Depth domain seismic data-based calibration and interpretation method

Abstract

The invention provides a calibration and interpretation method based on depth domain seismic data, and the method comprises the steps: S1, carrying out the speed structure analysis of VSP data, and carrying out the reliability evaluation; s2, well seismic calibration interpretation is carried out according to the VSP data; s3, longitudinally and transversely analyzing deviation distribution characteristics, and exploring main control factors of deviation distribution; and S4, according to the main control factors, obtaining a structure interpretation result map after depth correction. According to a calibration result, depth domain error characteristics are analyzed, depth domain interpretation longitudinal deviation is reduced, depth domain structure interpretation precision is improved, and the problem that depth domain imaging depth error correction is difficult is solved.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration, and more particularly to a method for calibration and interpretation of depth-domain seismic data. Background Technology

[0002] Pre-stack depth migration is currently one of the most accurate migration methods for seismic data processing, particularly advantageous in areas with complex velocity or tectonic structures. It is primarily used to address wavefield imaging problems of complex geological bodies such as salt domes, complex basements, complex fault blocks, and bioherms. Compared to time migration, pre-stack depth migration improves imaging accuracy. On a high-quality time-migrated seismic profile, the phase axis can only accurately represent the horizontal relative position of subsurface structural surfaces, while the vertical relative position is distorted, sometimes severely. This is especially problematic for structures related to velocity anomalies such as igneous rocks, deep low-amplitude structures, and complex thrust and nip structures in compression basins, where time-domain profiles have limitations, potentially reflecting structural morphology that differs from the subsurface structure. Depth-domain seismic data offers a more intuitive advantage in addressing this issue, providing accurate spatial geometry of geological bodies, including depth, attitude, structural highs, and fault discontinuities, thus accurately reflecting subsurface structural morphology and possessing significant geological meaning. Domestic and international exploration practices have confirmed that pre-stack depth migration seismic imaging technology is one of the key technologies for reducing exploration risks. Therefore, how to perform subsequent calibration and interpretation of seismic data after pre-stack depth migration becomes a problem that must be addressed.

[0003] Previous researchers have conducted extensive studies on the problems existing in the interpretation of depth-domain seismic data. He Xinghua, starting from the description of seismic wavefields in the time and depth domains, proved that the geophysical concepts, principles, and methods in the time and depth domains are interconnected, and their mathematical expressions are similar. He also provided reasonable explanations for practical problems such as the selection of sampling intervals in depth-time conversion, the relationship between frequency spectrum and wavenumber spectrum data, and the video rate changes on the depth-time conversion profile. Zhang Xuejian et al. argued that synthetic seismic records should also be used for stratigraphic calibration in the depth domain. Due to the uncertainty of the Fourier transform in the depth domain, they proposed using the standard Ricker wavelet instead of statistical wavelets to create synthetic seismic records in the depth domain, and proposed a method suitable for creating synthetic seismic records in the depth domain. Hao Xiaohong et al. explored the interpretation of depth-domain seismic data, proposing a method for interpretation in the depth domain that adapts as closely as possible to the current interpretation system's working habits and approaches. Zhou Shang et al. explored the interpretation methods and processes of depth-domain seismic data, utilizing wavenumber-type attributes for hydrocarbon detection. Han Biwu et al. used extracted depth-domain variance, dip, curvature, and coherence attributes for principal component analysis, fusing the results with ant-like attributes for structural interpretation. Based on the analysis of depth-domain data characteristics, Cao Danping converted inter-well seismic data in the depth domain to the time domain using regional velocity and tomographic imaging velocity. By coarsening and reducing frequencies, they established a similarity relationship between inter-well seismic data and ground-based seismic data, gaining insights into the low-frequency reflection characteristics of inter-well seismic data. Combined with the calibration of synthetic seismic records from adjacent wells, they established the reflection characteristics of inter-well seismic data, accurately interpreting the reflection horizons of inter-well seismic data. However, the accuracy problem of depth-domain structures after time-depth conversion has not been effectively solved. In particular, the current shale oil exploration and development process, with its long horizontal wells and three-dimensional development, places higher demands on the accuracy of structural description, and traditional depth domain interpretation methods are difficult to meet the current needs of shale oil exploration and development.

[0004] Based on this, we conducted research on depth domain layer calibration methods, depth domain interpretation methods, and depth domain mapping methods. We found that the current depth domain interpretation faces two problems: ① "Difficulty in calibration", as depth domain calibration lacks an accurate velocity scale; ② "Difficulty in correction", as accurate correction of longitudinal deviations in depth domain interpretation is very difficult. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method for calibration and interpretation of depth-domain seismic data that overcomes or at least partially solves the above problems.

[0006] According to one aspect of the present invention, a calibration and interpretation method based on depth-domain seismic data is provided, the calibration and interpretation method comprising:

[0007] Step S1: Perform velocity structure analysis on the VSP data and conduct a reliability evaluation;

[0008] Step S2: Interpretation of well seismic calibration based on the VSP data;

[0009] Step S3: Analyze the characteristics of the deviation distribution in both longitudinal and transverse directions, and explore the main controlling factors of the deviation distribution;

[0010] Step S4: Based on the main control factors, obtain the interpretation result diagram after depth correction.

[0011] Optionally, step S1: performing velocity structure analysis on the VSP data specifically includes:

[0012] By comparing the velocity structure of the time-depth curves of all VSP wells in the work area with the velocity structure of the general velocity-time-depth relationship in the region, it is ensured that the variation pattern is normal and consistent, with no outliers.

[0013] By comparing the changes with the sonic logging curves of this well, we can ensure that the velocity structure of the VSP is consistent with the velocity structure represented by the sonic logging curves.

[0014] Optionally, the reliability evaluation specifically includes:

[0015] Refer to the core logging data and observe whether the variation range of the velocity curve matches the variation of the lithological profile.

[0016] By combining well stratification with the correspondence between seismic reflections, we analyze whether the velocity relationships provided by VSP data are consistent with geological understanding, clarify the quality of VSP data, and provide a velocity scale for subsequent well-seismic calibration.

[0017] Optionally, step S2: interpretation of well seismic calibration based on the VSP data specifically includes:

[0018] Step 201: Frequency upsampling of seismic data to obtain seismic data that best matches the VSP corridor stacking profile;

[0019] Utilizing the property that VSPLOG and seismic data have the same wavelet frequency band and consistent waveform;

[0020] By comparing the reflected wave impedance of the VSP corridor overlay profile with the reflected wave group of the depth domain seismic data profile, the two sets of data are matched and imaged, and the accurate stratigraphic calibration of the seismic profile is achieved based on the known geological stratification of the well.

[0021] Step 202: Compare the wave impedance characteristics of the depth domain seismic data of the VSP well with the core data from actual drilling to obtain the correspondence, clarify the longitudinal velocity structure of the formation and the wave impedance correspondence with the seismic profile, assign the geological information revealed by the drilling and logging data to the seismic data, and realize the well-seismic interpretation of the surrounding wells by comparing the core data from the surrounding wells with the seismic wave impedance, so as to guide the interpretation work of the whole area from point to line to surface.

[0022] Optionally, step S3: analyzing the characteristics of the deviation distribution in both longitudinal and transverse directions, and exploring the main controlling factors of the deviation distribution, specifically includes:

[0023] The study area exhibits deviation patterns based on its geological conditions;

[0024] The imaging deviations of all wells in the work area were statistically analyzed in both longitudinal and transverse directions to explore the main controlling factors of the deviation distribution.

[0025] Optionally, the deviation patterns in the study area based on geological conditions specifically include:

[0026] Based on geological understanding, several factors related to the deviation distribution characteristics of this work area were considered. These factors include factor A, factor B, and factor C.

[0027] By fitting correlation functions, the correlation between a single influencing factor and imaging bias was analyzed;

[0028] Based on the first factor A, the known correction quantities in the work area are classified, and the correlation between the imaging deviation and the first factor A is fitted to obtain the first regression coefficient R1.

[0029] By analogy with the correlation between the fitting imaging bias and the second factor B and the third factor C, we can obtain the second regression coefficient R2 and the third regression coefficient R3.

[0030] Optionally, the statistical analysis of imaging deviations in the depth domain of all wells within the work area, including longitudinal and lateral analysis of deviation distribution characteristics, and the exploration of the main controlling factors of deviation distribution, specifically includes:

[0031] Based on the corresponding regression coefficients, the parameters are classified.

[0032] Based on the consistency between the correlation between a single geological parameter and the deviation distribution and actual geological laws, the parameters are divided into parameters that conform to geological laws and parameters that do not conform to geological laws.

[0033] Based on a clear understanding of the correlation between deviation distribution and influencing factors, and constrained by geological laws, the main controlling factors of imaging deviation distribution in the work area are determined.

[0034] Optionally, step S4: obtaining the interpretation result map after depth correction based on the main controlling factors specifically includes:

[0035] Based on the main controlling factors, the work area is divided into zones, the functional relationship of the depth domain imaging deviation in each zone is explored, and a theoretical model of the deviation law is established.

[0036] The theoretical model is used to simulate the correction amount for areas with low well control. Seismic interpretation results are used as hard data, and the correction amount is used as soft data constraint. The optimal interval is determined based on the variogram function.

[0037] The corrected interpretation results are obtained by using a multi-grid approximation algorithm for spatial extrapolation interpolation, and the deep-corrected interpretation result map is obtained.

[0038] Optionally, the step of dividing the work area into zones based on the main controlling factors, exploring the functional relationship of the depth domain imaging deviation in each zone, and establishing a theoretical model of the deviation law specifically includes:

[0039] The existing wells in the study area are concentrated in the central part of the work area, and the well control level does not meet the correction requirements. Therefore, the correction amount of the northern and southern parts with low well control level is obtained to control the correction of the whole area.

[0040] Assuming that after partitioning, the existing well imaging deviations from A to B are q1, q2, q3, q4, q5, ..., qn respectively, and according to step S3, it is found that the deviations exhibit a consistent statistical regularity from top to bottom in the partitioned case, and the fitted result is Q. n =An 2 ±Bn±C, making R 2 ≥0.8;

[0041] Where Qn is the deviation value with index n, and n is any value in the integer sequence;

[0042] Based on the known well imaging deviation data, the theoretical model parameters A, B, and C are fitted to make the R² value close to 1, and a theoretical model of the deviation law of a certain zone in the work area is established.

[0043] The correction amount for areas with low well control is obtained through simulation using the aforementioned deviation law theoretical model.

[0044] Optionally, the correction amount for areas with low well control obtained through simulation using the theoretical model specifically includes:

[0045] In the process of correcting seismic interpretation results based on actual drilling corrections, the density of correction points determines the degree of correction.

[0046] Through the variation function Determine the optimal interval for the correction points;

[0047] Where h is the number of intervals between two correction points, and one interval is 50m; It is the correction value at the correction point; the variogram reveals the similarity of the correction values ​​between two spatial locations at a distance h; γ (h) The smaller the value, the closer the correction values ​​at the two positions are; conversely, the larger the value, the greater the difference.

[0048] The maximum interval obtained by the variogram is hn. The value of the variogram no longer increases and stabilizes near a limit value. The correction amount is correlated within the range of hn, and the correction amount no longer has spatial correlation outside the range of hn. hn is the optimal interval for interpolation of the correction point.

[0049] This invention provides a calibration and interpretation method based on depth-domain seismic data. The method includes: Step S1: performing velocity structure analysis and reliability evaluation on VSP data; Step S2: performing well-seismic calibration and interpretation based on the VSP data; Step S3: analyzing the longitudinal and transverse deviation distribution characteristics and exploring the main controlling factors of the deviation distribution; Step S4: obtaining the depth-corrected structural interpretation result map based on the main controlling factors. Based on the calibration results, the method analyzes the depth-domain error characteristics, reduces the longitudinal deviation in depth-domain interpretation, improves the accuracy of depth-domain structural interpretation, and solves the problem of difficult depth error correction in depth-domain imaging.

[0050] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating a specific embodiment of a calibration and interpretation method based on depth-domain seismic data provided by this invention;

[0053] Figure 2 This is a comparison diagram of the change curves of the VSP velocity structure and the acoustic layer velocity in a specific embodiment of the present invention;

[0054] Figure 3 This is a comparison chart of VSP data and logging lithology data in a specific embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of the matching imaging of VSP corridor overlay profile reflection impedance and depth domain seismic data profile reflection wave group in a specific embodiment of the present invention.

[0056] Figure 5 This is a deviation distribution theoretical model established in a specific embodiment of the present invention;

[0057] Figure 6 This is a schematic diagram illustrating the principle of determining the optimal interval based on the variation function in a specific embodiment of the present invention;

[0058] Figure 7This is a graph showing the functional relationship between the variation function value and the optimal interval in a specific embodiment of the present invention;

[0059] Figure 8 This is a schematic diagram of spatial extrapolation correction based on the optimal interval, which is involved in a specific embodiment of the present invention. Detailed Implementation

[0060] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0061] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0062] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0063] Example 1

[0064] In a specific embodiment 1 of the present invention, such as Figure 1 As shown, the present invention, based on the calibration and interpretation method of depth-domain seismic data, specifically includes:

[0065] In step 1, velocity analysis and evaluation are performed on the VSP data, including:

[0066] Step 101: Velocity structure analysis. First, compare the velocity structure of the time-depth curves of all VSP wells in the work area with the velocity structure of Dongying well to ensure that the variation patterns are normal and consistent, with no outliers. Second, compare the changes with the sonic logging curve of this well to ensure that the VSP velocity structure is normal. For example... Figure 2 The figure shows a comparison of the velocity variations of the VSP velocity structure and the acoustic layer velocity. Figure 3 As shown, this is a comparison chart of VSP data and logging lithology data.

[0067] Step 102: Reliability evaluation. First, refer to the core logging data and observe whether the variation range of the velocity curve matches the variation of the lithological profile. Second, combine the correspondence between well stratification and seismic reflection to analyze whether the velocity relationship provided by the VSP data is consistent with geological understanding, thereby clarifying the quality of the VSP data and providing a velocity "benchmark" for subsequent well-seismic calibration.

[0068] Step 2, interpretation of wellbore calibration based on VSP data, including:

[0069] Step 201: Frequency upsampling of the seismic data yields the seismic data that best matches the VSP corridor stacking profile. This utilizes the property that the VSP and seismic data have the same wavelet frequency band and consistent waveform.

[0070] like Figure 4 As shown in the figure, the comparison diagram of VSP data and well logging lithology data is used to compare the reflection wave impedance of the VSP corridor superimposed profile with the reflection wave group of the depth domain seismic data profile to achieve matching imaging of the two sets of data. Based on the known geological stratification of the well, the accurate stratigraphic calibration of the seismic profile is achieved.

[0071] Step 202: Compare the wave impedance characteristics of the depth domain seismic data of the VSP well with the core data from actual drilling to obtain the correspondence, clarify the longitudinal velocity structure of the formation and the wave impedance correspondence with the seismic profile, assign the geological information revealed by the drilling and logging data to the seismic data, and realize the well-seismic interpretation of the surrounding wells by comparing the core data from the surrounding wells with the seismic wave impedance, so as to guide the interpretation work of the whole area from point to line to surface.

[0072] Step 3: Statistically analyze the imaging deviations of all wells in the work area across the depth domain, and examine the distribution characteristics of these deviations both longitudinally and laterally to explore the main controlling factors of the deviation distribution. For example... Figure 5 The diagram shows a schematic of the deviation distribution theoretical model.

[0073] Step 301: Analysis of Controlling Factors. Based on geological understanding, the distribution characteristics of deviations in this work area are considered to be related to factors such as structure, lithofacies, and depth. Computer fitting of correlation functions is used to analyze the correlation between single influencing factors and imaging deviations. Specifically, based on structural factors, the work area is divided into zones according to the structural pattern of "two depressions, two ridges, and two slopes." Imaging deviations from actual drilling are fitted within each zone, and the average regression coefficient R1 is obtained. Based on lithofacies factors, the work area is divided into zones according to the lithology of argillaceous limestone, argillaceous mudstone, felsic rocks, and conglomerate. Imaging deviations are fitted within each zone, and the average regression coefficient R2 is obtained. The correlation between imaging deviation and depth is fitted to obtain the regression coefficient R3.

[0074] Step 302: Since the factors influencing the deviation distribution pattern are not unique, the correlation between a single factor and the deviation distribution is not obvious, but the magnitude of the correlation can reflect the relative strength of the influence of each factor. Based on the magnitude of the regression coefficients in the corresponding correlation analysis, the parameters are classified: parameters with large regression coefficients are highly correlated, and parameters with small regression coefficients are less correlated. Based on the consistency between the correlation between a single geological parameter and the deviation distribution and actual geological laws, the parameters are divided into parameters that conform to geological laws and parameters that do not conform to geological laws.

[0075] Step 303: Based on the clear correlation between the deviation distribution and influencing factors, and constrained by geological laws, the main controlling factor of the imaging deviation distribution in this work area is determined to be tectonics. Statistical patterns show that tectonics are highly correlated with deviations. Based on the structural pattern of "two depressions, two ridges, and two slopes" in the work area, the work area is divided into 6 zones from west to east.

[0076] Step 4: Explore the functional relationships of imaging deviations in the six zones of the study area, and use multivariate regression to fit an algorithmic formula that can characterize the distribution of deviations for correction, so as to correct imaging deviations to the greatest extent while ensuring the original structural trend.

[0077] Step 401: Existing wells in the study area are mainly concentrated in the central part of the work area, and the well control level does not meet the correction requirements. To solve this problem, this step obtains the correction amounts for the northern and southern areas with low well control levels to control the correction of the entire area. Assuming that after partitioning, the existing well imaging deviations from A to B are q1, q2, q3, q4, q5, ..., qn respectively, and based on the findings in Step 3, the deviations exhibit a consistent statistical regularity from top to bottom in the partitioned case, and the fitted value Q is obtained. n =An 2 ±Bn±C, making R 2 ≥0.8, where Qn is the deviation value with index n, and n is any value in the integer sequence. Based on the known well imaging deviation data, the theoretical model parameters A, B, and C are fitted to ensure R... 2 When the value is close to 1, a theoretical model of the deviation law of a certain zone in the work area can be established, and the correction amount of the area with low well control degree can be obtained through model simulation.

[0078] like Figure 6 The diagram shows the principle of determining the optimal interval based on the variation function.

[0079] Step 402: In the process of correcting seismic interpretation results based on actual drilling corrections, the density of correction points determines the degree of correction. A high density of correction points may lead to overcorrection, resulting in correction results that do not conform to geological understanding. Conversely, a low density of correction points may fail to control the entire area, leading to correction residuals in the interpretation results between wells and at work area boundaries. To address this issue, a method using variograms is proposed. To determine the optimal interval for correction points.

[0080] like Figure 7 The graph shows the functional relationship between the variogram values ​​and the optimal interval.

[0081] Where h is the number of intervals between two correction points, and one interval is 50m; This is the correction amount at the correction point. This formula, through the variogram function, reveals the similarity of the correction amounts at two spatial locations at a distance h. γ (h)The smaller the value, the closer the correction values ​​at the two locations; conversely, the larger the value, the greater the difference. The maximum interval h is obtained through the variogram function. n After that, the value of the variation function no longer increases and stabilizes near a limit value, h n The correction values ​​within the range are correlated, h n Outside the range, the variable correction amount no longer has spatial correlation, therefore it is considered that h n The optimal interval for interpolating the correction points.

[0082] Step 403: Use the seismic interpretation results as hard data, and the actual drilling correction amount obtained in step 401 as soft data constraint. Based on step 402, determine the h-value using the variogram function. n To obtain the optimal interval, the corrected interpretation result is obtained through spatial extrapolation interpolation using a multi-grid approximation algorithm. For example... Figure 8 As shown, this is a schematic diagram of spatial extrapolation correction based on the optimal interval.

[0083] Beneficial effects: Based on VSP data, this invention establishes a correspondence between VSP corridor overlay data and actual drilling core data, clarifies the characteristics of formation longitudinal velocity structure variation, guides well-seismic calibration, and solves the problem of lacking an accurate velocity scale and difficulty in depth domain calibration. Based on the calibration results, the depth domain error characteristics are analyzed, and a theoretical model simulation coupling correction method is proposed to reduce longitudinal deviation in depth domain interpretation, improve the accuracy of depth domain structural interpretation, and solve the problem of difficulty in depth domain imaging depth error correction.

[0084] This invention provides a calibration and interpretation method based on depth-domain seismic data. It performs velocity analysis and evaluation on VSP data, establishes the correspondence between VSP corridor overlay data and actual drilling core data, clarifies the characteristics of vertical velocity structure changes in formations, guides well-seismic calibration, and solves the problem of "calibration difficulty." Based on the calibration results, it analyzes the depth-domain error characteristics of the study area, proposes a depth correction method, reduces the vertical deviation in depth-domain interpretation, solves the problem of "correction difficulty," and obtains depth-corrected structural interpretation results that meet the needs of efficient development. This method improves the error problem of traditional depth-domain calibration and interpretation methods, achieving high interpretation accuracy and ease of implementation, laying a geological foundation for subsequent well deployment and drilling.

[0085] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A calibration and interpretation method based on depth-domain seismic data, characterized in that, The calibration and interpretation methods include: Step S1: Perform velocity structure analysis on the VSP data and conduct a reliability evaluation; Step S2: Interpretation of well seismic calibration based on the VSP data; Step S3: Analyze the characteristics of the deviation distribution in both longitudinal and transverse directions, and explore the main controlling factors of the deviation distribution; Step S4: Based on the main control factors, obtain the interpretation result diagram after depth correction.

2. The calibration and interpretation method based on depth-domain seismic data according to claim 1, characterized in that, Step S1: Velocity structure analysis of VSP data specifically includes: By comparing the velocity structure of the time-depth curves of all VSP wells in the work area with the velocity structure of the general velocity-time-depth relationship in the region, it is ensured that the variation pattern is normal and consistent, with no outliers. By comparing the changes with the sonic logging curves of this well, we can ensure that the velocity structure of the VSP is consistent with the velocity structure represented by the sonic logging curves.

3. The calibration and interpretation method based on depth-domain seismic data according to claim 1, characterized in that, The reliability evaluation specifically includes: Refer to the core logging data and observe whether the variation range of the velocity curve matches the variation of the lithological profile. By combining well stratification with the correspondence between seismic reflections, we analyze whether the velocity relationships provided by VSP data are consistent with geological understanding, clarify the quality of VSP data, and provide a velocity scale for subsequent well-seismic calibration.

4. The calibration and interpretation method based on depth-domain seismic data according to claim 1, characterized in that, Step S2: The interpretation of well seismic calibration based on the VSP data specifically includes: Step 201: Frequency upsampling of seismic data to obtain seismic data that best matches the VSP corridor stacking profile; Utilizing the property that VSPLOG and seismic data have the same wavelet frequency band and consistent waveform; By comparing the reflected wave impedance of the VSP corridor overlay profile with the reflected wave group of the depth domain seismic data profile, the two sets of data are matched and imaged, and the accurate stratigraphic calibration of the seismic profile is achieved based on the known geological stratification of the well. Step 202: Compare the wave impedance characteristics of the depth domain seismic data of the VSP well with the core data from actual drilling to obtain the correspondence, clarify the longitudinal velocity structure of the formation and the wave impedance correspondence with the seismic profile, assign the geological information revealed by the drilling and logging data to the seismic data, and realize the well-seismic interpretation of the surrounding wells by comparing the core data from the surrounding wells with the seismic wave impedance, so as to guide the interpretation work of the whole area from point to line to surface.

5. The calibration and interpretation method based on depth-domain seismic data according to claim 1, characterized in that, Step S3: Analyzing the characteristics of the deviation distribution in both longitudinal and transverse directions, and exploring the main controlling factors of the deviation distribution, specifically includes: The study area exhibits deviation patterns based on its geological conditions; The imaging deviations of all wells in the work area were statistically analyzed in both longitudinal and transverse directions to explore the main controlling factors of the deviation distribution.

6. The calibration and interpretation method based on depth-domain seismic data according to claim 5, characterized in that, The study area exhibits specific deviation patterns based on geological conditions, including: Based on geological understanding, several factors related to the deviation distribution characteristics of this work area were considered. These factors include factor A, factor B, and factor C. By fitting correlation functions, the correlation between a single influencing factor and imaging bias was analyzed; Based on the first factor A, the known correction quantities in the work area are classified, and the correlation between the imaging deviation and the first factor A is fitted to obtain the first regression coefficient R1. By analogy with the correlation between the fitting imaging bias and the second factor B and the third factor C, we can obtain the second regression coefficient R2 and the third regression coefficient R3.

7. The calibration and interpretation method based on depth-domain seismic data according to claim 5, characterized in that, The imaging deviations of all wells in the statistical work area were analyzed in both longitudinal and transverse directions. The main controlling factors of the deviation distribution were explored, including: Based on the corresponding regression coefficients, the parameters are classified. Based on the consistency between the correlation between a single geological parameter and the deviation distribution and actual geological laws, the parameters are divided into parameters that conform to geological laws and parameters that do not conform to geological laws. Based on a clear understanding of the correlation between deviation distribution and influencing factors, and constrained by geological laws, the main controlling factors of imaging deviation distribution in the work area are determined.

8. The calibration and interpretation method based on depth-domain seismic data according to claim 1, characterized in that, Step S4: Based on the main controlling factors, the specific steps for constructing the interpretation result map after depth correction include: Based on the main controlling factors, the work area is divided into zones, the functional relationship of the depth domain imaging deviation in each zone is explored, and a theoretical model of the deviation law is established. The theoretical model is used to simulate the correction amount for areas with low well control. Seismic interpretation results are used as hard data, and the correction amount is used as soft data constraint. The optimal interval is determined based on the variogram function. The corrected interpretation results are obtained by using a multi-grid approximation algorithm for spatial extrapolation interpolation, and the deep-corrected interpretation result map is obtained.

9. The calibration and interpretation method based on depth-domain seismic data according to claim 8, characterized in that, The process of dividing the work area into zones based on key controlling factors, exploring the functional relationships of depth domain imaging deviations in each zone, and establishing a theoretical model for the deviation patterns specifically includes: The existing wells in the study area are concentrated in the central part of the work area, and the well control level does not meet the correction requirements. Therefore, the correction amount of the northern and southern parts with low well control level is obtained to control the correction of the whole area. Assuming that after partitioning, the existing well imaging deviations from A to B are q1, q2, q3, q4, q5, ..., qn respectively, and according to step S3, it is found that the deviations exhibit a consistent statistical regularity from top to bottom in the partitioned case, and the fitted result is Q. n =An 2 ±Bn±C, making R 2 ≥0.8; Where Qn is the deviation value with index n, and n is any value in the integer sequence; Based on the known well imaging deviation data, the theoretical model parameters A, B, and C are fitted to make the R² value close to 1, and a theoretical model of the deviation law of a certain zone in the work area is established. The correction amount for areas with low well control is obtained through simulation using the aforementioned deviation law theoretical model.

10. The calibration and interpretation method based on depth-domain seismic data according to claim 8, characterized in that, The correction amount for areas with low well control obtained through simulation using the theoretical model specifically includes: In the process of correcting seismic interpretation results based on actual drilling corrections, the density of correction points determines the degree of correction. Through the variation function Determine the optimal interval for the correction points; Where h is the number of intervals between two correction points, and one interval is 50m; It is the correction value at the correction point; the variogram reveals the similarity of the correction values ​​between two spatial locations at a distance h; γ (h) The smaller the value, the closer the correction values ​​at the two positions are; conversely, the larger the value, the greater the difference. The maximum interval obtained by the variogram is hn. The value of the variogram no longer increases and stabilizes near a limit value. The correction amount is correlated within the range of hn, and the correction amount no longer has spatial correlation outside the range of hn. hn is the optimal interval for interpolation of the correction point.

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