Irregular seismic data shale reservoir fracture density prediction method, medium and device

Abstract

The invention provides an irregular seismic data shale reservoir fracture density prediction method, medium and device, and the method comprises the steps: carrying out the seismic data quality analysis of seismic data needed by fracture prediction, and analyzing and judging whether the seismic data coverage times and the shot point uniformity degree meet the demands or not; if the requirements are not met, five-dimensional seismic data regularization of multiple space sampling points is carried out, and reconstructed seismic data are obtained; post-stack seismic curvature calculation is carried out on the basis of reconstructed seismic data, basic geological fracture morphological characteristics are further described, and effective constraints are provided for subsequent fracture prediction; and based on the post-stack seismic curvature calculation result and the ground fracture data, the pre-stack amplitude anisotropic fracture density calculation result is constrained, the geological form is strengthened, and a final shale fracture density result is obtained. The problems that existing partial irregular seismic data cannot meet the fracture density prediction requirement, and the geological regularity of the pre-stack fracture density prediction result is not high can be solved.

Description

Technical Field

[0001] This invention relates to the field of natural fracture prediction technology in geophysical exploration of oil and gas, and more specifically, to a method, medium, and device for predicting fracture density in shale reservoirs based on irregular seismic data. Background Technology

[0002] Pre-stack seismic data fracture prediction mainly utilizes azimuth anisotropy parameter inversion technology. This involves using azimuth anisotropy characteristics exhibited by seismic waves passing through fractured media to predict fracture location and density using azimuth anisotropy fracture detection technology. Based on relevant domestic and international research on fracture prediction, the seismic data required for fracture prediction should meet three conditions:

[0003] (1) The earthquake data meets the characteristics of wide azimuth;

[0004] (2) Most of the shot points and receiver points in the data should meet the conditions of regular and uniform distribution;

[0005] (3) The number of seismic coverages should be roughly the same for different shot-receiver distances.

[0006] Chinese invention patent CN109143351A discloses a method for inverting pre-stack anisotropic characteristic parameters. This method includes: obtaining reflection coefficients based on pre-stack seismic gathers; performing simultaneous pre-stack inversion on pre-stack seismic data to obtain the P-wave velocity, S-wave velocity, and density of the upper and lower media, respectively; and inverting the anisotropic parameters of the upper and lower media based on the reflection coefficients, P-wave velocities, S-wave velocities, and densities of the upper and lower media according to the anisotropic parameter inversion formula to obtain the anisotropic parameters of the upper and lower media. A transformation is made to the Ruger formula to reflect the potential relationship between formation anisotropic parameters and fracture development.

[0007] The existing technology has at least the following problems:

[0008] (1) The seismic data required for crack prediction mostly do not meet the condition of uniform distribution of shot and receiver points, and the number of times covered by different shot-receiver distances is inconsistent.

[0009] (2) Most of the existing pre-stack fracture prediction results lack obvious geological patterns and are highly random. Summary of the Invention

[0010] The present invention aims to provide a method, medium and device for predicting fracture density in shale reservoirs using irregular seismic data, in order to solve the problem that some existing irregular seismic data do not meet the requirements for fracture density prediction, and the geological regularity of pre-stack fracture density prediction results is not strong.

[0011] This invention provides a method for predicting fracture density in shale reservoirs using irregular seismic data, comprising the following steps:

[0012] Step 101: Conduct seismic data quality analysis on the seismic data required for crack prediction, and analyze and determine whether the seismic data coverage and shot-receiver point uniformity meet the requirements.

[0013] Step 102: If the requirements are not met, perform five-dimensional seismic data regularization of multi-spatial sampling points to obtain reconstructed seismic data;

[0014] Step 103: Based on the reconstructed seismic data, post-stack seismic curvature calculation is carried out to further characterize the basic geological fault morphology and provide effective constraints for subsequent fracture prediction.

[0015] Step 104: Based on the post-stack seismic curvature calculation results and surface fracture data, the pre-stack amplitude anisotropic fracture density calculation results are constrained to enhance the geological morphology and obtain the final shale fracture density results.

[0016] Furthermore, in step 102, the five-dimensional seismic data regularization of multi-spatial sampling points refers to the Fourier interpolation calculation of multi-spatial sampling points under different dimensions of seismic data.

[0017] Furthermore, for a time-frequency domain slice y(x) of a data volume, where x represents some spatial variable information, assuming there are A points, the wavenumber spectrum is Y(k), and there are B wavenumber coefficients. The specific formula is as follows:

[0018]

[0019] Where i is the imaginary unit, k is the wavenumber, N is the number of sampling points, and Θ A×B This is the frequency wavenumber domain result for this slice, where θ is a single wavenumber component;

[0020] Combining equation (1), the forward and inverse Fourier transforms can be rewritten as follows:

[0021] f = ΘF, F = Θ H f(2)

[0022] Where F is the frequency domain function, f is the time domain function, and H is the conjugate function. The specific calculation process for frequency interpolation is as follows:

[0023] F i+1 =F i -F i (s)Θ H θ s (3)

[0024] Where s is the largest element of the inner product, calculated using equation (4):

[0025]

[0026] In the formula, x a For data sampling points, and i = 1, 2…d; d can be adjusted and modified as needed; ω is the frequency. For x a The inner product interpolation result at point Δx is the data point interval;

[0027] The above four formulas are used to interpolate the longitudinal, lateral, offset, and azimuth spatial information of seismic data, ultimately achieving the regularization of five-dimensional seismic data from multiple spatial sampling points.

[0028] Furthermore, in step 103, the formula for calculating the post-stack seismic curvature is as follows:

[0029]

[0030] Among them, K x d represents the curvature along the x-direction. x The viewing angle is the tilt angle.

[0031] Furthermore, in step 104, the surface fracture data is the fracture density data calculated from imaging logging data; the pre-stack amplitude anisotropic fracture density is the fracture density calculation guided by AVAZ theory.

[0032] Furthermore, the AVAZ theoretical formula is as follows:

[0033]

[0034] Among them, U iso U is the rate of change of isotropic amplitude with azimuth angle. ani Let AZ be the rate of change of anisotropic amplitude with azimuth angle, and AZ be the azimuth angle from the excitation point to the receiver. θ is the azimuth angle of the crack direction, P is the amplitude of the reflected wave when the P wave is perpendicularly incident, and θ is the incident angle.

[0035] When the angle of incidence remains constant, it simplifies to:

[0036] R(θ,AZ)=A+B(AZ)sin 2 θ (7)

[0037] In equation (7), A reflects the amplitude change related to the shot-receiver distance, B(AZ) reflects the amplitude change with the shot-receiver azimuth angle, and B / A is a parameter representing the crack density.

[0038] Furthermore, in step 104, the calculation results of pre-stack amplitude anisotropic fracture density are constrained based on the post-stack seismic curvature calculation results and surface fracture data, including:

[0039] (1) In areas with large seismic curvature, the calculation results of pre-stack amplitude anisotropic fracture density are emphasized. In areas with small seismic curvature, the calculation results of pre-stack amplitude anisotropic fracture density are appropriately reduced. Finally, the preliminary corrected shale fracture density results are obtained.

[0040] (2) The shale fracture density results around the well were adjusted using the fracture density calculation results of the well imaging logging to obtain the final shale fracture density results.

[0041] The present invention also provides a computer terminal storage medium storing computer terminal executable instructions, which are used to execute the above-described method for predicting fracture density in shale reservoirs based on irregular seismic data.

[0042] The present invention also provides a computing device, comprising:

[0043] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method for predicting fracture density in shale reservoirs based on irregular seismic data.

[0044] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0045] This invention first analyzes the seismic data required for fracture prediction, and reconstructs irregular seismic data based on the number of seismic data coverages and the uniformity of shot-receiver points. Second, it calculates post-stack seismic curvature volumes based on the reconstructed seismic data to further characterize basic geological features. Finally, it uses the post-stack seismic curvature volumes and surface fracture data to constrain and correct the pre-stack amplitude anisotropic fracture density calculation results, obtaining the final shale fracture density result. This invention solves the problems that some existing irregular seismic data do not meet the requirements for fracture density prediction, and that the geological regularity of pre-stack fracture density prediction results is weak. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the method for predicting fracture density in shale reservoirs using irregular seismic data, as described in an embodiment of the present invention.

[0048] Figure 2aThis is a diagram showing the effect of single-shot seismic data reconstruction before reconstruction in the five-dimensional seismic data regularization method based on multiple spatial sampling points in an embodiment of the present invention.

[0049] Figure 2b This is a diagram showing the effect of reconstructing single-shot seismic data in the five-dimensional seismic data regularization method based on multiple spatial sampling points in an embodiment of the present invention.

[0050] Figure 3a This is a diagram showing the effect of reconstructing superimposed seismic data in the five-dimensional seismic data regularization method based on multiple spatial sampling points in an embodiment of the present invention.

[0051] Figure 3b This is a diagram showing the effect of reconstructing superimposed seismic data in the five-dimensional seismic data regularization method based on multiple spatial sampling points in an embodiment of the present invention.

[0052] Figure 4 This is a rendering of the predicted fracture density of shale reservoirs based on irregular seismic data in an embodiment of the present invention. The larger the color scale value, the greater the fracture density and the more developed the fractures. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0054] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0055] Example

[0056] like Figure 1 As shown, this embodiment proposes a method for predicting fracture density in shale reservoirs using irregular seismic data. First, the seismic data required for fracture prediction is analyzed. Then, based on the seismic data coverage frequency and the uniformity of shot-receiver points, the irregular seismic data is reconstructed. For example... Figure 2a , Figure 2b As shown; secondly, based on the reconstructed seismic data, post-stack seismic curvature volume calculations are performed to further characterize the basic geological morphological features, such as... Figure 3a , Figure 3bAs shown; finally, the pre-stack amplitude anisotropic fracture density calculation results are constrained and corrected using post-stack seismic curvature body and surface fracture data to obtain the final shale fracture density results, as shown. Figure 4 As shown. The method includes the following steps:

[0057] Step 101: Conduct seismic data quality analysis on the seismic data required for crack prediction, and analyze and determine whether the seismic data coverage and shot-receiver point uniformity meet the requirements.

[0058] Step 102: If the requirements are not met, perform five-dimensional seismic data regularization of multi-spatial sampling points to obtain reconstructed seismic data;

[0059] In step 102, the five-dimensional seismic data regularization of multi-spatial sampling points refers to the Fourier interpolation calculation of multi-spatial sampling points under different dimensions of seismic data.

[0060] For a time-frequency domain slice y(x) of a data volume, where x represents a spatial variable, assuming there are A points, the wavenumber spectrum is Y(k), and there are B wavenumber coefficients. The specific formula is as follows:

[0061]

[0062] Where i is the imaginary unit, k is the wavenumber, N is the number of sampling points, and Θ A×B This is the frequency wavenumber domain result for this slice, where θ is a single wavenumber component;

[0063] Combining equation (1), the forward and inverse Fourier transforms can be rewritten as follows:

[0064] f = ΘF, F = Θ H f(2)

[0065] Where F is the frequency domain function, f is the time domain function, and H is the conjugate function. The specific calculation process for frequency interpolation is as follows:

[0066] F i+1 =F i -F i (s)Θ H θ s (3)

[0067] Where s is the largest element of the inner product, calculated using equation (4):

[0068]

[0069] In the formula, x a For data sampling points, and i = 1, 2…d; d can be adjusted and modified as needed; ω is the frequency. For x a The inner product interpolation result at the location is given, where Δx is the data point interval; this method can fully consider the information patterns between multiple spatial sampling points in the same dimension.

[0070] Since seismic data is generally regular in terms of time sampling intervals, the above four formulas are used to interpolate the longitudinal, lateral, offset, and azimuth spatial information of seismic data, ultimately realizing the regularization of five-dimensional seismic data from multiple spatial sampling points.

[0071] Step 103: Based on the reconstructed seismic data, post-stack seismic curvature calculation is carried out to further characterize the basic geological fault morphology and provide effective constraints for subsequent fracture prediction.

[0072] In step 103, curvature is an index characterizing the geometric features of strata. The calculation formula is as follows:

[0073]

[0074] Among them, K x d represents the curvature along the x-direction. x The viewing angle is the tilt angle.

[0075] Step 104: Based on the post-stack seismic curvature calculation results and surface fracture data, the pre-stack amplitude anisotropic fracture density calculation results are constrained to enhance the geological morphology and obtain the final shale fracture density results.

[0076] In step 104, the surface fracture data generally refers to the fracture density data (the number of fractures identified per unit fracture area) calculated from imaging logging data. Pre-stack amplitude anisotropic fracture density is generally calculated based on AVAZ theory.

[0077] The basic theoretical formula for AVAZ is as follows:

[0078]

[0079] In the above formula, U iso U is the rate of change of isotropic amplitude with azimuth angle. ani Let AZ be the rate of change of anisotropic amplitude with azimuth angle, and AZ be the azimuth angle from the excitation point to the receiver. Let θ be the azimuth angle of the crack direction, P be the amplitude of the reflected P-wave when it is incident perpendicularly, and θ be the incident angle. When the incident angle remains constant, it can be simplified to:

[0080] R(θ,AZ)=A+B(AZ)sin 2 θ (7)

[0081] In equation (7), A reflects the amplitude change related to the shot-receiver distance, B(AZ) reflects the amplitude change with the shot-receiver azimuth angle, and B / A is a parameter representing the crack density.

[0082] Constraints are applied to the pre-stack amplitude anisotropic fracture density calculation results based on seismic curvature results and surface fracture data, including:

[0083] (1) Where the earthquake curvature value is large, the calculation results of pre-stack amplitude anisotropic fracture density are emphasized. Where the earthquake curvature is small, the calculation results of pre-stack amplitude anisotropic fracture density are appropriately reduced. Finally, the preliminary corrected shale fracture density results are obtained.

[0084] (2) The shale fracture density results around the well were adjusted using the fracture density calculation results of the well imaging logging to obtain the final shale fracture density results.

[0085] Furthermore, in some embodiments, a computer terminal storage medium is proposed, storing computer terminal executable instructions for executing the irregular seismic data shale reservoir fracture density prediction method as described in the preceding embodiments. Examples of computer storage media include magnetic storage media (e.g., floppy disks, hard disks, etc.), optical recording media (e.g., CD-ROMs, DVDs, etc.), or memory such as memory cards, ROMs, or RAMs. The computer storage medium can also be distributed across a network-connected computer system, for example, as an application store.

[0086] Furthermore, in some embodiments, a computing device is proposed, comprising: at least one processor; and a memory communicatively connected to said at least one processor; wherein the memory stores instructions executable by said at least one processor, said instructions being executed by said at least one processor to enable said at least one processor to perform the irregular seismic data shale reservoir fracture density prediction method as described in the foregoing embodiments. Examples of computing devices include PCs, tablets, smartphones, or PDAs, etc.

[0087] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An irregular seismic data shale reservoir fracture density prediction method, characterized in that, It comprises the following steps: Step 101, seismic data quality analysis is carried out on the seismic data required for fracture prediction, and whether the fold number and the uniformity of shot-receiver are satisfied is analyzed and judged; Step 102, if the requirement is not met, five-dimensional seismic data regularization of multiple spatial sampling points is carried out to obtain reconstructed seismic data; Step 103, based on the reconstructed seismic data, post-stack seismic curvature calculation is carried out to further depict the basic geological fracture morphology characteristics and provide effective constraints for subsequent fracture prediction; Step 104, based on the post-stack seismic curvature calculation results and the well fracture data, the pre-stack amplitude anisotropy fracture density calculation results are constrained to strengthen the geological morphology, and the final shale fracture density results are obtained.

2. The method of irregular seismic data shale reservoir fracture density prediction of claim 1, wherein, In step 102, the five-dimensional seismic data regularization of multiple spatial sampling points refers to the Fourier interpolation calculation of multiple spatial sampling points in different dimensions of seismic data.

3. The method of irregular seismic data shale reservoir fracture density prediction of claim 2, wherein, For a time-frequency domain slice y(x) of a data body, x is a certain spatial variable information, assuming that there are A point positions, at this time the wave number spectrum is Y(k), there are B wave number coefficients, and the specific formula is as follows: where i is the imaginary unit, k is the wave number, N is the number of sampling points, Θ A×B is the frequency-wavenumber domain result for the slice, and θ is a single wavenumber component; Combined with formula (1), the forward and inverse transforms of Fourier are rewritten as the following formulas: f = ΘF, F = Θ H f (2) Wherein, F is a frequency domain function, f is a time domain function, and H is a conjugate function. The specific calculation process of frequency interpolation is represented as: F i+1 = F i - F i (s) Θ H θ s (3) Wherein, s is the maximum element of the inner product, which is calculated by formula (4): where x a is the data sampling point, and d is adjusted and modified as needed; ω is the frequency, is the inner product interpolation result at x a , and Δx is the data point interval. Through the above four formulas, the interpolation of the vertical, horizontal, offset, and azimuth spatial information of the seismic data is completed, and finally the five-dimensional seismic data regularization of multiple spatial sampling points is realized.

4. The method of irregular seismic data shale reservoir fracture density prediction of claim 1, wherein, In step 103, the post-stack seismic curvature calculation formula is as follows: where K x represents the curvature along the x-direction, d x is the apparent inclination angle.

5. The method of irregular seismic data shale reservoir fracture density prediction of claim 1, wherein, In step 104, the well fracture data is the fracture density data calculated from the imaging logging data; and the pre-stack amplitude anisotropy fracture density is the fracture density calculation under the guidance of AVAZ theory.

6. The method of irregular seismic data shale reservoir fracture density prediction of claim 5, wherein, The AVAZ theory formula is as follows: where U iso is the rate of change of the isotropic amplitude with azimuth, U ani is the rate of change of the anisotropic amplitude with azimuth, AZ is the azimuth from the excitation to the reception, is the azimuth of the fracture strike, P is the reflected wave amplitude of the P-wave at normal incidence, and θ is the angle of incidence. When the incidence angle is constant, it is simplified as: R(θ, AZ) = A + B(AZ)sin 2 θ (7) In formula (7), A reflects the amplitude change related to offset, and B(AZ) reflects the change of amplitude with shot azimuth; B / A is a parameter representing fracture density.

7. The method of irregular seismic data shale reservoir fracture density prediction of claim 6, wherein, In step 104, based on the post-stack seismic curvature calculation results and the well fracture data, the pre-stack amplitude anisotropy fracture density calculation results are constrained, which comprises: (1) In the place where the seismic curvature value is large, the pre-stack amplitude anisotropy fracture density calculation results are highlighted, and in the place where the seismic curvature is small, the pre-stack amplitude anisotropy fracture density calculation results are appropriately reduced, and finally the preliminary corrected shale fracture density results are obtained; (2) The well imaging logging fracture density calculation results are used to adjust the shale fracture density results around the well, and finally the final shale fracture density results are obtained.

8. A computer terminal storage medium storing computer terminal executable instructions, characterized in that, The computer terminal executable instructions are used to execute the irregular seismic data shale reservoir fracture density prediction method in any one of claims 1-7.

9. A computing device, comprising: It comprises: At least one processor; and a memory communicatively connected with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for irregular seismic data shale reservoir fracture density prediction according to any one of claims 1-7.

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