Speed modeling method and device for oil reservoir static model and medium

Abstract

The invention belongs to the technical field of oil and gas field development, and discloses a velocity modeling method and device for an oil reservoir static model and a medium, and the method comprises the steps: S1, extracting the velocity of a stratum depth midpoint at a well point based on an interval transit time logging curve and time domain seismic data; s2, based on the seismic velocity field, extracting the velocity of a stratum depth midpoint at the control point; s3, carrying out sampling and statistics on the stratum depth midpoint velocities at the well point and the control point, and calculating a stratum velocity surface; s4, according to a plurality of layers of velocity surfaces, establishing a velocity model of the whole region; and S5, performing time-depth conversion by using the speed model, and completing adjustment and improvement of the speed model on the basis of well correction. The time domain can be accurately and efficiently converted into the depth domain, and the working efficiency of oil and gas field development and production is improved.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field development technology, specifically a velocity modeling method, equipment and medium for static reservoir models. Background Technology

[0002] Velocity modeling is a crucial step in static reservoir modeling, converting time-domain data to the depth domain. The velocity model is vital in static reservoir modeling; its accuracy affects not only the reservoir's structural morphology in the depth domain but also the accuracy of subsequent reservoir attribute models. Currently, data used for velocity modeling primarily comes from two sources: wellpoint velocities (sonic logging or synthetic seismic records) and three-dimensional time-domain seismic velocity spectra. A velocity model suitable for subsequent static reservoir models typically needs to meet four criteria: first, it must match the velocities at the wellpoints; second, it must match the seismic velocity spectrum between wells; third, within the well network coverage area, the depth-domain stratigraphic layering error obtained from the velocity model should be controlled within a few meters compared to the wellpoint layering error, with no obvious trend; and fourth, outside the well network coverage area, the depth-domain stratigraphic layering obtained from the velocity model should closely approximate the interpretation results of depth-domain seismic data.

[0003] The existing velocity modeling methods have the following drawbacks:

[0004] One issue is the low efficiency of velocity modeling. The existing conventional velocity modeling process is shown in the attached document. Figure 1 As shown, the modeling process includes basic data, velocity models, time-depth conversion, static models, dynamic models, and iterative feedback. This process needs to consider the rationality of wellpoint and seismic velocities and validate the velocity modeling method. When facing complex geological conditions, it is necessary to try different velocity modeling methods (e.g., V0-k method, mapping method, 3D mesh method, etc.) to assess their time-depth conversion effects and select the optimal method. After determining the velocity modeling method, iterative optimization of the velocity modeling parameters is required. From basic data quality control to the final time-depth conversion, a considerable amount of time is typically spent analyzing the uncertainties. The velocity model, from establishment to refinement, generally takes 2-3 months, which is relatively inefficient.

[0005] Secondly, existing technologies often face the challenge of tuning well velocities to seismic velocities. Due to the different scales of well velocities and seismic velocities, well-seismic velocity mismatches frequently occur in conventional velocity modeling methods. When the difference between well and seismic velocities is significant, after time-depth conversion of the velocity model established using conventional methods, depth anomalies (bull's-eyes) inevitably appear at the well points on the depth domain structural surface. Therefore, conventional velocity field establishment methods typically require repeated quality control to correct these anomalies.

[0006] Thirdly, maintenance costs are high. Conventional velocity modeling methods require quality control of the data one by one and constantly face the problem of data updates. As the number of wells increases, it is necessary to continuously update the data of newly added wells, which is costly in terms of time and manpower.

[0007] Fourth, it cannot efficiently capture the heterogeneity of formation velocity. Conventional velocity modeling methods require multiple iterations and refinements, and methods relying solely on well or seismic data are limited by the data scale and cannot efficiently capture the heterogeneity of formation velocity. Therefore, the depth of the structural surface after time-depth conversion has significant uncertainties. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention aims to provide a velocity modeling method, equipment, and medium for static reservoir models, in order to accurately and efficiently convert the time domain to the depth domain and improve the efficiency of oil and gas field development and production.

[0009] To achieve the above objectives, the technical solution adopted by this invention is as follows: A velocity modeling method for a static reservoir model, comprising the following steps:

[0010] S1. Based on sonic time-of-flight logging curves and time-domain seismic data, extract the velocity at the midpoint of the formation depth at the well point;

[0011] S2. Based on the seismic velocity field, extract the velocity at the midpoint of the formation depth at the control point;

[0012] S3. Sample and statistically analyze the formation velocity at the midpoint of the formation depth at the well point and control point, and calculate the formation velocity surface;

[0013] S4. Based on several velocity surfaces, establish a velocity model for the entire region;

[0014] S5. Use the velocity model for time-depth conversion, and on the basis of well calibration, adjust and improve the velocity model.

[0015] As a limitation of the present invention, S1. Seismic records are synthesized using sonic transit time and density logging curves, and well-seismic calibration is performed in combination with time-domain seismic data to form time-depth pairs at well points, and then the average velocity curve at the well point is generated; the depths of the top and bottom of the formation are calculated based on the well layers, and the velocity at the midpoint of the formation depth at the well point is extracted.

[0016] S2. Based on the complexity of the geological structure and the well control range, uniformly distributed control points are formed. The seismic velocity spectrum is thinned according to the line number where the control point is located. The time-depth pair of the control point is extracted. Based on the depth of the seismic interpretation layer at the control point, the depth of the top and bottom of the strata at the control point is estimated. The velocity at the midpoint of the strata depth of each control point is extracted.

[0017] S3. The velocity at the midpoint of the formation depth from the well point and the control point is uniformly used as the velocity sample point, and the velocity surface is calculated using geostatistical algorithms;

[0018] S4. The calculated velocity surfaces of several layers are vertically accumulated, and the accumulated velocity surfaces are corrected again with the layer velocities at the well points to establish a velocity model for the entire oilfield.

[0019] S5. Use the final velocity model to perform time-depth conversion on the time-domain data, compare the residuals at well points and the structural morphology at control points in the depth domain, and then make local adjustments and improvements to the velocity model.

[0020] As a further limitation of the present invention, in step S1: the average velocity curve at the well point is generated using formula 1;

[0021]

[0022] In Equation 1, Vavg n Z represents the average velocity from altitude 0 meters to the nth stratum. n Let Z0 be the depth at the nth meter below the elevation, and T be the depth at the elevation of 0 meters. n Let T0 be the sound travel time at the nth meter below the altitude, and T0 be the sound travel time at the altitude of 0 meters.

[0023] Based on Formula 2, the velocity at the midpoint of the formation at the well point is extracted:

[0024]

[0025] In Equation 2, V n Z represents the depth of the midpoint of the nth stratum. n Z represents the depth of the nth stratum top. n+1 T represents the depth of the (n+1)th stratum top. n When the sound waves travel to the top of the nth stratum, T n+1 The sound travel time is the time taken to travel to the top of the (n+1)th stratum.

[0026] As a further limitation of the present invention, in step S2: the velocity of the midpoint of each stratum depth at each control point is extracted according to formula 2.

[0027] As a further limitation of the present invention, in step S3: for a specific formation, the velocity at the midpoint of the formation depth at the well point and the control point is taken as a known point, and the velocity surface of the entire formation is calculated by interpolating other areas in the work area using formula 3.

[0028]

[0029] In Equation 3, v x',y' v is the unknown velocity in the strata.x,y Let λ be the velocity at the midpoint of the formation depth at the well point and control point, i = 1, 2, 3, ..., n, where n is the number of known velocity points at the well point and control point; i The interpolation weights are calculated using formulas 4 and 5.

[0030]

[0031] In equations 4 and 5, i = 1, 2, 3…n, where n is the number of known velocity points at the well points and control points; C(v x,y -v x',y' ) represents the covariance between known velocity points and unknown velocity points.

[0032] As another limitation of the present invention, in step S5: the time-domain data is converted to time depth using a velocity model and quality control is performed;

[0033] At the well point, the depth domain layer is subtracted from the depth at the well point, and the velocity model is locally adjusted and improved based on the layer residuals obtained at the well point. In the wellless area, the depth domain layer is compared with the corresponding stratum reflection characteristics in the depth domain seismic data. If the depth difference or shape of the seismic reflection peak or trough between the layer and the corresponding stratum is too large, the velocity model needs to be locally adjusted and improved again.

[0034] The present invention also provides a computer device, the technical solution of which is as follows: it includes a memory and a processor, the memory and the processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the above-mentioned velocity modeling method for reservoir static models.

[0035] The present invention also provides a computer-readable storage medium storing computer instructions for enabling the velocity modeling method for a reservoir static model described above.

[0036] By adopting the above-described technical solution, the beneficial effects achieved by this invention compared to the prior art are as follows:

[0037] This invention primarily addresses the challenge of converting time-domain data to the depth domain in static reservoir modeling. Starting with the challenges posed by multiple data scales and formation heterogeneity, it integrates well data, seismic data, and geostatistical algorithms. It unifies the midpoint velocities of reservoir segments calculated from well and seismic data into velocity samples, and uses geostatistical methods to calculate layer velocity surfaces covering the entire oilfield in each formation. These layer velocity surfaces are then used as input data to establish a velocity model. This invention efficiently and accurately converts time-domain data and research results to the depth domain, reducing the difficulty of oil and gas reservoir description and management, facilitating subsequent static reservoir modeling, and improving the efficiency of oil and gas field development and production. Simultaneously, this invention lays the foundation for the accuracy and predictability of static reservoir models, reducing uncertainties in oil and gas field development and production. Specifically:

[0038] First, the modeling parameter optimization process is streamlined. This invention eliminates the need to compare the time-depth conversion effects of different velocity modeling methods, directly utilizing existing data to quickly integrate a relatively accurate velocity model. Furthermore, this method achieves a relatively accurate velocity model without repeated parameter adjustments. For a detailed explanation of how to use this method from basic data quality control to final time-depth conversion, please refer to [reference needed]. Figure 1 It usually only takes about one month, which improves the efficiency of integrated reservoir development projects.

[0039] Second, it utilizes tunable multi-scale data. This invention unifies the midpoint velocity at formation depths at well points and control points as velocity samples, effectively leveraging multi-scale data and resolving the problem of velocity mismatch across different scales. After time-depth conversion, the velocity model established using this method is less prone to depth anomalies (bull's-eyes) on the depth domain structural surface.

[0040] refer to Figure 3 When using conventional velocity modeling methods, after converting the top surface of the formation in the time domain to the depth domain using the velocity model, a significant depression is observed at the well point (indicated by the arrow). These depressions are called depth anomalies (bull's eyes). Depth anomalies do not conform to the geological patterns of formation development, and their occurrence is due to the inaccuracy of the velocity model. However, when using this invention, after converting the top surface of the formation in the time domain to the depth domain using the velocity model, the depression at the same well point location (indicated by the arrow) is significantly improved. The resulting depth domain structural top surface is smoother and more natural, and its morphology conforms to the geological patterns of formation development, indicating that the established velocity model is highly accurate.

[0041] Third, it overcomes the limitations of the quantity and quality of basic data. This invention combines well data and seismic data, leveraging the advantages of both in well-controlled and well-free areas. Compared to conventional velocity modeling methods, it eliminates the need for individual data quality control; it only requires checking for anomalies at the velocity level, and removing any outliers. Especially in well-free areas, this method effectively thins out seismic data at regular intervals, reducing the time cost of quality control.

[0042] Fourth, it efficiently captures formation velocity heterogeneity. This invention can reasonably simplify formation velocity heterogeneity by using the velocity (well velocity, seismic velocity) at the midpoint of the formation depth as a sample point and employing geostatistical methods to capture horizontal velocity heterogeneity. Furthermore, the superposition of multiple vertical formation velocity surfaces also captures vertical velocity heterogeneity to a certain extent. Especially in well-free areas, this method can utilize seismic data to reduce structural uncertainties.

[0043] exist Figure 4 In this invention, the depth domain structural surface obtained by time-depth conversion using the velocity model is compared with the well point stratification to form a depth difference histogram. The histogram shows that the difference between the depth domain structural map and its corresponding well point stratification is randomly distributed without a clear trend, and the difference ranges from -11 to 12 meters, with an average depth difference of 0.3 meters. In contrast, the depth difference histogram obtained using conventional velocity modeling methods (taking the most common V0-k method as an example) shows a difference range of 2 to 40 meters between the well point stratification and the depth domain structural map, indicating that the overall depth of the depth domain structural map is greater than the depth of the corresponding well point stratification, suggesting a significant systematic error. Furthermore, in well-free areas within the oilfield (the area shown by the ellipse in the figure), the top surface structural maps of the depth domain obtained using this invention show a higher degree of agreement (depth and trend) with the reflection phase axes (the combination of peaks or troughs generated by the reflection of seismic waves) of the depth domain seismic data than those obtained using conventional velocity modeling methods.

[0044] In summary, this invention can quickly establish a velocity model that meets the prediction accuracy requirements in a short period of time. Especially for oilfields with multiple vertically integrated oil and gas reservoirs, this method can utilize existing data to efficiently capture the velocity heterogeneity caused by the superposition of multiple oil and gas reservoirs, and establish a velocity model that can meet the accuracy requirements for subsequent reservoir static model establishment, which can generate significant economic and social benefits. Attached Figure Description

[0045] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0046] Figure 1 A comparison of the steps and time required between the conventional velocity modeling method described in the background art and the velocity model of the present invention;

[0047] Figure 2 This is a schematic diagram illustrating the process of establishing the velocity model of the present invention;

[0048] Figure 3 A comparison chart of the time-depth conversion effects at the well point between other velocity modeling methods and the velocity modeling method of this invention;

[0049] Figure 4 A comparison chart of the accuracy of conventional V0-k velocity modeling method and the velocity modeling method of the present invention at well points and between wells in an oilfield.

[0050] Figure 5 This is a plot of velocity versus depth at the midpoint of the formation at a well point in an oilfield extraction site.

[0051] Figure 6 This is a schematic diagram of the velocity profile of a reservoir in an oil field.

[0052] Figure 7 The velocity surface obtained by interpolating the velocities of the Lower Fars, Mishrif, and Nahr Umr formations in a certain oil field;

[0053] Figure 8 To accumulate velocity surfaces in each stratum and establish a velocity model diagram. Detailed Implementation

[0054] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustrative and understanding purposes only and are not intended to limit the scope of the invention.

[0055] An embodiment of a velocity modeling method, equipment, and medium for static reservoir models.

[0056] refer to Figures 1 to 2 As shown, a velocity modeling method for a reservoir static model includes the following steps:

[0057] S1. Based on sonic time-of-flight logging curves and time-domain seismic data, extract the velocity at the midpoint of the formation depth at the well point.

[0058] Specifically, seismic records are synthesized using sonic transit time and density logging curves, and well-seismic calibration is performed in conjunction with time-domain seismic data to form time-depth pairs at well points, and then the average velocity curve at the well point is generated; the depths of the top and bottom of the formation are calculated based on the well layers, and the velocity at the midpoint of the formation depth at the well point is extracted.

[0059] More specifically, a synthetic seismic record is created using sonic transit time and density logging curves. This record is then matched with the seismic reflection waveform from the wellbore access path in terms of amplitude and frequency to perform fine-grained well-seismic calibration, resulting in a time-depth pair at the wellpoint. The distance from the wellbore access path to the wellpoint should be less than 100 meters. The average velocity curve at the wellpoint is generated using Formula 1.

[0060]

[0061] In Equation 1, Vavg n Z represents the average velocity from altitude 0 meters to the nth stratum. n Let Z0 be the depth at the nth meter below the elevation, and T be the depth at the elevation of 0 meters. n Let Tn be the sound wave travel time at the nth meter below the elevation, and T0 be the sound wave travel time at the elevation of 0 meters. The average velocity curve obtained by Equation 1 can be regarded as a function of the average formation velocity at the wellbore and time.

[0062] Optimize well strata to extract velocity values ​​at the midpoint of the formation at the well point. The well strata must cover the top and bottom of the main vertically developed reservoirs in the oilfield, with an average time interval between 50-100 meters. The velocity at the midpoint of this formation at the well point is extracted using Formula 2:

[0063]

[0064] In Equation 2, V n Z represents the depth of the midpoint of the nth stratum. n Z represents the depth of the nth stratum top. n+1 T represents the depth of the (n+1)th stratum top. n When the sound waves travel to the top of the nth stratum, T n+1 This refers to the sound travel time at the top of the (n+1)th stratum. (See reference...) Figure 5 This is a plot of velocity and depth at the midpoint of the formation at a well point in an oilfield extraction site.

[0065] S2. Based on the seismic velocity field, extract the velocity at the midpoint of the stratum depth at the control point.

[0066] Specifically, based on the complexity of the work area structure and the well control range, control points are formed at a certain distance, with the interval between control points ranging from 50 to 200 meters, to ensure that the control point density can cover the edge of the structure or the area without drilling.

[0067] The dix formula, i.e.:

[0068]

[0069] In the dix formula, T n-1 The sound travel time is the time taken to travel to the top of the (n-1)th stratum.

[0070] Using the Dix formula, the root mean square velocity of an earthquake can be converted into layer velocity. (See reference...) Figure 6Next, the seismic mean velocity volume is thinned according to the coordinates of the control point and the sequence number of the seismic trace it should receive. The time-depth pair of the control point is extracted, and the depth domain seismic interpretation horizon corresponding to the selected well layer is used to estimate the depth of the top and bottom of the strata at the control point. Finally, the velocity at the midpoint of the strata depth at each control point is extracted according to Formula 2.

[0071] S3. Sample and statistically analyze the formation velocity at the midpoint of the formation depth at the well point and control point, and calculate the formation velocity surface.

[0072] Specifically, the velocity at the midpoint of the formation depth at the well point and the control point is uniformly used as the velocity sample point, and the velocity surface is calculated using geostatistical algorithms.

[0073] More specifically, for a particular formation, the velocity at the midpoint of the formation depth at the well point and the control point is taken as a known point. Formula 3 is used to interpolate other areas within the work area to calculate the velocity surface of the entire formation.

[0074]

[0075] In Equation 3, v x',y' v is the unknown velocity in the strata. x,y Let λ be the velocity at the midpoint of the formation depth at the well point and control point, i = 1, 2, 3, ..., n, where n is the number of known velocity points at the well point and control point; i The interpolation weights are calculated using formulas 4 and 5.

[0076]

[0077] In equations 4 and 5, i = 1, 2, 3…n, where n is the number of known velocity points at the well points and control points; C(v x,y- v x',y' ( ) represents the covariance between points with known velocities and points with unknown velocities. (Reference) Figure 7 The velocity surface of the Lower Fars, Mishrif, and Nahr Umr formations in a certain oil field is obtained by interpolating the velocity at the midpoint of the formation depth based on the well points and control points.

[0078] S4. Based on several velocity surfaces, establish a velocity model for the entire region.

[0079] Specifically, the calculated velocity surfaces of several layers are vertically accumulated, and the accumulated velocity surfaces are then corrected again using the layer velocities at the well points, thus establishing a velocity model for the entire oilfield.

[0080] More specifically, using the horizontally layered formation assumption, the interpolated formation velocity surfaces are vertically accumulated, and the resulting velocity surfaces are considered as the layer velocities of the corresponding formations. The accumulated layer velocity surfaces are then corrected again using the average velocities at each layer of the well point, thus establishing a velocity model for the entire oilfield. The software Petrel is used here for the vertical accumulation of formation velocity surfaces and the establishment of the velocity model, such as... Figure 8 As shown.

[0081] S5. Use the velocity model to perform time-depth conversion, and based on well calibration and geological understanding, adjust and improve the velocity model.

[0082] Specifically, the final velocity model is used to perform time-depth conversion on the time-domain data, and the residuals at the well points and the structural morphology at the control points are compared in the depth domain. Then, the velocity model is locally adjusted and improved.

[0083] More specifically, the velocity model is used to perform time-depth conversion on the time-domain data and quality control is applied. At the well point, the depth-domain bedding plane is subtracted from the depth at the well point to obtain the layered residuals at the well point. If there are outliers, significant systematic biases in the residuals (overall deeper or shallower than the well layer), or significant bullseye anomalies (depressions) appear in the obtained depth-domain structural map at the well point, then the velocity model needs to be locally adjusted and improved. In well-free areas, the obtained depth-domain bedding plane is compared with the corresponding stratigraphic reflection characteristics in the depth-domain seismic data. If the depth difference or morphology of the seismic reflection peaks or troughs between the bedding plane and the corresponding stratigraphic layer is too large, then the velocity model needs to be further locally adjusted and improved.

[0084] The optional embodiments of this application also provide a computer device, the computer device including: a memory and a processor that are communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the above-described velocity modeling method for a reservoir static model.

[0085] In addition, this application also provides a computer-readable storage medium storing computer instructions for causing a computer to execute the above-described velocity modeling method for a reservoir static model.

[0086] It should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A velocity modeling method for a static reservoir model, characterized in that: Includes the following steps: S1. Based on sonic time-of-flight logging curves and time-domain seismic data, extract the velocity at the midpoint of the formation depth at the well point; S2. Based on the seismic velocity field, extract the velocity at the midpoint of the formation depth at the control point; S3. Sample and statistically analyze the formation velocity at the midpoint of the formation depth at the well point and control point, and calculate the formation velocity surface; S4. Based on several velocity surfaces, establish a velocity model for the entire region; S5. Use the velocity model for time-depth conversion, and on the basis of well calibration, adjust and improve the velocity model.

2. The velocity modeling method for a static reservoir model according to claim 1, characterized in that: S1. Seismic records are synthesized using sonic transit time and density logging curves, and well-seismic calibration is performed in conjunction with time-domain seismic data to form time-depth pairs at well points, and then the average velocity curve at the well point is generated; the depths of the top and bottom of the formation are calculated based on the well layers, and the velocity at the midpoint of the formation depth at the well point is extracted. S2. Based on the complexity of the geological structure and the well control range, uniformly distributed control points are formed. The seismic velocity spectrum is thinned according to the line number where the control point is located. The time-depth pair of the control point is extracted. Based on the depth of the seismic interpretation layer at the control point, the depth of the top and bottom of the strata at the control point is estimated. The velocity at the midpoint of the strata depth of each control point is extracted. S3. The velocity at the midpoint of the formation depth from the well point and the control point is uniformly used as the velocity sample point, and the velocity surface is calculated using geostatistical algorithms; S4. The calculated velocity surfaces of several layers are vertically accumulated, and the accumulated velocity surfaces are corrected again with the layer velocities at the well points to establish a velocity model for the entire oilfield. S5. Use the final velocity model to perform time-depth conversion on the time-domain data, compare the residuals at well points and the structural morphology at control points in the depth domain, and then make local adjustments and improvements to the velocity model.

3. The velocity modeling method for a static reservoir model according to claim 2, characterized in that: In step S1: the average velocity curve at the well point is generated using formula 1; In Equation 1, Vavg n Z represents the average velocity from altitude 0 meters to the nth stratum. n Let Z0 be the depth at the nth meter below the elevation, and T be the depth at the elevation of 0 meters. n Let T0 be the sound travel time at the nth meter below the altitude, and T0 be the sound travel time at the altitude of 0 meters. Based on Formula 2, the velocity at the midpoint of the formation at the well point is extracted: In Equation 2, V n Z represents the depth of the midpoint of the nth stratum. n Z represents the depth of the nth stratum top. n+1 T represents the depth of the (n+1)th stratum top. n When the sound travels to the top of the nth stratum, T n+1 The sound travel time is the time taken to travel to the top of the (n+1)th stratum.

4. The velocity modeling method for a static reservoir model according to claim 3, characterized in that: In step S2: the velocity at the midpoint of each stratum depth at each control point is extracted according to formula 2.

5. The velocity modeling method for a static reservoir model according to claim 2, characterized in that: In step S3: For a specific formation, the velocity at the midpoint of the formation depth at the well point and the control point is taken as the known point. Using formula 3, interpolation is performed on other areas within the work area to calculate the velocity surface of the entire formation. In Equation 3, v x',y' v is the unknown velocity in the strata. x,y Let λ be the velocity at the midpoint of the formation depth at the well point and control point, i = 1, 2, 3, ..., n, where n is the number of known velocity points at the well point and control point; i The interpolation weights are calculated using formulas 4 and 5. In equations 4 and 5, i = 1, 2, 3…n, where n is the number of known velocity points at the well points and control points; C(v x,y -v x',y' ) represents the covariance between known velocity points and unknown velocity points.

6. A velocity modeling method for a reservoir static model according to any one of claims 1-5, characterized in that: In step S5: the time-domain data is converted to time depth using a velocity model, and quality control is performed. At the well point, the depth domain layer is subtracted from the depth at the well point, and the velocity model is locally adjusted and improved based on the layer residuals obtained at the well point. In the wellless area, the depth domain layer is compared with the corresponding stratum reflection characteristics in the depth domain seismic data. If the depth difference or shape of the seismic reflection peak or trough between the layer and the corresponding stratum is too large, the velocity model needs to be locally adjusted and improved again.

7. A computer device, characterized in that: include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the velocity modeling method for a reservoir static model as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions for causing the computer to execute the velocity modeling method for a reservoir static model as described in any one of claims 1 to 6.

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