A method and system for establishing a low-frequency model based on multi-attribute fusion

Abstract

The present invention discloses a method and system for establishing a low-frequency model based on multi-attribute fusion. The method for establishing a low-frequency model based on multi-attribute fusion includes: creating a compressional wave velocity compaction trend body in a work area; constructing a pre-stack parameter elastic very low frequency model based on the compressional wave velocity compaction trend body and the seismic layer velocity body; establishing a well interpolation model based on a three-dimensional structural grid; and merging the pre-stack parameter elastic very low frequency model and the well interpolation model in the frequency domain to obtain a multi-attribute fusion low-frequency model. The present invention fully integrates seismic velocity information, regional compaction trend information, well logging information, and geological model information to participate in establishing the low-frequency model, improving the "bull's eye" phenomenon of conventional well interpolation low-frequency models. The overall law is consistent with the structural background, solving the problem of low accuracy of low-frequency models caused by uneven drilling distribution, strong reservoir heterogeneity, and rapid lateral changes.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas exploration and development, and in particular relates to a method and system for establishing a low-frequency model based on multi-attribute fusion. Background Art

[0002] Seismic data has a limited bandwidth, lacking both high- and low-frequency information. The loss of high-frequency information affects the resolution of thin layers, while the lack of low-frequency information complicates reservoir boundary characterization and quantitative interpretation. Constrained sparse pulse inversion can increase the bandwidth of seismic data, but without a low-frequency model to constrain the boundaries, reliable absolute P- and S-wave impedances cannot be generated.

[0003] Conventional low-frequency modeling is primarily influenced by two factors: the framework model based on structurally interpreted horizons and faults; and well logging data. Building a seismic low-frequency reservoir model involves interpolating well logging data along the layers within the constraints of the geological framework model to obtain a comprehensive initial parameter model. Interpolation can be performed using a variety of methods, including trigonometric interpolation, inverse distance weighted (IDW), local kriging, and global kriging. However, this modeling approach is generally only suitable for areas with relatively simple sedimentary environments and minimal lateral variation. Furthermore, due to the interpolation algorithm, the resulting model often exhibits lateral "bull's eyes," which can directly affect subsequent seismic inversion results. Summary of the Invention

[0004] In response to the above problems, the present invention discloses a method for establishing a low-frequency model based on multi-attribute fusion, comprising the following steps:

[0005] Create a compaction trend body of longitudinal wave velocity in the work area;

[0006] Based on the P-wave velocity compaction trend body and the seismic layer velocity body, a prestack parametric elastic very low frequency model is constructed;

[0007] Based on the 3D structural grid, a well interpolation model is established;

[0008] The pre-stack parameter elastic very low frequency model and the well interpolation model are merged in the frequency domain to obtain a multi-attribute fusion low frequency model.

[0009] Furthermore, the specific steps of creating the work area longitudinal wave velocity compaction trend body are as follows:

[0010] According to the linear relationship between the depth of the wells in the work area and the P-wave velocity, the P-wave velocity compaction trend relationship in the work area is determined;

[0011] Based on the longitudinal wave velocity compaction trend relationship, a longitudinal wave velocity compaction trend body of the work area is created.

[0012] Furthermore, the specific steps of constructing the pre-stack parametric elastic very low frequency model based on the P-wave velocity compaction trend body and the seismic layer velocity body are as follows:

[0013] The optimized interval velocity body is corrected based on the compressional wave velocity compaction trend body and the compressional wave velocity of the target well in the work area to obtain the well-controlled interval velocity body;

[0014] The linear functional relationships between P-wave velocity, P-wave impedance, P-wave velocity ratio and density were established.

[0015] Based on the linear function relationship, the well-controlled layer velocity body is converted into P-wave impedance, P-wave velocity ratio and density elastic trend body, and a pre-stack parametric elastic very low frequency model constructed based on the seismic layer velocity body is obtained.

[0016] Furthermore, the specific steps of establishing the well interpolation model based on the three-dimensional structural grid are as follows:

[0017] Based on the 3D structural grid and well-controlled interval velocity body, the P-wave impedance, P-wave velocity ratio and density curve are interpolated with well-controlled interval velocity body constraints to obtain a well interpolation model driven by the 3D structural grid and interval velocity.

[0018] Furthermore, the frequency range of the pre-stack parametric elastic very low frequency model is 0-2 Hz.

[0019] Furthermore, the frequency range of the well interpolation model is 2-8 Hz.

[0020] The present invention also discloses a system for establishing a low-frequency model based on multi-attribute fusion, comprising:

[0021] Create a unit to create the longitudinal wave velocity compaction trend body of the work area;

[0022] A construction unit is used to construct a prestack parametric elastic very low frequency model based on the P-wave velocity compaction trend body and the seismic layer velocity body;

[0023] Establishing units for building well interpolation models based on a three-dimensional structural grid;

[0024] The merging unit is used to merge the pre-stack parameter elastic very low frequency model and the well interpolation model in the frequency domain to obtain a multi-attribute fusion low frequency model.

[0025] Furthermore, the creation unit is specifically configured to:

[0026] According to the linear relationship between the depth of the wells in the work area and the P-wave velocity, the P-wave velocity compaction trend relationship in the work area is determined;

[0027] Based on the longitudinal wave velocity compaction trend relationship, a longitudinal wave velocity compaction trend body of the work area is created.

[0028] Furthermore, the construction unit is specifically used to:

[0029] The optimized interval velocity body is corrected based on the compressional wave velocity compaction trend body and the compressional wave velocity of the target well in the work area to obtain the well-controlled interval velocity body;

[0030] The linear functional relationships between P-wave velocity, P-wave impedance, P-wave velocity ratio and density were established.

[0031] Based on the linear function relationship, the well-controlled layer velocity body is converted into P-wave impedance, P-wave velocity ratio and density elastic trend body, and a pre-stack parametric elastic very low frequency model constructed based on the seismic layer velocity body is obtained.

[0032] Furthermore, the establishing unit is specifically configured to:

[0033] Based on the 3D structural grid and well-controlled interval velocity body, the P-wave impedance, P-wave velocity ratio and density curve are interpolated with well-controlled interval velocity body constraints to obtain a well interpolation model driven by the 3D structural grid and interval velocity.

[0034] Compared with the prior art, the embodiments of the present invention have at least the following advantages: it can fully integrate seismic velocity information, regional compaction trend information, logging information and geological model information to participate in the establishment of a low-frequency model, improve the "bull's eye" phenomenon of the conventional well interpolation low-frequency model, and the overall law is consistent with the structural background, solving the problem of uneven drilling distribution, strong reservoir heterogeneity, and rapid lateral changes resulting in low accuracy of the low-frequency model.

[0035] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 A flowchart of a method for establishing a low-frequency model based on multi-attribute fusion according to an embodiment of the present invention is shown;

[0038] Figure 2 shows a cross-analysis diagram of longitudinal wave velocity and different elastic parameters according to an embodiment of the present invention;

[0039] Figure 3 A pre-stack parametric elastic very low frequency model according to an embodiment of the present invention is shown;

[0040] Figure 4 shows a three-dimensional structural grid and well interpolation profile according to an embodiment of the present invention;

[0041] Figure 5 A low-frequency model of longitudinal wave impedance of a complex fault block according to an embodiment of the present invention is shown;

[0042] Figure 6 A low-frequency model of the P-wave and S-wave velocity ratios of a complex fault block according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0044] When the wells in the study area are unevenly distributed, the reservoir is highly heterogeneous, and the lateral changes are rapid, interpolation using only logging curves will be greatly affected by well control in the lateral direction, which poses certain risks.

[0045] Seismic data provides two types of information: amplitude and velocity. Seismic velocities obtained through seismic processing can, to a certain extent, reflect sedimentary trends. Therefore, this paper proposes to establish a low-frequency model (0-8 Hz) required for sedimentary rock sequence inversion by combining compaction trend correction with interwell interpolation under seismic velocity control (0-2 Hz).

[0046] Figure 1 FIG. 1 is a flow chart showing a method for establishing a low-frequency model based on multi-attribute fusion according to an embodiment of the present invention. Figure 1 As shown, the present invention proposes a method for establishing a low-frequency model based on multi-attribute fusion, comprising the following steps:

[0047] Create a compaction trend body of longitudinal wave velocity in the work area;

[0048] Based on the P-wave velocity compaction trend body and the seismic layer velocity body, a prestack parametric elastic very low frequency model is constructed;

[0049] Based on the 3D structural grid, a well interpolation model is established;

[0050] The pre-stack parameter elastic very low frequency model and the well interpolation model are merged in the frequency domain to obtain a multi-attribute fusion low frequency model.

[0051] The present invention obtains a low-frequency model by fusing information from different frequency bands in the frequency domain, ensuring that the low-frequency model conforms to the actual underground sedimentary characteristics as much as possible, and provides good support for reservoir prediction and inversion.

[0052] In some embodiments, the specific steps of creating a work area longitudinal wave velocity compaction trend body are as follows:

[0053] Based on the linear relationship between depth and P-wave velocity of typical wells in the work area, the P-wave velocity compaction trend relationship within the work area is determined. Typical wells generally have complete data, long curves, and include wells of different reservoir types. Their distribution should also be controlled within the plane range of the work area as much as possible, so as to characterize the regional sedimentary trend.

[0054] Based on the P-wave velocity compaction trend relationship, a P-wave velocity compaction trend body for the work area is created. This is applicable to any stratum with a compaction trend in the work area, such as a monocline structure.

[0055] The compaction trend is an internal relationship of the region, reflecting the relationship between the depth and elastic properties of the entire region. The frequency of this trend is lower, generally 0-1Hz, which is also a very low frequency.

[0056] The present invention is mainly aimed at areas where the target layer has a compaction trend, and can take the compaction trend into consideration and fully utilize the information of seismic layer velocity.

[0057] In some embodiments, the specific steps of constructing a pre-stack parametric elastic very low frequency model based on the P-wave velocity compaction trend body and the seismic layer velocity body are as follows:

[0058] The seismic interval velocity volume (i.e., interval velocity volume) of the work area is quality controlled and optimized to obtain an optimized interval velocity volume. Specifically, the quality control and optimization processing includes outlier removal, smoothing, and high-frequency shearing of the interval velocity volume. High-frequency shearing removes all signals above a cutoff frequency of 3 Hz, retaining only valid signals below 3 Hz.

[0059] The optimized interval velocity volume is corrected based on the P-wave velocity compaction trend volume and the P-wave velocity of the target well (typical well) in the work area to obtain a well-controlled interval velocity volume. The correction process specifically includes performing a three-dimensional compaction trend analysis on the optimized interval velocity volume based on the P-wave velocity compaction trend volume. Then, the interval velocity curve at the well point and the P-wave curve on the well are extracted for linear correlation analysis. Based on the linear relationship, the optimized interval velocity volume is converted into a well-controlled interval velocity volume.

[0060] Based on the rock physics laws of the work area, linear functional relationships were established between P-wave velocity and the regional P-wave impedance, P-wave velocity ratio, and density. The rock physics laws can reflect the regional P-wave velocity and P-wave impedance, P-wave velocity ratio, and density. The P-wave impedance, P-wave velocity ratio, and density curves were pre-corrected for single-well outliers and processed for multi-well consistency, resulting in relatively consistent patterns across wells.

[0061] Based on the linear function relationship, the well-controlled layer velocity body is converted into P-wave impedance, P-wave velocity ratio and density elastic trend body, and a pre-stack parametric elastic very low frequency model constructed based on the seismic layer velocity body is obtained.

[0062] The effective frequency band information of the seismic layer velocity body is 0-2Hz, which belongs to very low frequency and can reflect the sedimentary laws of a large set of strata.

[0063] There are differences in the velocity ranges of the interval velocity body and the wellbore velocity, and the interval velocity may also have velocity reversal in a certain layer segment, which is inconsistent with the compaction trend. After correction by the P-wave velocity compaction trend body and the P-wave velocity of the target well in the work area, the interval velocity body is consistent with the wellbore velocity trend and can also reflect the compaction trend.

[0064] By statistically analyzing the relationship between P-wave velocity and P-wave impedance, P-wave velocity ratio and density, the velocity body of the well-controlled layer (the velocity body of the well-controlled layer is consistent with the P-wave velocity on the well) is converted into a P-wave impedance, P-wave velocity ratio and density elastic trend body.

[0065] In some embodiments, the specific steps of establishing a well interpolation model based on a three-dimensional structural grid are as follows:

[0066] Based on the 3D structural grid and well-controlled interval velocity volume, we rationally selected typical wells within the work area and interpolated the optimized P-wave impedance, P-wave velocity ratio, and density curves of these wells using the well-controlled interval velocity volume. This resulted in a 3D structural grid and interval velocity-driven well interpolation model. The 3D structural grid was derived by interpreting the structural horizons using the 3D seismic data volume combined with well logging data.

[0067] The three-dimensional structural framework can characterize the sedimentary trend and ensure that when the well curve is interpolated, the interpolation results between well points follow the structural trend. Adding the well-controlled layer velocity body constraint can ensure that the interpolation results between well points are not simply mathematical interpolation but follow the trend of layer velocity changes.

[0068] The method of establishing a low-frequency model based on multi-attribute fusion of the present invention can fully integrate seismic velocity information, regional compaction trend, logging information and geological model information to participate in the establishment of the low-frequency model, improve the "bull's eye" phenomenon of the conventional well interpolation low-frequency model, and the overall law is consistent with the structural background, solving the problems of uneven drilling distribution, strong reservoir heterogeneity, and rapid lateral changes resulting in low accuracy of the low-frequency model.

[0069] In some embodiments, the frequency range of the pre-stack parametric elastic very low frequency model is 0-2 Hz.

[0070] In some embodiments, the frequency range of the well interpolation model is 2-8 Hz.

[0071] The study area, covering 300 square kilometers, is located on the slope of the Oriente Basin in Ecuador, South America. It primarily hosts low-amplitude structural-lithologic traps, with reservoirs primarily composed of tidal flat sand bars and tidal channel deposits. The reservoirs are highly heterogeneous and exhibit rapid lateral variations. Approximately 300 wells are drilled in the study area, but their distribution is uneven. Interpolating low-frequency models solely using well logs poses significant lateral constraints to well control, posing a risk.

[0072] 1) Create a compaction trend body in the monocline structural area

[0073] The analysis of the compaction trend in the monocline structure of the Andes T-central area shows that there is a certain compaction trend in the area. The compaction trend of P-wave velocity objectively reflects the sedimentary characteristics of the strata in the monocline structure area with increasing depth. According to the linear relationship between the depth and P-wave velocity of typical wells in the work area, the P-wave velocity compaction trend relationship in the area is statistically analyzed, and the P-wave velocity compaction trend body of the monocline structure area is created based on this relationship.

[0074] 2) Constructing a pre-stack parametric elastic very low frequency model based on seismic layer velocity volume

[0075] The study area has complex geological characteristics, necessitating full utilization of information from seismic interval velocity volumes (or interval velocity volumes). This interval velocity volume is optimized to obtain a processed interval velocity volume. This optimized interval velocity volume is then calibrated based on the P-wave velocities of typical wells within the study area and the P-wave velocity compaction trend volume from Step 1 to obtain a well-controlled interval velocity volume. Rock physics analysis at the well and in the area indicates a correlation between velocity and various elastic parameters. Therefore, relationships between P-wave impedance, density, P-wave velocity ratio, and P-wave velocity are established. This relationship is selected based on trends that are as close to mudstone as possible.

[0076] Figure 2 (a) is the intersection analysis diagram of longitudinal wave velocity and longitudinal wave impedance, as shown in Figure 2 As shown in (a), the y-axis is the longitudinal wave impedance and the x-axis is the velocity. The specific relationship is y = 3.10998x - 2559.91.

[0077] Figure 2 (b) is the intersection analysis diagram of longitudinal wave velocity and density, as shown in Figure 2 As shown in (b), the y-axis is density and the x-axis is speed, and the specific relationship is y=0.000425x+1.05.

[0078] Figure 2 (c) is the cross analysis diagram of the longitudinal wave velocity and the longitudinal and transverse wave velocity ratio, as shown in Figure 2 As shown in (c), the y-axis is the ratio of the longitudinal and transverse wave velocities, and the x-axis is the velocity. The specific relationship is y = 0.0000042*x^3 - 0.0000012*x^2 - 0.000175x + 2.252.

[0079] Then, the velocity field is converted into an elastic parameter data volume using the above relationship as a prestack parametric elastic very low frequency model. Figure 3 (a) is the low-frequency model of longitudinal wave impedance based on the layer velocity body; Figure 3 (b) is the density low-frequency model based on the layer velocity body; Figure 3 (c) is the low-frequency model of the P-wave and S-wave velocity ratio based on the layer velocity body; Figure 3 As shown in (a), 3(b), and 3(c), the three prestack parameter elastic very low frequency models can reflect the sedimentary change trend of a large set of strata and have a good correlation with the well elastic curve.

[0080] 3) Establish a 3D structural framework and well interpolation model driven by interval velocity

[0081] Reasonably select wells in the work area, and perform well interpolation constrained by the well-controlled layer velocity body based on the 3D structural grid and the well-controlled layer velocity body in step 2, as well as the optimized P-wave impedance curves, P-wave and S-wave velocity ratio curves, and density curves of these wells, to obtain a well interpolation model driven by the 3D structural grid and layer velocity.

[0082] The present invention uses the logging curve interpolation method to establish a low-frequency model based on the three-dimensional structural grid. The model is interpolated along the structural trend, such as Figure 4 As shown. Figure 4 (a) is the three-dimensional structural grid of the work area; Figure 4 (b) is the low-frequency model section of the well interpolation longitudinal wave impedance. Figure 4 (b) Well interpolation model and Figure 4 The three-dimensional structural framework in (a) has the same trend.

[0083] 4) Establishment of multi-attribute fusion low-frequency model

[0084] The prestack parameter elastic very low frequency model based on the seismic layer velocity body in step 2 and the well interpolation model driven by the three-dimensional structural grid and layer velocity in step 3 are merged in the frequency domain. The very low frequency model retains the information of 0-2 Hz, and the well interpolation model retains the information of 2-8 Hz, thus establishing a multi-attribute fusion low-frequency model.

[0085] like Figure 5 (a) is the root mean square attribute diagram of the interpolated P-wave impedance model of the M1 layer well; Figure 5 (b) is the root mean square attribute diagram of the comprehensive longitudinal wave impedance model of the M1 layer; Figure 6 (a) is the root mean square attribute diagram of the interpolated P-wave and S-wave velocity ratio model of the M1 layer well; Figure 6 (b) is the root mean square attribute diagram of the comprehensive longitudinal and transverse wave velocity ratio model of the M1 layer. Figure 5 (a) and Figure 6 As shown in (a), the results show that the sedimentary pattern reflected by the low-frequency model plane map obtained by the well interpolation method alone is that there is a "bull's eye" phenomenon at the center point, and the overall pattern is inaccurate; Figure 5 (b) and Figure 6 As shown in (b), the velocity changes reflected by the layer velocity volume are consistent with sedimentary patterns. Based on sedimentary geological understanding, the layer velocity volume reflects more accurate patterns. In the case of compaction trends controlled by the layer velocity volume, the final multi-attribute fusion low-frequency model, obtained using the multi-information coupling low-frequency model establishment process, shows that the lithologic variation trends reflected in the extracted velocity ratio plan are consistent with sedimentary geological understanding, effectively improving the accuracy of the low-frequency model.

[0086] Based on the above-mentioned method for establishing a low-frequency model based on multi-attribute fusion, the present invention further proposes a system for establishing a low-frequency model based on multi-attribute fusion, comprising:

[0087] Create a unit to create the longitudinal wave velocity compaction trend body of the work area;

[0088] A construction unit is used to construct a prestack parametric elastic very low frequency model based on the P-wave velocity compaction trend body and the seismic layer velocity body;

[0089] Establishing units for building well interpolation models based on a three-dimensional structural grid;

[0090] The merging unit is used to merge the pre-stack parameter elastic very low frequency model and the well interpolation model in the frequency domain to obtain a multi-attribute fusion low frequency model.

[0091] In some embodiments, the creating unit is specifically configured to:

[0092] According to the linear relationship between the depth of the wells in the work area and the P-wave velocity, the P-wave velocity compaction trend relationship in the work area is determined;

[0093] Based on the longitudinal wave velocity compaction trend relationship, a longitudinal wave velocity compaction trend body of the work area is created.

[0094] In some embodiments, the building block is specifically used to:

[0095] The optimized interval velocity body is corrected based on the compressional wave velocity compaction trend body and the compressional wave velocity of the target well in the work area to obtain the well-controlled interval velocity body;

[0096] The linear functional relationships between P-wave velocity, P-wave impedance, P-wave velocity ratio and density were established.

[0097] Based on the linear function relationship, the well-controlled layer velocity body is converted into P-wave impedance, P-wave velocity ratio and density elastic trend body, and a pre-stack parametric elastic very low frequency model constructed based on the seismic layer velocity body is obtained.

[0098] In some embodiments, the establishing unit is specifically configured to:

[0099] Based on the 3D structural grid and well-controlled interval velocity body, the P-wave impedance, P-wave velocity ratio and density curve are interpolated with well-controlled interval velocity body constraints to obtain a well interpolation model driven by the 3D structural grid and interval velocity.

[0100] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for establishing a low-frequency model based on multi-attribute fusion, characterized in that: The following steps are involved: Create a compaction trend body of longitudinal wave velocity in the work area; Based on the P-wave velocity compaction trend body and the seismic layer velocity body, a prestack parametric elastic very low frequency model is constructed; Based on the 3D structural grid, a well interpolation model is established; Merging the prestack parameter elastic very low frequency model and the well interpolation model in the frequency domain to obtain a multi-attribute fusion low frequency model; The specific steps of creating the work area longitudinal wave velocity compaction trend body are as follows: According to the linear relationship between the depth of the wells in the work area and the P-wave velocity, the P-wave velocity compaction trend relationship in the work area is determined; Based on the longitudinal wave velocity compaction trend relationship, a longitudinal wave velocity compaction trend body of the work area is created; The specific steps of constructing the pre-stack parametric elastic very low frequency model based on the P-wave velocity compaction trend body and the seismic layer velocity body are as follows: The optimized interval velocity body is corrected based on the compressional wave velocity compaction trend body and the compressional wave velocity of the target well in the work area to obtain the well-controlled interval velocity body; The linear functional relationships between P-wave velocity, P-wave impedance, P-wave velocity ratio and density were established. Based on the linear function relationship, the well-controlled layer velocity body is converted into the compressional wave impedance, compressional and shear wave velocity ratio and density elastic trend body, and the pre-stack parametric elastic very low frequency model based on the seismic layer velocity body is obtained. The specific steps of establishing the well interpolation model based on the three-dimensional structural grid are as follows: Based on the 3D structural grid and well-controlled interval velocity body, the P-wave impedance, P-wave velocity ratio and density curve are interpolated with well-controlled interval velocity body constraints to obtain a well interpolation model driven by the 3D structural grid and interval velocity.

2. The method for establishing a low-frequency model based on multi-attribute fusion according to claim 1, characterized in that: The frequency range of the pre-stack parametric elastic very low frequency model is 0-2 Hz.

3. The method for establishing a low-frequency model based on multi-attribute fusion according to claim 1, characterized in that: The frequency range of the well interpolation model is 2-8 Hz.

4. A system for establishing a low-frequency model based on multi-attribute fusion, characterized in that: include: Create a unit to create the longitudinal wave velocity compaction trend body of the work area; A construction unit is used to construct a prestack parametric elastic very low frequency model based on the P-wave velocity compaction trend body and the seismic layer velocity body; Establishing units for building well interpolation models based on a three-dimensional structural grid; a merging unit, configured to merge the prestack parameter elastic very low frequency model and the well interpolation model in the frequency domain to obtain a multi-attribute fusion low frequency model; The creation unit is specifically used to: According to the linear relationship between the depth of the wells in the work area and the P-wave velocity, the P-wave velocity compaction trend relationship in the work area is determined; Based on the longitudinal wave velocity compaction trend relationship, a longitudinal wave velocity compaction trend body of the work area is created; The building block is specifically used for: The optimized interval velocity body is corrected based on the compressional wave velocity compaction trend body and the compressional wave velocity of the target well in the work area to obtain the well-controlled interval velocity body; The linear functional relationships between P-wave velocity, P-wave impedance, P-wave velocity ratio and density were established. Based on the linear function relationship, the well-controlled layer velocity body is converted into the compressional wave impedance, compressional and shear wave velocity ratio and density elastic trend body, and the pre-stack parametric elastic very low frequency model based on the seismic layer velocity body is obtained. The establishing unit is specifically configured to: Based on the 3D structural grid and well-controlled interval velocity body, the P-wave impedance, P-wave velocity ratio and density curve are interpolated with well-controlled interval velocity body constraints to obtain a well interpolation model driven by the 3D structural grid and interval velocity.

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