Method and system for evaluating integrating degree of construction model and seismic data, and storage medium

Abstract

The invention discloses a construction model and seismic data integrating degree evaluation method and system and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining an original construction model and original seismic data; performing leveling operation on the original construction model to obtain a leveled construction model; comparing the leveled structure model with the original structure model to obtain a structure conversion amount, and migrating the structure conversion amount to the seismic data to obtain leveled seismic data; identifying and acquiring a seismic axis of a target layer based on the leveled seismic data, traversing the seismic axis of the target layer to perform seismic axis continuous length analysis, and acquiring a seismic axis continuous length data set; according to the seismic axis continuous length data set, calculating a corresponding integrating degree evaluation index, and obtaining a integrating degree evaluation value; and outputting a integrating degree evaluation result based on the integrating degree evaluation value, thereby achieving the technical effects of intuition improvement, working efficiency improvement and result quantification.

Description

Technical Field

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

[0002] In the field of geological exploration, the accuracy of structural models is crucial for resource assessment, risk prediction, and development planning. In order to ensure that the structural model can accurately reflect the actual situation of the underground structure, it must be compared and analyzed with the actual seismic data. This comparison involves not only the matching of geometric shapes, but also the correspondence in time (depth) and space. At present, the commonly used methods for measuring the fit between the structural model and the original seismic data body mainly include methods based on manual statistics, that is, relying on manual or semi-automatic tracking of the stratigraphic interface.

[0003] The existing technology has the following disadvantages: First, the manual statistical method is highly subjective. Since it relies on the experience and judgment of the interpreter, the results may vary from person to person and lack objectivity and consistency. Secondly, the complexity of the seismic data itself increases the difficulty of analysis, especially the rapid changes in the shape of the seismic axis make intuitive analysis difficult. Finally, traditional methods are difficult to quantify and can often only give qualitative evaluations such as "high", "medium" and "low", and it is difficult to provide specific values ​​to quantify the analysis results. Summary of the invention

[0004] The present invention provides an evaluation method, system and storage medium for the degree of fit between a structural model and seismic data, so as to solve the technical problems in the prior art of 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 degree of fit between a structural model and seismic data, wherein the method comprises:

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

[0007] Based on a preset structural restoration interpretation program, each layer in the original structural model is flattened 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 seismic axis of the target layer is identified and obtained, and the seismic axis of the target layer is traversed to perform a continuous length analysis of the seismic axis to obtain a continuous length data set of the seismic axis;

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

[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] A data acquisition module, used to obtain the original structural model and original seismic data;

[0014] A structural model flattening module 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;

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

[0016] A seismic axis identification and analysis module, 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;

[0017] A fit evaluation calculation module is used to calculate 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;

[0018] The fit evaluation result output module is used to output the fit evaluation result based on the fit 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 an evaluation 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; based on a preset structural recovery and interpretation program, flattening each layer in the original structural model to obtain a flattened structural model; comparing the flattened structural model with the original structural model, calculating a structural transformation amount, and migrating the transformation amount to the original seismic data, thereby obtaining the flattened seismic data; using the flattened seismic data, identifying and extracting the seismic axis of a target layer, traversing the seismic axis of the target layer for continuous length analysis, and generating a seismic axis continuous length data set; calculating a fit evaluation index, specifically a mean of the seismic axis continuous length, based on the seismic axis continuous length data set, to obtain a fit evaluation value; based on the fit evaluation value, outputting a final fit evaluation result. The evaluation 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 It is a flow chart of the method for evaluating the degree of fit between the structural model and seismic data of the present invention;

[0022] Figure 2 It is a structural schematic diagram 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 of 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 are shown in the drawings, rather than all of them.

[0025] Embodiment 1

[0026] Figure 1 The figure is 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 source includes 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, align the spatial coordinates of the original structural model with the seismic data to ensure the consistency of the model with the seismic wave signal. Then, verify the data integrity and accuracy, including checking whether the model file is missing hierarchical information, whether there are abnormal sampling intervals in the seismic data, etc., to provide 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 process of the strata. Exemplary modeling software include Petrel, GOCAD, etc.

[0032] Specifically, the structural restoration interpretation program has a powerful geomechanical engine embedded in it, which can simulate the stress and strain changes of the strata during the geological history. By analyzing the mechanical behavior of the strata, the deformation characteristics and trends of the strata can be identified, providing a mechanical basis for the leveling operation.

[0033] Specifically, the structural restoration interpretation program constructs a balanced section of the strata to display the shape and position of the strata in different geological periods, eliminates the deformation effects of the strata, and restores the original shape of the strata, thereby flattening the structural model and presenting a straight or nearly 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 objectives; 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, a relative time difference with 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, a leveling operation based on structural restoration is performed, and the leveled structural model is generated.

[0037] Specifically, the structural restoration interpretation program is used to simulate and reconstruct the history and morphology of geological structures. Among them, time zero is the reference 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, in the original structural model, a stable geological interface, such as a known sedimentary datum or a stable stratigraphic interface, is selected as the time zero point of the leveling benchmark. This time zero point appears as an obvious reflection interface in the seismic data and is relatively stable in geological history; then, the relative time difference between each structural layer and the time zero point is calculated using seismic data and geological models. For example, for a specific structural layer, the delay time of the layer relative to the time zero point is calculated by analyzing the propagation time of the seismic wave between the layer and the time zero point, combined with the known stratigraphic velocity model. This delay time reflects the degree of deformation of the stratum during the geological history.

[0039] Furthermore, according to the relative time difference, combined with the fault characteristics and geological knowledge in the seismic data, the faults and horizons are defined. For example, the location where the stratigraphic interface is obviously displaced near the fault is identified and defined as a fault; at the same time, the specific location and morphology of each horizon are determined according to 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 the automated structural restoration interpretation program. Based on this information, the program simulates the evolution of the strata during the geological history process, adjusts the position and morphology of the stratigraphic interface, and makes it appear straight or approximately straight on the flattened datum. Finally, a flattened structural model is generated, which can better reflect the true distribution characteristics and structural morphology of the strata, and provides 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 recovery interpretation program, which can effectively simplify the complexity of the seismic axis morphology and 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 the 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 faults of the stratum 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 stratum interface; a mapping relationship is established between the original tectonic model and the seismic data, and the tectonic transformation amounts are correspondingly migrated to the seismic data, so that the reflection event axes in the seismic data present a straight 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 amount, and migrating the structural transformation amount 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 and the original structural model is compared 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 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. First, multiple model sample points are defined in the original structural 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, combined with the sampling interval and spatial resolution of the seismic data, the model sample points are mapped to 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 difference in the stratigraphic interface. For example, a stratigraphic interface appears straight in the flattened model, but appears inclined 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 longitudinal axis coordinate attributes, including time or depth, are extracted. If the longitudinal axis coordinate attributes of the structural model and 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 the 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, 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 level the seismic data, that is, the reflection event axes in the seismic data are adjusted accordingly by vertical translation and rotation; after the adjustment, the reflection event axes 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, identifying the seismic axis of the target layer based on the flattened seismic data and performing continuous length analysis can effectively evaluate the continuity of the seismic axis, thereby indirectly quantifying the fit between the structural model and the seismic data.

[0051] In some embodiments, based on the flattened seismic data, identifying and acquiring the seismic axis of the target layer, and traversing the seismic axis of the target layer to perform seismic axis continuous length analysis to acquire a seismic axis continuous length data set 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, and if so, the lengths between adjacent time points are added to the continuous length statistics; if not, the current continuous length is output as a seismic axis continuous length value, and the continuous length is calculated again 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 is used to automatically identify the peak position in the seismic signal, that is, the peak point of the seismic reflection wave. These peak points usually correspond to the reflection characteristics of the stratum interface and are the basis for seismic axis identification; for example, by calculating the first-order derivative of the seismic signal or using the sliding window comparison method, the turning point from increasing to decreasing signal amplitude is detected, thereby identifying the peak. At the same time, an amplitude threshold is set to exclude pseudo peaks caused by noise and ensure 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 = |t2-t1| is calculated; then, a time difference threshold is set, such as 1ms. If Δt < 1ms, t1 and t2 are considered to be continuous, and this length is added to the continuous length statistics; otherwise, it is considered that there is a discontinuity or fault.

[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 are accumulated, and finally a continuous length data set of the earthquake axis is formed; wherein the continuous length data set of the earthquake axis includes a data set of multiple continuous lengths, such as {L1, L2, L3, ...}, wherein each length reflects the continuity of the earthquake 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, according to the continuous length data set of the seismic 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 seismic axis, including:

[0060] Calculate the sum of all the 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 value of the seismic axis continuous length; use the mean value of the 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 seismic axis continuous length data set are added up to obtain the total continuous length; then, the number of continuous length segments contained in the seismic axis continuous length data set, that is, the number of elements in the data set, is counted. 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 value of the seismic axis continuous length, and the result is output as a 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 good 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 fit evaluation value, outputting a fit 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 standard judgment result is output as that the constructed model is up to standard; the standard 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 according to the accuracy requirements of the structural model and the quality of the actual seismic data. The threshold is used to determine whether the fit between the structural model and the seismic data reaches 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 fail 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 standard, and the fit evaluation value is [mean value 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 discrimination 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, based on the fit evaluation value and the seismic axis continuous length data set, calculating the auxiliary fit evaluation index of the seismic axis continuous length, and obtaining 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 seismic axis) is taken as the average value of the continuous length data set of the seismic 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 seismic data.

[0075] Specifically, the maximum and minimum values ​​in the continuous length data set of the seismic 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 it can be considered that the structural model has a higher fit with the seismic data.

[0076] Specifically, polynomial regression analysis was performed on the leveled 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 variance, range and fitting evaluation value of the data set are weighted to generate an auxiliary fit evaluation value. The weights are determined according to the 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 the seismic data.

[0079] In summary, the method for evaluating the degree of fit between the 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 the seismic axis continuous length analysis to obtain the seismic axis continuous length data set; according to the seismic axis continuous length data set, the corresponding fit evaluation index is calculated to obtain the 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] Embodiment 2

[0082] Figure 2 Schematic diagram of the structure of the evaluation system for the fit between the structural model and seismic data of the present invention. 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 evaluation method for the degree of fit between the structural model and the seismic data in the above embodiment, the present invention also provides an evaluation system for the degree of fit between the structural model and the 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, acquire structural conversion amounts, and migrate the structural conversion amounts to the seismic data to acquire 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 value of the seismic axis continuous length.

[0089] The fit evaluation result output module 16 is used to output the fit evaluation result based on the fit 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, a relative time difference with 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, a leveling operation based on structural restoration is performed, and the leveled structural model is generated.

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

[0093] Based on the original construction model, define a plurality of model sample points to obtain 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 the difference information of the model sample point set in the flattened structural model and the original structural model to obtain the structural transformation 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 execution steps of the seismic 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, and if so, the lengths between adjacent time points are added to the continuous length statistics; if not, the current continuous length is output as a seismic axis continuous length value, and the continuous length is calculated again 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 execution steps of the fit evaluation calculation module 15 include:

[0101] Calculate the sum of all the 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 value of the seismic axis continuous length; use the mean value of the 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 execution steps of 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 standard judgment result is output as that the constructed model is up to standard; the standard judgment result is associated with the fit evaluation value, and the output is the fit evaluation result.

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

[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] Embodiment 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 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. The method for evaluating the degree of fit between a structural model and seismic data is implemented by running the software programs, instructions and modules stored in the computer-readable storage medium.

[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 fit between the structural model and seismic data described in embodiment two. For the sake of brevity of the specification, they will not be further elaborated 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 fit between the structural model and seismic data described in embodiment two. For the sake of brevity of the specification, they will not be further elaborated 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 above-mentioned embodiments. It should be understood that those skilled in the art can still modify the technical solutions recorded in the above-mentioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope 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 the original structural model and original seismic data; Based on a preset structural restoration interpretation program, each layer in the original structural model is flattened 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 seismic axis of the target layer is identified and obtained, and the seismic axis of the target layer is traversed to perform a continuous length analysis of the seismic axis to obtain a continuous length data set of the seismic axis; According to the continuous length data set of the seismic axis, a corresponding fit evaluation index is calculated to obtain a fit evaluation value, wherein the fit evaluation index is the mean value of the continuous length of the seismic axis; 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, characterized in that: Based on a preset structural restoration interpretation program, each layer in the original structural model is flattened 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 with the flattened benchmark is calculated, and faults and horizons are defined according to the relative time difference; The faults and the 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, characterized in that: 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 the flattened seismic data, including: Based on the original construction model, define a plurality of model sample points to obtain a model sample point set; Mapping the model sample point set to the seismic data to obtain a data sample point set; Comparing the difference information of the model sample point set in the flattened structural model and the original structural model to obtain the structural transformation 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, characterized in that: 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 a continuous length analysis of the seismic axis to obtain a continuous length data set of the seismic axis, including: Using a peak extraction function to automatically extract each time point on the seismic axis of the target layer in the flattened seismic data that meets the detection condition, 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, and 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 calculated again from the next time point until all the time point sets have been accumulated; Based on a plurality of the seismic axis continuous length values, the seismic axis continuous length data set is formed.

5. The method for evaluating the degree of fit between a structural model and seismic data according to claim 4, characterized in that: According to the continuous length data set of the seismic 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 seismic axis, including: Calculating the sum of all the seismic axis continuous length values ​​in the seismic axis continuous length data set, and counting the number of items of the seismic axis continuous length values; Dividing the sum of the continuous length values ​​of the seismic axis by the number of items of the continuous length values ​​of the seismic axis to obtain the mean value of the continuous length of the seismic 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, characterized in that: 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 fit evaluation value, and the fit evaluation result is output.

7. The method for evaluating the degree of fit between a structural model and seismic data according to claim 1, characterized in that: The method further comprises: 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.

8. The method for evaluating the degree of fit between a structural model and seismic data according to claim 7, characterized in that: 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, further comprising: Taking the fit evaluation value as the average value of the continuous length data set of the earthquake axis, the corresponding data set variance is calculated; Calculate and obtain the data set range of the continuous length data set 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 execute the method for evaluating the degree of fit between the structural model and seismic data according to any one of claims 1 to 8, and the system comprises: A data acquisition module, used to obtain the original structural model and original seismic data; A structural model flattening module 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; A structural conversion amount acquisition and migration module is used to compare the flattened structural model with the original structural model to acquire structural conversion amounts, and migrate the structural conversion amounts to the seismic data to acquire flattened seismic data; A seismic axis identification and analysis module, 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; A fit evaluation calculation module is used to calculate 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; The fit evaluation result output module is used to output the fit evaluation result based on the fit 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 as described in any one of claims 1 to 8 is implemented.

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