Method and System for Characterizing Small-Scale Fracture Development Zones Based on a High-Precision Velocity Model

Abstract

The present invention relates to the technical field of oil and gas exploration, and particularly relates to a method and system for characterizing small-scale fracture development areas based on a high-precision velocity model. The seismic layer velocity function of the target area is corrected using logging data, and the layer velocity information is obtained using the layer velocity function to establish a high-precision velocity model; the adaptive normal illumination data is obtained using the connectivity attribute volume based on multi-parameter optimization, and the adaptive normal illumination data in the time domain is converted into the adaptive normal illumination data in the depth domain using the high-precision velocity model; an azimuth guidance field is established using the fracture azimuth and fracture density, and the initial fracture grid model of different wells is obtained using the azimuth guidance field optimized layer by layer by logging and the fracture density. Through model weight assignment, screening of the optimal weight value, and high-density grid connection, a dynamic small-scale fracture grid model is output. The fracture development area is determined based on the small-scale fracture density and azimuth, and the dynamic small-scale fracture grid model is used to characterize the fracture development area and the spatial distribution characteristics. The present invention can quickly and accurately determine the spatial distribution law of small-scale fracture-intensive development areas.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to a method and system for characterizing a small-scale fracture development zone based on a high-precision velocity model. Background Art

[0002] Fractures are widely developed in the crustal medium. They are formed under the action of crustal stress after a long period of geological tectonic movement. Fractures themselves can serve as oil and gas storage space, as well as important channels for oil and gas migration, controlling the formation and distribution of oil and gas reservoirs. Therefore, fractures are important seismic parameters for oil and gas reservoir exploration and development, and many scholars have used a variety of fracture modeling methods to study them.

[0003] Conventional fracture modeling methods: First, multi-parameter analysis is performed on pre-stack seismic data, and the problems of near-channel multiple wave interference noise and limited bandwidth in three-dimensional seismic data are systematically analyzed, and the optimization processing of relevant seismic gathers is further carried out. Then, the fracture sensitivity attributes are extracted from the denoised seismic data. Further, through comparative analysis of different parameters such as incident angles and azimuth angles, the optimal fracture sensitivity attribute data in the time domain and the normal illumination data in the time domain are calculated in turn, so as to roughly determine the distribution range of the fracture development zone. Conventional fracture modeling methods can identify the laws of reservoir fracture development zones in areas with large-scale fracture development by using discrete fracture grid modeling technology. However, in areas with small-scale fracture development, the application effect is poor. Summary of the invention

[0004] To this end, the present invention provides a method and system for characterizing small-scale fracture development zones based on a high-precision velocity model to solve the problem of unsatisfactory application effect of small-scale fracture modeling in oil and gas exploration.

[0005] According to the design scheme provided by the present invention, on the one hand, a method for characterizing a small-scale fracture development zone based on a high-precision velocity model is provided, comprising:

[0006] Obtain pre-stack OVT domain seismic gather data and perform noise analysis;

[0007] AVO fitting compensation gather optimization processing, wherein the AVO fitting compensation gather optimization processing includes fitting a cubic polynomial, removing noise and target gather energy compensation;

[0008] Use logging data to correct the seismic layer velocity function in the target area, and use the layer velocity function to obtain layer velocity information to establish a high-precision velocity model;

[0009] The time domain adaptive normal lighting data is obtained by using the connectivity attribute body based on multi-parameter optimization, and the time domain adaptive normal lighting data is converted into depth domain adaptive normal lighting data by using a high-precision velocity model;

[0010] An azimuth guidance field is established using the fracture azimuth and fracture density. An initial small-scale fracture grid model and a dynamic small-scale fracture grid model are established using the azimuth guidance field corrected layer by layer by a neural network and the fracture density. The dynamic small-scale fracture grid model is used to characterize the fracture development area and the spatial distribution characteristics.

[0011] Based on the small-scale fracture density and fracture azimuth and using the dynamic small-scale fracture grid model, the fracture development area is determined to characterize the small-scale fracture development area.

[0012] As the method for characterizing the small-scale fracture development area based on the high-precision velocity model of the present invention, further, the fitting cubic polynomial in the AVO fitting compensation gather optimization process is expressed as T = P + Gx + P*Gx 2 + Cx 3 , where T is the reflection coefficient, x is the incident angle, P is the intercept, G is the gradient, P*G is the AVO indicator factor, and C is the curvature.

[0013] As the method for characterizing the small-scale fracture development area based on the high-precision velocity model of the present invention, further, the seismic layer velocity data of the target area is corrected using well logging data, including:

[0014] The layer velocity data at the target well point is extracted, and the seismic layer velocity and the layer velocity at the well point are subjected to correlation analysis and velocity correction to obtain the corrected seismic layer velocity data;

[0015] The corrected seismic layer velocity data is successively thinned, interpolated, and smoothed to obtain a high-precision velocity model.

[0016] As the method for characterizing the small-scale fracture development area based on the high-precision velocity model of the present invention, further, the correlation analysis and velocity correction of the seismic layer velocity and the layer velocity at the well point include:

[0017] The cross-correlation between the seismic layer velocity and the layer velocity at the well point is analyzed using a preset cross-correlation analysis function to obtain the difference between the seismic layer velocity and the well point layer velocity of different strata;

[0018] Based on the difference between the seismic layer velocity and the well point layer velocity of different strata and using the layer velocity value of the well and the seismic layer velocity value, a correction function is constructed to correct the seismic layer velocity value using the correction function;

[0019] The corrected seismic layer velocity is successively subjected to three-dimensional high-density grid processing to obtain a high-precision velocity model through thinning, interpolation, and smoothing processing.

[0020] As the method for characterizing small-scale fracture development zones based on a high-precision velocity model of the present invention, further, the process of converting the adaptive normal illumination data in the time domain into the adaptive normal illumination data in the depth domain by using the high-precision velocity model is expressed as: C F (h) = V vol ·ΔγC F (t), where C F (t) is the adaptive normal illumination data in the time domain, Δγ is the adaptive factor, C F (h) is the adaptive normal illumination data in the depth domain, and V vol is the seismic layer velocity function in the high-precision velocity model.

[0021] As the method for characterizing small-scale fracture development zones based on a high-precision velocity model of the present invention, further, under the constraint conditions of the azimuth guidance field of different wells, an initial fracture grid model and a dynamic small-scale fracture grid model are obtained by using the azimuth guidance field optimized layer by layer based on neural network units and the fracture density, including:

[0022] Construct a fracture density function and a fracture azimuth function according to the fracture circular discrete space function, the fracture circular space radius, and the vertical and horizontal coordinates;

[0023] Normalize the fracture density function according to the maximum and minimum values of the fracture density, perform high-density grid processing on the normalized fracture density and the fracture azimuth, and obtain the azimuth guidance field;

[0024] Establish an azimuth guidance field according to the fracture azimuth and the fracture density, use the azimuth guidance fields of different wells to correct the azimuth guidance field in the neural network unit layer by layer, and establish an initial small-scale fracture grid model for multiple wells by using the corrected azimuth guidance field and the fracture density. Further, perform model weight assignment, screen the optimal weight value, and high-density grid connection on the initial small-scale fracture grid model for multiple wells in sequence to obtain a dynamic small-scale fracture grid model.

[0025] As the method for characterizing small-scale fracture development zones based on a high-precision velocity model of the present invention, further, determine the fracture development zones according to the small-scale fracture density and azimuth, including:

[0026] Perform linear regression on the fracture density and the fracture azimuth, and determine the range and law of the small-scale fracture development zones in combination with the plane distribution characteristics of the fracture development zones in the dynamic small-scale fracture grid model.

[0027] On the other hand, the present invention also provides a system for characterizing small-scale fracture development zones based on a high-precision velocity model, including: a data acquisition module, a data optimization module, a velocity model acquisition module, a data conversion module, a deep learning module for establishing a target model, and a regional characterization module, where

[0028] A data acquisition module, configured to acquire pre-stack OVT domain seismic trace gather data and perform noise analysis;

[0029] A data optimization module, configured to perform optimization processing on AVO fitting compensation gather, and the AVO fitting compensation gather optimization processing includes fitting a cubic polynomial, removing noise, and compensating the energy of the target gather;

[0030] A velocity model acquisition module, configured to correct the seismic layer velocity function of the target area by using well logging data, obtain layer velocity information by using the layer velocity function, and establish a high-precision velocity model;

[0031] A data conversion module, configured to obtain time-domain adaptive normal illumination data by using a connectivity attribute volume based on multi-parameter optimization, and convert the time-domain adaptive normal illumination data into depth-domain adaptive normal illumination data by using the high-precision velocity model;

[0032] A target model establishment deep learning module, configured to establish an azimuth guidance field by using the fracture azimuth and fracture density, perform layer-by-layer correction of the azimuth guidance field in the neural network unit by using the azimuth guidance fields of different wells, establish an initial small-scale fracture grid model of multiple wells by using the corrected azimuth guidance field and fracture density, and further perform model weight assignment, screening of the optimal weight value, and high-density grid connection on the initial small-scale fracture grid model of multiple wells in sequence to obtain a dynamic small-scale fracture grid model, and the dynamic small-scale fracture grid model is used to characterize the fracture development area and spatial distribution characteristics;

[0033] A regional characterization module, configured to determine the fracture development area based on the small-scale fracture density and azimuth and based on the dynamic small-scale fracture grid model to characterize the small-scale fracture development area.

[0034] Advantages of the present invention:

[0035] The present invention corrects seismic layer velocity data through well logging data to establish a high-precision velocity model, providing high-precision velocity field information for seismic data in the depth domain. Conduct (CF) connectivity calculations on seismic data with near-channel multiple interference removed to obtain a connectivity attribute volume. Screen adaptive normal illumination data in the time domain through parameters such as incident angle and azimuth angle, and convert it into adaptive normal illumination data in the depth domain using the high-precision velocity model, which can accurately reflect the distribution law of small-scale fracture development areas. Establish an azimuth guidance field based on fracture azimuth and fracture density. Under the constraint conditions of the azimuth guidance fields of different wells, use the azimuth guidance fields of different wells to correct the azimuth guidance field in the neural network unit layer by layer. Establish an initial small-scale fracture grid model for multiple wells in sequence with the corrected azimuth guidance field and fracture density. Further, perform model weight assignment, screen the optimal weight value, and connect high-density grids for the initial small-scale fracture grid model of multiple wells to obtain a dynamic small-scale fracture grid model. The dynamic small-scale fracture grid model can better reflect the development law of the small-scale fracture system, providing an important basis for subsequent well location deployment. Based on studying the planar distribution characteristics of Class 1 fracture development areas in the dynamic small-scale grid model, conduct linear regression analysis on fracture density and fracture azimuth, focusing on studying the classification rules and scope of the development areas of the small-scale fracture system in the key research area, so as to quickly and accurately determine the spatial distribution law of small-scale fracture intensive development areas, facilitate subsequent reasonable well location deployment and oilfield exploitation, and have good application prospects in the field of oilfield exploration. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of the characterization process of small-scale fracture development areas based on a high-precision velocity model in the embodiment;

[0037] Figure 2 Before the optimization and processing of the AVO fitting compensation gather in the embodiment;

[0038] Figure 3 After the optimization and processing of the AVO fitting compensation gather in the embodiment;

[0039] Figure 4 High-precision three-dimensional velocity model in the embodiment;

[0040] Figure 5 Planar diagram of adaptive normal illumination in the depth domain in the embodiment;

[0041] Figure 6 Planar distribution diagram of fracture azimuth - A in the embodiment;

[0042] Figure 7 Planar distribution diagram of fracture azimuth - B in the embodiment;

[0043] Figure 8 Dynamic small-scale fracture grid model - A in the embodiment;

[0044] Figure 9 For the dynamic small-scale fracture grid model - B in the embodiment;

[0045] Figure 10 For the three-dimensional dynamic small-scale fracture grid model - B in the embodiment;

[0046] Figure 11 For the linear regression analysis in the fracture development area in the embodiment;

[0047] Figure 12 For the distribution map of the 1st-level fracture development area of the dynamic small-scale fracture grid model in the embodiment. Specific implementation manners

[0048] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and technical solutions.

[0049] In view of the fact that the application of the conventional fracture development area characterization method in oil and gas exploration has insufficient accuracy of time-domain data, it is difficult to effectively identify the area where small-scale fractures are relatively developed, and it is easy to add interference information of seismic data while identifying the small-scale fracture development area. In the embodiment of the present invention, referring to Figure 1 as shown, a method for characterizing a small-scale fracture development area based on a high-precision velocity model is provided, including:

[0050] P001. Obtain pre-stack OVT domain seismic trace gather data and conduct noise analysis.

[0051] Taking the three-dimensional pre-stack seismic data of the target oil and gas block as the input pre-stack trace gather data of the OVT domain, multi-parameter analysis is carried out on the pre-stack trace gather data. Noise analysis is carried out on the original seismic trace gather data. Multiple wave interference is not conducive to the later study of the fracture development area, and it is optimized by removing the multiple waves in the near traces.

[0052] P002. Optimize the AVO fitting compensation trace gather. The optimization of the AVO fitting compensation trace gather includes fitting a cubic polynomial, removing noise, and compensating the energy of the target trace gather.

[0053] Through application in a certain work area, taking the three-dimensional pre-stack seismic data of this work area as an example, the formation dip angle in the study area is relatively gentle, and small-scale fractures are relatively developed. For the problem of multiple wave interference in the near traces in the study area, first, AVO fitting compensation processing is carried out on the pre-stack trace gather data of the OVT domain, specifically including establishing a cubic fitting equation and compensating the energy of the target trace gather to separate the multiple wave interference noise and the effective signal in the near traces. The specific steps can be described as follows:

[0054] The specific steps are as follows:

[0055] In the first step, the pre-stack OVT seismic data is input. Through gather analysis, it can be seen that the multiple interference is relatively serious. It is urgent to establish a cubic fitting equation for the amplitude varying with the offset. This formula has a parabolic characteristic, where the terms to be determined are the incident angle x, P (intercept), G (gradient), P*G (AVO indicator factor), and C (curvature). The specific formula is as follows:

[0056] T = P + Gx + P*Gx 2 + Cx 3 (1)

[0057]

[0058] From formulas (1) and (2), T is the reflection coefficient, P, G, P*G, and C are the coefficient terms of the equation, V P 、V S 、D ρ are the velocities of the P-wave impedance, the S-wave impedance, and the density respectively, ΔV P 、ΔV S and ΔD ρ are the rate of change of the velocity of the P-wave impedance, the rate of change of the velocity of the S-wave impedance, and the rate of change of the density respectively, and δ is the incident angle. Define Q as the forward modeling function, and perform the convolution operation on the reflection coefficient and the Ricker wavelet. The specific expression is as follows:

[0059] Q = T * W (3)

[0060] From formula (3), Q is the forward modeling function, T is the reflection coefficient, W is the Ricker wavelet, and * is the convolution operation symbol.

[0061] In the second step, according to the error function calculation, retain the effective wave values with smaller errors and eliminate the near-offset multiples with larger errors. The AVO fitting compensation expression is as follows:

[0062]

[0063] From formula (4), Z is the fitting function, S is the compensation factor, f is the measured function, Q is the forward modeling function, and [] E is the transpose.

[0064] Through Figure 2 and Figure 3 comparison, it can be seen that the removal effect of the near-offset multiples is obvious, the continuity of the event axis becomes better, and the signal-to-noise ratio is significantly improved, providing an important data source for the later small-scale fracture characterization.

[0065] P003. Use well logging data to correct the seismic layer velocity function of the target area, and use the layer velocity function to obtain the layer velocity information to establish a high-precision velocity model.

[0066] Specifically, correcting the seismic layer velocity data of the target area using well logging data can be designed to include:

[0067] Extract the layer velocity data at the target well points, perform correlation analysis and velocity correction on the seismic layer velocity and the layer velocity at the well points to obtain the corrected seismic layer velocity data;

[0068] Perform thinning, interpolation, and smoothing processing on the corrected seismic layer velocity data in sequence to obtain a high-precision velocity model.

[0069] Since in areas with small formation dip angles and well-developed small-scale fractures, the traditional fracture grid modeling method cannot effectively reflect the spatial distribution law of fracture density, in the embodiments of this case, the best connectivity attribute body is obtained by adjusting and gradually optimizing multiple parameters, and the adaptive normal illumination data in the time domain is calculated for the best connectivity attribute. On this basis, it is converted into the adaptive normal illumination data in the depth domain through a high-precision velocity model.

[0070] Among them, performing correlation analysis and velocity correction on the seismic layer velocity and the layer velocity at the well points may include:

[0071] Use a preset cross-correlation analysis function to calculate the cross-correlation between the seismic layer velocity and the layer velocity at the well points to obtain the differences between the seismic layer velocities and the well-point layer velocities of different formations;

[0072] Based on the differences between the seismic layer velocities and the well-point layer velocities of different formations and using the layer velocity value of the well and the seismic layer velocity value, construct a correction function to correct the seismic layer velocity value using the correction function;

[0073] Perform three-dimensional high-density grid processing on the corrected seismic layer velocity in sequence to obtain a high-precision velocity model through thinning, interpolation, and smoothing processing.

[0074] Establishing a high-precision velocity model can be mainly summarized as including the following 4 steps. The specific steps are as follows:

[0075] In the first step, define the target layer section and use the horizon data to establish different formation structure surfaces.

[0076] In the second step, define the well logging stratification data to correct different formation structure surfaces and generate an accurate geological framework model.

[0077] In the third step, define the seismic root-mean-square velocity V RMS , the seismic layer velocity is V vol , the layer velocity at the well point is W vol , convert the seismic root-mean-square velocity into the seismic layer velocity, and the expression is as follows:

[0078]

[0079] From Equation (5), V SMR is the root-mean-square velocity, V vol is the seismic interval velocity, t top is the time value of the top interface of the formation, t down is the time value of the bottom interface of the formation, is the formation dip angle.

[0080] Perform cross-correlation analysis on the seismic interval velocity and the interval velocity at the well point, and analyze the differences between the seismic interval velocities and the interval velocities at the well point in different formations. The specific expression is as follows:

[0081]

[0082] From Equation (6), α is the correlation factor, t is the time value, υ is the time delay value, W vol is the interval velocity function at the well point, V vol is the seismic interval velocity function.

[0083] Furthermore, use the interval velocity value of the well and the seismic interval velocity value to construct a correction function. As described in Table 1, obtain a seismic interval velocity value with higher accuracy. The expression of the correction function is as follows:

[0084]

[0085] From Equation (7), t is the time value, W vol is the interval velocity function at the well point, V vol is the seismic interval velocity function, L2 is the L2 norm function, that is, the correction function. The smaller the correction function, the closer the interval velocity of the well and the seismic interval velocity are, indicating that the accuracy of the seismic interval velocity is higher.

[0086] Table 1 Velocity parameter information of seismic and logging

[0087]

[0088] Fourthly, analyze the small-scale fracture characteristics in the study area. Perform three-dimensional high-density grid processing on the corrected interval velocities in sequence, carry out thinning, interpolation, and smoothing processing, and output a three-dimensional high-precision velocity model, as Figure 4 shown.

[0089] P004. Obtain the adaptive normal illumination data in the time domain using the connectivity attribute volume based on multi-parameter optimization, and convert the adaptive normal illumination data in the time domain into the adaptive normal illumination data in the depth domain using the high-precision velocity model.

[0090] Calculate the connectivity (CF) attribute volume for the seismic data volume after multiple wave removal. Through multi-parameter comparative analysis, including different incident angles, azimuth angles, and frequency information, obtain the optimal connectivity (CF) attribute volume. Calculate the adaptive normal illumination data in the time domain for the optimal connectivity attribute volume. Based on a high-precision velocity model, convert the time-domain data into depth-domain data, as Figure 5 shown, define the corrected seismic layer velocity function V vol , and the specific formula is as follows:

[0091] C F (h) = V vol ·ΔγC F (t) (8)

[0092] From formula (8), C F (t) is the adaptive normal illumination data in the time domain screened by incident angle, azimuth angle, and frequency information. It should be noted that the adaptive factor Δγ is added to the adaptive normal illumination data in the time domain. C F (h) is the adaptive normal illumination data in the depth domain, and V vol is the corrected seismic layer velocity function.

[0093] P005. Establish an azimuth guidance field using the fracture azimuth and fracture density. Using the azimuth guidance fields of different wells as constraint conditions, establish an initial small-scale fracture grid model for multiple wells using the azimuth guidance field and fracture density corrected layer by layer by neural network units. Through model weight assignment, screening the optimal weight value, and high-density grid connection processing, obtain a dynamic small-scale fracture grid model, and the dynamic small-scale fracture grid model is used to characterize the fracture development area and spatial distribution characteristics. Specifically, it can be designed to include:

[0094] The first step: Construct a fracture density function and a fracture azimuth function based on the fracture circular discrete space function, fracture circular space radius, and vertical and horizontal coordinates;

[0095] The second step: Normalize the fracture density function according to the maximum and minimum values of the fracture density, perform high-density grid processing on the normalized fracture density and fracture azimuth, and obtain the azimuth guidance field;

[0096] The third step: Perform discretization processing based on the azimuth guidance field function and the fracture density function, establish a multi-layer neural network unit, use the azimuth guidance fields of different wells to correct the azimuth guidance field in the neural network unit layer by layer, and obtain an initial small-scale fracture grid model for multiple wells using the corrected azimuth guidance field and fracture density.

[0097] In the fourth step, analyze the neural network units of different layers in multiple wells, and sequentially perform model weight assignment, screening of the optimal weight value, and high-density grid connection on the initial small-scale fracture grid model of multiple wells, and output a dynamic small-scale fracture grid model suitable for the study area.

[0098] Among them, in the first step, the functional expressions of fracture density and fracture orientation can be expressed as follows:

[0099]

[0100] From formulas (9) and (10), P(x, y) is the fracture density, θ(x, y) is the fracture orientation, S() is the fracture circular discrete space function, (x, y) is the abscissa and ordinate of the fracture circular space, R is the radius of the fracture circular space, D is the fracture density coefficient, and θ is the fracture orientation coefficient.

[0101] In the second step, normalize the fracture density, and the functional expression is as follows:

[0102]

[0103] From formula (11), is the fracture density after normalization processing, (x, y) is the abscissa and ordinate of the fracture space, is the fracture density function after normalization processing, max() is the maximum value of the fracture density, and min() is the maximum value of the fracture density.

[0104] Perform high-density grid processing on the fracture density and fracture orientation to obtain the expression of the orientation guidance field ω:

[0105]

[0106] From formula (12), ω(x, y) is the orientation guidance field, (x, y) is the abscissa and ordinate of the fracture space, ω is the fracture density function, is the normalized fracture density function, θ is the normalized fracture orientation function, and λ is the anisotropy coefficient, is the fracture density after normalization processing, θ(x, y) is the fracture orientation, * is the convolution operation symbol, and Q is a constant.

[0107] In the third step, discretize the orientation guidance field and fracture density, and the functional expression of the orientation guidance field is as follows:

[0108]

[0109] Among them, ω RZ (x, y) is the orientation guidance field at different times, R is the lateral offset increment, Z is the depth increment, [] is the discretization matrix, and ω 11(x, y), ω 12 (x, y), …, ω 1n (x, y) is the azimuth guidance field with different offsets in the first layer; ω 21 (x, y), ω 22 (x, y), …, ω 2n (x, y) is the azimuth guidance field with different offsets in the second layer ω n1 (x, y), ω n2 (x, y) … ω nm (x, y) is the azimuth guidance field with the same horizontal offset, representing the azimuth guidance fields with different offsets in the nth layer. R ∈ [1, n], Z ∈ [1, m], where n and m are the maximum values of the horizontal offset and depth respectively.

[0110] Define the fracture density at different depths as The expression of the fracture density discretization function is:

[0111]

[0112] Among them, is the discretized fracture density at different depths, is the discretized fracture density function.

[0113] Utilize ω RZ Establish neural network units for different layers using the (x, y) azimuth guidance field matrix variable, and use the azimuth guidance fields of different wells to correct the azimuth guidance fields in the neural network units layer by layer. The specific implementation process: Combine ω 11 (x, y), ω 12 (x, y) … ω 1m (x, y) to form the neural network of the first layer; then, combine ω 21 (x, y), ω 22 (x, y) … ω 2m (x, y) to form the neural network unit of the second layer; and so on, combine ω n1 (x, y), ω n2 (x, y) … ω nm (x, y) to form the neural network unit of the nth layer. Use the azimuth guidance fields of Well MB1, Well MB2, Well MB3, Well MB4, and Well MB5 to correct the azimuth guidance fields of the neural network units layer by layer to obtain the azimuth guidance fields ω RZ-MB1 (x, y), ω RZ-MB2 (x, y), ω RZ-MB3 (x, y), ω RZ-MB4 (x, y) and ω RZ-MB5 (x, y).

[0114] Orient the guidance fields of different wells respectively with the fracture density at different depths for fracture modeling, thereby obtaining the initial small-scale fracture grid models of Well MB1, Well MB2, Well MB3, Well MB4, and Well MB5.

[0115] In the fourth step, analyze the neural network units of different layers of multiple wells, and sequentially perform model weight assignment, screening of the optimal weight value, and high-density grid connection on the initial small-scale fracture grid models of multiple wells. The weight assignment table for the initial small-scale fracture models of multiple wells is as follows:

[0116] Table 2 Weight Assignment Table for Initial Small-Scale Fracture Models

[0117]

[0118] The model weight coefficients of Well MB1, Well MB2, Well MB3, Well MB4, and Well MB5 are shown in Table 2. The optimal weight value screened out is 0.27, that is, the initial small-scale fracture grid model of Well MB5. Perform high-density grid connection calculation on the initial small-scale fracture model to obtain the dynamic small-scale fracture grid model.

[0119] Figure 6 and 8 are the fracture orientation and small-scale discrete fracture grid model obtained by using the conventional small-scale fracture characterization method. Figure 7 、 Figure 9 and 10 are the fracture orientation, small-scale fracture grid model, and three-dimensional small-scale fracture grid model obtained by using this solution. Among them, the conventional small-scale fracture characterization method can be mainly summarized as including the following steps:

[0120] T001: Input the pre-stack gather data in the OVT domain, as Figure 2 shown, and conduct multi-parameter analysis of the pre-stack seismic gathers. Conduct noise analysis on the original seismic data;

[0121] T002: For the problem of near-offset multiple interference in the study area, conduct AVO fitting compensation processing on the pre-stack gather data in the OVT domain, specifically including establishing a cubic fitting equation and energy compensation for the target gather, so as to separate the near-offset multiple interference noise and effective signals.

[0122] T003: Calculate the connectivity CF attribute volume for the seismic data volume after removing multiples. Through multi-parameter comparative analysis, including different incident angles, azimuth angles, and frequency information, obtain the optimal connectivity CF attribute volume. Further, calculate the normal illumination data in the time domain for the optimal connectivity attribute volume.

[0123] T004: Establish an azimuth guidance field for fracture density and fracture azimuth, and further establish a small-scale discrete fracture grid model using the azimuth guidance field to determine the small-scale fracture development area.

[0124] From Figure 6 and Figure 7 By comparison, it can be seen that the small-scale fracture system in this solution is depicted more precisely. Through Figure 8 , Figure 9 and Figure 10 By comparison, it can be seen that the three-dimensional dynamic small-scale fracture grid model in this solution can better characterize the fracture development area and its spatial distribution characteristics.

[0125] P006: Perform linear regression on fracture density and fracture azimuth to determine the first-level fracture development area in the cross-plot.

[0126] Among them, the specific steps of the linear regression analysis can be described as follows:

[0127] L = λX1 2 + δX2 + Z (15)

[0128] In formula (15), L is the regression equation function of the normalized fracture density and fracture azimuth, X1 is the fracture density term, X2 is the fracture azimuth term, λ is the fracture density coefficient, δ is the isotropic coefficient, and Z is a constant.

[0129] From Figure 11 it can be seen that the vertical axis represents fracture density, the horizontal axis represents fracture azimuth, and the blue scatter plot has good linear characteristics. Therefore, the blue scatter plot area is divided into the first-class fracture development area.

[0130] P007: Determine the range and law of the small-scale fracture development area in combination with the planar distribution characteristics of the fracture development area in the dynamic small-scale fracture grid model.

[0131] On the basis of comprehensively analyzing the results of the linear regression analysis of the fracture development area and combining the planar distribution characteristics of the first-class fracture development area in the dynamic small-scale grid model, the range and law of the small-scale fracture development area are basically determined.

[0132] Figure 11 is the linear regression analysis of the fracture development area, Figure 12 is the planar distribution map of the first-class fracture development area of the dynamic small-scale grid model, Figure 11 the first-class fracture development area represented by the blue scatter plot in Figure 12 the red range in also represents the first-class fracture development area. Finally, the range of the small-scale fracture development area in the study area can be determined.

[0133] Furthermore, based on the above method, an embodiment of the present invention further provides a small-scale fracture development area characterization system based on a high-precision velocity model, including: a data acquisition module, a data optimization module, a velocity model acquisition module, a data conversion module, a target model establishment module, and a region characterization module, where

[0134] The data acquisition module is used to acquire pre-stack OVT domain seismic trace gather data and perform noise analysis;

[0135] The data optimization module is used for AVO fitting compensation gather optimization processing, and the AVO fitting compensation gather optimization processing includes fitting a cubic polynomial, removing noise, and energy compensation for the target gather;

[0136] The velocity model acquisition module is used to correct the seismic layer velocity function of the target area using well logging data, obtain layer velocity information using the layer velocity function, and establish a high-precision velocity model;

[0137] The data conversion module is used to obtain time-domain adaptive normal illumination data using a connectivity attribute volume based on multi-parameter optimization, and convert the time-domain adaptive normal illumination data into depth-domain adaptive normal illumination data using the high-precision velocity model;

[0138] The target model establishment deep learning module is used to establish an azimuth guidance field using fracture azimuth and fracture density, perform layer-by-layer correction of the azimuth guidance field in the neural network unit using the azimuth guidance fields of different wells, establish an initial small-scale fracture grid model for multiple wells using the corrected azimuth guidance field and fracture density, and further perform model weight assignment, screening of the optimal weight value, and high-density grid connection on the initial small-scale fracture grid model for multiple wells to obtain a dynamic small-scale fracture grid model, and the dynamic small-scale fracture grid model is used to characterize the fracture development area and spatial distribution characteristics;

[0139] The region characterization module is used to determine the fracture development area based on the small-scale fracture density and azimuth and based on the dynamic small-scale fracture grid model to characterize the small-scale fracture development area.

[0140] Unless otherwise specifically stated, the relative steps, numerical expressions, and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0141] Each embodiment in this specification is described in a progressive manner. The key points of each embodiment are the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0142] The units and method steps of the examples described in combination with the embodiments disclosed in this document can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those of ordinary skill in the art can use different methods to implement the described functions for each specific application, but such implementation is not considered to exceed the scope of the present invention.

[0143] Those of ordinary skill in the art can understand that all or part of the steps in the above methods can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a magnetic disk, or an optical disc, etc. Optionally, all or part of the steps of the above embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module / unit in the above embodiments can be implemented in the form of hardware or in the form of a software functional module. The present invention is not limited to any specific form of the combination of hardware and software.

[0144] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present invention, used to illustrate the technical solutions of the present invention, and are not intended to limit them. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions recorded in the foregoing embodiments or easily conceive of changes, or perform equivalent replacements for some of the technical features; and these modifications, changes, or replacements 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, and should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for characterizing small-scale fracture development zones based on a high-precision velocity model, characterized in that, Including: Obtaining pre-stack OVT domain seismic gather data and performing noise analysis; AVO fitting compensation gather optimization processing, where the AVO fitting compensation gather optimization processing includes fitting a cubic polynomial, removing noise, and compensating the energy of the target gather; Calibrating the seismic layer velocity function of the target area using well logging data, obtaining layer velocity information using the layer velocity function to establish a high-precision velocity model; Obtaining time-domain adaptive normal illumination data using the connectivity attribute volume based on multi-parameter optimization, and converting the time-domain adaptive normal illumination data to depth-domain adaptive normal illumination data using the high-precision velocity model; Establishing an azimuth guidance field using the fracture azimuth and fracture density, correcting the azimuth guidance field in the neural network unit layer by layer using the azimuth guidance fields of different wells, establishing an initial small-scale fracture grid model using the corrected azimuth guidance field and fracture density, and sequentially performing model weight assignment, screening the optimal weight value, and high-density grid connection for the initial small-scale fracture grid model to obtain a dynamic small-scale fracture grid model, where the dynamic small-scale fracture grid model is used to characterize the fracture development area and spatial distribution characteristics; Determining the fracture development area based on the small-scale fracture density and azimuth and based on the dynamic small-scale fracture grid model to characterize the small-scale fracture development area.

2. The method for characterizing small-scale fracture development zones based on a high-precision velocity model according to claim 1, wherein, The fitting cubic polynomial in the optimization process of AVO fitting compensation gather is expressed as T = P + Gx + P*Gx 2 + Cx 3 , where T is the reflection coefficient, x is the incident angle, P is the intercept, G is the gradient, P*G is the AVO indicator factor, and C is the curvature.

3. The method for characterizing a small-scale fracture development area based on a high-precision velocity model according to claim 1, wherein Calibrating the seismic layer velocity function of the target area using well logging data, including: Extracting the layer velocity data at the target well point, performing correlation analysis and velocity correction on the seismic layer velocity and the layer velocity at the well point to obtain the corrected seismic layer velocity data; Sequentially performing thinning, interpolation, and smoothing processing on the corrected seismic layer velocity data to obtain a high-precision velocity model.

4. The method for characterizing small-scale fracture development zones based on a high-precision velocity model according to claim 3, wherein, Performing correlation analysis and velocity correction on the seismic layer velocity and the layer velocity at the well point, including: Analyzing the cross-correlation between the seismic layer velocity and the layer velocity at the well point using a preset cross-correlation analysis function to obtain the difference between the seismic layer velocity and the well point layer velocity of different strata; Constructing a correction function based on the difference between the seismic layer velocity and the well point layer velocity of different strata and using the layer velocity value of the well and the seismic layer velocity value to correct the seismic layer velocity value using the correction function; Sequentially performing three-dimensional meshing processing on the corrected seismic layer velocity to obtain a high-precision velocity model through thinning, interpolation, and smoothing processing.

5. The method for characterizing small-scale fracture development zones based on a high-precision velocity model according to claim 1, wherein The process of converting the adaptive normal illumination data in the time domain to the adaptive normal illumination data in the depth domain using a high-precision velocity model is expressed as: C F (h) = V vol ·ΔγC F (t), where C F (t) is the adaptive normal illumination data in the time domain, Δγ is the adaptive factor, and C F (h) is the adaptive normal illumination data in the depth domain, and Vvol is the seismic layer velocity function in the high-precision velocity model.

6. The method for characterizing small-scale fracture development zones based on a high-precision velocity model according to claim 1, wherein Obtaining an initial fracture grid model and a dynamic small-scale fracture grid model using the azimuth guidance field and fracture density optimized layer by layer based on neural network units, including: Constructing a fracture density function and a fracture azimuth function based on the fracture circular discrete space function, fracture circular space radius, and vertical and horizontal coordinates; Normalizing the fracture density function according to the maximum and minimum values of the fracture density, performing high-density meshing processing on the normalized fracture density and fracture azimuth, and obtaining an azimuth guidance field; Establishing a neural network unit according to the azimuth guidance field function, correcting the azimuth guidance field in the neural network unit layer by layer using the azimuth guidance field functions of different wells, and obtaining an initial small-scale fracture grid model for the azimuth guidance field and fracture density corrected layer by layer; According to the initial small-scale fracture grid model, model weight distribution, screening of the optimal weight value, and high-density grid connection are carried out in sequence, and a dynamic small-scale fracture grid model suitable for the study area is output.

7. The method for characterizing small-scale fracture development zones based on a high-precision velocity model according to claim 1 or 6, characterized in that Determine the fracture development area based on the small-scale fracture density and orientation, including: Perform linear regression on the fracture density and fracture orientation, and determine the range and law of the small-scale fracture development area in combination with the plane distribution characteristics of the fracture development area in the dynamic small-scale fracture grid model.

8. A small-scale fracture development area characterization system based on a high-precision velocity model, characterized in that, Including: a data acquisition module, a data optimization module, a velocity model acquisition module, a data conversion module, a deep learning module for establishing a target model, and a regional characterization module, where The data acquisition module is used to acquire pre-stack OVT domain seismic trace gather data and perform noise analysis; The data optimization module is used for AVO fitting compensation gather optimization processing, and the AVO fitting compensation gather optimization processing includes fitting a cubic polynomial, removing noise, and compensating the energy of the target gather; The velocity model acquisition module is used to correct the seismic layer velocity function of the target area using well logging data, obtain layer velocity information using the layer velocity function, and establish a high-precision velocity model; The data conversion module is used to obtain time-domain adaptive normal illumination data using a connectivity attribute volume based on multi-parameter optimization, and convert the time-domain adaptive normal illumination data into depth-domain adaptive normal illumination data using the high-precision velocity model; The deep learning module for establishing a target model is used to establish an azimuth guidance field using the fracture orientation and fracture density, correct the azimuth guidance field in the neural network unit layer by layer using the azimuth guidance fields of different wells, establish an initial small-scale fracture grid model using the corrected azimuth guidance field and fracture density, and perform model weight distribution, screening of the optimal weight value, and high-density grid connection on the initial small-scale fracture grid model in sequence to obtain a dynamic small-scale fracture grid model, and the dynamic small-scale fracture grid model is used to characterize the fracture development area and spatial distribution characteristics; The regional characterization module is used to determine the fracture development area based on the small-scale fracture density and fracture orientation and based on the dynamic small-scale fracture grid model to characterize the small-scale fracture development area.

9. An electronic device, characterized in that, Including: At least one processor, and a memory coupled to the at least one processor; Wherein, the memory stores a computer program, and the computer program can be executed by the at least one processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium, and when the computer program is executed, it can implement the method according to any one of claims 1 to 7.

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