastic information from the wellbore into space through well interpolation methods) often fail to accurately estimate low-frequency elastic information. Due to the lack of spatial constraints, this affects the final elastic inversion results.

[0005] In a first aspect, this disclosure provides a post-stack inversion method based on phased low-frequency modeling, including:

[0006] Seismic attributes are extracted from the raw seismic data, wherein the seismic attributes are used to characterize the geological sedimentary facies of the target work area;

[0007] The geological statistical simulation process of the target work area is constrained based on the seismic attributes and the sand body thickness of the target work area to generate a geological statistical model of the target work area; wherein, the sand body thickness is generated from the well logging data of the target work area;

[0008] Low-frequency impedance data is generated based on the well logging data and the geostatistical model.

[0009] The P-wave impedance of the target work area is determined by inversion based on the low-frequency impedance data, the original seismic data, and the post-stack synthetic seismic record.

[0010] In some embodiments of this disclosure, the geostatistical simulation process of the target work area is constrained based on the seismic attributes and the sand body thickness of the target work area to generate a geostatistical model of the target work area, including:

[0011] The sand-to-land ratio of the target work area is determined based on the earthquake properties and the sand body thickness.

[0012] Based on the aforementioned sand-to-land ratio generation lithological probability constraint conditions;

[0013] The geological statistical simulation process of the target work area is constrained according to the lithological probability constraints to generate a geological statistical model of the target work area.

[0014] In some embodiments of this disclosure, low-frequency impedance data is generated based on the well logging data and a geostatistical model, including:

[0015] Based on the well logging data, determine the impedance data corresponding to each lithology in the target work area;

[0016] Based on the impedance data, generate the impedance probability distribution function corresponding to the lithology of the target work area;

[0017] Low-frequency impedance data are generated based on the impedance probability distribution function and the geostatistical model.

[0018] In some embodiments of this disclosure, inversion is performed based on the low-frequency impedance data, the original seismic data, and the post-stack synthetic seismic record, including:

[0019] A low-frequency variation trend objective function is generated based on the low-frequency impedance data during the inversion process;

[0020] A seismic data objective function is generated based on the original seismic data and the post-stack synthetic seismic record.

[0021] An inversion objective function is generated based on the low-frequency variation trend objective function, the seismic data objective function, and the lithological spatial variation function for inversion.

[0022] In some embodiments of this disclosure, after determining the longitudinal wave impedance of the target work area, the method further includes:

[0023] Based on the rock physical characteristics in the well logging data, determine the lithological impedance thresholds in different strata;

[0024] The three-dimensional lithological data of the target work area are determined based on the longitudinal wave impedance and the lithological impedance threshold.

[0025] In some embodiments of this disclosure, the seismic property is the energy half-decay time.

[0026] Secondly, this disclosure provides a post-stack inversion device based on phased-array low-frequency modeling, comprising:

[0027] The seismic attribute extraction module is used to extract seismic attributes from the raw seismic data, wherein the seismic attributes are used to characterize the geological sedimentary facies of the target work area;

[0028] The geological statistical simulation constraint module is used to constrain the geological statistical simulation process of the target work area based on the seismic attributes and the sand body thickness of the target work area, so as to generate a geological statistical model of the target work area; wherein, the sand body thickness is generated from the well logging data of the target work area;

[0029] The low-frequency impedance generation module is used to generate low-frequency impedance data based on the well logging data and the geostatistical model.

[0030] The P-wave impedance determination module is used to perform inversion based on the low-frequency impedance data, the original seismic data, and the post-stack synthetic seismic record to determine the P-wave impedance of the target work area.

[0031] In some embodiments of this disclosure, the geostatistical simulation constraint module includes:

[0032] A sand-to-land ratio determination unit is used to determine the sand-to-land ratio of the target work area based on the seismic properties and the sand body thickness.

[0033] The lithological probability constraint generation unit is used to generate lithological probability constraint conditions based on the sand-land ratio.

[0034] The geostatistical model generation unit is used to constrain the geostatistical simulation process of the target work area according to the lithological probability constraint conditions, so as to generate the geostatistical model of the target work area.

[0035] In some embodiments of this disclosure, the low-frequency impedance generation module includes:

[0036] Impedance data determination unit, used to determine the impedance data corresponding to each lithology in the target work area based on the well logging data;

[0037] An impedance probability distribution function generation unit is used to generate an impedance probability distribution function corresponding to the lithology of the target work area based on the impedance data.

[0038] The low-frequency impedance generation unit is used to generate low-frequency impedance data based on the impedance probability distribution function and the geostatistical model.

[0039] In some embodiments of this disclosure, the longitudinal wave impedance determination module includes:

[0040] The low-frequency variation trend objective function generation unit is used to generate the low-frequency variation trend objective function in the inversion process based on the low-frequency impedance data.

[0041] The seismic data objective function generation unit is used to generate a seismic data objective function based on the original seismic data and the post-stack synthetic seismic record.

[0042] The inversion objective function generation unit is used to generate an inversion objective function based on the low-frequency variation trend objective function, the seismic data objective function, and the lithological spatial variation function, so as to perform inversion.

[0043] In some embodiments of this disclosure, a post-stack inversion device based on phased-array low-frequency modeling further includes:

[0044] The lithological impedance threshold determination module is used to determine the lithological impedance threshold in different strata based on the rock physical characteristics in the well logging data.

[0045] The three-dimensional lithological body data determination module is used to determine the three-dimensional lithological body data of the target work area based on the longitudinal wave impedance and the lithological impedance threshold.

[0046] In some embodiments of this disclosure, the seismic property is the energy half-decay time.

[0047] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the foregoing aspects.

[0048] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the methods described in the above aspects.

[0049] Fifthly, this disclosure provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the methods described in the foregoing aspects.

[0050] This disclosure provides a post-stack inversion method, apparatus, equipment, storage medium, and computer program based on phase-controlled low-frequency modeling. The corresponding post-stack inversion method based on phase-controlled low-frequency modeling includes: First, by deeply analyzing the seismic attributes in the original seismic data, the most suitable seismic attributes are selected for geological sedimentary facies analysis. Using well logging data and lithological thickness information, the sand-to-soil ratio on the geological plane is accurately determined, laying the foundation for the construction of the phase-controlled geological model. Next, the sand-to-soil ratio on the geological plane is used as a probabilistic constraint on sandstone and mudstone lithology and input into the geostatistical simulation process. Simultaneously, the impedance probability distribution function information corresponding to the lithology in the well logging data is statistically analyzed, and appropriate range and variation are set to achieve accurate construction of phase-controlled low-frequency impedance. Subsequently, the constructed phase-controlled low-frequency impedance is used as the low-frequency input of a deterministic inversion algorithm. Combined with seismic data and wavelet information (post-stack synthetic seismic records), the accurate result of the true subsurface P-wave impedance is successfully inverted, providing technical support for efficient prediction of P-wave impedance. Finally, through detailed statistical analysis of the rock physical characteristics in the well logging data and calculation based on the impedance-lithology threshold of different layers, three-dimensional lithological information was successfully obtained.

[0051] The method disclosed herein not only opens up new avenues for obtaining low-frequency elastic information under complex geological structures, but also provides strong support for technological advancements and practical applications in related fields, demonstrating broad application potential and significant practical value.

[0052] In summary, the method provided in this disclosure can accurately analyze low-frequency elastic information under complex geological conditions, thus providing strong technical support for the three-dimensional elastic prediction of this type of oil and gas reservoir. The method not only optimizes existing seismic exploration techniques but also lays a solid foundation for the efficient and safe development of future oil and gas resources. Attached Figure Description

[0053] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0054] Figure 1 This is a schematic flowchart of a post-stack inversion method based on phased low-frequency modeling provided in an embodiment of this disclosure.

[0055] Figure 2 This is a flowchart illustrating step 200 of a post-stack inversion method based on phased low-frequency modeling provided in an embodiment of this disclosure.

[0056] Figure 3 This is a flowchart illustrating step 300 of a post-stack inversion method based on phased low-frequency modeling provided in an embodiment of this disclosure.

[0057] Figure 4This is a flowchart illustrating step 400 of a post-stack inversion method based on phased low-frequency modeling provided in an embodiment of this disclosure.

[0058] Figure 5 This is another flowchart illustrating a post-stack inversion method based on phased low-frequency modeling provided in this disclosure.

[0059] Figure 6 This is a flowchart illustrating a post-stack inversion method based on phased low-frequency modeling, which is provided as an application example of this disclosure.

[0060] Figure 7 A schematic diagram of the energy half-life properties provided for an application example of this disclosure.

[0061] Figure 8 A schematic diagram of sand-to-land ratio provided as an application example of this disclosure.

[0062] Figure 9 A schematic diagram of phase-controlled constrained low-frequency longitudinal wave impedance provided as an application example of this disclosure.

[0063] Figure 10 A schematic diagram of the longitudinal wave impedance results provided for an application example of this disclosure.

[0064] Figure 11 A schematic diagram of the lithological intersection analysis results provided for this application example.

[0065] Figure 12 A three-dimensional schematic diagram of a lithological body provided as an application example of this disclosure.

[0066] Figure 13 A schematic diagram of the planar sand body distribution provided as an application example of this disclosure.

[0067] Figure 14 A block diagram of a post-stack inversion device based on phased low-frequency modeling provided in this disclosure embodiment.

[0068] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0069] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0070] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0071] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0072] Example 1

[0073] This disclosure provides a post-stack inversion method based on phased low-frequency modeling. Figure 1 This is a flowchart illustrating a post-stack inversion method based on phased-controlled low-frequency modeling, provided in an embodiment of this disclosure. Figure 1 As shown, a post-stack inversion method based on phased-controlled low-frequency modeling includes:

[0074] Step 100: Extract seismic attributes from the raw seismic data, wherein the seismic attributes are used to characterize the geological sedimentary facies of the target work area;

[0075] Step 200: Constrain the geostatistical simulation process of the target work area based on the seismic attributes and the sand body thickness of the target work area to generate a geostatistical model of the target work area; wherein, the sand body thickness is generated from the well logging data of the target work area;

[0076] Step 300: Generate low-frequency impedance data based on the well logging data and the geostatistical model;

[0077] Step 400: Perform inversion based on the low-frequency impedance data, the original seismic data, and the post-stack synthetic seismic record to determine the P-wave impedance of the target work area.

[0078] This disclosure provides a post-stack inversion method based on phase-controlled low-frequency modeling, comprising: first, extracting seismic attributes from the original seismic data, wherein the seismic attributes are used to characterize the geological sedimentary facies of the target work area; constraining the geostatistical simulation process of the target work area according to the seismic attributes and the sand body thickness of the target work area to generate a geostatistical model of the target work area; wherein the sand body thickness is generated from the well logging data of the target work area; generating low-frequency impedance data according to the well logging data and the geostatistical model; and performing inversion based on the low-frequency impedance data, the original seismic data, and the post-stack synthetic seismic record to determine the P-wave impedance of the target work area.

[0079] In summary, the method provided in this disclosure solves the technical challenge of establishing complex models under different geological backgrounds in order to obtain reliable elastic inversion results. The method significantly improves the accuracy of seismic inversion and effectively acquires low-frequency elastic information. Furthermore, this method can significantly optimize seismic inversion results, reduce the impact of missing low-frequency data, and further provide more accurate three-dimensional elastic predictions for oil and gas reservoirs under complex geological conditions.

[0080] Example 2

[0081] The seismic attributes in step 100 refer to features or parameters extracted from seismic exploration data. These attributes are used to identify and assess subsurface oil and gas reservoirs. Preferably, the seismic attributes in step 100 include:

[0082] Amplitude: Reflects the intensity of seismic waves reflected at stratigraphic interfaces. Variations in amplitude can indicate the interfaces between different geological layers, identifying the boundaries and thicknesses of oil and gas reservoirs.

[0083] Phase: The phase information of seismic waves provides the relative time of reflection events. Phase reversal can be an indicator of the presence of oil and gas, especially in high-porosity reservoirs.

[0084] Frequency: Frequency component analysis of seismic waves helps distinguish different types of formations and fluids. Typically, the reflection signals from oil and gas reservoirs have specific frequency characteristics.

[0085] Velocity: Changes in the propagation velocity of seismic waves can reflect the density and elastic modulus of underground rock formations. Velocity analysis is used to determine the lithology and porosity of formations and can help calculate reservoir depth.

[0086] Amplitude variation with displacement: AVO analysis examines the relationship between amplitude and distance variation between the seismic receiver and the source, and is used to identify variations in fluid type (such as gas, water, oil) and reservoir lithology.

[0087] Waveforms: Morphological analysis of seismic waveforms helps identify the properties of subsurface structures and fluids. Waveform comparison techniques can be used to detect subtle changes in geological formations.

[0088] Reflection coefficient: The intensity of seismic wave reflection at different stratigraphic interfaces, reflecting the differences in physical properties on both sides of the interface. A high reflection coefficient usually indicates a significant change in density or wave velocity, which may be related to oil and gas reservoirs.

[0089] Time-depth conversion: The time information in seismic data is converted into depth information through a velocity model to create a depth map of the subsurface structure, which is crucial for determining the actual depth of the reservoir.

[0090] Elastic impedance: A parameter that links seismic reflection data to the elastic properties of the formation, used to refine AVO analysis results and enhance the identification of reservoir fluid and lithological variations.

[0091] Anisotropic properties: These describe the variation in the propagation velocity of seismic waves in different directions within subsurface rocks. These properties can reveal the density and directionality of fractures in formations, helping to assess reservoir permeability.

[0092] Co-reflection point overlay: By overlaying multiple reflection data from the same point, the signal-to-noise ratio is enhanced, providing a clearer underground image.

[0093] Inversion properties: Subsurface physical parameters obtained through seismic data inversion, such as acoustic impedance and shear impedance. These properties directly reflect the lithology, porosity, and fluid properties of the strata.

[0094] Velocity analysis: used to construct subsurface velocity models to aid in the time-depth conversion of seismic data and quantitative evaluation of reservoirs.

[0095] Step 200, generating a lithological model using geostatistical methods combined with well logging data and geostatistical data, is a comprehensive task that typically includes the following steps:

[0096] Acquire relevant well logging data, such as natural gamma-ray logging (GR), sonic logging (AC), resistivity logging (RT), density logging (RHOB), and neutron logging (NPHI). This data provides information on the physical properties of different formations, which helps in lithology identification. Collect relevant geological statistics, such as formation thickness, sedimentary environment information, and lithofacies distribution. This data provides spatial distribution information, which helps in constructing lithological models. Collect well point data, including well location coordinates, depth, and lithological descriptions.

[0097] Next, the well logging data is preprocessed, including noise removal, missing data filling, and standardization. Preliminary statistical analysis is then performed on the well logging data and geological statistics to identify the well logging response characteristics of different lithologies and to establish statistical models for each lithology type.

[0098] Using well logging data, cross-plots or cluster analysis are employed to classify formations into different lithological categories (such as sandstone, mudstone, and limestone). Combined with existing lithological descriptions (such as core analysis and drilling reports), well logging curves are calibrated to determine the lithology corresponding to each curve.

[0099] The variation function of well logging data is calculated to analyze its spatial variability and determine anisotropy and autocorrelation distance. Finally, Monte Carlo simulation or other stochastic simulation methods are used to generate multiple possible lithological distribution models. This method, based on geostatistical models, generates possible lithological distributions, reflecting the uncertainty of lithology.

[0100] The generated two-dimensional lithology distribution is extended along the wellbore depth to construct a three-dimensional lithology model. This model can display the spatial distribution of subsurface lithology, providing a basis for subsequent oil and gas reservoir evaluation.

[0101] Regarding step 300, low-frequency impedance data refers to the rock layer impedance information calculated through seismic wave reflection and transmission characteristics, particularly the impedance characteristics related to low-frequency components. Low-frequency impedance data can penetrate thick sedimentary layers, providing information about deep geological structures. This is of great significance for identifying deep oil and gas reservoirs and basement structures. Furthermore, low-frequency impedance data helps identify large-scale geological structural features, such as faults, folds, and large sedimentary units, which are often difficult to distinguish in high-frequency data.

[0102] Step 400, inversion, is a method for extracting subsurface structural and physical property parameters from seismic wave data. Specifically, it involves using seismic wave reflection, refraction, or transmission data, combined with mathematical models and algorithms, to infer the physical properties of subsurface geological bodies (such as velocity, density, elastic modulus, etc.). The inversion process aims to deduce the most realistic subsurface model from observational data.

[0103] Example 3

[0104] Based on the above embodiments, see Figure 2 Step 200 includes:

[0105] Step 201: Determine the sand-to-land ratio of the target work area based on the seismic attributes and the sand body thickness;

[0106] First, a thorough study was conducted on seismic properties and well logging sand body thickness. To more accurately describe the geological sedimentary facies, the most suitable seismic property was selected. Through combined analysis of this property and sand body thickness, a geological lithofacies distribution map was determined to ascertain the sand-to-soil ratio.

[0107] In the field of petroleum geology, the sandstone-to-soil ratio refers to the ratio of the volume or thickness of sandstone in a reservoir to the total volume or thickness of the entire reservoir, usually expressed as a percentage. For example, if a reservoir has a total thickness of 100 meters, of which 50 meters are sandstone, then the sandstone-to-soil ratio is 50%.

[0108] Step 202: Generate lithological probability constraints based on the sand-land ratio;

[0109] Step 203: Constrain the geological statistical simulation process of the target work area according to the lithological probability constraint conditions to generate a geological statistical model of the target work area.

[0110] For steps 202 and 203: When specific distribution characteristics exist in the spatial lithology, the inversion process typically relies on the three-dimensional constraints of seismic data to obtain inversion results that conform to sedimentary characteristics. However, when seismic data lacks low-frequency components reflecting sedimentary trends, the constraint effect of seismic data on the distribution trend of sedimentary trends is significantly reduced. When the statistical distribution of lithological elastic parameters is severely overlaid, the constraint effect of seismic data is further weakened, causing seismic data to lose its guiding role in sedimentary trends. In this case, it is necessary to incorporate the constraints of sedimentary facies and sedimentary model research results into the geostatistical inversion to compensate for the inadequacy of seismic constraints. In facies-controlled or lithology-constrained geostatistical inversions, the constrained data volumes include vertical one-dimensional lithological assemblages, planar two-dimensional lithofacies / lithology maps (such as sand-to-land ratio maps), and spatial three-dimensional lithological data volumes (such as special lithological bodies like coal seams).

[0111] Based on the above embodiments, see Figure 3 Step 300 includes:

[0112] Step 301: Determine the impedance data corresponding to each lithology in the target work area based on the well logging data;

[0113] The purpose of step 301 is to make the individual lithologies in the lithology model have a certain degree of distinguishability in terms of elastic statistical distribution (when the elastic parameter statistical distributions of individual lithologies overlap significantly, it is necessary to merge lithologies to reduce the uncertainty of lithology prediction).

[0114] Step 302: Generate the impedance probability distribution function corresponding to the lithology of the target work area based on the impedance data;

[0115] Step 303: Generate low-frequency impedance data based on the impedance probability distribution function and the geostatistical model.

[0116] Based on the above embodiments, see Figure 4 Step 400 includes:

[0117] Step 401: Generate an objective function for the low-frequency variation trend during the inversion process based on the low-frequency impedance data;

[0118] Step 402: Generate a seismic data objective function based on the original seismic data and the post-stack synthetic seismic record;

[0119] Step 403: Generate an inversion objective function based on the low-frequency variation trend objective function, the seismic data objective function, and the lithological spatial variation function, and perform inversion.

[0120] Specifically, the preferred inversion method is the post-stack constrained sparse pulse inversion method (which inverts post-stack seismic data into a sequence of stratigraphic reflection coefficients). This method adds constraints to the traditional sparse pulse inversion method to process post-stack seismic data.

[0121] Specifically, it is assumed that the reflection coefficient sequence of the seismic record is sparse, meaning that only a few reflection events occur at any given time. This assumption is consistent with the characteristics of the layered structure of the subsurface medium. To enhance the stability and reliability of the inversion, geological or geophysical constraints are incorporated into the inversion process, such as the sign of the reflection coefficients, the continuity of the strata, and constraints on well data. These constraints can prevent overfitting and unreasonable solutions in the inversion results. The subsurface reflection coefficient sequence is obtained by minimizing the residuals of the inversion model (i.e., the difference between observed seismic data and synthetic seismic data) and the sparsity constraint of the reflection coefficients.

[0122] Based on the above post-stack constrained sparse pulse inversion method, for steps 401 to 403:

[0123] Post-stack constrained sparse impulse inversion, based on maximum likelihood inversion, incorporates sparsity assumptions, trend constraints, and spatial constraints into the inversion objective function F:

[0124] F = F seismic +F contrast +Ftrend +F spatial

[0125] Among them, F contrast It is the objective function of elastic parameters, representing the sparsity and constraints of estimating the longitudinal wave impedance, transverse wave impedance, and density relative reflection coefficient.

[0126] F trend It is the objective function for low-frequency change trends;

[0127] F spatial It is the objective function for spatial change trends;

[0128] F seismic This is the objective function for seismic data, representing the degree of fit between actual observed seismic data and synthetic seismic records. Its mathematical expression is:

[0129]

[0130] Where seismic is the actual seismic data after stacking (i.e., full stacking, Substacks=1);

[0131] synthetic refers to post-stack synthetic seismic records;

[0132] Multiplier seismic represents the weighting coefficient of earthquakes in the objective function.

[0133] Example 4

[0134] Based on the above embodiments, see Figure 5 A post-stack inversion method based on phased low-frequency modeling further includes the following steps after step 400:

[0135] Step 500: Determine the lithological impedance thresholds in different strata based on the rock physical characteristics in the well logging data;

[0136] First, well logging data including sonic transit time (DT), density (RHOB), and resistivity (RES) are collected. Then, based on the changes in the well logging curves and existing geological data, different lithological layers (such as sandstone, mudstone, and limestone) are identified. The lithological impedance (Z) is calculated using the following formula:

[0137] Z = RHOB × Vp

[0138] Where Vp is the velocity of sound and RHOB is the density.

[0139] Finally, statistical analysis was performed on the lithological impedance values ​​of each stratum, calculating the mean, standard deviation, etc. Based on the statistical results, a lithological impedance threshold was set. The threshold can be determined using quantile methods (such as quartiles) to facilitate the differentiation of different lithologies.

[0140] Step 600: Determine the three-dimensional lithological data of the target work area based on the longitudinal wave impedance and the lithological impedance threshold.

[0141] Based on the above embodiments, the seismic attribute is the energy half-decay time.

[0142] To more accurately describe geological sedimentary facies, the most suitable seismic attribute, energy half-decay attribute, was selected. The energy half-decay attribute refers to the relative time position at which the seismic reflected wave energy reaches 1 / 2 within a given analysis window. Its basic algorithm first calculates the cumulative amplitude of each sampling point within the given time window, then calculates the relative time position when the energy reaches 1 / 2, i.e., the relative change in amplitude within the analysis window.

[0143] This disclosure provides a post-stack inversion method based on phase-controlled low-frequency modeling, comprising: first, extracting seismic attributes from the original seismic data, wherein the seismic attributes are used to characterize the geological sedimentary facies of the target work area; constraining the geostatistical simulation process of the target work area according to the seismic attributes and the sand body thickness of the target work area to generate a geostatistical model of the target work area; wherein the sand body thickness is generated from the well logging data of the target work area; generating low-frequency impedance data according to the well logging data and the geostatistical model; and performing inversion based on the low-frequency impedance data, the original seismic data, and the post-stack synthetic seismic record to determine the P-wave impedance of the target work area.

[0144] The method provided in this disclosure can effectively solve the problem of inaccurate inversion caused by the lack of low-frequency data, and can provide more accurate three-dimensional elastic prediction for oil and gas reservoir exploration in complex geological backgrounds.

[0145] Example 5

[0146] To further illustrate the solution, based on the above embodiments, this embodiment takes region A as an example, see [link to example]. Figure 6 This paper provides an application example to further explain a post-stack inversion method based on phased low-frequency modeling.

[0147] S1: Determine the sand-to-land ratio on the geological plane.

[0148] First, a thorough study was conducted on seismic properties and well logging sandbody thickness. To more accurately describe geological sedimentary facies, the most suitable seismic property, namely the energy half-decay property, was selected. By combining this property with well logging sandbody thickness analysis, a geological lithofacies distribution map was determined, providing basic data and guidance for subsequent steps.

[0149] Specifically, by performing seismic attribute analysis on the raw seismic data, the energy half-life attribute with the best performance was selected for geological sedimentary facies analysis (see [link]). Figure 7Based on well logging data and lithological thickness, the sand-to-soil ratio on the geological plane is determined (see...). Figure 8 This allows for the establishment of a phase-controlled geological model.

[0150] S2: Establish a lithological model.

[0151] Building upon geostatistical simulations, this study further deepened the understanding of lithology, leading to the development of a lithological model. This model not only considers sedimentary patterns and reservoir targets but also takes into account the distinctiveness of lithologies in their elastic parameters. Ensuring that each lithology exhibits significant differences in its elastic statistical distribution improves the model's accuracy.

[0152] Specifically, using geostatistical methods, combined with well logging and geostatistical data, a horizontally continuous lithological model with high consistency with seismic data was generated. The goal of this step was to ensure that the generated discrete lithology and continuous elastic parameter results were consistent with the geostatistical model and seismic data.

[0153] In lithological models, lithology differs from conventional geological modeling. It is not only based on sedimentary patterns and reservoir targets but also requires that each lithology exhibit a certain degree of distinguishability in its elastic statistical distribution. When the statistical distributions of elastic parameters of individual lithologies severely overlap, lithologies need to be merged to reduce the uncertainty of lithology prediction. Lithology proportion refers to the percentage of each lithology within the entire inversion parameter interval, usually expressed as a percentage. Similar to the probability of sand-soil ratio, it can typically be estimated through multi-well lithological statistics or sedimentary facies and sedimentary patterns. Lithological spatial variability functions are mathematical tools describing lithological continuity, divided into vertical and lateral variability functions. These variability functions describe the degree to which regionalized variables change with distance.

[0154] Lithological spatial variability is a mathematical tool used to describe the spatial continuity of lithology, and it is divided into vertical variability and lateral variability. A variability function is a tool for describing how the spatial variability of a regionalized variable changes with distance. Let u represent a spatial location (dimensionless), and Z(u) represent a random variable at u (regionalized variable, dimensionless), then the variability function of Z can be expressed as:

[0155]

[0156] Where h is the lag distance (dimensionless), i.e., the spatial vector distance, and E represents the mean of the statistical sample (dimensionless). Assuming Z(u+h)-Z(u)=0, the variogram can be written as:

[0157]

[0158] The variogram can take various mathematical forms, such as exponential, Gaussian, and mixed types, depending on the rate of spatial variation. The spatial correlation range of lithological variables is described by the range parameter. Within the range, the data are correlated; outside the range, the data are uncorrelated, meaning that observations outside the range do not affect the estimation results.

[0159] It should be noted that the premise of using variograms to describe the spatial variation of lithology is that the spatial part of lithology is random, that is, there is no specific vertical lithological combination structure or lateral lithological distribution trend.

[0160] S3: Establish a low-frequency trend model.

[0161] To supplement the missing low-frequency information in measured seismic data, a facies-controlled geologically constrained low-frequency model was specifically designed. This model considers vertical lithological assemblage, planar lithofacies distribution, and spatial three-dimensional lithological characteristics, thus providing a more comprehensive description of the low-frequency characteristics of the strata.

[0162] Specifically, the geological plane sand-soil ratio is used as a probabilistic constraint on sandstone and mudstone lithology in the geostatistical simulation, and the impedance probability distribution function information corresponding to the lithology in the well logging data is statistically analyzed. Appropriate range and variability are set (here, the plane range is 3000 meters, and the vertical resolution is 1 millisecond), and low-frequency impedance information is simulated (see...). Figure 9 This enables the construction of phase-controlled low-frequency impedance.

[0163] S4: Perform post-stack constrained sparse impulse inversion.

[0164] After obtaining the phase-controlled geologically constrained low-frequency model, it is used for post-stack constrained sparse pulse inversion. This step aims to obtain the absolute wave impedance model and merge the unconstrained reflection coefficients with the low-frequency model to obtain more complete and accurate seismic inversion results.

[0165] Seismic reflection originates from the elastic phenomenon where differences in elastic parameters such as impedance within different strata cause seismic waves to be reflected at the stratum interface (elastic interface). For a given vertically incident seismic wave, the intensity of the reflected wave at the elastic interface is determined by the reflection coefficients of the longitudinal wave impedance on both sides of the interface.

[0166]

[0167] Post-stack constrained sparse impulse inversion, based on maximum likelihood inversion, incorporates sparsity assumptions, trend constraints, and spatial constraints into the inversion objective function.

[0168] Furthermore, using phase-controlled low-frequency impedance as the low-frequency input to the deterministic inversion algorithm, combined with seismic data and wavelet information, the actual subsurface P-wave impedance results are obtained (see...). Figure 10This enables the prediction of longitudinal wave impedance.

[0169] It should be noted that constrained sparse pulse inversion yields an absolute wave impedance model. The low-frequency components missing in the measured seismic data cannot be directly obtained using sparse pulse inversion; a low-frequency trend model needs to be established as a constraint for the inversion. In the reflectivity domain, unconstrained reflection coefficient inversion of the seismic data is first performed. The resulting reflection coefficient sequence and the low-frequency trend model are then combined and integrated for wave impedance calculation. When selecting the merging frequency of the unconstrained reflection coefficients and the low-frequency model, it is crucial to avoid missing frequencies in the amplitude spectrum and to obtain as much information as possible from the seismic data.

[0170] S5: Obtain the results of longitudinal wave impedance and lithology.

[0171] Deterministic inversion was performed using a phase-controlled geologically constrained low-frequency model to obtain the final P-wave impedance results. Based on these results and statistical analysis of well logging petrophysical characteristics, the impedance threshold of sandstone and mudstone was determined, thereby obtaining the three-dimensional lithological results.

[0172] Specifically, statistical analysis was performed based on the petrophysical characteristics in the well logging data. Among these, the impedance threshold value of the mudstone and sandstone in the upper sub-member of the fourth section was 1.23e. 7 kg / m 3 m / s, the impedance threshold values ​​for mudstone and sandstone in the lower sub-members of the fourth and second sub-members are 1.3e 7 kg / m 3 m / s. For example... Figure 11 As shown, the horizontal axis represents longitudinal wave impedance, and the vertical axis represents density. Red represents sandstone, and gray represents mudstone.

[0173] Three-dimensional P-wave impedance data were obtained through deterministic inversion using a phase-controlled geologically constrained low-frequency model. Based on the impedance-lithology thresholds of different layers, a three-dimensional lithological body was calculated (see [link to model]). Figure 12 Finally, extract the sand body planar distribution map (see...). Figure 13 ).

[0174] As described above, the post-stack inversion method based on phase-controlled low-frequency modeling provided in this disclosure successfully obtains accurate elastic results by using a phase-controlled low-frequency model for elastic sparse pulse inversion. This effectively solves the problem of traditional methods relying on a single model, making it difficult to capture complex subsurface low-frequency information features. It provides substantial verification of the prediction results, thus intuitively confirming the reliability of the reservoir lithology identification method.

[0175] More importantly, the equivalent elastic parameters obtained through the post-stack inversion method based on phase-controlled low-frequency modeling can be directly applied to the physical property prediction work of reservoir prediction, providing more accurate quantitative guidance for oil and gas reservoir exploration and development. Furthermore, the equivalent elastic parameters obtained through the post-stack inversion method based on phase-controlled low-frequency modeling can be directly used for physical property prediction in reservoir prediction, providing better quantitative guidance for oil and gas reservoir exploration and development.

[0176] Example 6

[0177] Based on the same inventive concept, this application also provides a post-stack inversion device based on phased-controlled low-frequency modeling, which can be used to implement the method described in the above embodiments, as shown in the following embodiments. Since the principle of the post-stack inversion device based on phased-controlled low-frequency modeling is similar to that of the post-stack inversion method based on phased-controlled low-frequency modeling, the implementation of the post-stack inversion device based on phased-controlled low-frequency modeling can refer to the implementation of the post-stack inversion method based on phased-controlled low-frequency modeling, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0178] Embodiments of the present invention provide a specific implementation of a post-stack inversion device based on phased low-frequency modeling capable of realizing a post-stack inversion method based on phased low-frequency modeling, wherein, see... Figure 14 A post-stack inversion device based on phased-array low-frequency modeling includes:

[0179] The seismic attribute extraction module 10 is used to extract seismic attributes from the raw seismic data, wherein the seismic attributes are used to characterize the geological sedimentary facies of the target work area;

[0180] The geological statistical simulation constraint module 20 is used to constrain the geological statistical simulation process of the target work area based on the seismic attributes and the sand body thickness of the target work area, so as to generate a geological statistical model of the target work area; wherein, the sand body thickness is generated from the well logging data of the target work area;

[0181] The low-frequency impedance generation module 30 is used to generate low-frequency impedance data based on the well logging data and the geostatistical model.

[0182] The P-wave impedance determination module 40 is used to perform inversion based on the low-frequency impedance data, the original seismic data, and the post-stack synthetic seismic record to determine the P-wave impedance of the target work area.

[0183] In some embodiments of this disclosure, the geostatistical simulation constraint module includes:

[0184] A sand-to-land ratio determination unit is used to determine the sand-to-land ratio of the target work area based on the seismic properties and the sand body thickness.

[0185] The lithological probability constraint generation unit is used to generate lithological probability constraint conditions based on the sand-land ratio.

[0186] The geostatistical model generation unit is used to constrain the geostatistical simulation process of the target work area according to the lithological probability constraint conditions, so as to generate the geostatistical model of the target work area.

[0187] In some embodiments of this disclosure, the low-frequency impedance generation module includes:

[0188] Impedance data determination unit, used to determine the impedance data corresponding to each lithology in the target work area based on the well logging data;

[0189] An impedance probability distribution function generation unit is used to generate an impedance probability distribution function corresponding to the lithology of the target work area based on the impedance data.

[0190] The low-frequency impedance generation unit is used to generate low-frequency impedance data based on the impedance probability distribution function and the geostatistical model.

[0191] In some embodiments of this disclosure, the longitudinal wave impedance determination module includes:

[0192] The low-frequency variation trend objective function generation unit is used to generate the low-frequency variation trend objective function in the inversion process based on the low-frequency impedance data.

[0193] The seismic data objective function generation unit is used to generate a seismic data objective function based on the original seismic data and the post-stack synthetic seismic record.

[0194] The inversion objective function generation unit is used to generate an inversion objective function based on the low-frequency variation trend objective function, the seismic data objective function, and the lithological spatial variation function, so as to perform inversion.

[0195] In some embodiments of this disclosure, a post-stack inversion device based on phased-array low-frequency modeling further includes:

[0196] The lithological impedance threshold determination module is used to determine the lithological impedance threshold in different strata based on the rock physical characteristics in the well logging data.

[0197] The three-dimensional lithological body data determination module is used to determine the three-dimensional lithological body data of the target work area based on the longitudinal wave impedance and the lithological impedance threshold.

[0198] In some embodiments of this disclosure, the seismic property is the energy half-decay time.

[0199] This disclosure provides a post-stack inversion device based on phase-controlled low-frequency modeling, comprising: a seismic attribute extraction module for extracting seismic attributes from raw seismic data, wherein the seismic attributes characterize the geological sedimentary facies of the target work area; a geostatistical simulation constraint module for constraining the geostatistical simulation process of the target work area based on the seismic attributes and the sand body thickness of the target work area to generate a geostatistical model of the target work area; wherein the sand body thickness is generated from well logging data of the target work area; a low-frequency impedance generation module for generating low-frequency impedance data based on well logging data and the geostatistical model; and a P-wave impedance determination module for performing inversion based on the low-frequency impedance data, raw seismic data, and post-stack synthetic seismic records to determine the P-wave impedance of the target work area.

[0200] In summary, the apparatus provided in this disclosure can accurately analyze low-frequency elastic information under complex geological conditions, thus providing strong technical support for the three-dimensional elastic prediction of this type of oil and gas reservoir. The method provided in this disclosure not only optimizes existing seismic exploration techniques but also lays a solid foundation for the efficient and safe development of future oil and gas resources.

[0201] Example 7

[0202] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0203] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the steps of the method described in the above embodiments, specifically including the following:

[0204] Seismic attributes are extracted from the raw seismic data, wherein the seismic attributes are used to characterize the geological sedimentary facies of the target work area;

[0205] The geological statistical simulation process of the target work area is constrained based on the seismic attributes and the sand body thickness of the target work area to generate a geological statistical model of the target work area; wherein, the sand body thickness is generated from the well logging data of the target work area;

[0206] Low-frequency impedance data is generated based on the well logging data and the geostatistical model.

[0207] The P-wave impedance of the target work area is determined by inversion based on the low-frequency impedance data, the original seismic data, and the post-stack synthetic seismic record.

[0208] In some embodiments of this disclosure, the geostatistical simulation process of the target work area is constrained based on the seismic attributes and the sand body thickness of the target work area to generate a geostatistical model of the target work area, including:

[0209] The sand-to-land ratio of the target work area is determined based on the earthquake properties and the sand body thickness.

[0210] Based on the aforementioned sand-to-land ratio generation lithological probability constraint conditions;

[0211] The geological statistical simulation process of the target work area is constrained according to the lithological probability constraints to generate a geological statistical model of the target work area.

[0212] In some embodiments of this disclosure, low-frequency impedance data is generated based on the well logging data and a geostatistical model, including:

[0213] Based on the well logging data, determine the impedance data corresponding to each lithology in the target work area;

[0214] Based on the impedance data, generate the impedance probability distribution function corresponding to the lithology of the target work area;

[0215] Low-frequency impedance data are generated based on the impedance probability distribution function and the geostatistical model.

[0216] In some embodiments of this disclosure, inversion is performed based on the low-frequency impedance data, the original seismic data, and the post-stack synthetic seismic record, including:

[0217] A low-frequency variation trend objective function is generated based on the low-frequency impedance data during the inversion process;

[0218] A seismic data objective function is generated based on the original seismic data and the post-stack synthetic seismic record.

[0219] An inversion objective function is generated based on the low-frequency variation trend objective function, the seismic data objective function, and the lithological spatial variation function for inversion.

[0220] In some embodiments of this disclosure, after determining the longitudinal wave impedance of the target work area, the method further includes:

[0221] Based on the rock physical characteristics in the well logging data, determine the lithological impedance thresholds in different strata;

[0222] The three-dimensional lithological data of the target work area are determined based on the longitudinal wave impedance and the lithological impedance threshold.

[0223] In some embodiments of this disclosure, the seismic property is the energy half-decay time.

[0224] In some embodiments of this example, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implements the steps of the method described in the above embodiments, specifically including the following:

[0225] Seismic attributes are extracted from the raw seismic data, wherein the seismic attributes are used to characterize the geological sedimentary facies of the target work area;

[0226] The geological statistical simulation process of the target work area is constrained based on the seismic attributes and the sand body thickness of the target work area to generate a geological statistical model of the target work area; wherein, the sand body thickness is generated from the well logging data of the target work area;

[0227] Low-frequency impedance data is generated based on the well logging data and the geostatistical model.

[0228] The P-wave impedance of the target work area is determined by inversion based on the low-frequency impedance data, the original seismic data, and the post-stack synthetic seismic record.

[0229] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0230] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0231] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0232] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0233] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0234] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0235] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0236] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0237] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A post-stack inversion method based on phased-controlled low-frequency modeling, characterized in that, include: Seismic attributes are extracted from the raw seismic data, wherein the seismic attributes are used to characterize the geological sedimentary facies of the target work area; The geological statistical simulation process of the target work area is constrained based on the seismic attributes and the sand body thickness of the target work area to generate a geological statistical model of the target work area; wherein, the sand body thickness is generated from the well logging data of the target work area; Low-frequency impedance data is generated based on the well logging data and the geostatistical model. The P-wave impedance of the target work area is determined by inversion based on the low-frequency impedance data, the original seismic data, and the post-stack synthetic seismic record.

2. The post-stack inversion method according to claim 1, characterized in that, The geological statistical simulation process of the target work area is constrained based on the seismic attributes and the sand body thickness of the target work area to generate a geological statistical model of the target work area, including: The sand-to-land ratio of the target work area is determined based on the earthquake properties and the sand body thickness. Based on the aforementioned sand-to-land ratio generation lithological probability constraint conditions; The geological statistical simulation process of the target work area is constrained according to the lithological probability constraints to generate a geological statistical model of the target work area.

3. The post-stack inversion method according to claim 1, characterized in that, Low-frequency impedance data is generated based on the well logging data and the geostatistical model, including: Based on the well logging data, determine the impedance data corresponding to each lithology in the target work area; Based on the impedance data, generate the impedance probability distribution function corresponding to the lithology of the target work area; Low-frequency impedance data are generated based on the impedance probability distribution function and the geostatistical model.

4. The post-stack inversion method according to claim 1, characterized in that, Inversion is performed based on the low-frequency impedance data, the original seismic data, and the post-stack synthetic seismic record, including: A low-frequency variation trend objective function is generated based on the low-frequency impedance data during the inversion process; A seismic data objective function is generated based on the original seismic data and the post-stack synthetic seismic record. An inversion objective function is generated based on the low-frequency variation trend objective function, the seismic data objective function, and the lithological spatial variation function for inversion.

5. The post-stack inversion method according to claim 1, characterized in that, After determining the longitudinal wave impedance of the target work area, the following steps are also included: Based on the rock physical characteristics in the well logging data, determine the lithological impedance thresholds in different strata; The three-dimensional lithological data of the target work area are determined based on the longitudinal wave impedance and the lithological impedance threshold.

6. The post-stack inversion method according to any one of claims 1 to 5, characterized in that, The earthquake attribute is the energy half-decay time.

7. A post-stack inversion device based on phased-array low-frequency modeling, characterized in that, include: The seismic attribute extraction module is used to extract seismic attributes from the raw seismic data, wherein the seismic attributes are used to characterize the geological sedimentary facies of the target work area; The geological statistical simulation constraint module is used to constrain the geological statistical simulation process of the target work area based on the seismic attributes and the sand body thickness of the target work area, so as to generate a geological statistical model of the target work area; wherein, the sand body thickness is generated from the well logging data of the target work area; The low-frequency impedance generation module is used to generate low-frequency impedance data based on the well logging data and the geostatistical model. The P-wave impedance determination module is used to perform inversion based on the low-frequency impedance data, the original seismic data, and the post-stack synthetic seismic record to determine the P-wave impedance of the target work area.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the post-stack inversion method based on phased low-frequency modeling as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the post-stack inversion method based on phased low-frequency modeling as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When executed by a processor, the computer program implements the steps of the post-stack inversion method based on phased low-frequency modeling as described in any one of claims 1 to 6.

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