Modeling method based on seismic transverse difference attributes

Abstract

The invention belongs to the technical field of geophysical exploration, and particularly relates to a seismic transverse difference attribute-based modeling method, which comprises the following steps of: extracting data; calculating a characteristic value; establishing an initial attribute model M; carrying out data normalization; calculating a seismic waveform correction coefficient; correcting the residual curve; and establishing a final attribute model body. The method has the following advantages: through seismic wavelet decomposition, influence factors of seismic wavelets are fully analyzed, influences of stretching and equalization processing in acquisition environments such as seismic source and detector placement conditions and a processing process are eliminated, and response of effective information is fundamentally improved; the geological thin sand layer with the large burial depth can be calculated, limitation of the stratum burial depth is avoided, and the sand layer prediction precision is improved; the practical application cost is low and the practicability is high.

Description

Technical Field

[0001] The invention belongs to the technical field of geophysical exploration, and in particular relates to a method for modeling based on seismic lateral difference attributes. Background Art

[0002] Seismic geological modeling is the process of describing and modeling geological bodies by comprehensively utilizing information such as earthquakes, geology, logging, and drilling. The resulting geological model can be used to describe the geometric shape of geological bodies, stratigraphic contact relationships, and various attribute characteristics. Geological models generally include structural models and attribute models.

[0003] Attribute modeling is the process of integrating various quantitative models of attribute parameters (such as seismic parameters and reservoir parameters) generated by seismic, geological, logging, drilling and other information on the basis of structural model (also known as stratigraphic framework model, which is composed of stratigraphic interface model and fault model). Attribute model summarizes a variety of information and interpretation results, which can be used to quantitatively describe the characteristics of reservoir space and analyze the heterogeneity and continuity of reservoir from multiple scales. It is a relatively effective means of studying reservoir. Attribute model is mainly used in the following aspects in oilfield scientific research and production: 1. Provide various parameter models for reservoir prediction; 2. Provide three-dimensional geological data body for numerical simulation of oil reservoir; 3. Used to calculate oil and gas pore volume or reserves; 4. Intuitively describe the characteristics of reservoir space and help well deployment; 5. Effective communication platform. Currently, the commonly used attribute modeling methods can be divided into two categories: deterministic modeling and stochastic modeling.

[0004] Deterministic attribute modeling is mainly based on methods such as inverse distance, local weighting, simple kriging, radial basis function, etc., which interpolate well point attributes under the control of the structural model to obtain a deterministic attribute model. This type of method is simple to implement, has high computational efficiency and fast speed, but its results have obvious modeling characteristics. The reservoir attributes are very continuous in space, but they are often inconsistent with the fast-changing sedimentary characteristics of domestic continental basins, thus limiting the application scenarios of this method.

[0005] Random attribute modeling mainly uses the variogram to describe the spatial variation characteristics of the reservoir, and obtains a statistical attribute model by randomly interpolating the well point attributes under the control of the structural model. This type of method has high accuracy and the results have certain statistical laws, but the variogram is too rough to describe the spatial characteristics of the reservoir, resulting in low lateral resolution of the results, and the plane laws are often inconsistent with the complex sedimentary patterns. The results are highly random and suitable for reserve assessment and numerical modeling, but not for specific geological target decisions.

[0006] The above two types of attribute modeling methods only use well data (well curves and well point distances) for calculation and predict spatially through mathematical algorithms and statistical laws. The predictability and accuracy of inter-well attributes often have problems of strong modeling or strong randomness due to the lack of reference standards. At present, three-dimensional seismic data, as the only densely distributed geophysical information between wells, has been widely used in structural interpretation, reservoir prediction, etc. Using seismic data as a reference to correct and adjust the attribute model is a feasible technical path to improve the predictability of attribute modeling and reduce modeling and randomness. Summary of the invention

[0007] In view of the above problems, the present invention provides a method for modeling based on seismic lateral difference attributes, comprising the following steps:

[0008] Extract data: Based on structural interpretation and time-depth calibration methods, extract the well curves, wellside seismic traces, and seismic traces of the points to be predicted within the comparison time window;

[0009] Calculate the characteristic value: perform arithmetic averaging on the extracted seismic traces near the well to obtain the standard seismic trace W, and calculate the mean value of the seismic trace of the predicted point within the comparison time window. and standard deviation As the characteristic value of each seismic trace;

[0010] Establishing the initial attribute model M: taking the arithmetic average of the well curves extracted in step 1 to obtain the residual curve ΔZ, and performing model interpolation based on the well curves and structural interpretation results to obtain the initial attribute model M;

[0011] Data normalization;

[0012] Calculate the earthquake waveform correction coefficient: Use the normalized mean and standard deviation of the earthquake trace at the predicted point and the mean and standard deviation of the standard earthquake trace to calculate the earthquake waveform correction coefficient Q of the predicted point. x ;

[0013] Correction residual curve;

[0014] Establish the final attribute model: treat the model curve M of the seismic trace of the prediction point x Perform residual correction calculations on each channel to form a residual data volume ΔM X , the initial attribute model M and ΔM x The data bodies are merged to obtain the final attribute model body.

[0015] A further preferred technical solution is: the data normalization is specifically:

[0016] Calculate the mean and standard deviation of the standard seismic trace W within the comparison time window as the characteristic values ​​of the trace, and normalize the mean of the standard seismic trace, the mean of the seismic trace of the point to be predicted, the standard deviation of the standard seismic trace W and the mean of the seismic trace of the point to be predicted respectively. The normalized mean of the standard seismic trace W is , and the normalized mean of the seismic trace of the point to be predicted is ; the normalized standard deviation of the standard seismic trace is , and the normalized standard deviation of the seismic trace of the point to be predicted is .

[0017] A further preferred technical solution is to correct the residual curve, specifically:

[0018] The earthquake waveform correction coefficient Q of the predicted point x As the weight coefficient, the residual curve ΔZ is corrected to obtain the corrected residual curve ΔZ x .

[0019] Its further preferred technical solution is: the mean value of the seismic trace of the predicted point The calculation formula is as follows:

[0020]

[0021] in, is the mean value of the seismic traces of each predicted point, L x is the wavelength, i.e. the number of sampling points of the wave, x i is the value of the i-th sampling point.

[0022] Its further preferred technical solution is: the standard deviation of the seismic trace of the predicted point The calculation formula is as follows:

[0023]

[0024] in, is the mean value of the seismic traces of each predicted point, L x is the wavelength, i.e. the number of sampling points of the wave, x i is the value of the i-th sampling point; is the standard deviation of the seismic trace of each point to be predicted.

[0025] Its further preferred technical solution is: the mean value of the standard seismic trace W The calculation formula is as follows:

[0026]

[0027] in, is the mean value of the standard seismic trace, L w is the wavelength, i.e. the number of sampling points of the wave, w i is the value of the i-th sampling point.

[0028] Its further preferred technical solution is: the standard deviation of the standard seismic trace W The calculation formula is as follows:

[0029]

[0030] in, is the mean value of the standard seismic trace, L w is the wavelength, i.e. the number of sampling points of the wave, w i is the value of the i-th sampling point, is the standard deviation of the standard seismic trace, L w is the wavelength, i.e. the number of sampling points of the wave.

[0031] Its further preferred technical solution is: the earthquake waveform correction coefficient Q of the point to be predicted x The calculation formula is as follows:

[0032] Among them, Q x is the earthquake waveform correction coefficient of the point to be predicted, is the mean of the normalized standard seismic trace, is the normalized mean value of the seismic traces of each prediction point, is the standard deviation of the normalized standard seismic trace, is the normalized standard deviation of the seismic traces of each prediction point.

[0033] A further preferred technical solution is: the calculation formula of the corrected residual curve is as follows:

[0034] ΔZ x =Q x ΔZ

[0035] Among them, ΔZ x is the residual curve after correction, Q x is the seismic waveform correction coefficient, and ΔZ is the residual curve.

[0036] A further preferred technical solution is: in the data extraction, the well curves, wellside seismic traces, and seismic traces of the points to be predicted of the wells within the comparison time window are extracted according to the structural interpretation and time-depth calibration method.

[0037] Beneficial effects of the invention: Starting from the perspective of geophysics, the invention explores the fundamental causes of errors in the prediction of deeply buried thin sand layers, conducts in-depth analysis and research on illusions caused by various reasons, combines structural geological factors, and uses the spectrum analysis of the sand layers in the target layer to determine the frequency range and sub-wave sequence of the main rock strata, scientifically and effectively eliminate the influence of interference waves and the illusion of a single sub-wave, and effectively improve the prediction accuracy of thin sand layers.

[0038] Compared with previous research results, this technology has the following advantages: ① Seismic wavelet decomposition is based on an in-depth analysis of the geophysical characteristics of seismic signal reflection, and studies the effective wavelet according to the time-varying nature of its reflection travel process, which is a big step forward compared to the previous method of using a single wavelet; ② Wavelet decomposition fully analyzes the influencing factors of seismic wavelets, eliminates the influence of acquisition environments such as earthquake sources and detector placement conditions, as well as the effects of stretching and equalization processing during the processing process, and fundamentally improves the response of effective information; ③ It can calculate geological thin sand layers with greater burial depths, without being restricted by the burial depth of the strata, thus improving the accuracy of sand layer prediction; ④ The actual application cost is low and the practicability is strong.

[0039] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or 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, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces 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 creative work.

[0041] Figure 1 It is a technical flow chart of the method of the present invention;

[0042] Figure 2 It is the seismic trace and logging curve diagram beside the sample well W1 of the present invention;

[0043] Figure 3 It is the seismic trace and logging curve diagram beside the sample well W2 of the present invention;

[0044] Figure 4 It is a schematic diagram of all the seismic traces of the points to be predicted in a certain area;

[0045] Figure 5 It is a standard seismic trace diagram;

[0046] Figure 6 It is a standard curve graph;

[0047] Figure 7 is the initial attribute model profile;

[0048] Figure 8 It is the model curve diagram of the seismic trace of the point to be predicted;

[0049] Fig. 9 It is the residual curve of a certain point to be predicted;

[0050] Fig.10 It is the residual curve after correction of a certain point to be predicted;

[0051] Fig.11 It is the residual data volume profile obtained by calculating channel by channel.

[0052] Fig.12 It is the cross-section diagram of the corrected attribute model body. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0054] Example 1

[0055] The Triassic in the Jiefangqu area is a continental deposit of braided river delta to meandering river deposits. The thickness of the sedimentary sand body is thin, with the thinnest sand body only 2-5m, and the lateral changes are fast. The width of the river phase is about 30-100m. The inter-river bays and river channel deposits are inter-distributed in the vertical direction of the river. The reservoir distribution is poor, the degree of drilling control is low, and the reservoir changes between wells are random, making it difficult to effectively establish a reservoir attribute model. The deterministic modeling results are inconsistent with the actual geological understanding and development dynamics, and conventional attribute modeling technology is difficult to accurately describe the distribution of the reservoir. In this case, a method based on seismic lateral difference attribute modeling was adopted for this area, and the reservoir model was re-implemented. The specific methods are as follows: Figure 1 shown.

[0056] 1. Extract the wellside seismic traces, curves and curves of the predicted points of the drilled wells: According to the structural interpretation and time-depth calibration method, extract the well curves, wellside seismic traces and the predicted points of the drilled wells within the comparison time window; according to the structural interpretation of the area, synthetic seismic record calibration and other methods, extract the well curves, wellside seismic traces (W1 wellside seismic traces and logging curves such as JF1-7-3 wells drilled within the comparison time window) Figure 2 As shown, the seismic traces and logging curves near Well W2 are as follows Figure 3 As shown) and the seismic trace of the point to be predicted (the seismic trace of the point to be predicted is as shown Figure 4 shown).

[0057] 2. Calculate the standard seismic trace and characteristic value: Perform arithmetic averaging on the extracted well-side seismic traces to obtain the standard seismic trace, such as Figure 5 As shown, the mean value of the seismic traces of each predicted point in the comparison time window is calculated. and standard deviation The calculation method is as follows:

[0058]

[0059]

[0060] in, is the mean value of the standard seismic trace, L w is the wavelength, i.e. the number of sampling points of the wave, w i is the value of the i-th sampling point. The standard deviation of the standard seismic trace can be calculated according to the above formula, where is the standard deviation of the standard seismic trace, L w is the wavelength, i.e. the number of sampling points of the wave, w i is the value of the i-th sampling point, is the mean value of the standard seismic trace. Compare the mean values ​​of the seismic traces of each predicted point in the time window and standard deviation As shown in Table 1, it is used as the characteristic value of each seismic trace;

[0061] Table 1 Schematic diagram of the mean and standard deviation of each seismic trace in the comparison time window

[0062] Mean Standard Deviation Earthquake trace of point 1 to be predicted -1789.00 4556.98 Earthquake trace of point 2 to be predicted -1542.78 4517.15 Earthquake trace of point 3 to be predicted -1499.39 4904.39 Earthquake trace of point 4 to be predicted -1402.32 5172.42 Earthquake trace of point 5 to be predicted -1504.30 5110.08 Earthquake trace of point 6 to be predicted -1421.75 5495.85 Earthquake trace of point 7 to be predicted -1575.78 5916.90 To be predicted point 8 seismic trace -1310.71 6370.05 Earthquake track 9 of the predicted point -1585.49 6132.70 10 earthquake traces at the predicted point -1629.19 5426.65 To be predicted point 11 seismic trace -1867.46 5250.93 To be predicted point 12 seismic trace -2155.39 4822.11 To be predicted point 13 earthquake track -2329.56 5026.89 To be predicted point 14 seismic trace -2468.48 5432.53 To be predicted point 15 earthquake track -2079.92 5692.18 To be predicted point 16 seismic trace -2090.47 5203.01 To be predicted point 17 earthquake track -2380.95 5287.41 To be predicted point 18 seismic trace -2377.50 5276.75 To be predicted point 19 earthquake track -2145.74 5525.76 To be predicted point 20 earthquake track -2201.20 5210.38 To be predicted point 21 seismic trace -2224.15 5685.43 To be predicted point 22 seismic trace -2234.99 5874.07

[0063] 3. Establish the initial attribute model: Take the arithmetic average of the logging curves extracted in step 1 to obtain the residual curve ΔZ, such as Figure 6 As shown in the figure, the model is interpolated according to the well curve and structural interpretation results to obtain the initial attribute model M. The profile of the initial attribute model is shown in Figure 7 shown.

[0064] 4. Calculate the mean of the standard seismic traces within the comparison time window and standard deviation As the characteristic value of this trace, the values ​​are shown in Table 2; and the average value of the standard seismic trace is The mean value of each seismic channel and the standard deviation of the standard seismic trace and the mean value of each seismic channel They are normalized respectively, and the standard deviation of the normalized standard seismic trace is The standard deviation of the normalized mean of each seismic channel As shown in Table 3.

[0065] Table 2 Mean and standard deviation of standard seismic traces

[0066] Mean Standard Deviation Standard seismic channel -1930.93 5693.64

[0067] Table 3 Normalized standard seismic traces and the mean and standard deviation of each seismic trace

[0068] Normalized standard deviation Standard seismic channel 0.63 Earthquake trace of point 1 to be predicted 0.02 Earthquake trace of point 2 to be predicted 0.00 Earthquake trace of point 3 to be predicted 0.21 Earthquake trace of point 4 to be predicted 0.35 Earthquake trace of point 5 to be predicted 0.32 Earthquake trace of point 6 to be predicted 0.53 Earthquake trace of point 7 to be predicted 0.76 To be predicted point 8 seismic trace 1.00 Earthquake track 9 of the predicted point 0.87 10 earthquake traces at the predicted point 0.49 To be predicted point 11 seismic trace 0.40 To be predicted point 12 seismic trace 0.16 To be predicted point 13 earthquake track 0.28 To be predicted point 14 seismic trace 0.49 To be predicted point 15 earthquake track 0.63 To be predicted point 16 seismic trace 0.37 To be predicted point 17 earthquake track 0.42 To be predicted point 18 seismic trace 0.41 To be predicted point 19 earthquake track 0.54 To be predicted point 20 earthquake track 0.37 To be predicted point 21 seismic trace 0.63 To be predicted point 22 seismic trace 0.73

[0069] 5. Calculate the earthquake waveform correction coefficient: extract the model curve M of the earthquake trace at the predicted point x ,like Figure 8 As shown, the model curve M x Subtract the residual curve ΔZ from the residual curve ΔZ x ,like Fig. 9 The seismic waveform correction coefficient Q of the predicted point is calculated by using the normalized mean and standard deviation of the seismic trace of the predicted point and the mean and standard deviation of the standard seismic trace. x , earthquake waveform correction factor Q x The calculation formula is as follows:

[0070]

[0071] Among them, Q x is the seismic waveform correction coefficient of the seismic trace at the point to be predicted, is the mean of the normalized standard seismic trace, is the normalized mean value of the seismic trace of the predicted point, is the standard deviation of the normalized standard seismic trace, is the standard deviation of the normalized seismic trace of the predicted point. The seismic waveform correction coefficient Q of the predicted point x As shown in Table 4;

[0072] Table 4 Earthquake waveform correction coefficients at predicted points

[0073] Earthquake waveform correction factor Earthquake trace of point 1 to be predicted 0.2551 Earthquake trace of point 2 to be predicted 0.7221 Earthquake trace of point 3 to be predicted 0.5386 Earthquake trace of point 4 to be predicted 0.4357 Earthquake trace of point 5 to be predicted 0.3937 Earthquake trace of point 6 to be predicted 0.1592 Earthquake trace of point 7 to be predicted 0.1254 To be predicted point 8 seismic trace 0.2398 Earthquake track 9 of the predicted point 0.1274 10 earthquake traces at the predicted point 0.0444 To be predicted point 11 seismic trace 0.3093 To be predicted point 12 seismic trace 0.4203 To be predicted point 13 earthquake track 0.0003 To be predicted point 14 seismic trace 0.1238 To be predicted point 15 earthquake track 0.2891 To be predicted point 16 seismic trace 0.2944 To be predicted point 17 earthquake track 0.0570 To be predicted point 18 seismic trace 0.2065 To be predicted point 19 earthquake track 0.0038 To be predicted point 20 earthquake track 0.0867 To be predicted point 21 seismic trace 0.2219 To be predicted point 22 seismic trace 0.6634

[0074] 6. Correction of residual curve: Use the seismic waveform correction coefficient of the predicted channel as the weight coefficient to correct the residual curve and obtain the corrected residual curve ΔZ x , the corrected residual curve ΔZ x The calculation formula is as follows:

[0075] ΔZ x =Q x ΔZ

[0076] Among them, ΔZ x is the corrected residual curve of the channel to be predicted, Q x is the seismic waveform correction coefficient, ΔZ is the residual curve; the residual curve after correction ΔZ x like Fig.10 As shown;

[0077] 7. Establish the final attribute model: perform residual correction calculation on each seismic data volume to form a residual data volume ΔMX , its cross-section diagram is as follows Fig.11 As shown; the initial impedance model M and the data volume ΔM x The modified attribute model body cross-section is obtained by merging, which is the final attribute model body. Fig.12 shown.

[0078] 8. Application effect:

[0079] According to the seismic lateral difference attribute modeling method, attribute modeling was carried out on the Triassic reservoir layer in the Jiefangqudong area, and 5 new closures were discovered and implemented, among which the Jiefang 150 closure has been successfully drilled, and the predicted resource volume of crude oil is 1.2 million tons. The present invention uses structural interpretation and time-depth calibration methods to extract the well curves, wellside seismic traces, and seismic traces of the points to be predicted in the comparison time window, and combines the lateral difference characteristics of the seismic waveform and the drilled curve to establish a residual data body, and corrects the model interpolation attribute body to obtain the final attribute model. The modeling method of the present invention is simple to implement, has high computational efficiency and high speed, can adapt to the fast-changing sedimentary characteristics of domestic continental basins, has high lateral resolution, is suitable for specific geological target decision-making, improves the predictability of attribute modeling, and reduces modeling and randomness.

[0080] It should be noted that the terms "first", "second" etc. in the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged in appropriate circumstances, so that the embodiments of the present application described here. In the present application, the orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inside", "outside", "middle", "vertical", "horizontal", "lateral", "longitudinal" etc. are based on the orientation or positional relationship shown in the accompanying drawings.

[0081] 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 substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for modeling based on seismic lateral difference attributes, characterized in that: The following steps are involved: Extract data: Based on structural interpretation and time-depth calibration methods, extract the well curves, wellside seismic traces, and seismic traces of the points to be predicted within the comparison time window; Calculate the characteristic value: perform arithmetic averaging on the extracted seismic traces near the well to obtain the standard seismic trace W, and calculate the mean value of the seismic trace of the predicted point within the comparison time window. and standard deviation As the characteristic value of each seismic trace; Establish the initial attribute model M: perform arithmetic averaging on the extracted well curves to obtain the residual curve ΔZ, perform model interpolation based on the well curves and structural interpretation results to obtain the initial attribute model M; Data normalization; Calculate the earthquake waveform correction coefficient: Use the normalized mean and standard deviation of the earthquake trace at the predicted point and the mean and standard deviation of the standard earthquake trace to calculate the earthquake waveform correction coefficient Q of the predicted point. x ; Correction residual curve; Establish the final attribute model: treat the model curve M of the seismic trace of the prediction point x Perform residual correction calculations on each channel to form a residual data volume ΔM X , the initial attribute model M and ΔM x The data bodies are merged to obtain the final attribute model body.

2. The method for modeling based on seismic lateral difference attributes according to claim 1, characterized in that: The data normalization is specifically as follows: Calculate the mean value of the standard seismic trace W within the comparison time window and standard deviation As the characteristic value of the trace, and the mean value of the standard seismic trace The mean value of the seismic trace at the predicted point and the standard deviation of the standard seismic trace W and the mean value of the seismic trace of the predicted point They are normalized respectively, and the normalized mean value of the standard seismic trace W is The normalized mean value of the seismic trace at the predicted point is The standard deviation of the normalized standard seismic trace is The normalized standard deviation of the seismic trace at the predicted point is 3. The method for modeling based on seismic lateral difference attributes according to claim 1, characterized in that: The modified residual curve is specifically: The earthquake waveform correction coefficient Q of the predicted point x As the weight coefficient, the residual curve ΔZ is corrected to obtain the corrected residual curve ΔZ x .

4. The method for modeling based on seismic lateral difference attributes according to claim 2, characterized in that: The mean value of the seismic trace of the predicted point The calculation formula is as follows: in, is the mean value of the seismic trace of the predicted point, L x is the wavelength, i.e. the number of sampling points of the wave, x i is the value of the i-th sampling point.

5. The method for modeling based on seismic lateral difference attributes according to claim 2, characterized in that: The standard deviation of the seismic trace of the point to be predicted The calculation formula is as follows: in, is the mean value of the seismic traces of each predicted point, L x is the wavelength, i.e. the number of sampling points of the wave, x i is the value of the i-th sampling point; is the standard deviation of the seismic trace of each point to be predicted.

6. The method for modeling based on seismic lateral difference attributes according to claim 2, characterized in that: The mean value of the standard seismic trace W The calculation formula is as follows: in, is the mean value of the standard seismic trace, L w is the wavelength, i.e. the number of sampling points of the wave, w i is the value of the i-th sampling point.

7. The method for modeling based on seismic lateral difference attributes according to claim 2, characterized in that: The standard deviation of the standard seismic trace W The calculation formula is as follows: in, is the mean value of the standard seismic trace, L w is the wavelength, i.e. the number of sampling points of the wave, w i is the value of the i-th sampling point, is the standard deviation of the standard seismic trace, L w is the wavelength, i.e. the number of sampling points of the wave.

8. The method for modeling based on seismic lateral difference attributes according to claim 2, characterized in that: The earthquake waveform correction coefficient Q of the point to be predicted x The calculation formula is as follows: Among them, Q x is the earthquake waveform correction coefficient of the point to be predicted, is the mean of the normalized standard seismic trace, is the normalized mean value of the seismic traces of each prediction point, is the standard deviation of the normalized standard seismic trace, is the normalized standard deviation of the seismic traces of each prediction point.

9. The method for modeling based on seismic lateral difference attributes according to claim 1, characterized in that: The calculation formula of the corrected residual curve is shown as follows: ΔZ x =Q x ·ΔZ Among them, ΔZ x is the residual curve after correction, Q x is the seismic waveform correction coefficient, and ΔZ is the residual curve.

10. A method for modeling based on seismic lateral difference attributes according to any one of claims 1 to 9, characterized in that: The extracted data is extracted based on structural interpretation and time-depth calibration methods to extract well curves, wellside seismic traces, and seismic traces of points to be predicted within a comparison time window.

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