Angle gather expansion method and device based on AVO feature compensation, electronic equipment and medium
By using an AVO feature compensation method, the narrow incident angle gather in pre-stack inversion is extended, solving the problems of low signal-to-noise ratio and imaging position deviation, and achieving higher accuracy reservoir inversion and prediction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2024-10-30
- Publication Date
- 2026-05-01
AI Technical Summary
In pre-stack inversion, existing technologies suffer from low signal-to-noise ratios in gather data with narrow incident angles, leading to large errors in reservoir inversion results. Furthermore, reverse-time migration is sensitive to velocity, resulting in significant imaging position deviations, and there is a lack of effective narrow incident angle extension techniques.
A method based on AVO feature compensation is adopted. By acquiring seismic and velocity data, the ray incident angle is determined, the mid-to-far gather reflection coefficient is calculated, the seismic wavelet at each incident angle is extracted, and the gather is extended by fitting the reflection coefficient. The Fatti equation and the least squares method are used to solve the problem simultaneously to obtain the broadband sparse reflection coefficient, and then pre-stack inversion is performed.
It improved the signal-to-noise ratio of seismic data, enhanced reservoir continuity, significantly improved reservoir prediction performance, and demonstrated significant segmentation characteristics with a substantial increase in signal-to-noise ratio.
Smart Images
Figure CN121956129A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of oil and gas exploration and development technology, and more specifically, to an angle gather expansion method, apparatus, electronic device and medium based on AVO feature compensation. Background Technology
[0002] Pre-stack inversion fully utilizes the AVO (Average Volatility Variation) characteristics of seismic data. AVO represents the amplitude variation with offset. After converting the offset to the incident angle, the elastic physical parameters of the reservoir can be obtained by fully utilizing the functional relationship between the reflection coefficient and the density and velocity of the media above and below the reflection interface. Furthermore, reservoir porosity, fluid content, and lithology can be evaluated. Pre-stack inversion calculates the reflection coefficient at different incident angles. Since the reflection coefficient is a function of angle, it reflects the seismic wave amplitude observed from different angles. Therefore, the inversion accuracy is significantly affected by the incident angle of the gather.
[0003] The quality of pre-stack gather processing determines the quality of post-stack processing results and the accuracy of AVO interpretation results. Therefore, the optimization of pre-stack gather processing is of great significance.
[0004] Current angle gather optimization methods primarily focus on improving the signal-to-noise ratio (SNR) by processing the original incident angle data. The main method for gather optimization is the pre-stack depth migration method based on the two-way wave equation. This method, grounded in the two-way wave equation, is better suited to complex subsurface media and provides ideal imaging results for high-dipping strata and structures with significant lateral velocity variations. However, it also suffers from high computational and storage requirements, as well as low-frequency noise. Furthermore, reverse-time migration is highly sensitive to velocity; even small velocity errors can lead to imaging position deviations and artifacts. No extension techniques for narrow incident angles have yet been discovered. When the incident angle is too small, the original gather only yields a narrow azimuth partially stacked data volume, resulting in a low SNR, poor convergence, and large errors in reservoir inversion results.
[0005] Therefore, it is necessary to develop an angle gather expansion method, device, electronic device, and medium based on AVO feature compensation.
[0006] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention
[0007] This invention proposes an angle gather extension method, device, electronic device and medium based on AVO feature compensation, which can extend small angle gather information, improve the signal-to-noise ratio of seismic data and make the pre-stack inversion more accurate.
[0008] In a first aspect, embodiments of this disclosure provide an angle gather expansion method based on AVO feature compensation, including:
[0009] By acquiring seismic and velocity data, determining the incident angle of the rays, and then obtaining partial superpositions with different incident angles;
[0010] Calculate the reflection coefficient of the mid-to-long-range collection;
[0011] Extract seismic wavelets at each incident angle;
[0012] By fitting the reflection coefficient of the mid-to-long-range gather using the seismic wavelet, mid-to-long-range information is obtained, thereby enabling gather expansion.
[0013] As a specific implementation of this disclosure, calculating the reflection coefficient of the mid-to-long-range data set includes:
[0014] Based on the constrained algorithm of pre-stack simultaneous inversion, wavelets are extracted from the partial stacks at different angles, and the reflection coefficients of the partial stacks at different angles are obtained by maximum likelihood deconvolution.
[0015] Substituting the reflection coefficients of partially superimposed bodies at different angles into the Fatti equation, and then solving the equations simultaneously using the least squares method under the constraints of the low-frequency elastic parameter trend model, the reflection coefficients of the mid-to-far gathers are obtained by minimizing the solution.
[0016] As one specific implementation of this disclosure, the solution results include the L1 norm of the reflection coefficients of AI, SI, and ρ.
[0017] As a specific implementation of this disclosure, extracting seismic wavelets at each incident angle includes:
[0018] The reflection coefficient sequence above the well is calculated by using velocity and density curves from well logging data, and the corresponding seismic wavelet is calculated by the convolution model using the seismic traces near the well.
[0019] As a specific implementation of this disclosure, the reflection coefficient sequence of the well on the partial superposition body at different angles is calculated by the Zoeppritz equation.
[0020] As a specific implementation of this disclosure, it also includes:
[0021] Under the constraints of P-wave velocity, S-wave velocity, density data and the angular range of corresponding partial superpositions, seismic wavelets at corresponding angles are calculated by combining well logging data and partial superpositions at different angles.
[0022] As a specific implementation of this disclosure, it also includes:
[0023] Pre-stack inversion was performed on the gather data containing the aforementioned mid-to-long-range information, and the reservoir prediction results before and after the inversion were compared.
[0024] Secondly, embodiments of this disclosure also provide an angle gather expansion device based on AVO feature compensation, comprising:
[0025] The angle-splitting module acquires seismic and velocity data, determines the incident angle of rays, and then obtains partial superpositions with different incident angles.
[0026] The calculation module calculates the reflection coefficient of the mid-to-long-range data set;
[0027] Extraction module, extracts seismic wavelets at each incident angle;
[0028] The fitting module fits the reflection coefficient of the mid-to-long-distance gather using the seismic wavelet to obtain mid-to-long-distance information and achieve gather expansion.
[0029] As a specific implementation of this disclosure, calculating the reflection coefficient of the mid-to-long-range data set includes:
[0030] Based on the constrained algorithm of pre-stack simultaneous inversion, wavelets are extracted from the partial stacks at different angles, and the reflection coefficients of the partial stacks at different angles are obtained by maximum likelihood deconvolution.
[0031] Substituting the reflection coefficients of partially superimposed bodies at different angles into the Fatti equation, and then solving the equations simultaneously using the least squares method under the constraints of the low-frequency elastic parameter trend model, the reflection coefficients of the mid-to-far gathers are obtained by minimizing the solution.
[0032] As one specific implementation of this disclosure, the solution results include the L1 norm of the reflection coefficients of AI, SI, and ρ.
[0033] As a specific implementation of this disclosure, extracting seismic wavelets at each incident angle includes:
[0034] The reflection coefficient sequence above the well is calculated by using velocity and density curves from well logging data, and the corresponding seismic wavelet is calculated by the convolution model using the seismic traces near the well.
[0035] As a specific implementation of this disclosure, the reflection coefficient sequence of the well on the partial superposition body at different angles is calculated by the Zoeppritz equation.
[0036] As a specific implementation of this disclosure, it also includes:
[0037] Under the constraints of P-wave velocity, S-wave velocity, density data and the angular range of corresponding partial superpositions, seismic wavelets at corresponding angles are calculated by combining well logging data and partial superpositions at different angles.
[0038] As a specific implementation of this disclosure, it also includes:
[0039] Pre-stack inversion was performed on the gather data containing the aforementioned mid-to-long-range information, and the reservoir prediction results before and after the inversion were compared.
[0040] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:
[0041] Memory, which stores executable instructions;
[0042] A processor that executes the executable instructions in the memory to implement the angle gather expansion method based on AVO feature compensation.
[0043] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned angle gather expansion method based on AVO feature compensation.
[0044] Its beneficial effects are as follows:
[0045] This invention, without altering the fundamental characteristics of AVO, employs a constrained algorithm based on pre-stack simultaneous inversion to determine the gather reflection coefficients. Wavelet extracts are extracted from partial stacks at different angles, and the maximum likelihood deconvolution method is used to obtain the reflection coefficients of these partial stacks at different angles. These coefficients are then substituted into the Fatti equation and solved simultaneously using the least squares method. Constrained by a low-frequency elastic parameter trend model, pre-stack inversion is performed. The L1 norm of the reflection coefficients AI, SI, and ρ obtained from the inversion is minimized to obtain broadband, sparse reflection coefficients. Convolution of the fitted reflection coefficients yields mid-to-long-range information. Pre-stack inversion is then performed on the expanded data volume, significantly enhancing reservoir continuity, revealing distinct sectional features, and substantially improving the signal-to-noise ratio.
[0046] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description
[0047] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.
[0048] Figure 1 A flowchart illustrating the steps of an angle gather expansion method based on AVO feature compensation according to an embodiment of the present invention is shown.
[0049] Figure 2 A schematic diagram of the incident angle gather features according to an embodiment of the present invention is shown.
[0050] Figure 3 A schematic diagram of the 22-degree simulated gather inversion effect according to an embodiment of the present invention is shown.
[0051] Figure 4 A schematic diagram of the 30-degree simulated gather inversion effect according to an embodiment of the present invention is shown.
[0052] Figure 5 A schematic diagram of the original seismic gather according to an embodiment of the present invention is shown.
[0053] Figure 6 A schematic diagram of the original seismic gather reflection coefficients is shown according to an embodiment of the present invention.
[0054] Figure 7 A schematic diagram of an AVO long-range fitting mode according to an embodiment of the present invention is shown.
[0055] Figure 8 A schematic diagram comparing the Bortfield and Shuey fitting methods according to an embodiment of the present invention is shown.
[0056] Figure 9 A schematic diagram showing a comparison of AVO features between an extended gather and an original gather according to an embodiment of the present invention is provided.
[0057] Figure 10 A schematic diagram of an extended gather with four parts superimposed on a data body according to an embodiment of the present invention is shown.
[0058] Figure 11 A schematic diagram showing a comparison of the pre-stack inversion results of the original gather and the extended gather according to an embodiment of the present invention is provided.
[0059] Figure 12 A block diagram of an angle gather expansion device based on AVO feature compensation according to an embodiment of the present invention is shown.
[0060] Explanation of reference numerals in the attached figures:
[0061] 201. Angle division module; 202. Calculation module; 203. Extraction module; 204. Fitting module. Detailed Implementation
[0062] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.
[0063] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that these examples are merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.
[0064] Example 1
[0065] Figure 1 A flowchart illustrating the steps of an angle gather expansion method based on AVO feature compensation according to an embodiment of the present invention is shown.
[0066] like Figure 1 As shown, the angle gather expansion method based on AVO feature compensation includes:
[0067] Step 101: Acquire seismic data and velocity data, determine the incident angle of the rays, and then obtain partial superpositions with different incident angles;
[0068] Step 102: Calculate the reflection coefficient of the mid-to-far focus;
[0069] Step 103: Extract the seismic wavelet for each incident angle;
[0070] Step 104: Fit the reflection coefficient of the mid-to-long-range gathers using seismic wavelet to obtain mid-to-long-range information and realize gather expansion.
[0071] In one example, calculating the reflection coefficient of a mid-to-long distance gathering includes:
[0072] Based on the constrained algorithm of pre-stack simultaneous inversion, wavelets are extracted from the partial stacks at different angles, and the reflection coefficients of the partial stacks at different angles are obtained by maximum likelihood deconvolution.
[0073] Substituting the reflection coefficients of partially superimposed bodies at different angles into the Fatti equation, and then solving the equations simultaneously using the least squares method under the constraints of the low-frequency elastic parameter trend model, the reflection coefficients of the mid-to-far gathers are obtained by minimizing the solution.
[0074] In one example, the solution includes the L1 norm of the reflection coefficients of AI, SI, and ρ.
[0075] In one example, extracting the seismic wavelet for each incident angle includes:
[0076] The reflection coefficient sequence above the well is calculated by using velocity and density curves from well logging data, and the corresponding seismic wavelet is calculated by the convolution model using the seismic traces near the well.
[0077] In one example, the reflection coefficient sequence of the well at different angles on the partial superposition volume is calculated using the Zoeppritz equation.
[0078] In one example, it also includes:
[0079] Under the constraints of P-wave velocity, S-wave velocity, density data and the angular range of corresponding partial superpositions, seismic wavelets at corresponding angles are calculated by combining well logging data and partial superpositions at different angles.
[0080] In one example, it also includes:
[0081] Pre-stack inversion was performed on gather data containing mid-to-long-range information, and the reservoir prediction performance before and after the inversion was compared.
[0082] Specifically, seismic data and velocity data are acquired, and the incident angle of the rays is determined based on the seismic data and velocity data.
[0083] The reflection coefficients of mid-to-long-range gathers were determined using a constrained algorithm based on pre-stack simultaneous inversion. Wavelet extracts were extracted from partial stacks at different angles, and the reflection coefficients of these partial stacks at different angles were then obtained using the maximum likelihood deconvolution method. These coefficients were substituted into the Fatti equation and solved simultaneously using the least squares method. Pre-stack inversion was performed under the constraint of a low-frequency elastic parameter trend model. The L1 norm of the reflection coefficients AI, SI, and ρ obtained from the inversion was minimized to obtain broadband, sparse reflection coefficients.
[0084] Wavelet extraction is performed for each incident angle. First, the reflection coefficient sequence above the well is calculated using the velocity and density curves from the well logging data. Then, the seismic wavelet is obtained from the convolution model by combining the seismic traces near the well. The extraction of the seismic wavelet differs from that of post-stack data. The reflection coefficient sequence of the well at different angles of the partial stack is calculated using the Zoeppritz equation. This requires inputting P-wave velocity, S-wave velocity, density data, and the angle range of the corresponding partial stack. The seismic wavelet at the corresponding angle is estimated by combining the well logging data and the partial stacks at different angles.
[0085] Mid-to-long-range information is fitted. After extracting reflection coefficient information from the CRP gather, the mid-to-long-range information is obtained by convolving the fitted reflection coefficients with the extracted seismic wavelet, thus achieving gather extension. Pre-stack inversion is performed based on the extended data, and the reservoir prediction effect before and after the inversion is compared.
[0086] Example 2
[0087] The present invention also provides an angle gather expansion device based on AVO feature compensation, comprising:
[0088] The angle-splitting module acquires seismic and velocity data, determines the incident angle of rays, and then obtains partial superpositions with different incident angles.
[0089] The calculation module calculates the reflection coefficient of the mid-to-long-range data set;
[0090] Extraction module, extracts seismic wavelets at each incident angle;
[0091] The fitting module uses seismic wavelet to fit the reflection coefficient of the mid-to-long-range gathers to obtain mid-to-long-range information and realize gather expansion.
[0092] In one example, calculating the reflection coefficient of a mid-to-long distance gathering includes:
[0093] Based on the constrained algorithm of pre-stack simultaneous inversion, wavelets are extracted from the partial stacks at different angles, and the reflection coefficients of the partial stacks at different angles are obtained by maximum likelihood deconvolution.
[0094] Substituting the reflection coefficients of partially superimposed bodies at different angles into the Fatti equation, and then solving the equations simultaneously using the least squares method under the constraints of the low-frequency elastic parameter trend model, the reflection coefficients of the mid-to-far gathers are obtained by minimizing the solution.
[0095] In one example, the solution includes the L1 norm of the reflection coefficients of AI, SI, and ρ.
[0096] In one example, extracting the seismic wavelet for each incident angle includes:
[0097] The reflection coefficient sequence above the well is calculated by using velocity and density curves from well logging data, and the corresponding seismic wavelet is calculated by the convolution model using the seismic traces near the well.
[0098] In one example, the reflection coefficient sequence of the well at different angles on the partial superposition volume is calculated using the Zoeppritz equation.
[0099] In one example, it also includes:
[0100] Under the constraints of P-wave velocity, S-wave velocity, density data and the angular range of corresponding partial superpositions, seismic wavelets at corresponding angles are calculated by combining well logging data and partial superpositions at different angles.
[0101] In one example, it also includes:
[0102] Pre-stack inversion was performed on gather data containing mid-to-long-range information, and the reservoir prediction performance before and after the inversion was compared.
[0103] Specifically, seismic data and velocity data are acquired, and the incident angle of the rays is determined based on the seismic data and velocity data.
[0104] The reflection coefficients of mid-to-long-range gathers were determined using a constrained algorithm based on pre-stack simultaneous inversion. Wavelet extracts were extracted from partial stacks at different angles, and the reflection coefficients of these partial stacks at different angles were then obtained using the maximum likelihood deconvolution method. These coefficients were substituted into the Fatti equation and solved simultaneously using the least squares method. Pre-stack inversion was performed under the constraint of a low-frequency elastic parameter trend model. The L1 norm of the reflection coefficients AI, SI, and ρ obtained from the inversion was minimized to obtain broadband, sparse reflection coefficients.
[0105] Wavelet extraction is performed for each incident angle. First, the reflection coefficient sequence above the well is calculated using the velocity and density curves from the well logging data. Then, the seismic wavelet is obtained from the convolution model by combining the seismic traces near the well. The extraction of the seismic wavelet differs from that of post-stack data. The reflection coefficient sequence of the well at different angles of the partial stack is calculated using the Zoeppritz equation. This requires inputting P-wave velocity, S-wave velocity, density data, and the angle range of the corresponding partial stack. The seismic wavelet at the corresponding angle is estimated by combining the well logging data and the partial stacks at different angles.
[0106] Mid-to-long-range information is fitted. After extracting reflection coefficient information from the CRP gather, the mid-to-long-range information is obtained by convolving the fitted reflection coefficients with the extracted seismic wavelet, thus achieving gather extension. Pre-stack inversion is performed based on the extended data, and the reservoir prediction effect before and after the inversion is compared.
[0107] Example 3
[0108] Figure 2 A schematic diagram of the incident angle gather features according to an embodiment of the present invention is shown.
[0109] Partial stacking of pre-stack CRP gathers was performed. Using a suitable velocity field and a one-dimensional ray tracing method, the pre-stack CRP gathers were partitioned from the offset domain to the angular domain. Then, the CRP angular gathers were divided into different angular ranges. Finally, the angular gathers of different angular ranges were stacked separately to obtain partial stacks at different angles. In this study, the maximum incident angle of the data was 22 degrees. To obtain reliable Poisson's ratio information, at least a medium angle requirement of 30 degrees is needed. Figure 2 As shown.
[0110] Determining the reflection coefficient of gathers at different angles. It is generally assumed that the seismic wavelet W(θ1) varying with angle is related to the P-wave reflection coefficient R at different angles calculated using the Aki-Richarts equations. ppThe result obtained by convolution (θ1) is approximately equal to the pre-stack gather data S without stacking. pp (θ1):
[0111] S pp (θ1)=W(θ1)*R pp (θ1)
[0112] Wavelets are extracted from the partial superpositions at different angles, and the reflection coefficients of the partial superpositions at different angles are obtained by using the maximum likelihood deconvolution method.
[0113] When the reflection coefficients of the partially superimposed bodies at different angles are obtained, they need to be substituted into the Fatti equation and solved simultaneously using the least squares method. Finally, these reflection coefficients are converted into the corresponding rate of change of elastic parameters of the subsurface interface (such as R). AI R SI and Rρ):
[0114] R pp (θ1)=aR AI +bR SI +c′R d
[0115] R pp (θ2)=aR AI +bR SI +c′R d
[0116] ...
[0117] R pp (θ n )=aR AI +bR SI +c′R d
[0118] In the formula, R pp (θ1), R pp (θ2), R pp (θ n ) represents the reflection coefficient of the partial superposition at different angles, n represents the number of partial superpositions, θ1, θ2, θ... n To represent different incident angles of the longitudinal wave, a = 1 + tan 2 θ, b = -8k sat sin 2 θ,
[0119] By constraining the low-frequency elastic parameter trend model, pre-stack synchronous inversion is performed to optimize the objective function of the following equation:
[0120]
[0121] In the formula, AI, SI, and ρ represent the retrieved P-wave impedance, S-wave impedance, and density, respectively; a is the control sparse pulse factor, which, by adjusting this parameter, can prevent random noise in seismic data from being identified as reflected waves; i represents the i-th partial stack, j represents the j-th sampling time on the seismic profile, and R... ij S represents the reflection coefficient as a function of angle. data ij S represents partially superimposed seismic data from different angles. ij (AI,SI,ρ) represents the seismic data obtained from forward modeling based on AI, SI, and ρ obtained from inversion; AI low SI low and ρ low These represent the corresponding P-wave impedance, S-wave impedance, and density in the trend model, respectively. The first expression on the right-hand side of the equation is the L2 norm, which represents the residual between the forward-modeled seismic data and the actual measured seismic data; the second expression represents the L1 norm of the reflection coefficients AI, SI, and ρ obtained from the inversion. Minimizing this norm yields broadband, sparse reflection coefficients.
[0122] Wavelet extraction from data at different incident angles. All algorithms based on convolutional seismic models require seismic data and seismic wavelets as input. Estimation of high-quality seismic wavelets is crucial for reflection coefficient extraction. Using low-quality wavelets will affect the extraction results, consequently impacting the subsequent fitting of long-range features from the gather.
[0123] The deterministic wavelet extraction method first calculates the reflection coefficient sequence above the well using velocity and density curves from well logging data. Then, it uses a convolution model to derive the seismic wavelet from the adjacent seismic traces. In gather data, seismic wavelet extraction differs from post-stack data. The reflection coefficient sequence on partial stacks at different angles is calculated using the Zoeppritz equation, which requires inputting P-wave velocity data, S-wave velocity data, density data, and the corresponding angular range of the partial stacks. The method combines well logging data and partial stacks at different angles to estimate the seismic wavelet at the corresponding angle.
[0124] In the wavelet estimation step, the optimal wavelet is obtained based on least-squares calculations of the synthetic record and seismic calculation data. Following White's method, the error of the following function is minimized:
[0125] ε tota l = ε 地震 +ε 子波 *α
[0126] Where, ε total It is the total error, ε 地震It is the seismic error, which is the L2 norm of the difference between the seismic data calculated based on the current elastic parameter model and the current wavelet and the synthetic data. ε 子波 α is the wavelet error, which is the L2 norm of the difference between the current wavelet and the previous wavelet. α is the wavelet stability factor, which can vary between 0 and ∑. A value of 1 is where the two error terms are balanced.
[0127] Figure 3 A schematic diagram of the 22-degree simulated gather inversion effect according to an embodiment of the present invention is shown.
[0128] Figure 4 A schematic diagram of the 30-degree simulated gather inversion effect according to an embodiment of the present invention is shown.
[0129] The fitting angle was determined based on AVO characteristics. Forward modeling analysis was conducted, comparing simulated gathers at 22 degrees and 30 degrees. Analysis of the inversion results showed that the 22-degree gather had poor prediction performance for reservoirs within the shadow formation, while the 30-degree gather significantly improved the prediction performance. Figures 3-4 As shown.
[0130] Figure 5 A schematic diagram of the original seismic gather according to an embodiment of the present invention is shown.
[0131] Figure 6 A schematic diagram of the original seismic gather reflection coefficients is shown according to an embodiment of the present invention.
[0132] like Figures 5-6 As shown, the AVO fitting formula is used to fit the AVO features of the original gather data within 22 degrees. The reflection coefficients of the corresponding mid-to-long-range channels are obtained based on the fitting trend and added to the mid-to-long-range gather data.
[0133] Figure 7 A schematic diagram of an AVO long-range fitting mode according to an embodiment of the present invention is shown.
[0134] Figure 8 A schematic diagram comparing the Bortfield and Shuey fitting methods according to an embodiment of the present invention is shown.
[0135] Figure 9 A schematic diagram showing a comparison of AVO features between an extended gather and an original gather according to an embodiment of the present invention is provided.
[0136] Figure 7The blue lines represent the actual AVO characteristics of the trace gather. Based on the fitting trend of the red line at 22 degrees near the trace, the AVO variation characteristics between 22 and 30 degrees can be obtained. Within 30 degrees, the AVO trend is consistent and stable. Beyond 30 degrees, the constraint decreases, and the AVO characteristics change significantly. Therefore, the AVO fitting compensation within 30 degrees is considered reliable. Figure 8 As shown, within 30 degrees, the AVO characteristics are basically the same, all being typical Type I AVOs. Among them, the reflection coefficient extension calculated using the Shuey method shows clearer long-range variation characteristics. Therefore, the Shuey method is preferred as the technical method for AVO extension. Figure 9 As shown.
[0137] Figure 10 A schematic diagram of an extended gather with four parts superimposed on a data body according to an embodiment of the present invention is shown.
[0138] Figure 11 A schematic diagram showing a comparison of the pre-stack inversion results of the original gather and the extended gather according to an embodiment of the present invention is provided.
[0139] A comparison of pre-stack inversion results. Using extended gathers for partial stacking yields four partial stacks, while the original gathers only produce three partial stacks in a narrow orientation, such as... Figure 10 As shown, pre-stack inversion was performed on the two types of superimposed data volumes before and after the expansion. Due to the narrow incident angle, the original data showed poor convergence, resulting in poor reservoir continuity and a low signal-to-noise ratio in the inversion results. However, using the expanded data for pre-stack inversion significantly enhanced reservoir continuity, revealed clear segmentation characteristics, and substantially improved the signal-to-noise ratio. Figure 11 As shown.
[0140] Example 4
[0141] Figure 12 A block diagram of an angle gather expansion device based on AVO feature compensation according to an embodiment of the present invention is shown.
[0142] like Figure 12 As shown, the angle gather expansion device based on AVO feature compensation includes:
[0143] The angle-splitting module 201 acquires seismic and velocity data, determines the incident angle of the rays, and then obtains a partial superposition of different incident angles.
[0144] Calculation module 202 calculates the reflection coefficient of the mid-to-long-range data set;
[0145] Extraction module 203 extracts seismic wavelets at each incident angle;
[0146] The fitting module 204 fits the reflection coefficient of the mid-to-long-distance gathers using seismic wavelets to obtain mid-to-long-distance information and realize gather expansion.
[0147] In one example, calculating the reflection coefficient of a mid-to-long distance gathering includes:
[0148] Based on the constrained algorithm of pre-stack simultaneous inversion, wavelets are extracted from the partial stacks at different angles, and the reflection coefficients of the partial stacks at different angles are obtained by maximum likelihood deconvolution.
[0149] Substituting the reflection coefficients of partially superimposed bodies at different angles into the Fatti equation, and then solving the equations simultaneously using the least squares method under the constraints of the low-frequency elastic parameter trend model, the reflection coefficients of the mid-to-far gathers are obtained by minimizing the solution.
[0150] In one example, the solution includes the L1 norm of the reflection coefficients of AI, SI, and ρ.
[0151] In one example, extracting the seismic wavelet for each incident angle includes:
[0152] The reflection coefficient sequence above the well is calculated by using velocity and density curves from well logging data, and the corresponding seismic wavelet is calculated by the convolution model using the seismic traces near the well.
[0153] In one example, the reflection coefficient sequence of the well at different angles on the partial superposition volume is calculated using the Zoeppritz equation.
[0154] In one example, it also includes:
[0155] Under the constraints of P-wave velocity, S-wave velocity, density data and the angular range of corresponding partial superpositions, seismic wavelets at corresponding angles are calculated by combining well logging data and partial superpositions at different angles.
[0156] In one example, it also includes:
[0157] Pre-stack inversion was performed on gather data containing mid-to-long-range information, and the reservoir prediction performance before and after the inversion was compared.
[0158] Example 5
[0159] This disclosure provides an electronic device, comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the aforementioned angle gather expansion method based on AVO feature compensation.
[0160] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
[0161] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.
[0162] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.
[0163] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.
[0164] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.
[0165] Example 6
[0166] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the angle gather expansion method based on AVO feature compensation.
[0167] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.
[0168] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
[0169] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.
[0170] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.
Claims
1. An angle gather expansion method based on AVO feature compensation, characterized in that, include: By acquiring seismic and velocity data, determining the incident angle of the rays, and then obtaining partial superpositions with different incident angles; Calculate the reflection coefficient of the mid-to-long-range collection; Extract seismic wavelets at each incident angle; By fitting the reflection coefficient of the mid-to-long-range gather using the seismic wavelet, mid-to-long-range information is obtained, thereby enabling gather expansion.
2. The angle gather expansion method based on AVO feature compensation according to claim 1, wherein, Calculating the reflection coefficient of a mid-to-long distance gathering includes: Based on the constrained algorithm of pre-stack simultaneous inversion, wavelets are extracted from partial stacks at different angles, and then the reflection coefficients of partial stacks at different angles are obtained by maximum likelihood deconvolution. Substituting the reflection coefficients of partially superimposed bodies at different angles into the Fatti equation, and then solving the equations simultaneously using the least squares method under the constraints of the low-frequency elastic parameter trend model, the reflection coefficients of the mid-to-far gathers are obtained by minimizing the solution.
3. The angle gather expansion method based on AVO feature compensation according to claim 2, wherein, The solution results include the L1 norm of the reflection coefficients of AI, SI, and ρ.
4. The angle gather expansion method based on AVO feature compensation according to claim 1, wherein, The seismic wavelets extracted at each incident angle include: The reflection coefficient sequence above the well is calculated by using velocity and density curves from well logging data, and the corresponding seismic wavelet is calculated by the convolution model using the seismic traces near the well.
5. The angle gather expansion method based on AVO feature compensation according to claim 4, wherein, The reflection coefficient sequence of the well at different angles on the partial superposition volume was calculated using the Zoeppritz equation.
6. The angle gather expansion method based on AVO feature compensation according to claim 4, wherein, Also includes: Under the constraints of P-wave velocity, S-wave velocity, density data and the angular range of corresponding partial superpositions, seismic wavelets at corresponding angles are calculated by combining well logging data and partial superpositions at different angles.
7. The angle gather expansion method based on AVO feature compensation according to claim 1, wherein, Also includes: Pre-stack inversion was performed on the gather data containing the aforementioned mid-to-long-range information, and the reservoir prediction results before and after the inversion were compared.
8. An angle gather expansion device based on AVO feature compensation, characterized in that, include: The angle-splitting module acquires seismic and velocity data, determines the incident angle of rays, and then obtains partial superpositions with different incident angles. The calculation module calculates the reflection coefficient of the mid-to-long-range data set; Extraction module, extracts seismic wavelets at each incident angle; The fitting module fits the reflection coefficient of the mid-to-long-distance gather using the seismic wavelet to obtain mid-to-long-distance information and achieve gather expansion.
9. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the angle gather expansion method based on AVO feature compensation as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the angle gather expansion method based on AVO feature compensation as described in any one of claims 1-7.