A boundary-preserving seismic wave impedance inversion method, device, equipment and medium

Abstract

The application discloses a boundary-keeping seismic wave impedance inversion method and device, equipment and medium, and relates to the technical field of geological exploration. The application obtains historical seismic data and well logging curve data including well logging impedance parameters of a target area, extracts seismic wavelet data from the historical seismic data, processes the seismic wavelet data to obtain target seismic wavelet data, constructs an initial impedance parameter model according to the well logging curve data, performs forward simulation on the initial impedance parameter model, constructs a composite objective function including a boundary-keeping constraint term and a prior information constraint term, updates the initial impedance parameter model based on the composite objective function to obtain a target impedance parameter model, and obtains an output impedance parameter inversion curve through the target impedance parameter model, thereby improving inversion precision and efficiency, and improving inversion stability and geological restoration capability.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, specifically to a boundary-preserving seismic impedance inversion method, apparatus, equipment, and medium. Background Technology

[0002] Impedance inversion schemes in seismic services can improve the imaging accuracy of subsurface structures and predict reservoir distribution characteristics, playing an important role in the exploration and development of oil and gas reservoirs.

[0003] Existing wave impedance inversion schemes include linear wave impedance inversion methods, Gaussian-constrained smoothness constraint methods, and sparse constraint methods. However, linear wave impedance inversion methods ignore the non-homogeneity of the subsurface medium and the nonlinear effects in wave field propagation, which limits their inversion accuracy under complex geological conditions. Gaussian-constrained smoothness constraint methods tend to make the inversion output too smooth, weakening the detailed features of the stratigraphic interfaces and causing the rock layer boundaries to become blurred, which seriously affects the ability to restore the true stratigraphic morphology. Due to the strong nonlinearity of its objective function, the sparse constraint method cannot avoid local minima, resulting in unstable inversion results or deviations from the true geological conditions.

[0004] Therefore, the existing scheme has the drawbacks of low inversion accuracy, poor ability to restore geological conditions, and poor stability of inversion results. Summary of the Invention

[0005] The technical problem to be solved by the present invention is that the existing schemes have the defects of low inversion accuracy, poor reconstruction ability and poor stability of inversion results. The purpose is to provide a boundary-preserving seismic impedance inversion method to solve the problems of low inversion accuracy, poor reconstruction ability and poor stability of inversion results in the existing schemes.

[0006] This invention is achieved through the following technical solution:

[0007] In a first aspect, this application provides a boundary-preserving seismic impedance inversion method, comprising:

[0008] Acquire historical seismic data and well logging curve data for the target area, and extract seismic wavelet data from the historical seismic data, wherein the well logging curve data includes well logging impedance parameters;

[0009] The seismic wavelet data is processed to obtain the target seismic wavelet data;

[0010] Based on the well logging data, an initial impedance parameter model is constructed, and forward modeling is performed on the initial impedance parameter model to obtain theoretical seismic gathers.

[0011] Construct a composite objective function, wherein the composite objective function is used to indicate the inversion residual between the theoretical seismic gather and the target seismic wavelet data;

[0012] Based on the composite objective function, the initial impedance parameter model is updated to obtain the target impedance parameter model, and the output impedance parameter inversion curve is obtained through the target impedance parameter model.

[0013] In one possible design, the data processing of the seismic wavelet data to obtain target seismic wavelet data includes:

[0014] The reflection coefficient is calculated based on the logging impedance parameters, wherein the reflection coefficient is used to indicate the seismic reflection coefficient of the vertical position in the logging.

[0015] The reflection coefficient and the seismic wavelet data are convolved to obtain a simulated seismic gather. The simulated seismic gather is then compared with the seismic wavelet data to obtain an amplitude scaling factor.

[0016] Based on the amplitude scaling factor and the seismic wavelet data, target seismic wavelet data is obtained so that the amplitude of the target seismic wavelet data matches that of the seismic wave data in the simulated seismic trace set.

[0017] In one possible design, constructing the initial impedance parameter model based on the well logging data includes:

[0018] Obtain the depositional features of the target region;

[0019] Based on the sedimentary characteristics and geological evolution laws, a tectonic sedimentary model is established, and spatial interpolation and extrapolation are performed based on the tectonic sedimentary model to construct the initial impedance parameter model;

[0020] In constructing the initial impedance parameter model, tectonic trend constraints and prior information on stratigraphic continuity are introduced to ensure that the initial impedance parameter model has geological rationality and spatial continuity in its lateral distribution.

[0021] In one possible design, constructing the initial impedance parameter model includes:

[0022] Based on the time-depth relationship of each vertical position in the well logging, the well logging curve data is converted from the depth domain to the time domain to obtain wave impedance information at the same time scale as the target seismic wavelet data.

[0023] In the time domain, well logging information at corresponding locations is scatter-pointed in different vertical geological layers, and well logging information at corresponding locations is scatter-pointed in the horizontal geological layers, so that the well logging information at each location is extended to the structural sedimentary model corresponding to the entire target area, as the initial impedance parameter model.

[0024] In one possible design, constructing the composite objective function includes:

[0025] Construct boundary preservation constraints and prior information constraints, wherein the boundary preservation constraints are used to indicate the boundary preservation operator, and the prior information constraints include seismic wave velocity;

[0026] The composite objective function is constructed based on the boundary preservation constraint term and the prior information constraint term.

[0027] In one possible design, the composite objective function is:

[0028] ;

[0029] in, For the target seismic wavelet data, For boundary-preserving constraint operators, z 0 represents the prior information constraint term. D For differential operators, z These are logging impedance parameters.

[0030] In one possible design, the boundary preservation operator is:

[0031] ;

[0032] in, G For Gaussian kernel function, For convolution operators, The gradient of the logging impedance parameters, The first boundary constraint scale parameter, The second boundary constraint scale parameter is given, where the first boundary constraint scale parameter is smaller than the second boundary constraint scale parameter.

[0033] Secondly, this application provides a boundary-preserving seismic impedance inversion device, comprising:

[0034] The acquisition module is used to acquire historical seismic data and well logging curve data of the target area, extract seismic wavelet data from the historical seismic data, wherein the well logging curve data includes well logging impedance parameters, and perform data processing on the seismic wavelet data to obtain target seismic wavelet data.

[0035] The processing module is used to construct an initial impedance parameter model based on the well logging curve data, and to perform forward modeling on the initial impedance parameter model to obtain theoretical seismic gathers, and to construct a composite objective function, wherein the composite objective function is used to indicate the inversion residual between the theoretical seismic gathers and the target seismic wavelet data;

[0036] The inversion module is used to update the initial impedance parameter model based on the composite objective function to obtain the target impedance parameter model, and to obtain the output impedance parameter inversion curve through the target impedance parameter model.

[0037] Thirdly, this application provides an electronic device, including: at least one processor and a memory;

[0038] The memory stores computer-executed instructions;

[0039] The at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to implement the boundary-preserving seismic impedance inversion method described above.

[0040] In a related aspect, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the boundary-preserving seismic impedance inversion method described above.

[0041] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0042] This application provides a boundary-preserving seismic impedance inversion method, apparatus, equipment, and medium. It acquires historical seismic data and well logging curve data including well logging impedance parameters for a target area, extracts seismic wavelet data from the historical seismic data, processes the seismic wavelet data to obtain target seismic wavelet data, constructs an initial impedance parameter model based on the well logging curve data, performs forward modeling on the initial impedance parameter model, constructs a composite objective function including boundary-preserving constraints and prior information constraints, updates the initial impedance parameter model based on the composite objective function to obtain the target impedance parameter model, and obtains the output impedance parameter inversion curve through the target impedance parameter model. This improves inversion accuracy and efficiency, and enhances inversion stability and geological reconstruction capabilities. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0044] Figure 1 A flowchart illustrating the boundary-preserving seismic impedance inversion method provided in this application embodiment. Figure 1 ;

[0045] Figure 2 This is a schematic diagram of the output of noiseless seismic wavelet data and initial impedance parameter model provided in the embodiments of this application;

[0046] Figure 3 This is a schematic diagram of the output of noisy seismic wavelet data and initial impedance parameter model provided in the embodiments of this application;

[0047] Figure 4 A flowchart illustrating the boundary-preserving seismic impedance inversion method provided in this application embodiment. Figure 2 ;

[0048] Figure 5 A flowchart illustrating the boundary-preserving seismic impedance inversion method provided in this application embodiment. Figure 3 ;

[0049] Figure 6 This diagram illustrates the impedance parameters obtained by using the boundary-preserving seismic impedance inversion method under noise-free conditions. Figure 1 ;

[0050] Figure 7 This diagram illustrates the impedance parameters obtained by using the boundary-preserving seismic impedance inversion method under noisy conditions. Figure 2 ;

[0051] Figure 8 A schematic diagram of the boundary-preserving seismic impedance inversion device provided in an embodiment of this application;

[0052] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0053] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0054] With the continuous development of the petroleum industry, oil and gas field exploration has become increasingly refined and efficient, placing higher demands on the accurate identification and comprehensive evaluation of oil and gas reservoir characteristics. Seismic inversion in seismic services plays a crucial role in improving the accuracy of subsurface structure imaging, predicting reservoir distribution characteristics, and guiding the formulation of oil and gas development plans.

[0055] Seismic inversion has wide applications in various engineering practices, such as geotechnical engineering, underground space exploration, and environmental geological assessment, providing a unified technical platform and reliable geological basis for oil exploration and engineering construction. Among these, acoustic impedance inversion, as one of the most mature and widely used techniques in seismic inversion, plays a crucial role in geological interpretation and reservoir prediction. Seismic impedance can effectively reflect the physical characteristics of strata, such as porosity, fluid saturation, and lithological features.

[0056] Existing wave impedance inversion schemes include linear wave impedance inversion methods, Gaussian constraint smoothness constraint methods, and sparse constraint methods.

[0057] For linear impedance retrieval methods, optimization is based on the least squares method. However, due to the obvious stratification of underground strata, their physical properties typically exhibit an approximately continuous vertical distribution and a relatively stable lateral distribution. Therefore, strata properties often show a certain "blocky" characteristic in spatial distribution, meaning that properties vary little within the same geological stratum, but there are significant differences between different strata, exhibiting obvious boundary characteristics, leading to low retrieval accuracy.

[0058] Gaussian constraints and smoothness constraints, such as L2 norm constraints, often lead to over-smoothing in the inversion results, making the stratigraphic boundaries less clear and accurate, and affecting the ability to identify geological structural details, i.e., poor reconstruction ability.

[0059] While sparse constraint methods can improve the resolution of inversion results and enhance the ability to characterize stratigraphic details to some extent, these methods still have certain limitations in practical applications. Because their objective functions often have strong nonlinearity, they are prone to getting trapped in local minima, causing the inversion process to converge to a non-unique solution, resulting in poor stability of the inversion results.

[0060] Existing solutions also include the differential Laplace block constraint method, which effectively overcomes the ambiguity problem caused by traditional smoothing constraints by emphasizing the local variation characteristics of the model. Combined with a multi-channel joint inversion strategy, it can improve inversion accuracy to some extent. However, because multi-channel inversion requires solving large-scale sparse matrices, the computational load is high and the overall computational efficiency is low. Therefore, existing solutions suffer from low inversion accuracy and efficiency, poor reconstruction ability, and poor stability of inversion results.

[0061] Therefore, this application provides a boundary-preserving seismic impedance inversion method. It acquires historical seismic data and well logging curve data including well logging impedance parameters for the target area, extracts seismic wavelet data from the historical seismic data, processes the seismic wavelet data to obtain target seismic wavelet data, constructs an initial impedance parameter model based on the well logging curve data, performs forward modeling on the initial impedance parameter model, constructs a composite objective function including boundary-preserving constraints and prior information constraints, updates the initial impedance parameter model based on the composite objective function to obtain the target impedance parameter model, and obtains the output impedance parameter inversion curve through the target impedance parameter model. This improves the inversion accuracy, efficiency, reconstruction capability, and stability.

[0062] The technical solutions of this application and how they solve the aforementioned technical problems are described in detail below using specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0063] Example 1

[0064] Figure 1 A flowchart illustrating the boundary-preserving seismic impedance inversion method provided in this application embodiment. Figure 1 , Figure 2 This is a schematic diagram of the output of the noiseless seismic wavelet data and initial impedance parameter model provided in the embodiments of this application. Figure 3 This is a schematic diagram of the output of noisy seismic wavelet data and initial impedance parameter model provided in the embodiments of this application, combined with Figures 1 to 3 As shown, the method includes:

[0065] S101. Obtain historical seismic data and well logging curve data of the target area, and extract seismic wavelet data from the historical seismic data, wherein the well logging curve data includes well logging impedance parameters.

[0066] S102. Perform data processing on the seismic wavelet data to obtain target seismic wavelet data.

[0067] Specifically, since the actual earthquake amplitude is usually a relative value, there is often a certain numerical difference between the amplitude of the earthquake data obtained by forward modeling through the post-stack earthquake reflection coefficient equation and the true amplitude. Therefore, it is necessary to process the extracted seismic wavelet data so that the amplitude of the processed target seismic wavelet data matches the amplitude of the seismic wave data in the simulated seismic trace set.

[0068] Furthermore, based on the logging impedance parameters, the reflection coefficient is calculated using the post-stack reflection coefficient equation function, where the reflection coefficient is used to indicate the seismic reflection coefficient of the vertical position in the logging. The post-stack reflection coefficient equation function is shown in equation (1) below:

[0069] (1);

[0070] in, z is Well logging impedance parameters r is Reflectance coefficient i and i +1 represents different vertical positions in the well logging curve data;

[0071] Furthermore, the reflection coefficient and the seismic wavelet data are convolved to obtain a simulated seismic gather, which is obtained by the following equation (2):

[0072] (2);

[0073] in, s Simulated seismic gathers are used to indicate synthetic seismic records. For convolution operators, w For seismic wavelet data, the simulated seismic gathers are compared with the seismic wavelet data to calculate the amplitude scaling factor.

[0074] Furthermore, based on the amplitude scaling factor and the seismic wavelet data, target seismic wavelet data is obtained so that the amplitude of the target seismic wavelet data matches that of the seismic wave data in the simulated seismic trace set.

[0075] S103. Based on the well logging curve data, construct an initial impedance parameter model and perform forward modeling on the initial impedance parameter model to obtain theoretical seismic gathers.

[0076] Specifically, after acquiring the target seismic wavelet data, a tectonic sedimentary model is established. Using the tectonic sedimentary model as a framework, spatial interpolation and reasonable extrapolation are performed on each well logging data in the well logging curve data to construct the initial impedance parameter model corresponding to each well logging curve data.

[0077] Furthermore, based on the post-stack reflection coefficient equation, i.e., equation (1) above, and combined with the initial impedance parameter model constructed in the time domain, the seismic response of the subsurface medium is simulated using equation (2) above to generate theoretical seismic gathers. The basic physical mechanisms of seismic wave propagation in the strata are fully considered, making the simulation results closer to the characteristics of seismic records under actual geological conditions. This forward modeling process verifies the rationality of the initial impedance parameter model and provides a reliable foundation for subsequent inversion.

[0078] S104. Construct a composite objective function, wherein the composite objective function is used to indicate the inversion residual between the theoretical seismic gather and the target seismic wavelet data.

[0079] Specifically, such as Figure 2 and Figure 3 As shown, after obtaining the theoretical seismic gathers, the theoretical seismic data obtained from the forward modeling are compared with the actual observed target seismic wavelet data. The difference between the theoretical seismic gathers and the target seismic wavelet data is calculated as the inversion residual. The inversion residual is calculated using the following formula (3):

[0080] (3);

[0081] in, s Synthetic seismic records from theoretical seismic data, d For the target seismic wavelet data to be observed, e The difference between the theoretical seismic data and the target seismic wavelet data is denoted by g, which represents the forward modeling process.

[0082] Furthermore, the inversion residual reflects the degree of deviation between the current initial impedance parameter model and the actual underground structure, and is an important indicator for evaluating model error. At the same time, the inversion residual also provides a clear quantitative basis for subsequent iterative optimization, guiding the continuous adjustment and updating of model parameters, so that the inversion results gradually approach the actual underground wave impedance distribution.

[0083] S105. Based on the composite objective function, update the initial impedance parameter model to obtain the target impedance parameter model, and obtain the output impedance parameter inversion curve through the target impedance parameter model.

[0084] Specifically, after obtaining the inversion residuals in the current iteration process, the parameters in the initial impedance parameter model are successively optimized and updated based on the inversion residuals using the iterative reweighted least squares method. The iterative reweighted least squares method enhances the robustness of the algorithm to abnormal errors by dynamically adjusting the weight matrix in each iteration, thereby improving the stability and convergence of the inversion process.

[0085] Furthermore, throughout the entire iteration process, the changing trend of the inversion residual is continuously monitored as an important basis for judging whether the model approaches the true solution. When the inversion residual drops to the preset convergence threshold, or the number of iterations reaches the set upper limit, the inversion operation is terminated, and the optimal target impedance parameter model is finally obtained.

[0086] This application provides a boundary-preserving seismic impedance inversion method. It acquires historical seismic data and well logging curve data including well logging impedance parameters for a target area. Seismic wavelet data is extracted from the historical seismic data, and the wavelet data is processed to obtain target seismic wavelet data. Based on the well logging curve data, an initial impedance parameter model is constructed, and forward modeling is performed on this model. A composite objective function, including boundary-preserving constraints and prior information constraints, is constructed. Based on this composite objective function, the initial impedance parameter model is updated to obtain the target impedance parameter model. The output impedance parameter inversion curve is obtained through the target impedance parameter model, thereby improving inversion accuracy, efficiency, reconstruction capability, and stability.

[0087] Example 2

[0088] Based on the above embodiment one, the construction of the initial impedance parameter model in step S103 will be further explained. Figure 4 A flowchart illustrating the boundary-preserving seismic impedance inversion method provided in this application embodiment. Figure 2 ,like Figure 4 As shown, the method includes:

[0089] S201. Based on the aforementioned sedimentary characteristics and geological evolution patterns, establish a tectonic sedimentary model.

[0090] Specifically, after obtaining the target seismic wavelet data, the sedimentary characteristics of the target area are obtained, and a tectonic sedimentary model that conforms to the actual geological conditions is established by combining the regional sedimentary characteristics with the geological evolution law.

[0091] S202. Based on the constructed deposition model, perform spatial interpolation and extrapolation to construct the initial impedance parameter model.

[0092] Specifically, after obtaining the structural sedimentary model, based on the time-depth relationship of each vertical position in the well log, the well log curve data is converted from the depth domain to the time domain to obtain wave impedance information at the same time scale as the target seismic wavelet data.

[0093] Furthermore, in the time domain, well logging information at corresponding locations is inserted into scattered points in different vertical geological layers of the structural sedimentary model, and well logging information at corresponding locations is also inserted into scattered points in the horizontal geological layers, so that the well logging information at each location is extended to the structural sedimentary model of the entire target area as an initial impedance parameter model, thereby realizing the calculation of impedance parameter values ​​at each spatial location underground.

[0094] Furthermore, in constructing the initial impedance parameter model, tectonic trend constraints and prior information on stratigraphic continuity were introduced to ensure that the constructed initial impedance parameter model has good geological rationality and spatial continuity in its lateral distribution. The final initial impedance parameter model not only faithfully reflects well logging data and seismic tectonic interpretation, but also meets the requirements of subsequent seismic inversion for the low-frequency components and structural stability of the initial impedance parameter model, laying the foundation for improving inversion accuracy and geological interpretability.

[0095] This application provides a boundary-preserving seismic impedance inversion method. It acquires historical seismic data and well logging curve data including well logging impedance parameters for a target area. Seismic wavelet data is extracted from the historical seismic data, and the wavelet data is processed to obtain target seismic wavelet data. Based on the well logging curve data, an initial impedance parameter model is constructed, and forward modeling is performed on this model. A composite objective function, including boundary-preserving constraints and prior information constraints, is constructed. Based on this composite objective function, the initial impedance parameter model is updated to obtain the target impedance parameter model. The output impedance parameter inversion curve is obtained through the target impedance parameter model, thereby improving inversion accuracy, efficiency, reconstruction capability, and stability.

[0096] Example 3

[0097] Based on the above embodiment one, the steps of constructing the composite objective function and updating the initial impedance parameter model will be further explained. Figure 5 A flowchart illustrating the boundary-preserving seismic impedance inversion method provided in this application embodiment. Figure 3 ,like Figure 5 As shown, the method includes:

[0098] S301. Construct boundary preservation constraints and prior information constraints, wherein the boundary preservation constraints are used to indicate the boundary preservation operator, and the prior information constraints include seismic wave velocity.

[0099] Specifically, for the boundary preservation operator, it is shown in the following equation (4):

[0100] (4);

[0101] in, G For Gaussian kernel function, For convolution operators, The gradient of the logging impedance parameters, The first boundary constraint scale parameter, The second boundary constraint scale parameter is given, where the first boundary constraint scale parameter is smaller than the second boundary constraint scale parameter.

[0102] Furthermore, when calculating the corresponding impedance parameters using the boundary preservation operator, the weight parameters corresponding to the boundary constraint operator are first calculated, as shown in equation (5) below:

[0103] (5);

[0104] in, G For Gaussian kernel function, For convolution operators, The gradient of the logging impedance parameters, The first boundary constraint scale parameter, The second boundary constraint scale parameter is used. Next, the impedance parameter after the boundary constraint operator is calculated, as shown in equation (6) below:

[0105] (6);

[0106] in, The impedance parameters of the boundary constraint operator to be performed. These are the impedance parameters after the boundary constraint operators have been applied. k The number of boundary constraint iterations. D For differential operators, I is a value greater than 0 used to control the strength of the boundary constraints, where I is the identity matrix. diag To transform the weight parameters corresponding to the boundary constraint operators into a diagonal matrix, These are the weight parameters corresponding to the boundary constraint operators.

[0107] S302. Construct the composite objective function based on the boundary preservation constraint term and the prior information constraint term.

[0108] Specifically, for the composite objective function, it is shown in equation (7) below:

[0109] (7);

[0110] in, For the target seismic wavelet data, For boundary-preserving constraint operators, z 0 represents the prior information constraint term. D For differential operators, z For well logging impedance parameters, the prior information constraints include seismic wave velocity, and of course, other prior information may also be included.

[0111] S303. Based on the composite objective function, update the initial impedance parameter model to obtain the target impedance parameter model.

[0112] Specifically, after obtaining the composite objective function, the parameters in the initial impedance parameter model are successively optimized and updated based on the composite objective function using the iterative reweighted least squares method. After each parameter update, the composite objective function indicating the inversion residual is updated, forming a closed-loop iterative process.

[0113] In the process of successively optimizing and updating the parameters in the initial impedance parameter model, the derivative of the composite objective function with respect to the impedance parameters is first obtained, as shown in the following equation (8):

[0114] (8);

[0115] in, For a composite objective function, z The logging impedance parameters are calculated based on the derivative of the composite objective function with respect to the impedance parameters. The corresponding model parameters are obtained as shown in equation (9) below:

[0116] (9);

[0117] in, The residual between the theoretical seismic data and the target seismic wavelet data. For weight parameters, To dynamically adjust the weight matrix, These are the parameters of the first model. For the second model parameters, Let be the derivative of the composite objective function with respect to the impedance parameters. For the updated logging impedance parameters, it is given by the following equation (10):

[0118] (10);

[0119] in, To update the step size for logging impedance parameters, To update the logging impedance parameters, the updated logging impedance parameters are substituted into the boundary constraint operator for calculation and update, as shown in the following equation (11):

[0120] (11);

[0121] in, This represents the constraint weights of the boundary constraint operators on the iteration results during the iteration process.

[0122] Furthermore, throughout the entire iteration process, the changing trend of the inversion residuals is continuously monitored as an important basis for judging whether the initial impedance parameter model approaches the true solution. When the inversion residuals decrease to the preset convergence threshold, or the number of iterations reaches the set upper limit, the inversion operation is terminated, and the optimal target impedance parameter model is finally obtained.

[0123] This application provides a boundary-preserving seismic impedance inversion method. It acquires historical seismic data and well logging curve data including well logging impedance parameters for a target area. Seismic wavelet data is extracted from the historical seismic data, and the wavelet data is processed to obtain target seismic wavelet data. Based on the well logging curve data, an initial impedance parameter model is constructed, and forward modeling is performed on this model. A composite objective function, including boundary-preserving constraints and prior information constraints, is constructed. Based on this composite objective function, the initial impedance parameter model is updated to obtain the target impedance parameter model. The output impedance parameter inversion curve is obtained through the target impedance parameter model, thereby improving inversion accuracy, efficiency, reconstruction capability, and stability.

[0124] Figure 6 This diagram illustrates the impedance parameters obtained by using the boundary-preserving seismic impedance inversion method under noise-free conditions. Figure 1 , Figure 7 This diagram illustrates the impedance parameters obtained by using the boundary-preserving seismic impedance inversion method under noisy conditions. Figure 2 Combining Figure 6 and Figure 7 The impedance parameter inversion curve is compared with the measured logging curve for illustration.

[0125] After obtaining the target impedance parameter model, the output impedance parameter inversion curve is obtained through the target impedance parameter model. The impedance parameter inversion curve is then compared with the measured logging curve at each well point to evaluate the accuracy and reliability of the inversion results.

[0126] By extracting the impedance values ​​of corresponding well locations from seismic inversion profiles and matching them layer by layer with the well logging data of those wells, a systematic consistency analysis can be conducted from multiple aspects, including morphological characteristics, variation trends, and numerical magnitudes. This comparison not only intuitively reflects the accuracy of the inversion results in terms of stratigraphic interface identification and lithological change response, but also effectively identifies the sources of deviation between the two, such as wavelet estimation errors, inaccurate initial impedance parameter models, or limitations of the inversion method itself. In-depth analysis of the deviation characteristics can determine the effectiveness and applicability of the current inversion method, providing crucial basis for subsequent optimization of inversion parameters, adjustment of constraints, and improvement of the modeling process.

[0127] like Figure 6 and Figure 7 As shown, the impedance parameters obtained by the boundary-preserving seismic impedance inversion method of this invention are illustrated. The vertical axis represents time (in seconds), and the horizontal axis represents impedance (in g / cm³•km / s). The boundary-preserving seismic impedance inversion method effectively preserves the boundary characteristics of the predicted impedance parameters. Figure 7The results shown are the inversion results when random noise with a signal-to-noise ratio of 4 is added. Boundary preservation and the introduction of the initial model play a key role in maintaining the stability of the inversion process and improving the boundary details of the inversion results.

[0128] This invention employs the above technical solutions, namely, a forward modeling method based on the accurate post-stack reflection coefficient equation to calculate seismic records. This fully considers the nonlinear factors in the wavefield propagation process, effectively avoiding the accuracy deficiencies of traditional linear approximation methods under complex geological conditions. Furthermore, it introduces boundary preservation constraints into the inversion objective function, enhancing the ability to characterize stratigraphic interfaces and enabling the impedance inversion results to better reflect the blocky characteristics of the strata, thus conforming to the actual distribution patterns of the subsurface medium. By solving the inversion problem using an iterative reweighted least squares method, the nonlinear characteristics are effectively handled, thereby avoiding the accuracy degradation problem caused by traditional linearization methods during the solution process.

[0129] In this embodiment of the invention, electronic devices or main control devices can be divided into functional modules according to the above method examples. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional module. It should be noted that the module division in this embodiment of the invention is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0130] Figure 8 A schematic diagram of the boundary-preserving seismic impedance inversion device provided in the embodiments of this application is shown below. Figure 8 As shown, the device 800 includes:

[0131] The acquisition module 801 is used to acquire historical seismic data and well logging curve data of the target area, extract seismic wavelet data from the historical seismic data, wherein the well logging curve data includes well logging impedance parameters, and perform data processing on the seismic wavelet data to obtain target seismic wavelet data.

[0132] The processing module 802 is used to construct an initial impedance parameter model based on the well logging curve data, and to perform forward modeling on the initial impedance parameter model to obtain theoretical seismic gathers and construct a composite objective function, wherein the composite objective function is used to indicate the inversion residual between the theoretical seismic gathers and the target seismic wavelet data.

[0133] The inversion module 803 is used to update the initial impedance parameter model based on the composite objective function to obtain the target impedance parameter model, and to obtain the output impedance parameter inversion curve through the target impedance parameter model.

[0134] Furthermore, the acquisition module 801 is specifically used to calculate and acquire the reflection coefficient based on the logging impedance parameters, wherein the reflection coefficient is used to indicate the seismic reflection coefficient of the vertical position in the logging.

[0135] The reflection coefficient and the seismic wavelet data are convolved to obtain a simulated seismic gather. The simulated seismic gather is then compared with the seismic wavelet data to obtain an amplitude scaling factor.

[0136] Based on the amplitude scaling factor and the seismic wavelet data, target seismic wavelet data is obtained so that the amplitude of the target seismic wavelet data matches that of the seismic wave data in the simulated seismic trace set.

[0137] Furthermore, the processing module 802 is specifically used to acquire the depositional features of the target area;

[0138] Based on the sedimentary characteristics and geological evolution laws, a tectonic sedimentary model is established, and spatial interpolation and extrapolation are performed based on the tectonic sedimentary model to construct the initial impedance parameter model;

[0139] In constructing the initial impedance parameter model, tectonic trend constraints and prior information on stratigraphic continuity are introduced to ensure that the initial impedance parameter model has geological rationality and spatial continuity in its lateral distribution.

[0140] Furthermore, the processing module 802 is specifically used to convert the logging curve data from the depth domain to the time domain based on the time-depth relationship of each vertical position in the logging, so as to obtain wave impedance information at the same time scale as the target seismic wavelet data.

[0141] In the time domain, well logging information at corresponding locations is scatter-interpolated in different vertical geological layers, and the same well logging information is scatter-interpolated in the same horizontal geological layers. This expands the well logging information at each location to the structural sedimentary model corresponding to the entire target area, serving as the initial impedance parameter model.

[0142] Further, the processing module 802 is specifically used to construct boundary preservation constraints and prior information constraints, wherein the boundary preservation constraints are used to indicate the boundary preservation operator, and the prior information constraints include seismic wave velocity;

[0143] The composite objective function is constructed based on the boundary preservation constraint term and the prior information constraint term.

[0144] Furthermore, the composite objective function in processing module 802 is:

[0145] ;

[0146] in, For the target seismic wavelet data, For boundary-preserving constraint operators, z 0 represents the prior information constraint term. D For differential operators, z These are logging impedance parameters.

[0147] Furthermore, the boundary preservation operator in processing module 802 is:

[0148] ;

[0149] in, G For Gaussian kernel function, For convolution operators, The gradient of the logging impedance parameters, The first boundary constraint scale parameter, The second boundary constraint scale parameter is given, where the first boundary constraint scale parameter is smaller than the second boundary constraint scale parameter.

[0150] Figure 9 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application, such as... Figure 9 As shown, the device 900 includes at least one processor 901 and a memory 902. The electronic device 900 also includes a communication component 903. The processor 901, memory 902, and communication component 903 are connected via a bus 904.

[0151] In the specific implementation process, at least one processor 901 executes the computer execution instructions stored in the memory 902, causing at least one processor 901 to execute the boundary-preserving seismic impedance inversion method as executed on the electronic device side as described above.

[0152] The specific implementation process of processor 901 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0153] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the boundary-preserving seismic impedance inversion method as described above.

[0154] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A boundary-preserving seismic impedance inversion method, characterized in that, The method includes: Acquire historical seismic data and well logging curve data for the target area, and extract seismic wavelet data from the historical seismic data, wherein the well logging curve data includes well logging impedance parameters; The seismic wavelet data is processed to obtain the target seismic wavelet data; Based on the well logging data, an initial impedance parameter model is constructed, and forward modeling is performed on the initial impedance parameter model to obtain theoretical seismic gathers. Construct a composite objective function, wherein the composite objective function is used to indicate the inversion residual between the theoretical seismic gather and the target seismic wavelet data; Based on the composite objective function, the initial impedance parameter model is updated to obtain the target impedance parameter model, and the output impedance parameter inversion curve is obtained through the target impedance parameter model. The construction of the composite objective function includes: Construct boundary preservation constraints and prior information constraints, wherein the boundary preservation constraints are used to indicate the boundary preservation operator, and the prior information constraints include seismic wave velocity; Based on the boundary preservation constraint and the prior information constraint, the composite objective function is constructed as follows: ;in, For the target seismic wavelet data, The boundary preservation operator is defined as follows: z0 is the prior information constraint term, D is the differential operator, z is the logging impedance parameter, g represents the forward modeling process, and the boundary preservation operator is: Where G is the Gaussian kernel function, For convolution operators, The gradient of the logging impedance parameters, The first boundary constraint scale parameter, The second boundary constraint scale parameter is defined as the first boundary constraint scale parameter being smaller than the second boundary constraint scale parameter.

2. The boundary-preserving seismic impedance inversion method according to claim 1, characterized in that, The data processing of the seismic wavelet data to obtain target seismic wavelet data includes: The reflection coefficient is calculated based on the logging impedance parameters, wherein the reflection coefficient is used to indicate the seismic reflection coefficient of the vertical position in the logging. The reflection coefficient and the seismic wavelet data are convolved to obtain a simulated seismic gather. The simulated seismic gather is then compared with the seismic wavelet data to obtain an amplitude scaling factor. Based on the amplitude scaling factor and the seismic wavelet data, target seismic wavelet data is obtained so that the amplitude of the target seismic wavelet data matches that of the seismic wave data in the simulated seismic trace set.

3. The boundary-preserving seismic impedance inversion method according to claim 1, characterized in that, The step of constructing an initial impedance parameter model based on the well logging curve data includes: Obtain the depositional features of the target region; Based on the sedimentary characteristics and geological evolution laws, a tectonic sedimentary model is established, and spatial interpolation and extrapolation are performed based on the tectonic sedimentary model to construct the initial impedance parameter model; In constructing the initial impedance parameter model, tectonic trend constraints and prior information on stratigraphic continuity are introduced to ensure that the initial impedance parameter model has geological rationality and spatial continuity in its lateral distribution.

4. The boundary-preserving seismic impedance inversion method according to claim 3, characterized in that, The construction of the initial impedance parameter model includes: Based on the time-depth relationship of each vertical position in the well logging, the well logging curve data is converted from the depth domain to the time domain to obtain wave impedance information at the same time scale as the target seismic wavelet data. In the time domain, well logging information at corresponding locations is scatter-pointed in different vertical geological layers, and well logging information at corresponding locations is scatter-pointed in the horizontal geological layers, so that the well logging information at each location is extended to the structural sedimentary model corresponding to the entire target area, as the initial impedance parameter model.

5. A boundary-preserving seismic impedance inversion device, characterized in that, This device is used in the boundary-preserving seismic impedance inversion method according to any one of claims 1-4, and the device comprises: The acquisition module is used to acquire historical seismic data and well logging curve data of the target area, extract seismic wavelet data from the historical seismic data, wherein the well logging curve data includes well logging impedance parameters, and perform data processing on the seismic wavelet data to obtain target seismic wavelet data. The processing module is used to construct an initial impedance parameter model based on the well logging curve data, and to perform forward modeling on the initial impedance parameter model to obtain theoretical seismic gathers, and to construct a composite objective function, wherein the composite objective function is used to indicate the inversion residual between the theoretical seismic gathers and the target seismic wavelet data; The inversion module is used to update the initial impedance parameter model based on the composite objective function to obtain the target impedance parameter model, and to obtain the output impedance parameter inversion curve through the target impedance parameter model.

6. An electronic device, characterized in that, include: At least one processor and memory; The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the boundary-preserving seismic impedance inversion method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the boundary-preserving seismic impedance inversion method as described in any one of claims 1 to 4.

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