Method, system and storage medium for evaluating the fit between structural model and seismic data

Abstract

The present invention discloses a method, system and storage medium for evaluating the degree of fit between a structural model and seismic data, and relates to the technical field of data processing. The method comprises: obtaining an original structural model and original seismic data; performing a flattening operation on the original structural model to obtain a flattened structural model; comparing the flattened structural model with the original structural model to obtain a structural conversion amount, and migrating the structural conversion amount to seismic data to obtain flattened seismic data; identifying and obtaining a target layer seismic axis based on the flattened seismic data, and traversing the target layer seismic axis to perform a seismic axis continuous length analysis to obtain a seismic axis continuous length data set; calculating a corresponding degree of fit evaluation index based on the seismic axis continuous length data set to obtain a degree of fit evaluation value; and outputting a degree of fit evaluation result based on the degree of fit evaluation value, thereby achieving the technical effects of improved intuitiveness, improved work efficiency and quantified results.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, system and storage medium for evaluating the degree of fit between a structural model and seismic data. Background Art

[0002] In geological exploration, the accuracy of structural models is crucial for resource assessment, risk prediction, and development planning. To ensure that structural models accurately reflect the true state of the subsurface structure, they must be compared and analyzed with actual seismic data. This comparison involves not only matching geometric shapes but also correspondences in time (depth) and space. Currently, commonly used methods for measuring the fit between structural models and original seismic data volumes mainly include manual statistical methods that rely on manually or semi-automatically traced stratigraphic interfaces.

[0003] Existing techniques have the following shortcomings: First, manual statistical methods are highly subjective. Because they rely on the interpreter's experience and judgment, results can vary from person to person, lacking objectivity and consistency. Second, the inherent complexity of seismic data complicates analysis, particularly the rapid changes in seismic axis morphology, which makes intuitive analysis difficult. Finally, traditional methods struggle with quantification, often only providing qualitative assessments such as "high," "medium," and "low," rather than providing specific numerical values ​​to quantify the results. Summary of the Invention

[0004] The present invention provides a method, system and storage medium for evaluating the degree of fit between structural models and seismic data, so as to solve the technical problems in the prior art such as poor objectivity and consistency, high analysis difficulty and inability to provide quantitative results, and achieve the technical effects of improved intuitiveness, increased work efficiency and quantified results.

[0005] In a first aspect, the present invention provides a method for evaluating the fit between a structural model and seismic data, wherein the method comprises:

[0006] Obtain original structural model and original seismic data;

[0007] Based on a preset structural restoration interpretation program, a flattening operation is performed on each layer in the original structural model to obtain a flattened structural model;

[0008] Comparing the flattened structural model with the original structural model to obtain structural transformation amount, and migrating the structural transformation amount to the seismic data to obtain flattened seismic data;

[0009] Based on the flattened seismic data, the target layer seismic axis is identified and obtained, and the seismic axis of the target layer is traversed to perform seismic axis continuous length analysis to obtain a seismic axis continuous length data set;

[0010] Calculating a corresponding fit evaluation index according to the seismic axis continuous length data set to obtain a fit evaluation value, wherein the fit evaluation index is the mean of the seismic axis continuous length;

[0011] Based on the compatibility evaluation value, a compatibility evaluation result is output.

[0012] In a second aspect, the present invention further provides a system for evaluating the degree of fit between a structural model and seismic data, wherein the system comprises:

[0013] Data acquisition module, used to obtain original structural model and original seismic data;

[0014] a structural model flattening module, configured to flatten each layer in the original structural model based on a preset structural restoration interpretation program to obtain a flattened structural model;

[0015] a structural conversion amount acquisition and migration module, configured to compare the flattened structural model with the original structural model, acquire structural conversion amounts, and migrate the structural conversion amounts to the seismic data to acquire flattened seismic data;

[0016] An earthquake axis identification and analysis module is used to identify and obtain the earthquake axis of the target layer based on the flattened earthquake data, and traverse the earthquake axis of the target layer to perform earthquake axis continuous length analysis to obtain an earthquake axis continuous length data set;

[0017] A fit evaluation calculation module is used to calculate a corresponding fit evaluation index based on the seismic axis continuous length data set to obtain a fit evaluation value, wherein the fit evaluation index is the mean of the seismic axis continuous length;

[0018] The compatibility evaluation result output module is used to output the compatibility evaluation result based on the compatibility evaluation value.

[0019] In a third aspect, the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for evaluating the degree of fit between a structural model and seismic data provided by the present invention.

[0020] The present invention discloses a method, system and storage medium for evaluating the degree of fit between a structural model and seismic data, comprising: obtaining an original structural model and original seismic data; flattening each layer in the original structural model based on a preset structural recovery interpretation program to obtain a flattened structural model; comparing the flattened structural model with the original structural model, calculating a structural conversion amount, and migrating the conversion amount to the original seismic data to obtain the flattened seismic data; using the flattened seismic data to identify and extract the seismic axis of the target layer, traversing the seismic axis of the target layer to perform a continuous length analysis to generate a seismic axis continuous length data set; calculating a fit evaluation index based on the seismic axis continuous length data set, specifically the mean of the seismic axis continuous length, to obtain a fit evaluation value; outputting a final fit evaluation result based on the fit evaluation value. The method, system and storage medium for evaluating the degree of fit between a structural model and seismic data disclosed in the present invention solve the technical problems of poor objectivity and consistency, high analysis difficulty and inability to provide quantitative results, and achieve the technical effects of improved intuitiveness, improved work efficiency and quantified results. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the flow of the method for evaluating the degree of fit between the structural model and seismic data of the present invention;

[0022] Figure 2 Schematic diagram of the structure of the evaluation system for the degree of fit between the structural model and seismic data of the present invention.

[0023] Explanation of reference numerals: data acquisition module 11 , structural model flattening module 12 , structural transformation quantity acquisition and migration module 13 , earthquake axis identification and analysis module 14 , fit evaluation calculation module 15 , fit evaluation result output module 16 . DETAILED DESCRIPTION

[0024] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the drawings.

[0025] Example 1

[0026] Figure 1 Schematic diagram of a flow chart of a method for evaluating the degree of fit between a structural model and seismic data according to the present invention, wherein the method comprises:

[0027] Obtain the original structural model and original seismic data.

[0028] Specifically, the original structural model and seismic data are first obtained to provide basic data support for subsequent processing and analysis. The original structural model comes from three-dimensional geological structure information obtained from seismic exploration or drilling data, output of geological modeling tools, etc.; the original seismic data is obtained through seismic acquisition equipment or based on data providers.

[0029] Optionally, the original structural model is spatially aligned with the seismic data to ensure consistency between the model and the seismic wave signal. Then, the data integrity and accuracy are verified, including checking whether the model file is missing hierarchical information and whether the seismic data has sampling interval anomalies, thereby providing a solid foundation for subsequent seismic analysis or geological interpretation.

[0030] Based on a preset structural restoration interpretation program, a flattening operation is performed on each layer in the original structural model to obtain a flattened structural model.

[0031] Specifically, the structural restoration interpretation program is a software tool used to reconstruct the history and morphology of geological structures. Based on the principles of geomechanics and seismic data, it restores and adjusts the structural model by simulating the deposition, deformation and evolution processes of the strata. Exemplary models include modeling software such as Petrel and GOCAD.

[0032] Specifically, the tectonic restoration interpretation program incorporates a powerful geomechanical engine capable of simulating the stress and strain changes in strata over geological history. By analyzing the mechanical behavior of the strata, it identifies deformation characteristics and trends, providing a mechanical basis for leveling operations.

[0033] Specifically, the structural restoration interpretation program constructs a balanced section of the stratum to show the shape and position of the stratum in different geological periods, eliminates the deformation effect of the stratum, and restores the original shape of the stratum, thereby flattening the structural model and presenting a straight or approximately straight state on the balanced section.

[0034] Optionally, the structural restoration interpretation program integrates a variety of automated algorithms, such as pattern recognition and optimization algorithms, to automatically identify structural features such as stratigraphic interfaces and faults, and quickly and accurately level the structural model according to preset leveling rules and goals. The application of automated algorithms improves the efficiency and accuracy of leveling operations and reduces human intervention and errors.

[0035] In some embodiments, based on a preset structural restoration interpretation program, a flattening operation is performed on each layer in the original structural model to obtain a flattened structural model, including:

[0036] A time zero point is selected from the original structural model as a leveling benchmark; for each structural layer in the original structural model, the relative time difference from the leveling benchmark is calculated, and faults and horizons are defined based on the relative time difference; the faults and horizons and the original structural model are input into an automated structural restoration interpretation program, and a leveling operation based on structural restoration is performed to generate the leveled structural model.

[0037] Specifically, the structural restoration interpretation program is used to simulate and reconstruct the history and morphology of geological structures. Among them, the time zero point is the benchmark point for the leveling operation. Usually, a stable geological interface or a known geological event is selected as the time zero point, which provides a reference for calculating the relative time difference of other structural layers and ensuring the accuracy and consistency of the leveling operation.

[0038] Specifically, first, a stable geological interface, such as a known sedimentary base level or a stable stratigraphic interface, is selected within the original tectonic model as the time zero point for the leveling benchmark. This time zero point appears as a distinct reflection interface in the seismic data and is relatively stable throughout geological history. Then, using the seismic data and the geological model, the relative time difference between each tectonic layer and time zero is calculated. For example, for a specific tectonic layer, the propagation time of the seismic wave between that layer and time zero is analyzed, combined with the known stratigraphic velocity model, to calculate the delay time of that layer relative to time zero. This delay time reflects the degree of deformation of the stratum over geological history.

[0039] Furthermore, based on the relative time difference, combined with the fault characteristics and geological knowledge in the seismic data, faults and horizons are defined. For example, the location where the stratigraphic interface is significantly displaced near the fault is identified and defined as a fault; at the same time, the specific position and morphology of each horizon are determined based on the relative time difference and spatial distribution characteristics of the strata. Finally, the defined fault and horizon information, as well as the original structural model, are input into an automated structural restoration and interpretation program. Based on this information, the program simulates the evolution of the strata during geological history and adjusts the position and morphology of the stratigraphic interface so that it appears straight or approximately straight on the flattened datum. Ultimately, a flattened structural model is generated, which can better reflect the true distribution characteristics and structural morphology of the strata, providing a solid foundation for subsequent seismic data processing and resource assessment.

[0040] Through the above steps, the original structural model is flattened based on the preset structural restoration interpretation program, which can effectively simplify the complexity of the seismic axis morphology, improve the accuracy of geological interpretation and the utilization efficiency of seismic data.

[0041] The flattened structural model is compared with the original structural model to obtain structural transformation amount, and the structural transformation amount is transferred to the seismic data to obtain flattened seismic data.

[0042] Specifically, by comparing the flattened tectonic model with the original tectonic model, the differences between the two are identified, and the differences are mainly reflected in the position, morphology and distribution of the stratigraphic interface; the identified differences are quantified as tectonic transformation amounts, which involves calculating parameters such as the vertical displacement, horizontal displacement and rotation angle of the stratigraphic interface; a mapping relationship is established between the original tectonic model and the seismic data, and the tectonic transformation amounts are correspondingly transferred to the seismic data, so that the reflection event axes in the seismic data appear to be straight in a state consistent with the flattened tectonic model, eliminating the influence of fault activity on the interpretation of seismic data, simplifying the complexity of the seismic axis morphology, and improving the intuitiveness of the analysis.

[0043] In some embodiments, comparing the flattened structural model with the original structural model to obtain structural transformation amounts, and migrating the structural transformation amounts to the seismic data to obtain the flattened seismic data includes:

[0044] Based on the original structural model, a plurality of model sample points are defined to obtain a model sample point set; the model sample point set is mapped to the seismic data to obtain a data sample point set; the difference information of the model sample point set in the leveled structural model is compared with that in the original structural model to obtain a structural transformation amount; the data coordinate attributes of the seismic data are extracted, and the structural transformation amount is metrically transformed according to the data coordinate attributes to obtain a target structural transformation amount; according to the mapping relationship between the model sample point set and the data sample point set, the target structural transformation amount is applied to perform data leveling on the seismic data.

[0045] Specifically, in geological exploration, the flattened tectonic model is compared with the original tectonic model to obtain tectonic transformation quantities, and the transformation quantities are transferred to seismic data to obtain flattened seismic data. First, multiple model sample points are defined in the original tectonic model. These points are usually distributed at key stratigraphic interfaces and fault positions and can represent the geometric morphology and structural characteristics of the strata, such as stratigraphic interfaces on both sides of the fault and important sedimentary interfaces. Then, the model sample points are mapped to the seismic data in combination with the sampling interval and spatial resolution of the seismic data to form a data sample point set to ensure the accuracy and consistency of the mapping.

[0046] Specifically, the flattened structural model is compared with the original structural model to identify the differences in the stratigraphic interfaces. For example, a stratigraphic interface appears straight in the flattened model, but tilted in the original model, and there is a height difference on both sides of the fault. These differences are then quantified to obtain the structural transformation amount, including parameters such as the vertical displacement, horizontal displacement, and rotation angle of the stratigraphic interface. Then, the data coordinate attributes of the seismic data, that is, the vertical axis coordinate attributes, including time or depth, are extracted. If the structural model and the vertical axis coordinate attributes of the seismic data are inconsistent, a time-depth conversion relationship is established based on the velocity model (Velocity Model) in the seismic data file or the seismic calibration data, and coordinate reprojection is performed to convert the structural transformation amount from the model coordinate system to the seismic data coordinate system to obtain the target structural transformation amount. Through the above steps, the structural transformation amount can be successfully mapped from the model coordinate system to the time or depth coordinate system of the seismic data, providing support for subsequent geological analysis and seismic interpretation.

[0047] Specifically, based on the mapping relationship between the model sample point set and the data sample point set, the target structural transformation amount is applied to flatten the seismic data, that is, the reflection events in the seismic data are adjusted accordingly by vertical translation and rotation; after adjustment, the reflection events in the seismic data present a straight state consistent with the flattened structural model, eliminating the influence of fault activity on the interpretation of seismic data.

[0048] Through the above steps, the flattened structural model is compared with the original structural model to obtain the structural transformation amount, and the transformation amount is transferred to the seismic data to obtain the flattened seismic data, which can effectively improve the interpretation accuracy of the seismic data and provide a more reliable basis for geological exploration.

[0049] Based on the flattened seismic data, the seismic axis of the target layer is identified and acquired, and the seismic axis continuous length analysis is performed on the seismic axis of the target layer to acquire a seismic axis continuous length data set.

[0050] Specifically, by identifying the seismic axis of the target layer based on the flattened seismic data and performing continuous length analysis, the continuity of the seismic axis can be effectively evaluated, thereby indirectly quantifying the fit between the structural model and the seismic data.

[0051] In some embodiments, based on the leveled seismic data, identifying and acquiring a target layer seismic axis, and traversing the target layer seismic axis to perform seismic axis continuous length analysis to acquire a seismic axis continuous length dataset includes:

[0052] A peak extraction function is used to automatically extract each time point that meets the detection conditions on the seismic axis of the target layer in the flattened seismic data to obtain a time point set; the time point set is traversed, and for each pair of adjacent time points, the corresponding time difference is calculated; it is determined whether the time difference is less than a preset time difference threshold. If it is less than, the lengths between adjacent time points are added to the continuous length statistics; if it is not less than, the current continuous length is output as a seismic axis continuous length value, and the continuous length is recalculated from the next time point until all the time point sets have been accumulated; based on multiple seismic axis continuous length values, the seismic axis continuous length data set is formed.

[0053] Specifically, the peak extraction function automatically identifies the peak locations in seismic signals, specifically the peak points of seismic reflection waves. These peaks typically correspond to reflection characteristics at bed interfaces and are the basis for identifying seismic axes. Peaks are identified by calculating the first-order derivative of the seismic signal or using a sliding window comparison method to detect the turning point where the signal amplitude changes from increasing to decreasing. An amplitude threshold is also set to eliminate spurious peaks caused by noise, ensuring the accuracy of the extracted time point set.

[0054] Specifically, the time point set is traversed, and for each pair of adjacent time points, the corresponding time difference is calculated. For example, for time points t1 and t2, Δt is calculated as |t2-t1|. A time difference threshold is then set, such as 1ms. If Δt < 1ms, t1 and t2 are considered continuous, and this length is added to the continuous length statistics. Otherwise, a discontinuity or fault is considered.

[0055] Specifically, in the process of traversing the time point set, when encountering a discontinuous time point pair, the current continuous length is output as a seismic axis continuous length value. For example, when the time difference between t3 and t4 is greater than the threshold, the continuous length from t1 to t3 is output as a value, and the continuous length is calculated again from t4.

[0056] Specifically, the time point set is traversed until all time points have been accumulated, and finally a seismic axis continuous length dataset is formed; wherein, the seismic axis continuous length dataset contains a plurality of continuous length datasets, such as {L1, L2, L3, ...}, where each length reflects the continuity of the seismic axis in different intervals.

[0057] By identifying the seismic axis of the target layer based on the flattened seismic data and performing continuous length analysis, the continuity of the seismic axis can be effectively evaluated, providing key data support for the evaluation of the fit between the structural model and the seismic data.

[0058] According to the seismic axis continuous length data set, a corresponding fit evaluation index is calculated to obtain a fit evaluation value, wherein the fit evaluation index is the mean of the seismic axis continuous length.

[0059] In some embodiments, a corresponding fit evaluation index is calculated based on the continuous length dataset of the seismic axis to obtain a fit evaluation value, wherein the fit evaluation index is the mean of the continuous length of the seismic axis, including:

[0060] Calculate the sum of all seismic axis continuous length values ​​in the seismic axis continuous length data set, and count the number of items of the seismic axis continuous length values; divide the sum of the seismic axis continuous length values ​​by the number of items of the seismic axis continuous length values ​​to obtain the mean seismic axis continuous length; use the mean seismic axis continuous length as a fit evaluation value to measure the fit between the original structural model and the original seismic data.

[0061] Specifically, first, all continuous length values ​​in the earthquake axis continuous length data set are accumulated to obtain the total continuous length; then, the number of continuous length segments contained in the earthquake axis continuous length data set is counted, that is, the number of elements in the data set. For example, if the data set is {L1, L2, L3, L4}, the number of continuous lengths is 4; then, the total continuous length is divided by the number of continuous lengths to obtain the mean of the earthquake axis continuous length, and output it as the fit evaluation value.

[0062] Specifically, the mean continuous length of the seismic axis can reflect the overall continuity level of the seismic axis. The higher the mean, the better the continuity of the seismic axis, which means that the structural model and the seismic data are in better agreement. By accurately calculating the mean continuous length of the seismic axis, we can provide a quantitative basis for evaluating the agreement between the structural model and the seismic data.

[0063] Based on the compatibility evaluation value, a compatibility evaluation result is output.

[0064] In some embodiments, based on the compatibility evaluation value, outputting a compatibility evaluation result includes:

[0065] Based on a preset fit threshold, the fit evaluation value is judged to be up to standard; if the fit evaluation value meets the fit threshold, the compliance judgment result is output as that the construction model meets the standard; the compliance judgment result is associated with the fit evaluation value, and the output is the fit evaluation result.

[0066] Specifically, first, a fit threshold is set based on the accuracy requirements of the structural model and the quality of the actual seismic data. This threshold is used to determine whether the fit between the structural model and the seismic data meets the expected target; then, the calculated fit evaluation value (i.e., the mean of the continuous length of the seismic axis) is compared with the preset fit threshold. If the fit evaluation value is greater than or equal to the threshold, the structural model is considered to meet the standard; otherwise, it is considered to meet the standard.

[0067] Furthermore, the compliance judgment result is associated with the fit evaluation value to form a complete fit evaluation result. For example, the output result is: "The structural model meets the standards, and the fit evaluation value is [mean of the continuous length of the earthquake axis]."

[0068] Through the above steps, the fit evaluation results can be accurately output, providing a strong basis for evaluating the fit between the structural model and seismic data, and facilitating the smooth progress of geological exploration projects.

[0069] Furthermore, the method further comprises:

[0070] Based on the fit evaluation value and the seismic axis continuous length data set, an auxiliary fit evaluation index of the seismic axis continuous length is calculated to obtain an auxiliary fit evaluation value; the auxiliary fit evaluation value is used as an auxiliary discriminant feature and added to the fit evaluation result.

[0071] Specifically, the auxiliary fit evaluation index is used to provide more dimensional quantitative indicators for the fit evaluation results, such as the statistical characteristics corresponding to the seismic axis continuous length data set, which helps to further judge the fit between the structural model and the seismic data. By combining the original fit evaluation value and the auxiliary fit evaluation value, the deviation of a single indicator can be effectively reduced, providing a more accurate model fit result.

[0072] In some embodiments, calculating an auxiliary fit evaluation index of the seismic axis continuous length based on the fit evaluation value and the seismic axis continuous length dataset to obtain the auxiliary fit evaluation value further includes:

[0073] The fit evaluation value is taken as the average value of the continuous length data set of the seismic axis, and the corresponding data set variance is calculated; the data set range of the continuous length data set of the seismic axis is calculated; a polynomial regression analysis is performed on the flattened seismic data, and the corresponding determination coefficient is extracted from the regression analysis result, and the output is a data set fitting evaluation value; the data set variance, the data set range and the data set fitting evaluation value are weighted to generate the auxiliary fit evaluation value.

[0074] Specifically, the fit evaluation value (mean of the continuous length of the earthquake axis) is taken as the average value of the continuous length data set of the earthquake axis, and the variance of the data set is calculated using the variance formula; the variance also reflects the degree of dispersion of the data set. The smaller the variance, the more concentrated the continuous length values ​​in the data set, and the higher the fit between the structural model and the earthquake data.

[0075] Specifically, the maximum and minimum values ​​in the continuous length data set of the earthquake axis are obtained, and the difference between the two is calculated, which is the range of the data set. The range represents the range of the data set. The smaller the range, the smaller the range of variation of the continuous length values ​​in the data set, and the higher the fit between the structural model and the earthquake data.

[0076] Specifically, polynomial regression analysis was performed on the flattened earthquake data to establish the fitted earthquake axis, and then the corresponding determination coefficient (R 2 ) as the data set fitting evaluation value; where the determination coefficient represents the degree of fit of the regression model to the data set, R 2 The closer it is to 1, the better the model fitting effect is, the stronger the regularity of the data set is, and the higher the fit between the structural model and the seismic data is.

[0077] Furthermore, the dataset variance, dataset range, and dataset fitting evaluation value are weighted to generate an auxiliary fit evaluation value. The weights in the weighting process are determined according to the degree of influence of each indicator on the fit. The smaller the auxiliary fit evaluation value, the higher the fit between the structural model and the seismic data.

[0078] Through the above steps, the auxiliary fit evaluation value can be accurately calculated and added to the fit evaluation result as an auxiliary discriminant feature, providing a more comprehensive and accurate basis for evaluating the fit between the structural model and seismic data.

[0079] In summary, the method for evaluating the fit between a structural model and seismic data provided by the present invention has the following technical effects:

[0080] By obtaining the original structural model and the original seismic data; based on the preset structural recovery interpretation program, each layer in the original structural model is flattened to obtain the flattened structural model; the flattened structural model is compared with the original structural model to obtain the structural transformation amount, and the structural transformation amount is transferred to the seismic data to obtain the flattened seismic data; based on the flattened seismic data, the seismic axis of the target layer is identified and obtained, and the seismic axis of the target layer is traversed to perform seismic axis continuous length analysis to obtain a seismic axis continuous length data set; according to the seismic axis continuous length data set, the corresponding fit evaluation index is calculated to obtain a fit evaluation value, wherein the fit evaluation index is the mean value of the seismic axis continuous length; based on the fit evaluation value, the fit evaluation result is output, thereby achieving the technical effects of improved intuitiveness, improved work efficiency and quantified results.

[0081] Example 2

[0082] Figure 2 This is a schematic diagram of the structure of the evaluation system for the degree of fit between the structural model and seismic data of the present invention. For example, Figure 1 The flow chart of the method for evaluating the degree of fit between the structural model and seismic data of the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0083] Based on the same concept as the method for evaluating the degree of fit between the structural model and seismic data in the above embodiment, the present invention also provides an evaluation system for the degree of fit between the structural model and seismic data, including:

[0084] The data acquisition module 11 is used to acquire the original structural model and original seismic data.

[0085] The structural model flattening module 12 is used to flatten each layer in the original structural model based on a preset structural restoration interpretation program to obtain a flattened structural model.

[0086] The structural conversion amount acquisition and migration module 13 is used to compare the flattened structural model with the original structural model to obtain structural conversion amounts, and migrate the structural conversion amounts to the seismic data to obtain flattened seismic data.

[0087] The seismic axis identification and analysis module 14 is used to identify and obtain the seismic axis of the target layer based on the flattened seismic data, and traverse the seismic axis of the target layer to perform seismic axis continuous length analysis to obtain a seismic axis continuous length data set.

[0088] The fit evaluation calculation module 15 is used to calculate the corresponding fit evaluation index according to the seismic axis continuous length data set to obtain a fit evaluation value, wherein the fit evaluation index is the mean of the seismic axis continuous length.

[0089] The compatibility evaluation result output module 16 is configured to output a compatibility evaluation result based on the compatibility evaluation value.

[0090] In some embodiments, the steps of constructing the model flattening module 12 include:

[0091] A time zero point is selected from the original structural model as a leveling benchmark; for each structural layer in the original structural model, the relative time difference from the leveling benchmark is calculated, and faults and horizons are defined based on the relative time difference; the faults and horizons and the original structural model are input into an automated structural restoration interpretation program, and a leveling operation based on structural restoration is performed to generate the leveled structural model.

[0092] In some embodiments, the steps of constructing the conversion amount acquisition and migration module 13 include:

[0093] defining a plurality of model sample points based on the original construction model, and obtaining a model sample point set;

[0094] Mapping the model sample point set to the seismic data to obtain a data sample point set;

[0095] Comparing difference information of the model sample point set in the flattened structural model and the original structural model to obtain a structural conversion amount;

[0096] Extracting data coordinate attributes of the seismic data, and performing metric conversion on the structural transformation amount according to the data coordinate attributes to obtain a target structural transformation amount;

[0097] According to the mapping relationship between the model sample point set and the data sample point set, the target structural transformation amount is applied to perform data flattening on the seismic data.

[0098] In some embodiments, the steps performed by the earthquake axis identification and analysis module 14 include:

[0099] A peak extraction function is used to automatically extract each time point that meets the detection conditions on the seismic axis of the target layer in the flattened seismic data to obtain a time point set; the time point set is traversed, and for each pair of adjacent time points, the corresponding time difference is calculated; it is determined whether the time difference is less than a preset time difference threshold. If it is less than, the lengths between adjacent time points are added to the continuous length statistics; if it is not less than, the current continuous length is output as a seismic axis continuous length value, and the continuous length is recalculated from the next time point until all the time point sets have been accumulated; based on multiple seismic axis continuous length values, the seismic axis continuous length data set is formed.

[0100] In some embodiments, the steps executed by the fit evaluation calculation module 15 include:

[0101] Calculate the sum of all seismic axis continuous length values ​​in the seismic axis continuous length data set, and count the number of items of the seismic axis continuous length values; divide the sum of the seismic axis continuous length values ​​by the number of items of the seismic axis continuous length values ​​to obtain the mean seismic axis continuous length; use the mean seismic axis continuous length as a fit evaluation value to measure the fit between the original structural model and the original seismic data.

[0102] In some embodiments, the steps of executing the fit evaluation result output module 16 include:

[0103] Based on a preset fit threshold, the fit evaluation value is judged to be up to standard; if the fit evaluation value meets the fit threshold, the compliance judgment result is output as that the construction model meets the standard; the compliance judgment result is associated with the fit evaluation value, and the output is the fit evaluation result.

[0104] In some implementations, the system further includes an auxiliary discrimination unit for calculating an auxiliary fit evaluation index of the continuous length of the seismic axis based on the fit evaluation value and the continuous length data set of the seismic axis, and obtaining an auxiliary fit evaluation value; the auxiliary fit evaluation value is added to the fit evaluation result as an auxiliary discrimination feature.

[0105] In some implementations, the execution steps of the auxiliary discrimination unit include: taking the fit evaluation value as the average value of the seismic axis continuous length data set, calculating the corresponding data set variance; calculating the data set range of the seismic axis continuous length data set; performing polynomial regression analysis on the flattened seismic data, and extracting the corresponding determination coefficient based on the regression analysis result, and outputting it as a data set fitting evaluation value; weighting the data set variance, the data set range and the data set fitting evaluation value to generate the auxiliary fit evaluation value.

[0106] Example 3

[0107] The present application provides a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the method for evaluating the degree of fit between a structural model and seismic data in an embodiment of the present invention. By running the software programs, instructions and modules stored in the computer-readable storage medium, the above-mentioned method for evaluating the degree of fit between a structural model and seismic data is implemented.

[0108] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the evaluation system for the degree of fit between the structural model and seismic data described in embodiment two. For the sake of brevity of the specification, no further elaboration will be given here.

[0109] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the evaluation system for the degree of fit between the structural model and seismic data described in embodiment two. For the sake of brevity of the specification, no further elaboration will be given here.

[0110] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention and are all included in the scope of protection of the present invention.

Claims

1. A method for evaluating the degree of fit between a structural model and seismic data, characterized in that: The method comprises: Obtain original structural model and original seismic data; Based on a preset structural restoration interpretation program, a flattening operation is performed on each layer in the original structural model to obtain a flattened structural model; Comparing the flattened structural model with the original structural model to obtain structural transformation amount, and migrating the structural transformation amount to the seismic data to obtain flattened seismic data; Based on the flattened seismic data, the target layer seismic axis is identified and obtained, and the seismic axis of the target layer is traversed to perform seismic axis continuous length analysis to obtain a seismic axis continuous length data set; Calculating a corresponding fit evaluation index according to the seismic axis continuous length data set to obtain a fit evaluation value, wherein the fit evaluation index is the mean of the seismic axis continuous length; Based on the compatibility evaluation value, a compatibility evaluation result is output.

2. The method for evaluating the degree of fit between a structural model and seismic data according to claim 1, wherein: Based on a preset structural restoration interpretation program, a flattening operation is performed on each layer in the original structural model to obtain a flattened structural model, including: Selecting a time zero point from the original structural model as a leveling reference; For each structural layer in the original structural model, a relative time difference from the leveling benchmark is calculated, and faults and horizons are defined according to the relative time difference; The faults and horizons and the original structural model are input into an automated structural restoration interpretation program, and a flattening operation based on structural restoration is performed to generate the flattened structural model.

3. The method for evaluating the degree of fit between a structural model and seismic data according to claim 2, wherein: Comparing the flattened structural model with the original structural model to obtain structural transformation amounts, and migrating the structural transformation amounts to the seismic data to obtain flattened seismic data, including: defining a plurality of model sample points based on the original construction model, and obtaining a model sample point set; Mapping the model sample point set to the seismic data to obtain a data sample point set; Comparing difference information of the model sample point set in the flattened structural model and the original structural model to obtain a structural conversion amount; Extracting data coordinate attributes of the seismic data, and performing metric conversion on the structural transformation amount according to the data coordinate attributes to obtain a target structural transformation amount; According to the mapping relationship between the model sample point set and the data sample point set, the target structural transformation amount is applied to perform data flattening on the seismic data.

4. The method for evaluating the degree of fit between a structural model and seismic data according to claim 3, wherein: Based on the flattened seismic data, the target layer seismic axis is identified and obtained, and the seismic axis continuous length analysis is performed on the target layer seismic axis to obtain a seismic axis continuous length data set, including: Using a peak extraction function to automatically extract each time point that meets a detection condition on the seismic axis of the target layer in the flattened seismic data to obtain a time point set; Traversing the set of time points, for each pair of adjacent time points, calculating the corresponding time difference; Determine whether the time difference is less than a preset time difference threshold; if so, add the lengths between adjacent time points to the continuous length statistics; If it is not less than, the current continuous length is output as a seismic axis continuous length value, and the continuous length is recalculated from the next time point until all the time points have been accumulated; The seismic axis continuous length data set is formed based on a plurality of seismic axis continuous length values.

5. The method for evaluating the degree of fit between a structural model and seismic data according to claim 4, wherein: According to the continuous length data set of the earthquake axis, a corresponding fit evaluation index is calculated to obtain a fit evaluation value, wherein the fit evaluation index is the mean of the continuous length of the earthquake axis, including: Calculating the sum of all earthquake axis continuous length values ​​in the earthquake axis continuous length data set, and counting the number of items of the earthquake axis continuous length values; Dividing the sum of the continuous length values ​​of the earthquake axis by the number of items of the continuous length values ​​of the earthquake axis to obtain the mean continuous length value of the earthquake axis; The mean value of the continuous length of the seismic axis is used as a fit evaluation value to measure the fit between the original structural model and the original seismic data.

6. The method for evaluating the degree of fit between a structural model and seismic data according to claim 1, wherein: Based on the fit evaluation value, a fit evaluation result is output, including: Based on a preset fit threshold, determining whether the fit evaluation value meets the standard; If the fit evaluation value satisfies the fit threshold, the compliance judgment result is outputted as the construction model meets the standards; The compliance determination result is associated with the compatibility evaluation value, and the compatibility evaluation result is output.

7. The method for evaluating the degree of fit between a structural model and seismic data according to claim 1, wherein: The method further comprises: Calculating an auxiliary fit evaluation index of the seismic axis continuous length based on the fit evaluation value and the seismic axis continuous length data set to obtain an auxiliary fit evaluation value; The auxiliary fit evaluation value is added to the fit evaluation result as an auxiliary discriminant feature.

8. The method for evaluating the degree of fit between a structural model and seismic data according to claim 7, wherein: Calculating an auxiliary consistency evaluation index of the seismic axis continuous length based on the consistency evaluation value and the seismic axis continuous length data set to obtain an auxiliary consistency evaluation value further includes: Taking the fit evaluation value as the average value of the earthquake axis continuous length data set, the corresponding data set variance is calculated; Calculating and obtaining the dataset range of the continuous length dataset of the earthquake axis; Performing polynomial regression analysis on the leveled seismic data, extracting corresponding determination coefficients based on the regression analysis results, and outputting the output as a data set fitting evaluation value; The data set variance, the data set range and the data set fitting evaluation value are weighted to generate the auxiliary fit evaluation value.

9. A system for evaluating the fit between a structural model and seismic data, characterized in that: The system is used to perform the method for evaluating the degree of fit between a structural model and seismic data according to any one of claims 1 to 8, and the system comprises: Data acquisition module, used to obtain original structural model and original seismic data; a structural model flattening module, configured to flatten each layer in the original structural model based on a preset structural restoration interpretation program to obtain a flattened structural model; a structural conversion amount acquisition and migration module, configured to compare the flattened structural model with the original structural model, acquire structural conversion amounts, and migrate the structural conversion amounts to the seismic data to acquire flattened seismic data; An earthquake axis identification and analysis module is used to identify and obtain the earthquake axis of the target layer based on the flattened earthquake data, and traverse the earthquake axis of the target layer to perform earthquake axis continuous length analysis to obtain an earthquake axis continuous length data set; A fit evaluation calculation module is used to calculate a corresponding fit evaluation index based on the seismic axis continuous length data set to obtain a fit evaluation value, wherein the fit evaluation index is the mean of the seismic axis continuous length; The compatibility evaluation result output module is used to output the compatibility evaluation result based on the compatibility evaluation value.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for evaluating the degree of fit between a structural model and seismic data according to any one of claims 1 to 8 is implemented.

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