Construction-oriented well control speed modeling method and application thereof

Abstract

The application discloses a well control velocity modeling method based on structure guidance and application thereof, and belongs to the technical field of seismic data processing in oil exploration. The application utilizes logging, VSP velocity and seismic velocity analysis, establishes a space variable factor field based on structure guidance, further uses the space variable factor field to constrain a multi-azimuth grid tomography residual velocity field, optimizes velocity update quantity, completes multi-azimuth grid tomography multi-round iteration of the well control velocity field based on structure guidance, and obtains the well control velocity model based on structure guidance. The well control velocity model obtained by using the modeling method provided by the application can consider the longitudinal resolution and the lateral change law of the velocity field, ensures that the updated velocity field is consistent with the well velocity, and the velocity change trend is also consistent with the seismic geology. The application can be used for establishing a high-precision velocity field and a Q field and for "true" surface TTI prestack depth migration, and is further applied to the fields of prestack inversion and calculation of geomechanical parameters.

Description

Technical Field

[0001] This invention belongs to the field of seismic data processing technology for petroleum exploration, and relates to a well control velocity modeling method, specifically a structure-guided well control velocity modeling method and its application. Background Technology

[0002] As seismic exploration shifts from conventional exploration to targeted precision exploration guided by geological needs, data processing has shifted from time-domain processing to pre-stack depth migration processing. The core of this shift is to establish high-fidelity, high-precision velocity models. How to further strengthen structure-guided well-controlled velocity modeling and iteration, and improve the well-seismic velocity consistency and the accuracy of velocity models, is a problem that data processing personnel are constantly exploring.

[0003] The main methods for well-controlled velocity modeling and their shortcomings are as follows: (1) Establishing a velocity field by interpolating the logging velocity based on the structural model. This method has high vertical resolution, but it is difficult to control the lateral velocity variation; (2) Calculating the δ along the layer through well-seismic velocity analysis, thereby updating the well-controlled velocity along the layer. This method has low accuracy, and the well-seismic fit after the velocity update is low; (3) Obtaining the gas variation factor curve through well-seismic velocity analysis, and interpolating to establish the gas variation factor field for velocity update. This method has good well-seismic velocity fit at the well point location, but the velocity trend does not conform to geological laws. The above-mentioned well-controlled velocity modeling methods have the shortcomings of being unable to simultaneously take into account both vertical resolution and lateral velocity variation, low velocity accuracy and well-seismic fit, and velocity trend not conforming to seismic geological laws. Summary of the Invention

[0004] To address the shortcomings of existing well-controlled velocity modeling methods, such as difficulty in simultaneously considering vertical resolution and lateral velocity variation, low velocity accuracy and well-seismic consistency, and inconsistencies between velocity trends and seismic geological patterns, this invention provides a well-controlled velocity modeling method. This method establishes a structure-guided spatial variation factor field through well-seismic comparative analysis to constrain velocity field updates, thereby improving the spatial resolution of velocity modeling (improving the vertical resolution of the velocity field through well control and optimizing the lateral velocity variation pattern through structure guidance). While ensuring that the updated velocity field matches the well velocity, it also ensures that the velocity variation trend conforms to seismic geology.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A well control velocity modeling method based on structure guidance, comprising the following steps performed sequentially:

[0007] S1. Based on the preprocessed well logging velocity data and the pre-constrained seismic layer velocity model, the well point location space variation factor curve is obtained and optimized to obtain the optimized well point location space variation factor curve; at the same time, the depth domain velocity control layer is determined by combining well logging stratification and seismic imaging data, and a depth domain structural model is established.

[0008] S2. Based on the optimized well point location gas variation factor curve and depth domain structural model, structurally guided spatial interpolation of the logging gas variation factor curve is carried out to establish a fine gas variation factor field;

[0009] S3. Use the fine spatial factor field to constrain the residual velocity field of multi-directional grid tomography, optimize the velocity update amount, and obtain the optimized velocity update amount;

[0010] S4. Apply the optimized velocity update to update the seismic layer velocity model, complete the multi-directional grid tomography iteration of the structure-guided well-controlled velocity field, and obtain the initial structure-guided well-controlled velocity field;

[0011] S5. Perform multiple iterations on the initial well control velocity field based on structure guidance to make the velocity field converge, thus obtaining the well control velocity model based on structure guidance.

[0012] As a first limitation of the invention's structure-guided well control velocity modeling method, in step S1, obtaining the preprocessed logging velocity data includes: collecting and organizing data to obtain logging data, seismic velocity data, and raw logging velocity data; it should be noted that the velocity involved in this invention is mainly P-wave velocity, but this method can also be used to process S-wave velocity.

[0013] By combining well logging data and seismic velocity data to analyze the well logging velocity trend, the original well logging velocity data is preprocessed to obtain preprocessed well logging velocity data.

[0014] The preprocessing method includes filtering and smoothing the raw logging velocity data. The purpose is to eliminate abnormal high-frequency interference caused by factors such as cycle jumps. The abnormal high-frequency interference is mainly caused by cycle jumps during sonic logging, which leads to sharp deflections or particularly large time difference values ​​on the time difference curve. These abnormal values ​​cannot reflect the true formation velocity.

[0015] As a further limitation on the first definition of the structure-guided well control velocity modeling method of the invention, the filtering process adopts a low-pass filter or a band-pass filter.

[0016] The smoothing process employs either a weighted average method or a median filtering method.

[0017] As a second limitation on the invention's structure-guided well control velocity modeling method, step S1, which involves obtaining and optimizing the well point location space variation factor curve, includes:

[0018] Based on the preprocessed logging velocity data and the pre-well control seismic velocity data, the seismic velocity curve at the well point location is extracted from the pre-well control seismic velocity data volume. The seismic velocity curve is correlated with the logging velocity curve to establish a fitting relationship. The space variation factor curve at the well point location is obtained by using the values ​​of the two curves and the fitting relationship.

[0019] Based on the well location space variation factor curve, the velocity variation law is analyzed by combining factors including logging velocity and geological structure variation law, and the well location space variation factor curve is optimized and adjusted.

[0020] As a third limitation on the invention of the structure-guided well control velocity modeling method, in step S2, the interpolation method is to use inverse distance weighted interpolation, kriging interpolation or minimum curvature interpolation between layers with structural model constraints.

[0021] As a fourth limitation on the invention of the well control velocity modeling method based on structure guidance, in step S3, the residual velocity field of multi-directional grid tomography constrained by fine spatial factor field includes flattening, interpolation and reverse flattening of the main target layer in the structure model, structure guidance spatial filtering, and multi-data volume operation of structure layer constraint fitting formula.

[0022] As a fifth limitation on the invention of the structure-oriented well control velocity modeling method, in step S4, the optimized velocity update amount is the velocity update amount that has been manually optimized after multi-directional grid tomography iteration through well seismic analysis and structural trend analysis.

[0023] As a sixth limitation on the invention of the structure-oriented well control velocity modeling method, in step S5, when the well-vibration relationship matching rate is not less than 98%, the iterative operation can be terminated.

[0024] This invention also provides the application of the above-mentioned structure-guided well-controlled velocity modeling method in the processing of pre-stack depth migration for TTI anisotropy on the true surface.

[0025] By adopting the above technical solution, the technical progress achieved by this invention compared with the prior art is as follows:

[0026] ① This invention utilizes well logging velocity and seismic velocity analysis to establish a spatial variation factor field based on structural guidance. This spatial variation factor field is then used to constrain the residual velocity field of multi-directional grid tomography, optimizing the velocity update amount. This completes multiple iterations of multi-directional grid tomography for the structure-guided well-controlled velocity field, resulting in a structure-guided well-controlled velocity model. The well-controlled velocity model obtained using the modeling method provided by this invention can balance the vertical resolution and lateral variation law of the velocity field. While ensuring that the updated velocity field matches the well velocity, the velocity variation trend also conforms to seismic geology.

[0027] ② In the well control velocity modeling method based on structure guidance provided by the present invention, the data volume is flattened along the target layer, and on this basis, data volume interpolation and smoothing are performed. Then, the structure shape is restored by reverse flattening of the data volume, thereby realizing structure guidance data volume interpolation and smoothing. This method can initially realize structure guidance constraints and make data interpolation and smoothing conform to the structural change law.

[0028] ③ Considering the lateral variation of formation thickness, this invention further fits the relational formula with information such as layer depth, interlayer thickness and data volume values ​​in the well control velocity modeling method based on structure guidance, and performs data volume calculation through the relational formula to realize spatiotemporally variable well control structure guidance modeling, overcoming the shortcomings of existing interlayer constraints that cannot be spatially variable and grid tomography iterations that cannot construct guidance.

[0029] ④ The well control velocity modeling method based on structure guidance provided by this invention combines information such as logging velocity, structural model, velocity model and multi-directional grid tomography residual velocity to carry out multi-information constraint velocity field update iteration, thereby establishing a layer velocity model that is more consistent with structural changes and has a higher well-seismic fit.

[0030] ⑤ The well control velocity modeling method based on structure guidance provided by this invention is easy to implement and operate in production, and is conducive to its widespread application.

[0031] In summary, the well control velocity modeling method based on structure guidance provided by this invention improves the vertical resolution through well control and enhances the accuracy of velocity modeling by constraining the lateral variation law through structure guidance.

[0032] The present invention provides a structure-guided well-controlled velocity modeling method, which can be applied to layer velocity modeling and further applied to pre-stack depth migration velocity model iteration, "true" surface TTI anisotropic pre-stack depth migration, and high-precision Q-field establishment. Attached Figure Description

[0033] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0034] Figure 1 This is a partial diagram of the original logging velocity curves of four wells in the study area in Embodiment 1 of the present invention, wherein... Figure 1 a) Represents Nanbao 2-15 well, Figure 1 b) Represents Well 2-26 of Nanbao, Figure 1 c) Represents Nanbao 2-27 well, Figure 1 d) represents Xinmiao No. 1 well;

[0035] Figure 2 This is a partial diagram of the pre-processed logging velocity curves of four wells in the study area in Embodiment 1 of the present invention, wherein... Figure 2 a) Represents Nanbao 2-15 well, Figure 2 b) Represents Well 2-26 of Nanbao, Figure 2 c) Represents Nanbao 2-27 well, Figure 2 d) represents Xinmiao No. 1 well;

[0036] Figure 3 The curves showing the well point location spatial variation factor of four wells in the study area in Embodiment 1 of this invention are shown. Figure 3 a) Represents Nanbao 2-15 well, Figure 3 b) Represents Well 2-26 of Nanbao, Figure 3 c) Represents Nanbao 2-27 well, Figure 3 d) represents Xinmiao No. 1 well;

[0037] Figure 4 The above are the optimized wellpoint location spatial variation factor curves for the four wells in the study area in Embodiment 1 of this invention, wherein... Figure 4 a) Represents Nanbao 2-15 well, Figure 4 b) Represents Well 2-26 of Nanbao, Figure 4 c) Represents Nanbao 2-27 well, Figure 4 d) represents Xinmiao No. 1 well;

[0038] Figure 5 This is the model for constructing the 1720 depth domain of the main control line in Embodiment 1 of the present invention;

[0039] Figure 6 This is a cross-sectional view of the fine spatial variation factor field of the main control line 1720 in Embodiment 1 of the present invention;

[0040] Figure 7 This refers to the well control velocity model based on structural guidance for the main control line 1720 in Embodiment 1 of the present invention.

[0041] Figure 8 This is a pre-stack depth offset and velocity superposition profile obtained by the data-driven velocity modeling method for the main control line 1720 in Embodiment 2 of the present invention.

[0042] Figure 9 This is a pre-stack depth offset and velocity superposition profile obtained by the well control velocity modeling method based on structure guidance for main control line 1720 in Embodiment 2 of the present invention.

[0043] Figure 10 This is an overlay diagram of the well logging velocity profile and the logging velocity curve obtained by the data-driven velocity modeling method in Embodiment 2 of the present invention. Curve a represents the logging velocity of well Nanbao 2-15, curve b represents the logging velocity of well Nanbao 2-26, curve c represents the logging velocity of well Nanbao 2-27, and curve d represents the logging velocity of well Xinmiao 1.

[0044] Figure 11This is an overlay diagram of the well velocity profile and logging velocity curve obtained by the well control velocity modeling method based on structure guidance in Embodiment 2 of the present invention. In this diagram, curve a represents the logging velocity of well Nanbao 2-15, curve b represents the logging velocity of well Nanbao 2-26, curve c represents the logging velocity of well Nanbao 2-27, and curve d represents the logging velocity of well Xinmiao 1.

[0045] Figure 12 This is an overlay diagram of well seismic velocity curves obtained by the data-driven velocity modeling method in Embodiment 2 of the present invention, wherein... Figure 12 a) Represents Nanbao 2-15 well, Figure 12 b) Represents Well 2-26 of Nanbao, Figure 12 c) Represents Nanbao 2-27 well, Figure 12 d) represents Xinmiao No. 1 well;

[0046] Figure 13 This is an overlay diagram of well vibration velocity curves obtained by the well control velocity modeling method based on structure guidance in Embodiment 2 of the present invention, wherein... Figure 13 a) Represents Nanbao 2-15 well, Figure 13 b) Represents Well 2-26 of Nanbao, Figure 13 c) Represents Nanbao 2-27 well, Figure 13 d) represents Xinmiao No. 1 well;

[0047] Figure 14 This is a bar chart showing the relative error between logging velocity and seismic velocity at some sampling points in wells Nanbao 2-15, Nanbao 2-26, Nanbao 2-27, and Xinmiao 1, using the data-driven velocity modeling method applied in Embodiment 2 of the present invention.

[0048] Figure 15 This is a bar chart showing the relative error between logging velocity and seismic velocity at some sampling points in wells Nanbao 2-15, Nanbao 2-26, Nanbao 2-27 and Xinmiao 1, based on the well control velocity modeling method based on structure guidance in Embodiment 2 of the present invention. Detailed Implementation

[0049] The present invention will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that the described embodiments are preferred examples of the present invention and are only used to explain the present invention and do not limit the present invention.

[0050] Example 1: A Well Control Velocity Modeling Method Based on Structure Guidance

[0051] (I) This embodiment takes the Jidong secondary contiguous research area as an example to provide a well control velocity modeling method based on structural guidance. The modeling method specifically includes the following steps performed in sequence:

[0052] S1. After collecting and organizing data and preprocessing well logging velocity data, calculate and optimize the well point location space variation factor curve, and establish a depth domain structural model. This includes the following steps:

[0053] S11. Collect and organize geological data of the secondary contiguous study area in eastern Hebei, as well as VSP velocity data, sonic logging data, logging interpretation data and seismic imaging data of 62 control wells selected in the study area, and obtain organized logging velocity data, logging stratification data, geological structure change patterns, seismic velocity data and raw logging velocity data.

[0054] S12. Combine the sorted logging data and seismic velocity data to analyze the logging velocity trend, filter and smooth the original logging velocity data to eliminate abnormal high-frequency interference caused by factors such as cycle jump, and obtain the preprocessed logging velocity data.

[0055] The filtering method described therein is low-pass filtering or band-pass filtering;

[0056] Smoothing can be achieved using methods such as weighted averaging or median filtering. In this embodiment, the weighted averaging method is used. Low-pass filtering is used to eliminate abnormal high-frequency interference. The so-called abnormal high-frequency interference mainly refers to the effect of cycle jump during sonic logging, which causes sharp deflection or particularly large time difference values ​​on the time difference curve. These abnormal values ​​cannot reflect the true formation velocity.

[0057] Partial figures of the original logging velocity curves of the four wells in the study area are shown below. Figure 1 As shown, where Figure 1 a) Represents Nanbao 2-15 well, Figure 1 b) Represents Well 2-26 of Nanbao, Figure 1 c) Represents Nanbao 2-27 well, Figure 1 d) represents the Xinmiao 1 well; the pre-processed logging velocity curves for the corresponding elevation sections of the four wells are shown in the figure below. Figure 2 As shown, where Figure 2 a) Represents Nanbao 2-15 well, Figure 2 b) Represents Well 2-26 of Nanbao, Figure 2 c) Represents Nanbao 2-27 well, Figure 2 d) represents Xinmiao 1 well.

[0058] Depend on Figures 1-2 The comparison shows that the velocity curve after filtering and smoothing is smoother. Velocity preprocessing can eliminate abnormal high-frequency interference from the original logging velocity, thereby ensuring the rationality of well control modeling.

[0059] S13. Based on the preprocessed logging velocity data and seismic velocity volume, firstly extract the seismic velocity curve at the well point location from the pre-well control seismic velocity data volume, then associate the seismic velocity curve with the logging velocity curve to establish a fitting relationship, and use the values ​​of the two curves and the fitting relationship to perform calculations to obtain the well point location air variation factor curve.

[0060] Among them, the well point location space variation factor curves of the four wells in the study area were obtained as follows: Figure 3 As shown, where Figure 3 a) Represents Nanbao 2-15 well, Figure 3 b) Represents Well 2-26 of Nanbao, Figure 3 c) Represents Nanbao 2-27 well, Figure 3 d) represents Xinmiao No. 1 well;

[0061] S14. Based on the well point location space variation factor curve, combined with factors including logging velocity and geological structure variation law, analyze the velocity variation law, optimize and adjust the well point location space variation factor curve, and obtain the optimized well point location space variation factor curve.

[0062] Among them, the optimized wellpoint location space variation factor curves of the four wells in the study area are as follows: Figure 4 As shown, where, Figure 4 a) Represents Nanbao 2-15 well, Figure 4 b) Represents Well 2-26 of Nanbao, Figure 4 c) Represents Nanbao 2-27 well, Figure 4 d) represents Xinmiao No. 1 well;

[0063] S15. Combine well logging stratification and seismic imaging data to determine the depth domain velocity control layer and establish a depth domain structural model;

[0064] Among them, the 1720-depth domain construction model of the main control line in this study area is as follows: Figure 5 As shown.

[0065] S2. Based on the optimized well point location gas variation factor curve and depth domain structural model, structurally guided spatial interpolation of the logging gas variation factor curve is carried out to establish a fine gas variation factor field;

[0066] The interpolation method is to use inverse distance weighted interpolation, Kriging interpolation, or minimum curvature interpolation between layers with the constraints of the construction model. In this embodiment, the interpolation method selected is Kriging interpolation.

[0067] Among them, the established main control line 1720 fine space variation factor field profile is shown in the figure below. Figure 6 As shown.

[0068] S3. By picking up the residuals of the azimuth gathers, the remaining velocity field is obtained through mesh tomography iteration. The remaining velocity field of multi-azimuth mesh tomography is constrained by the fine spatial factor field to optimize the velocity update amount and obtain the optimized velocity update amount.

[0069] The optimized velocity update values ​​for the target formations of the four wells are shown in Table 1.

[0070] Table 1. Statistics of the optimized velocity update for four wells.

[0071]

[0072] S4. Apply the optimized velocity update amount, update the seismic layer velocity model, complete the multi-directional grid tomography iteration of the structure-guided well-controlled velocity field, and obtain the initial structure-guided well-controlled velocity field. The optimized velocity update amount is the velocity update amount manually optimized by well-seismic analysis and structural trend analysis after multi-directional grid tomography iteration.

[0073] S5. Perform multiple iterations on the initial well control velocity field based on structure guidance until the velocity field tends to converge. The iteration ends when the well-vibration relationship match rate is not less than 98%, thus obtaining the well control velocity model based on structure guidance.

[0074] Using the structure-guided well control velocity modeling method provided by this invention, the structure-guided well control velocity model of the main control line 1720 is constructed as follows: Figure 7 As shown.

[0075] Example 2: Application of Structure-Guided Well Control Velocity Modeling Method

[0076] To examine the application effect of the structure-guided well control velocity modeling method provided by this invention in the field of pre-stack depth migration, the following experiments were conducted.

[0077] First, a data-driven velocity model for this study area is established based on conventional pre-stack depth migration velocity modeling methods. The specific method is as follows:

[0078] An initial velocity model is established using a time-domain construction model, and the velocity model is updated through layer-by-layer velocity updates and mesh tomography.

[0079] Then, the application effects of the well control velocity models obtained by the two modeling methods in the study area were compared.

[0080] (1) In terms of pre-stack depth migration and velocity superposition imaging

[0081] Two methods were used for modeling: the conventional data-driven modeling method and the structure-oriented well control velocity modeling method provided in this invention. The established well control velocity models were then used for layer velocity modeling, and a pre-stack depth migration and velocity superimposed profile was obtained by pre-stack depth migration.

[0082] The pre-stack depth migration and velocity superposition profile of the master control line 1720 obtained by the data-driven velocity modeling method is shown in the figure below. Figure 8 As shown; the pre-stack depth migration and velocity superposition profile of the master control line 1720 obtained by the structure-guided well control velocity modeling method is shown in the figure. Figure 9 As shown. By Figures 8-9 It can be seen that the well control velocity modeling method based on structure guidance significantly improves the velocity accuracy in the vertical direction, and the lateral velocity variation is more in line with the structural change law.

[0083] (2) In terms of well velocity profile imaging

[0084] After modeling using the above-mentioned data-driven velocity modeling method and the structure-guided well-controlled velocity modeling method provided by this invention, they are used for layer velocity modeling to obtain a continuous well velocity profile.

[0085] Among them, the overlay diagram of the well velocity profile and logging velocity curve obtained by the data-driven velocity modeling method is shown in the figure below. Figure 10 As shown, curve a represents well Nanbao 2-15, curve b represents well Nanbao 2-26, curve c represents well Nanbao 2-27, and curve d represents well Xinmiao 1; the overlay diagram of the well velocity profile and logging velocity curves in the study area obtained by the well-controlled velocity modeling method based on structural guidance is shown below. Figure 11 As shown, curve a represents well Nanbao 2-15, curve b represents well Nanbao 2-26, curve c represents well Nanbao 2-27, and curve d represents well Xinmiao 1. (The last sentence appears to be incomplete and possibly refers to a different context.) Figures 10-11 It can be seen that the velocity model established by the structure-oriented well control velocity modeling method matches the logging velocity better, has richer details, and the lateral velocity variation law is more in line with the structural change trend.

[0086] (3) In terms of the matching of well-seismic velocity curves

[0087] After modeling using the above-mentioned data-driven velocity modeling method and the structure-guided well control velocity modeling method provided by this invention, they are used for pre-stack depth migration velocity modeling, and the well-seismic velocity curve overlay is obtained by extracting the well point location velocity volume data.

[0088] Among them, the overlay diagram of well seismic velocity curves obtained by the data-driven velocity modeling method is shown in the figure. Figure 12 As shown, where, Figure 12 a) Represents Nanbao 2-15 well, Figure 12b) Represents Well 2-26 of Nanbao, Figure 12 c) Represents Nanbao 2-27 well, Figure 12 d) represents the Xinmiao 1 well; the overlay diagram of well-seismic velocity curves obtained by the well-controlled velocity modeling method based on structural guidance is shown below. Figure 13 As shown, where, Figure 13 a) Represents Nanbao 2-15 well, Figure 13 b) Represents Well 2-26 of Nanbao, Figure 13 c) Represents Nanbao 2-27 well, Figure 13 d) represents Xinmiao No. 1 well. (By...) Figures 12-13 It can be seen that the well-controlled velocity modeling method based on structure guidance has a better match with the seismic velocity, and the trends of well logging velocity and seismic velocity are basically consistent and the values ​​are close.

[0089] This embodiment randomly selects Figures 12-13 Multiple sampling points were used to calculate the relative errors between logging velocity and seismic velocity at different sampling points, and a comparison chart of the relative errors of well logging and seismic velocity under the two modeling methods was plotted. Specifically, four wells—Nanbao 2-15, Nanbao 2-26, Nanbao 2-27, and Xinmiao 1—were randomly selected, with 100 sampling points per well, totaling 400 sampling points. A bar chart of the relative errors between logging velocity and seismic velocity under the two well control velocity modeling methods was plotted as follows: Figures 14-15 As shown, where, Figure 14 A bar chart showing the relative error between logging velocity and seismic velocity at some sampling points in wells Nanbao 2-15, Nanbao 2-26, Nanbao 2-27 and Xinmiao 1, based on the application of a data-driven velocity modeling method; Figure 15 A bar chart showing the relative error between logging velocity and seismic velocity at some sampling points in wells Nanbao 2-15, Nanbao 2-26, Nanbao 2-27 and Xinmiao 1, based on the application of a structure-guided well control velocity modeling method.

[0090] Depend on Figures 14-15 It can be seen that the relative error between logging velocity and seismic velocity obtained by the "data-driven velocity modeling method" is significantly higher than that obtained by the "structure-guided well-controlled velocity modeling method" proposed in this invention. Therefore, the structure-guided well-controlled velocity modeling method greatly reduces the relative error between well logging and seismic velocity, further demonstrating that the velocity model established by the structure-guided well-controlled velocity modeling method proposed in this invention has a higher well-seismic fit than the conventional data-driven velocity modeling method.

[0091] The modeling method provided by this invention has been applied to the processing of seismic data from multiple blocks in eastern Hebei and northern Yunnan and Guizhou, achieving good results in depth migration velocity field characterization and further improving the accuracy of the velocity model. Meanwhile, with the increasing intensity of exploration and development, the well-controlled velocity modeling method based on structure guidance provided by this invention has broad application prospects.

[0092] The above description is merely an optional embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure.

Claims

1. A structure-oriented well control velocity modeling method, characterized in that, The modeling method comprises the following steps performed in sequence: S1. Based on the preprocessed logging velocity data and the pre-constrained seismic interval velocity model, a well point position space variable factor curve is calculated and optimized to obtain an optimized well point position space variable factor curve; meanwhile, a depth domain velocity control layer is determined in combination with logging layering and seismic imaging data to establish a depth domain structure model; In step S1, the calculation and optimization of the well point position space variable factor curve comprises: Based on the preprocessed logging velocity data and the pre-logging-constrained seismic velocity data, a fitting relationship is established by correlating the seismic velocity curve with the logging velocity curve to calculate the well point position space variable factor curve; Based on the well point position space variable factor curve, a velocity variation law is analyzed in combination with factors including logging velocity and geological structure variation law to optimize and adjust the well point position space variable factor curve; S2. Based on the optimized well point position space variable factor curve and the depth domain structure model, a structure-guided logging space variable factor curve interpolation is performed to establish a fine space variable factor field; In step S2, the interpolation method is to perform interpolation by using the inverse distance weighted interpolation method, the Kriging interpolation method or the minimum curvature interpolation method in layers under the constraint of the structure model; S3. The fine space factor field is used to constrain a multi-azimuth grid tomography residual velocity field to optimize a velocity update amount to obtain an optimized velocity update amount; In step S3, the use of the fine space factor field to constrain the multi-azimuth grid tomography residual velocity field comprises picking up a residual of a split-azimuth gather and iteratively calculating a residual velocity field by grid tomography; S4. The optimized velocity update amount is applied to update the seismic interval velocity model to complete multi-azimuth grid tomography iteration based on the structure-guided well-constrained velocity field to obtain an initial well-constrained velocity field based on the structure guidance; S5. The initial well-constrained velocity field based on the structure guidance is iterated for multiple rounds to make the velocity field tend to converge, and thus a well-constrained velocity model based on the structure guidance is obtained.

2. The construction orientation based well control velocity modeling method of claim 1, wherein, In step S1, the obtaining of the preprocessed logging velocity data comprises: collecting and arranging data to obtain logging data, seismic velocity data and original logging velocity data; The logging velocity trend is analyzed in combination with the logging data and the seismic velocity data to pre-process the original logging velocity data to obtain the preprocessed logging velocity data; The pre-processing method comprises filtering and smoothing the original logging velocity data.

3. The construction orientation based well control velocity modeling method of claim 2, wherein, The filtering adopts a low-pass filtering method or a band-pass filtering method; The smoothing adopts a weighted average method or a median filtering method.

4. The construction oriented well control velocity modeling method as claimed in claim 1, wherein, In step S4, the optimized velocity update amount is a velocity update amount optimized by manual optimization through well-seismic analysis and structure trend analysis after multi-azimuth grid tomography iteration.

5. The construction orientation-based well control velocity modeling method of claim 1, wherein, In step S5, when the well-seismic relationship coincidence rate is not less than 98%, the iteration operation can be ended.

6. Application of the structure-guided well-constrained velocity modeling method according to any one of claims 1-5 in "true" surface TTI anisotropic prestack depth migration processing.

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