Well control complex medium intelligent velocity field modeling method

Abstract

The invention provides an intelligent velocity field modeling method for a well control complex medium. The intelligent velocity field modeling method for the well control complex medium comprises the steps of 1, importing well seismic data; 2, calculating a time domain speed curve based on the well speed curve and the time-depth relation; step 3, acquiring event slope information, and calculating a local dip angle of the stratum structure; and 4, establishing a transverse prediction equation, and constructing constraint velocity field interpolation by using an inversion interpolation algorithm to obtain a final three-dimensional velocity field. According to the well control complex medium intelligent velocity field modeling method, well logging information is fully utilized, an inversion interpolation equation is established through seismic local dip angle structure trend constraint, and well control complex medium intelligent three-dimensional velocity field modeling is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of seismic exploration, and in particular to an intelligent velocity field modeling method for well-controlled complex media. Background Art

[0002] In seismic data processing, an accurate and reliable velocity field is the basis of a series of processing procedures. A reliable velocity field can not only reflect the background characteristics of the underground medium, but also is the key to whether the underground characteristics can be truly reflected after seismic data processing. Therefore, establishing a velocity field that conforms to the background characteristics of the underground medium can effectively improve the accuracy of seismic data processing.

[0003] However, in practical applications, the underground medium is complex, and it is difficult to obtain a sufficiently accurate velocity field based on seismic data alone. Well logging data has a high vertical resolution, and the measured data matches the characteristics of the underground medium, but the drilling cost is high, and it is difficult for well logging data to achieve full horizontal coverage. It can be seen that the combination of well and seismic data is an effective method to establish a reliable velocity field. Therefore, there is an urgent need for an efficient and intelligent method for modeling the velocity field of complex media under well control constraints.

[0004] In the Chinese patent application with application number: CN201911325343.4, a method for modeling a well-controlled velocity field is involved, and the steps are as follows: S1, establish a micro-logging shallow surface low-velocity body model, and calibrate the high-speed top interface on the low-velocity body model; S2, use the time-distance curve to perform time difference correction on the low-velocity body model to obtain a shallow area velocity model; S3, establish an initial velocity model for the deep area; S4, collect logging information to establish a well velocity model, and use the well velocity model to correct the initial velocity model; S5, offset the corrected initial velocity model to obtain a well-connected profile; S6, find velocity anomalies based on the well-connected profile, and correct the velocity anomalies; S7, repeat steps S5 and S6 until there are no velocity anomalies, obtain a deep area velocity model, and then obtain the final well-controlled velocity model. This invention method makes full use of micro-logging and logging data, and can effectively improve the accuracy of velocity modeling, reduce modeling errors, and adapt to a variety of terrains during actual use.

[0005] In the Chinese patent application with application number: CN201210462223.0, a modeling method of a layer velocity model of a formation is involved. The method includes: collecting seismic data of a work area; using the seismic data and the drilling data of the work area to generate a layer velocity field; performing reflection tomography velocity iteration processing on the layer velocity field to generate an optimized layer velocity field; extracting layer velocity plane maps of each formation from the optimized layer velocity field; correcting the extracted layer velocity plane maps of each formation using the drilling data; generating a layer velocity field based on the corrected layer velocity plane map; when the correction amount used for correction exceeds a predetermined range, proceeding to the step of performing reflection tomography velocity iteration processing on the generated layer velocity field.

[0006] In the Chinese patent application with application number: CN202010958720.4, a velocity modeling method, device, equipment and readable storage medium for a thrust tectonic zone are involved, wherein the method includes: gridding the interpreted horizons in the thrust tectonic zone, calculating the time gradients in the line and track directions respectively, and obtaining the time inclination of the stacking velocity; correcting the stacking velocity using the time inclination to obtain the root mean square velocity; establishing a framework model according to the stratigraphic units and faults in the thrust tectonic zone, within the framework model, for stratigraphic units without faults, converting the root mean square velocity of each stratigraphic unit into the stratigraphic velocity, establishing the average velocity field of each stratigraphic unit, for stratigraphic units with faults, converting the root mean square velocity of each fault block into the stratigraphic velocity, establishing the average velocity field of each fault block, and obtaining the velocity model of the thrust tectonic zone. This scheme can reflect the changes in velocity with the strata and the changes in the upper and lower plates of the faults, and the velocity model is more refined, making the velocity model more reasonable and accurate.

[0007] In the Chinese patent application with application number: CN201610018796.2, a three-dimensional high-precision velocity modeling method is involved, which includes the following steps: collecting geological information such as acoustic logging data, calculating the layer velocity Vi according to the time domain framework model through interpretation and analysis of the information, and processing and constraining the layer velocity Vi; combining the time domain construction interpretation model, and using the geological information to count the layer velocity Vi, and analyzing the distribution law of the layer velocity Vi; filling the two-dimensional layer and layer velocity Vi into the three-dimensional framework; using the layer velocity Vi inserted in the model, calculating the root mean square velocity and average velocity of a certain layer. The invention makes the process of converting seismic interpretation results from the time domain to the depth domain efficient and accurate, and can be maintained and updated in time when geological data information changes, and reduces the cost required to expand the model range.

[0008] The above existing technologies are all significantly different from the present invention and fail to solve the technical problem we want to solve. For this reason, we have invented a new intelligent velocity field modeling method for well control complex media. Summary of the invention

[0009] The purpose of the present invention is to provide a method for intelligent velocity field modeling of complex media for well control, which solves the problem of how to efficiently establish a velocity field model that conforms to the background of underground media.

[0010] The purpose of the present invention can be achieved by the following technical measures: a well control complex medium intelligent velocity field modeling method, the well control complex medium intelligent velocity field modeling method comprises:

[0011] Step 1, import well seismic data;

[0012] Step 2, calculating the time domain velocity curve based on the well velocity curve and the time-depth relationship;

[0013] Step 3, obtaining the slope information of the event axis and calculating the local dip angle of the stratigraphic structure;

[0014] Step 4: Establish the lateral prediction equation and use the inversion interpolation algorithm to interpolate the constrained velocity field to obtain the final three-dimensional velocity field.

[0015] The purpose of the present invention can also be achieved by the following technical measures:

[0016] In step 1, the imported well seismic data include well location coordinates, well curves, well layer data, and three-dimensional seismic data volume.

[0017] Step 1 specifically includes:

[0018] Establish an observation coordinate system for the target area of ​​study, and import well location coordinates, well curves, well stratification data, and 3D seismic data volumes in sequence;

[0019] Check the well seismic data, exclude old wells with weak vertical resolution and wells with poor borehole conditions, and select wells that meet the depth requirements of model construction and are consistent with the geological background of the underground medium.

[0020] In step 2, the well curve abnormal values ​​are corrected and the well velocity curve is calculated. The synthetic seismic record is calculated based on the well curve, and multi-well joint well-seismic calibration is performed to obtain the time-depth relationship. The time domain velocity curve is calculated based on the well velocity curve and the time-depth relationship.

[0021] Step 2 specifically includes:

[0022] Step 21, correcting the abnormal value of the well curve;

[0023] Step 22, establishing an accurate time-depth relationship;

[0024] Step 23, based on the time-depth relationship, time-depth conversion is performed according to the well curve to calculate the time domain velocity curve.

[0025] In step 21, the abnormally low values ​​of the well logging density curve and resistivity curve and the abnormally high values ​​of the acoustic wave time difference curve caused by the expansion of the wellbore diameter due to the wellbore collapse are corrected.

[0026] In step 22, synthetic seismic records are calculated based on the well logging density curve and the acoustic wave time difference curve. The synthetic seismic records are obtained by convolution of wave impedance calculated from the well logging data. Multi-well joint time-depth calibration is performed based on the synthetic seismic records and the actual seismic data to establish an accurate time-depth relationship.

[0027] In step 3, a plane wave decomposition filter is constructed using seismic data to obtain the event axis slope information and calculate the local dip of the stratigraphic structure.

[0028] Step 3 specifically includes:

[0029] Step 31: local inclination Make estimates;

[0030] Step 32, constructing a plane wave decomposition filter;

[0031] Step 33, using an iterative method to iterate the dip estimation formula, and then cyclically solving it to obtain the local dip corresponding to the seismic data volume.

[0032] In step 31, using the formula Local inclination To estimate, is the plane wave decomposition filter M(Z t ,Z x ) and the convolution of the 3D seismic data g.

[0033] In step 32, a plane wave decomposition filter is constructed.

[0034]

[0035] In the formula, M(Z t ,Z x ) is the plane wave decomposition filter in the Z transform domain, N(Z t ) / N(1 / Z t ) is equivalent to an all-pass filter

[0036]

[0037] in, is the local dip of the seismic data.

[0038] In step 33, the inclination angle estimation formula is linearly transformed into In the formula, is the change in slope, is the initial slope value, g is the 3D seismic data, express right The first partial derivative of the initial slope σ 0 Perform iterative updates, changing Δσ each time, and then cyclically solve to obtain the local dip angle corresponding to the seismic data volume.

[0039] In step 4, the local dip angle of the geological structure obtained from the seismic data and the velocity information obtained from the well data are used to establish a lateral prediction equation, and the structural constraint velocity field is interpolated using an inversion interpolation algorithm to obtain the final three-dimensional velocity field.

[0040] Step 4 specifically includes:

[0041] Step 41, establishing the relationship between the continuous velocity field signal a(x) and the logging velocity field signal a(n) according to the signal interpolation theory;

[0042] Step 42, establish the velocity field inversion interpolation equation by introducing the inclination angle smoothing constraint, solve the velocity field by using the least squares estimation, and obtain the final three-dimensional velocity field.

[0043] In step 41, according to the signal interpolation theory, the continuous velocity field signal a(x) and the logging velocity field signal a(n) are established as follows:

[0044]

[0045] Where W(x,n) is the weight function related to x and n; this weight function can be a Lagrange polynomial or a Sinc function, written as a matrix expression

[0046]

[0047] In step 42, the inversion interpolation algorithm uses the inversion framework to establish the relationship between the irregularly sampled regular data and the regular grid expected data:

[0048] Hm≈d

[0049] Construct the dip angle constraint condition and establish the velocity lateral prediction equation based on the inversion interpolation algorithm. Where d is the time domain logging velocity curve, m is the interpolation velocity field, H is the forward B-spline interpolation operator, λ is the regularization parameter, and Q is a regularization operator containing the inclination prediction: The local inclination angle of the geological structure obtained by using the seismic data;

[0050] Establishing the velocity field inversion interpolation equation by introducing the inclination smoothness constraint Solving the velocity field using least squares estimation The final three-dimensional velocity field is obtained.

[0051] The intelligent velocity field modeling method for well-controlled complex media in the present invention is based on accurate and reliable well seismic data, establishes a time-depth relationship through well seismic calibration, obtains a time domain velocity curve based on this, and uses plane wave decomposition to calculate the local dip angle corresponding to the seismic data body, and then uses the local dip angle of the geological structure obtained from the seismic data and the velocity information obtained from the well data to establish a lateral prediction equation, and uses the inversion interpolation algorithm to interpolate the structural constraint velocity field to obtain the final three-dimensional velocity field. Through the above method, the three aspects of geology, well logging and seismology are fully combined to realize the construction of a three-dimensional velocity field model for complex media, providing a reliable guarantee for subsequent seismic data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a schematic diagram of a time domain velocity curve after time-depth conversion in a specific embodiment of the present invention;

[0053] Figure 2 A schematic diagram of a complex medium seismic profile in a specific embodiment of the present invention;

[0054] Figure 3 A schematic diagram of a local tilt angle determined according to local plane wave decomposition in a specific embodiment of the present invention;

[0055] Figure 4 A schematic diagram of a three-dimensional velocity field model established by intelligent well control in a specific embodiment of the present invention;

[0056] Figure 5 The flowchart is a specific embodiment of the method for intelligent velocity field modeling of complex media for well control of the present invention. DETAILED DESCRIPTION

[0057] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0058] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations and / or combinations thereof.

[0059] The present invention is a method for intelligent well control velocity field modeling for complex media. The method includes: importing well seismic data, including well location coordinates, well curves, well stratification data, and three-dimensional seismic data bodies; correcting well curve abnormal values ​​and calculating well velocity curves, calculating synthetic seismic records based on the well curves, performing multi-well joint well seismic calibration to obtain time-depth relationships, and calculating time domain velocity curves based on the well velocity curves and time-depth relationships; using seismic data to obtain structural plane wave decomposition filters, obtain event axis slope information, and obtain local dip angles of formation structures; using the local dip angles of geological structures obtained from seismic data and the velocity information obtained from well data to establish lateral prediction equations, and using an inversion interpolation algorithm to interpolate the structural constraint velocity field to obtain the final three-dimensional velocity field. The above method makes full use of logging information, establishes inversion interpolation equations through local seismic dip structural strike constraints, and realizes intelligent three-dimensional velocity field modeling for complex media in well control.

[0060] The following are several specific embodiments of the present invention.

[0061] Example 1

[0062] In a specific embodiment 1 of the present invention, Figure 5 As shown, Figure 5 The flowchart of the method for intelligent velocity field modeling of complex media for well control of the present invention is shown in FIG. The method for intelligent velocity field modeling of complex media for well control of the present invention comprises the following steps:

[0063] Step 101, importing well seismic data, including well location coordinates, well curves, well layer data, and 3D seismic data volume. Specifically including:

[0064] An observation coordinate system for the research target area is established, and the well location coordinates, well curves, well stratification data and three-dimensional seismic data volumes are sequentially imported.

[0065] The well seismic data are checked to exclude old wells with weak vertical resolution and wells with poor borehole conditions, and wells that meet the depth requirements of model construction and are consistent with the geological background of the underground medium are selected.

[0066] Step 102, correct the well curve abnormal value and calculate the well velocity curve, calculate the synthetic seismic record according to the well curve, perform multi-well joint well seismic calibration to obtain the time-depth relationship, and calculate the time domain velocity curve based on the well velocity curve and the time-depth relationship. Specifically including:

[0067] The abnormal values ​​of the well curves are corrected, such as the abnormally low values ​​of the well logging density curve and the resistivity curve, and the abnormally high values ​​of the acoustic wave time difference curve caused by the expansion of the wellbore diameter due to the collapse of the wellbore.

[0068] A synthetic seismic record is calculated according to the well logging density curve and the acoustic wave time difference curve. The synthetic seismic record is obtained by the wave impedance convolution wavelet calculated from the well data. Multi-well joint time-depth calibration is performed based on the synthetic seismic record and actual seismic data to establish an accurate time-depth relationship.

[0069] Based on the time-depth relationship, time-depth conversion is performed according to the well curve to calculate the time domain velocity curve.

[0070] Step 103, constructing a plane wave decomposition filter using seismic data, obtaining event axis slope information, and obtaining a local dip angle of the stratigraphic structure; comprising:

[0071] Using the formula Local inclination To estimate, is the plane wave decomposition filter M(Z t ,Z x ) and the convolution of the three-dimensional seismic data g;

[0072] Constructing a plane wave decomposition filter In the formula, M(Z t ,Z x ) is the plane wave decomposition filter in the Z transform domain, N(Z t ) / N(1 / Z t ) is equivalent to an all-pass filter

[0073]

[0074] in, is the local dip of the seismic data.

[0075] The inclination angle estimation formula is linearly transformed into In the formula, is the change in slope, is the initial slope value, g is the 3D seismic data, express right The first partial derivative of the initial slope σ 0 Perform iterative updates, changing Δσ each time, and then cyclically solve to obtain the local dip angle corresponding to the seismic data volume.

[0076] Step 104, using the local dip angle of the geological structure obtained from the seismic data and the velocity information obtained from the well data, establish a lateral prediction equation, and use an inversion interpolation algorithm to interpolate the structural constraint velocity field to obtain the final three-dimensional velocity field, including:

[0077] According to the signal interpolation theory, the continuous velocity field signal a(x) and the logging velocity field signal a(n) are established as follows:

[0078]

[0079] Where W(x,n) is a weight function related to x and n. This weight function can be a Lagrange polynomial or a Sinc function, written as a matrix expression

[0080]

[0081] The inversion interpolation algorithm uses the inversion framework to establish the relationship between irregularly sampled regular data and regular grid expected data.

[0082] Hm≈d

[0083] Construct the dip angle constraint condition and establish the velocity lateral prediction equation based on the inversion interpolation algorithm. Where d is the time domain logging velocity curve, m is the interpolation velocity field, H is the forward B-spline interpolation operator, λ is the regularization parameter, and Q is a regularization operator containing the inclination prediction: is the local dip of the geological structure obtained using seismic data.

[0084] Establishing the velocity field inversion interpolation equation by introducing the inclination smoothness constraint Solving the velocity field using least squares estimation The final three-dimensional velocity field is obtained.

[0085] Example 2

[0086] In the specific embodiment 2 of the present invention, the well seismic data conforms to the geological background characteristics of the underground medium. Figure 1 As shown, it is a schematic diagram of a time domain velocity curve. In this schematic diagram, the velocity curves all reach a sufficient time depth and have no obvious abnormal values.

[0087] In this embodiment, the seismic data volume has no missing traces and has a comprehensive coverage. Figure 2 As shown, it is a schematic diagram of a complex medium seismic profile. It can be seen that the seismic profile better reflects the complex situation of the underground medium.

[0088] The plane wave decomposition filter is constructed using seismic data to obtain the event slope information and calculate the local dip of the stratigraphic structure.

[0089] The method of constructing a plane wave decomposition filter using seismic data to obtain event axis slope information and obtain the local dip angle of the stratum structure includes:

[0090] Using the formula Local inclination To estimate, is the plane wave decomposition filter M(Z t ,Z x ) and the convolution of the three-dimensional seismic data g;

[0091] Constructing a plane wave decomposition filter In the formula, M(Z t ,Z x ) is the plane wave decomposition filter in the Z transform domain, N(Z t ) / N(1 / Z t ) is equivalent to an all-pass filter

[0092]

[0093] in, is the local dip of the seismic data.

[0094] The inclination angle estimation formula is linearly transformed into In the formula, is the change in slope, is the initial slope value, g is the 3D seismic data, express right The first partial derivative of the initial slope σ 0 Perform iterative updates, changing Δσ each time, and then cyclically solve to obtain the local dip angle corresponding to the seismic data volume.

[0095] like Figure 3 As shown, it is a schematic diagram of the local dip angle corresponding to the seismic data volume obtained by using the above method. It can be seen that the above method can effectively obtain the local dip angle corresponding to the seismic data volume.

[0096] The local dip angle of the geological structure obtained from seismic data and the velocity information obtained from well data are used to establish the lateral prediction equation, and the inversion interpolation algorithm is used to interpolate the structural constraint velocity field to obtain the final three-dimensional velocity field.

[0097] Based on the velocity information obtained from the well data, the local inclination of the geological structure obtained from the seismic data can be directly used to perform lateral interpolation on the velocity to calculate the velocity field of the complex medium.

[0098] Using the local dip angle of the geological structure obtained from seismic data and the velocity information obtained from well data, a lateral prediction equation is established, and the inversion interpolation algorithm is used to interpolate the structural constraint velocity field to obtain the final three-dimensional velocity field, including:

[0099] According to the signal interpolation theory, the continuous velocity field signal a(x) and the logging velocity field signal a(n) are established as follows:

[0100]

[0101] Where W(x,n) is a weight function related to x and n. This weight function can be a Lagrange polynomial or a Sinc function, written as a matrix expression

[0102]

[0103] The inversion interpolation algorithm uses the inversion framework to establish the relationship between irregularly sampled regular data and regular grid expected data.

[0104] Hm≈d

[0105] Construct the dip angle constraint condition and establish the velocity lateral prediction equation based on the inversion interpolation algorithm. Where d is the time domain logging velocity curve, m is the interpolation velocity field, H is the forward B-spline interpolation operator, λ is the regularization parameter, and Q is a regularization operator containing the inclination prediction: is the local dip of the geological structure obtained using seismic data.

[0106] Establishing the velocity field inversion interpolation equation by introducing the inclination smoothness constraint Solving the velocity field using least squares estimation The final three-dimensional velocity field is obtained.

[0107] like Figure 4 As shown, it is a schematic diagram of the complex medium velocity field model obtained by interpolating the velocity curve. It can be seen that the constructed three-dimensional velocity field model is consistent with the underground medium background characteristics reflected by the well seismic data and better reflects the situation of the complex medium.

[0108] The intelligent velocity field modeling method for well-controlled complex media in the present invention makes full use of logging information, establishes inversion interpolation equations through seismic local dip structural strike constraints, and realizes intelligent three-dimensional velocity field modeling for well-controlled complex media.

[0109] Finally, it should be noted that the above is only 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 aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0110] Except for the technical features described in the specification, all other technical features are known technologies to those skilled in the art.

Claims

1. An intelligent velocity field modeling method for well control complex media, characterized in that: The intelligent velocity field modeling method for well control complex media includes: Step 1, import well seismic data; Step 2, calculating the time domain velocity curve based on the well velocity curve and the time-depth relationship; Step 3, obtaining the slope information of the event axis and calculating the local dip angle of the stratigraphic structure; Step 4: Establish the lateral prediction equation and use the inversion interpolation algorithm to interpolate the constrained velocity field to obtain the final three-dimensional velocity field.

2. The method for intelligent velocity field modeling of complex media for well control according to claim 1 is characterized in that: In step 1, the imported well seismic data include well location coordinates, well curves, well layer data, and three-dimensional seismic data volume.

3. The method for intelligent velocity field modeling of complex media for well control according to claim 2 is characterized in that: Step 1 specifically includes: Establish an observation coordinate system for the target area of ​​study, and import well location coordinates, well curves, well stratification data, and 3D seismic data volumes in sequence; Check the well seismic data, exclude old wells with weak vertical resolution and wells with poor borehole conditions, and select wells that meet the depth requirements of model construction and are consistent with the geological background of the underground medium.

4. The method for intelligent velocity field modeling of complex media for well control according to claim 1, characterized in that: In step 2, the well curve abnormal values ​​are corrected and the well velocity curve is calculated. The synthetic seismic record is calculated based on the well curve, and multi-well joint well-seismic calibration is performed to obtain the time-depth relationship. The time domain velocity curve is calculated based on the well velocity curve and the time-depth relationship.

5. The method for intelligent velocity field modeling of complex media for well control according to claim 4 is characterized in that: Step 2 specifically includes: Step 21, correcting the abnormal value of the well curve; Step 22, establishing an accurate time-depth relationship; Step 23, based on the time-depth relationship, time-depth conversion is performed according to the well curve to calculate the time domain velocity curve.

6. The method for intelligent velocity field modeling of complex media for well control according to claim 5 is characterized in that: In step 21, the abnormally low values ​​of the well logging density curve and resistivity curve and the abnormally high values ​​of the acoustic wave time difference curve caused by the expansion of the wellbore diameter due to the wellbore collapse are corrected.

7. The method for intelligent velocity field modeling of complex media for well control according to claim 5 is characterized in that: In step 22, synthetic seismic records are calculated based on the well logging density curve and the acoustic wave time difference curve. The synthetic seismic records are obtained by convolution of wave impedance calculated from the well logging data. Multi-well joint time-depth calibration is performed based on the synthetic seismic records and the actual seismic data to establish an accurate time-depth relationship.

8. The method for intelligent velocity field modeling of complex media for well control according to claim 1, characterized in that: In step 3, a plane wave decomposition filter is constructed using seismic data to obtain the event axis slope information and calculate the local dip of the stratigraphic structure.

9. The method for intelligent velocity field modeling of complex media for well control according to claim 8, characterized in that: Step 3 specifically includes: Step 31: local inclination Make estimates; Step 32, constructing a plane wave decomposition filter; Step 33, using an iterative method to iterate the dip estimation formula, and then cyclically solving it to obtain the local dip corresponding to the seismic data volume.

10. The method for intelligent velocity field modeling of complex media for well control according to claim 9, characterized in that: In step 31, using the formula Local inclination To estimate, is the plane wave decomposition filter M(Z t ,Z x ) and the convolution of the 3D seismic data g.

11. The method for intelligent velocity field modeling of complex media for well control according to claim 10, characterized in that: In step 32, a plane wave decomposition filter is constructed. In the formula, M(Z t ,Z x ) is the plane wave decomposition filter in the Z transform domain, N(Z t ) / N(1 / Z t ) is equivalent to an all-pass filter in, is the local dip of the seismic data.

12. The method for intelligent velocity field modeling of complex media for well control according to claim 11, characterized in that: In step 33, the inclination angle estimation formula is linearly transformed into In the formula, is the change in slope, is the initial slope value, g is the 3D seismic data, express right The first-order partial derivative of is used to iteratively update the initial slope σ0, changing Δσ each time, and then the local dip angle corresponding to the seismic data volume is obtained by cyclic solution.

13. The method for intelligent velocity field modeling of complex media for well control according to claim 1, characterized in that: In step 4, the local dip angle of the geological structure obtained from the seismic data and the velocity information obtained from the well data are used to establish a lateral prediction equation, and the structural constraint velocity field is interpolated using an inversion interpolation algorithm to obtain the final three-dimensional velocity field.

14. The method for intelligent velocity field modeling of complex media for well control according to claim 13, characterized in that: Step 4 specifically includes: Step 41, establishing the relationship between the continuous velocity field signal a(x) and the logging velocity field signal a(n) according to the signal interpolation theory; Step 42, establish the velocity field inversion interpolation equation by introducing the inclination angle smoothing constraint, solve the velocity field by using the least squares estimation, and obtain the final three-dimensional velocity field.

15. The method for intelligent velocity field modeling of complex media for well control according to claim 14, characterized in that: In step 41, according to the signal interpolation theory, the continuous velocity field signal a(x) and the logging velocity field signal a(n) are established as follows: Where W(x,n) is the weight function related to x and n; this weight function can be a Lagrange polynomial or a Sinc function, written as a matrix expression 16. The method for intelligent velocity field modeling of complex media for well control according to claim 15, characterized in that: In step 42, the inversion interpolation algorithm uses the inversion framework to establish the relationship between the irregularly sampled regular data and the regular grid expected data: Hm≈d Construct the dip angle constraint condition and establish the velocity lateral prediction equation based on the inversion interpolation algorithm. Where d is the time domain logging velocity curve, m is the interpolation velocity field, H is the forward B-spline interpolation operator, λ is the regularization parameter, and Q is a regularization operator containing the inclination prediction: The local inclination angle of the geological structure obtained by using the seismic data; Establishing the velocity field inversion interpolation equation by introducing the inclination smoothness constraint Solving the velocity field using least squares estimation The final three-dimensional velocity field is obtained.

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