A Progressive Fine Prediction Method for Reservoirs Based on Wide-Azimuth Seismic Change Rate Attributes

Abstract

The present invention relates to a progressive fine reservoir prediction method for wide-azimuth seismic change rate attributes in the technical field of reservoir prediction. It analyzes the parameters of the seismic acquisition observation system, defines the effective offset, and divides the seismic data by azimuth angle; within the defined offset, it generates the stacked seismic amplitude of the full-azimuth near-offset gather and the stacked seismic amplitude of the full-azimuth far-offset gather; it calculates and outputs the amplitude anisotropy value using the divided effective-offset azimuth gathers, and uses this result as the seismic prediction result of the sedimentary facies belt; it takes the difference between the stacked seismic amplitude of the full-azimuth far-offset gather and the stacked seismic amplitude of the full-azimuth near-offset gather to obtain the offset amplitude difference attribute and outputs the result, which is used as the prediction of the thickness of the reservoir prediction work area; it uses the gradient attribute change rate of the effective-offset azimuth gathers and outputs it as the prediction of the fluid.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir prediction, and in particular to a progressive fine reservoir prediction method for wide-azimuth seismic change rate attributes. Background Technique

[0002] Reservoir prediction is an important objective of seismic data interpretation. The accuracy of reservoir prediction directly affects the well location deployment and comprehensive evaluation in oil and gas field exploration. At present, there are various reservoir prediction methods. However, in terms of principle, they are mainly divided into two categories: one is the post-stack seismic prediction technology represented by conventional seismic attributes. This method mainly conducts formation structure interpretation by calibrating the synthetic seismogram of seismic data, determining the seismic reflection event of the target layer, and extracting attributes such as amplitude, frequency, and phase of the formation using horizons and time slices, or calculating extended attributes on such seismic attributes, such as amplitude-frequency ratio, etc. The other is the pre-stack seismic attribute prediction represented by P-wave and S-wave velocities and AVO attributes. This method analyzes the seismic response characteristics of geological bodies such as formations and fluids in the pre-stack gather, utilizes the difference in the propagation velocities of P-waves and S-waves in rocks, uses the stacked data of near, medium, and far gathers, and conducts elastic parameter prediction of pre-stack P-wave and S-wave velocity ratios, Young's modulus, and Poisson's ratio using the simplified Zoeppritz equation, or conducts seismic prediction of AVO attributes (intercept, gradient parameters) based on the change rate of the amplitude of the reservoir and fluid with the incident angle in the pre-stack gather.

[0003] With the continuous deepening of oil and gas field exploration, the targets of underground geological bodies are more complex, and higher requirements are also put forward for the accuracy of reservoir prediction. Although the macroscopic laws of seismic sedimentation can be identified through post-stack seismic attributes of P-wave impedance, due to the similar comprehensive velocities of different combinations and different lithologies, it is difficult to refine the description of reservoir facies zone boundaries, lithologic trap boundaries, and fluid boundaries. In terms of reservoir thickness prediction, the traditional method is to use the Widess principle between sand thickness and apparent amplitude, that is, within the tuning thickness, the apparent amplitude and sand thickness show a monotonically increasing relationship, and peak and amplitude and frequency are two of the most basic seismic attributes. When the formation thickness is less than one-fourth of the wavelength, these two seismic attributes are generally used to estimate the formation thickness. However, the non-linear relationship between these two seismic attributes and the formation thickness reduces the quantitative prediction accuracy of sand body thickness.

[0004] Pre-stack attributes are more applicable to complex geological structure areas compared to post-stack attributes. Pre-stack seismic attribute prediction not only considers the amplitude information of seismic waves, but also includes the prediction information of elastic parameters such as the longitudinal and transverse wave velocity ratio, Young's modulus, and Poisson's ratio. Compared with post-stack seismic attributes, the comprehensive application of these elastic information is more helpful to solve the post-stack influence of underground sedimentation and facies belt heterogeneity with the same phase. Using shear waves for prediction can provide a more comprehensive understanding of the underground geological structure. However, in terms of application, the results of pre-stack seismic attribute prediction largely depend on the accuracy of the geological model. If the geological model is inaccurate, the prediction results may also be unreliable. In addition, in some special geological environments, such as strongly anisotropic formations, traditional pre-stack seismic attribute prediction methods may no longer be applicable.

[0005] With the continuous progress of seismic exploration acquisition technology and processing technology, seismic data has gradually expanded from traditional 3D seismic data to "4D" information of 3D seismic + offset domain and "5D" information of 3D seismic + offset domain + azimuth domain. 5D seismic data has the advantages of OVT processing. Therefore, in summary, in the face of more dimensional seismic data, there is currently no good integrated technical process for using OVT seismic data to achieve reservoir facies belt division, reservoir thickness prediction, and fluid detection. Summary of the Invention

[0006] To solve the above problems, the present invention provides a progressive fine reservoir prediction method for wide-azimuth seismic change rate attributes.

[0007] Step 1: Collect OVT seismic data, interpreted horizon data, and seismic acquisition work area observation system parameters in the reservoir prediction work area.

[0008] Step 2: Analyze the seismic acquisition observation system parameters, determine the maximum north-south distance and maximum east-west distance of the shot-receiver points in the seismic acquisition work area, and take the minimum value of the two as the effective offset L.

[0009] Step 3: Limit the effective offset L and divide the OVT seismic data by azimuth.

[0010] Step 4: Generate the stacked seismic amplitude of the full azimuth near-offset gather and the stacked seismic amplitude of the full azimuth far-offset gather within the limited effective offset L.

[0011] Step 5: Calculate and output the amplitude anisotropy value using the divided effective offset azimuth gathers, and use this result as the seismic prediction result of the sedimentary facies belt.

[0012] Step 6: Use the difference between the stacked seismic amplitude of the full azimuth far-offset gather and the stacked seismic amplitude of the full azimuth near-offset gather to obtain the offset amplitude difference attribute and output the result, which is used as the prediction of the thickness of the reservoir prediction work area.

[0013] Step 7: According to the azimuth division method in Step 3, calculate the gradient attribute change rate using the effective offset partial azimuth gather, and output it as the prediction result of the fluid.

[0014] In the preferred embodiment, in Step 3, the division principle is to divide at least with a sector angle of 30 degrees, and the number of sectors shall not be less than 6. The azimuth is divided as 0 - 30, 30 - 60, 60 - 90, 90 - 120, 120 - 150, 150 - 180.

[0015] In the preferred embodiment, in Step 4, the near offset starts from 0m, the end offset is not greater than 1 / 2L, the far offset is not less than 1 / 2L, and the maximum is L.

[0016] In the preferred embodiment, in Step 5, use the divided effective offset partial azimuth seismic data to calculate the amplitude change rate value:

[0017]

[0018] );

[0019] In the formula, is the amplitude change rate, is the maximum amplitude value in the i-th azimuth, is the minimum amplitude value in the i-th azimuth, and i is the azimuth angle serial number.

[0020] In the preferred embodiment, in Step 7, according to the azimuth division method in Step 3, calculate the gradient attribute change rate G(i) using the effective offset partial azimuth gather:

[0021] ;

[0022] ;

[0023] In the formula, i = 1, 2, 3, 4, 5, 6; is the maximum effective offset amplitude value of the i-th azimuth angle, is the minimum offset amplitude value of the i-th azimuth angle.

[0024] In the preferred embodiment, ( ) are 6 azimuths divided and generated as 0 - 30, 30 - 60, 60 - 90, 90 - 120, 120 - 150, 150 - 180.

[0025] Compared with the prior art, the present invention has the following beneficial technical effects:

[0026] The present invention utilizes wide-azimuth seismic gather data. Compared with conventional 3D seismic data and prestack offset-domain seismic data, the wide-azimuth seismic gather data can not only solve the seismic prediction of refining reservoir boundaries using elastic parameters, but also solve the problem that traditional prestack attributes are no longer applicable in anisotropic formations.

[0027] By analyzing and clarifying the acquisition geometry of seismic data and determining the effective offset of OVT seismic data, the present invention can ensure the energy consistency of stacked gathers with different azimuths during azimuth division of data, eliminating the multi-solution problem of anisotropy prediction caused by energy differences due to inconsistent coverage.

[0028] A progressive fine reservoir prediction method based on rate-of-change attributes proposed by the present invention can realize the prediction of reservoirs from macroscopic sedimentary distribution to reservoirs and fluids. It achieves layer-by-layer constraint, avoids the multi-solution problem caused by single prediction, and improves the scientificity, rationality, and accuracy of reservoir prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of the technical solution of the present invention.

[0030] Figure 2 is an analysis diagram of the seismic acquisition geometry.

[0031] Figure 3 shows the seismic data divided by azimuth within the effective offset.

[0032] Figure 4 is an all-azimuth offset-stacked seismic section.

[0033] Figure 5 is a planar attribute map of the AvAz facies belt.

[0034] Figure 6 is a planar attribute map of the amplitude difference between far offset and near offset.

[0035] Figure 7 is a planar attribute map of AvoAz fluid. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0037] Progressive fine reservoir prediction method for wide-azimuth seismic change rate attributes of the present invention, collecting basic data such as seismic data and horizon interpretation data of the research work area; clarifying the acquisition geometry of seismic acquisition, determining the effective offset of OVT seismic data; after determining the effective offset, carrying out azimuth division of OVT gathers within the effective offset to generate azimuthal gathers of the effective offset; using OVT seismic data to carry out offset division in the full azimuth domain to generate full azimuth near-offset gathers and full azimuth far-offset gathers; using the divided azimuthal gathers of the effective offset to calculate the amplitude anisotropy value for seismic prediction of sedimentary facies zones; using the generated full azimuth near-offset gathers and full azimuth far-offset gathers to calculate the offset amplitude difference attribute to predict sedimentary thickness; calculating AVO gradient anisotropy using the AVO attribute of the azimuthal gathers of the effective offset.

[0038] As Figure 1 shown, it is a flow chart of the progressive fine reservoir prediction method for wide-azimuth seismic change rate attributes of the present invention, specifically including the following steps:

[0039] Step 1, collect OVT seismic data, interpreted horizon data, and acquisition geometry parameters of the seismic acquisition work area for the reservoir prediction.

[0040] OVT seismic data has offset information and azimuth information, which is conducive to carrying out fine geological body prediction; when studying the reservoir prediction work area, focus on and analyze basic data such as seismic interpretation data of the horizons of underground strata or geological bodies and acquisition geometry parameters of the seismic acquisition work area, mainly referring to the maximum vertical distance and maximum non-vertical distance of seismic data acquisition.

[0041] Step 2, analyze the acquisition geometry parameters of seismic acquisition, determine the maximum north-south distance and maximum east-west distance between shot points in the seismic acquisition work area, and take the minimum value of the two as the effective offset L.

[0042] Through the analysis of the OVT bin of the target horizon, it is considered that the maximum north-south distance between shot points of the OVT bin in the seismic acquisition work area is 3237.5 m, and the maximum east-west distance is 2037.5 m. In order to maintain the consistency of the coverage times of different azimuths and improve the accuracy of the azimuth attribute change rate of seismic data, the minimum value of the two, 2037.5, is used as the effective offset L.

[0043] As Figure 2 shown is the acquisition geometry of the seismic work area. As shown in Table 1 are the acquisition geometry parameters. The acquisition geometry mainly refers to the maximum non-vertical distance of 2037.5 as the effective offset L.

[0044] Table 1 Acquisition geometry parameters

[0045]

[0046] Step 3, define the effective offset L and divide the OVT seismic data by azimuth.

[0047] In the preferred embodiment, the effective offset L is defined as 2037.5 m, and the OVT seismic data is divided by azimuth into 0 - 30, 30 - 60, 60 - 90, 90 - 120, 120 - 150, 150 - 180 to obtain 6 sets of stacked seismic data, such as Figure 3 .

[0048] Step 4, within the defined effective offset L, generate the stacked seismic amplitude of the omnidirectional near-offset gather and the stacked seismic amplitude of the omnidirectional far-offset gather.

[0049] Within the defined offset of 2037.5 m, the stacked seismic data seismic_near of the omnidirectional near-offset gather and the stacked seismic data seismic_far of the omnidirectional far-offset gather are generated according to 0 m - 1018.75 m, 1018.75 m - 2037.5 m, such as Figure 4 .

[0050] Step 5, use the divided effective-offset azimuth gathers to calculate and output the amplitude anisotropy value, and use this result as the seismic prediction result of the sedimentary facies zone.

[0051] Previously, the effective offset was defined in Step 3, and then the seismic data was divided by azimuth into 0 - 30, 30 - 60, 60 - 90, 90 - 120, 120 - 150, 150 - 180 to obtain the effective-offset azimuth gathers.

[0052] Use the divided effective-offset azimuth seismic data to calculate the amplitude change rate value. The specific formula is:

[0053] );

[0054] In the formula, is the amplitude change rate, is the maximum amplitude in the i-th azimuth, is the minimum amplitude in the i-th azimuth, and i is the azimuth angle serial number.

[0055] Calculate and output, and use this result as the sedimentary facies zone result of the study area, such as Figure 5 shown.

[0056] Step 6, use the difference between the stacked seismic amplitude of the omnidirectional far-offset gather and the stacked seismic amplitude of the omnidirectional near-offset gather to obtain the offset amplitude difference attribute and output the result, which is used as the prediction of the thickness of the reservoir prediction area.

[0057] Extract the amplitude attributes of the target layer from two sets of seismic data, subtract the seismic_near amplitude attribute from the seismic_far amplitude attribute, and output the offset amplitude difference attribute. The obtained offset amplitude difference attribute is the reservoir sedimentation thickness of the study area, such as Figure 6 ;

[0058] Step 7: According to the azimuth division method in Step 3, calculate the gradient attribute change rate using the effective offset azimuthal gathers, and output it as the prediction result of the fluid.

[0059] According to the azimuth division scheme in Step 3, calculate the AVO gradient attribute change rate G(i) using the effective offset azimuthal gathers. The specific calculation formula is as follows:

[0060]

[0061] );

[0062] In the formula, is the maximum effective offset amplitude value of the i-th azimuth,[[]] is the minimum offset amplitude value of the i-th azimuth; L is the effective offset,[[]] is the maximum gradient attribute change rate of the i-th azimuth,[[]] is the minimum gradient attribute change rate of the i-th azimuth.

[0063] () is divided into 6 azimuths generated according to 0 - 30, 30 - 60, 60 - 90, 90 - 120, 120 - 150, 150 - 180.

[0064] Calculate the AVO gradient attribute change rate and output it. The result is used as the prediction of the fluid, such as Figure 7 shown.

[0065] The present invention utilizes wide-azimuth seismic gather data. Compared with conventional 3D seismic data and prestack offset domain seismic data, the wide-azimuth seismic gather data can not only solve the seismic prediction of refining reservoir boundaries using elastic parameters, but also solve the problem that traditional prestack attributes are no longer applicable in anisotropic formations.

[0066] By analyzing and clarifying the acquisition geometry of seismic data and determining the effective offset of OVT seismic data, the present invention can ensure the energy consistency of stacked gathers in different azimuths when performing azimuth division, eliminating the multi-solution problem of anisotropic prediction caused by energy differences due to inconsistent coverage.

[0067] A progressive fine reservoir prediction method based on the change rate attribute proposed by the present invention can realize the prediction of reservoirs from the macroscopic sedimentary distribution to the prediction of reservoirs and fluids. It realizes layer-by-layer constraints, avoids the multi-solution problem caused by single prediction, and improves the scientificity, rationality and accuracy of reservoir prediction.

[0068] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A progressive fine reservoir prediction method for wide-azimuth seismic change rate attributes, characterized in that: Step 1: Collect OVT seismic data, interpreted horizon data, and seismic acquisition work area observation system parameters in the reservoir prediction work area. Step 2: Analyze the seismic acquisition observation system parameters, determine the maximum north-south distance and the maximum east-west distance between shot points and receivers in the seismic acquisition work area, and take the minimum of the two as the effective offset L. Step 3: Limit the effective offset L and divide the OVT seismic data by azimuth. Step 4: Generate the stacked seismic amplitude of the full-azimuth near-offset gather and the stacked seismic amplitude of the full-azimuth far-offset gather within the limited effective offset L. Step 5: Use the divided effective-offset azimuth gathers to calculate and output the amplitude anisotropy value, and use this result as the seismic prediction result of the sedimentary facies belt. Step 6: Use the difference between the stacked seismic amplitude of the full-azimuth far-offset gather and the stacked seismic amplitude of the full-azimuth near-offset gather to obtain the offset amplitude difference attribute and output the result, which is used as the prediction of the thickness in the reservoir prediction work area. Step 7: According to the azimuth division method in Step 3, use the effective-offset azimuth gathers to calculate the gradient attribute change rate and output it as the prediction result of the fluid.

2. The progressive reservoir fine prediction method for wide-azimuth seismic change rate attributes according to claim 1, characterized in that In Step 3, the division principle is to divide with a minimum sector angle of 30 degrees, and the number of sectors shall not be less than 6. The azimuth is divided as 0 - 30, 30 - 60, 60 - 90, 90 - 120, 120 - 150, 150 - 180.

3. The progressive reservoir fine prediction method for wide-azimuth seismic change rate attributes according to claim 1, characterized in that In Step 4, the near offset starts from 0m, the end offset is not greater than 1 / 2L, the far offset is not less than 1 / 2L, and the maximum is L.

4. The progressive fine reservoir prediction method for wide-azimuth seismic change rate attributes according to claim 1, characterized in that In Step 5, use the divided effective-offset azimuth seismic data to calculate the amplitude change rate value: In the formula, Var(Amp) is the amplitude change rate, max{Amp i} is the maximum amplitude in the i-th azimuth, Min{Amp i} is the minimum amplitude in the i-th azimuth, and i is the azimuth angle serial number.

5. The progressive reservoir fine prediction method for wide-azimuth seismic change rate attributes according to claim 1, characterized in that In Step 7, according to the azimuth division method in Step 3, use the effective-offset azimuth gathers to calculate the gradient attribute change rate: where \(i = 1, 2, 3, 4, 5, 6\); Amp(i, L) is the maximum effective offset amplitude value at the \(i\)-th azimuth, and Amp(i, 0) is the minimum offset amplitude value at the \(i\)-th azimuth; max{G i} is the maximum gradient attribute change rate at the \(i\)-th azimuth, and Min{G i} is the minimum gradient attribute change rate at the \(i\)-th azimuth.

6. The progressive fine reservoir prediction method for wide-azimuth seismic change rate attributes according to claim 4 or 5, characterized in that i = (1, 2, 3, 4, 5, 6) are 6 azimuths generated by dividing according to 0 - 30, 30 - 60, 60 - 90, 90 - 120, 120 - 150, 150 - 180.

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