A loess plateau transition zone frequency consistency processing method

Abstract

The application discloses a loess plateau transition zone frequency consistency processing method, which comprises the following steps: obtaining a common midpoint / common reflection point gather A of a target area; obtaining a corresponding stack / migration profile according to the common midpoint / common reflection point gather A, and extracting a frequency attribute FA along a target layer on the stack / migration profile; processing the common midpoint / common reflection point gather A to obtain a high-resolution gather B, stacking the high-resolution gather B, and extracting a frequency attribute FB along the target layer on a profile obtained through the stacking; determining an expected main frequency FH conforming to a feature of the target area data; determining weight coefficients x and y of the frequency attribute FA and the frequency attribute FB according to a relationship among the expected main frequency FH, the frequency attribute FA and the frequency attribute FB; applying the weight coefficients x and y to the gather A and the gather B respectively to obtain an expected main frequency gather C; and stacking the expected main frequency gather C to obtain a corresponding result profile.

Description

Technical Field

[0001] This invention relates to the field of oil and gas geophysical exploration technology, and in particular to a method for frequency consistency processing in the Loess Plateau transition zone. Background Technology

[0002] In typical loess plateau-gully landforms, loess is characterized by its thickness and low velocity, resulting in strong frequency absorption and attenuation of seismic waves. Variations in loess thickness determine the dominant frequency and bandwidth of the original data, while the resolution of seismic data primarily depends on the bandwidth of the seismic waves. Currently, the most effective means of improving the frequency of seismic data is deconvolution. The effectiveness of deconvolution depends on the degree of conformity between the seismic data and the convolution model in the adopted deconvolution method. The complex surface conditions of the loess plateau make it difficult to meet the assumptions of surface-consistent deconvolution. Often, wavelet matching processing techniques are first used to eliminate inconsistencies in wavelet waveforms caused by different excitation factors. Then, near-surface Q-compensation is performed to eliminate Q-attenuation. Finally, spatiotemporal surface-consistent deconvolution is used to correct the phase, broaden the data spectrum, and improve data resolution. Although the above processing can eliminate some frequency inconsistencies, in the transition zone of the loess plateau, changes in gullies and soil moisture content still introduce frequency inconsistencies, forming frequency variations due to non-geological factors. These differences are unrelated to underground geological reflection information, thus affecting subsequent interpretations and judgments, and introducing multiple interpretations into later attribute studies. Summary of the Invention

[0003] To address the aforementioned technical problems, at least one embodiment of the present invention provides a frequency consistency processing method for the Loess Plateau transition zone to resolve the ambiguity problem.

[0004] In some optional embodiments, the method includes the following steps:

[0005] Obtain the common center point / common reflection point gather A of the target area;

[0006] Based on the common center point / common reflection point gather A, the corresponding stacking / offset profile is obtained, and the frequency attribute FA is extracted along the target layer on the stacking / offset profile.

[0007] The common center point / common reflection point domain gather A is processed to obtain a high-resolution gather B. The high-resolution gather B is then stacked, and the frequency attribute FB is extracted along the target layer on the cross-section obtained by stacking.

[0008] Determine the expected dominant frequency FH ​​that matches the characteristics of the data in the target area;

[0009] The weight coefficients x and y of frequency attribute FA and frequency attribute FB are determined based on the relationship between the expected main frequency FH, the frequency attribute FA and the frequency attribute FB.

[0010] The weighting coefficients x and y are applied to channel set A and channel set B respectively to obtain the desired main channel set C;

[0011] The desired main channel set C is overlaid to obtain the corresponding result profile.

[0012] In some optional embodiments, processing the common center point / common reflection point domain gather A to obtain a high-resolution gather B includes:

[0013] The common center point / common reflection point domain gather A is subjected to tuned deconvolution and spectral whitening to obtain high-resolution gather B.

[0014] In some optional embodiments, the step of stacking the high-resolution gather B and extracting the frequency attribute FB along the target layer on the profile obtained by stacking includes:

[0015] The high-resolution gather B is cleaned, and the cleaned gathers are superimposed to obtain a superimposed profile with improved resolution.

[0016] Frequency attribute FB is extracted along the target layer on the overlay profile after resolution improvement.

[0017] In some optional embodiments, determining the desired dominant frequency FH ​​that conforms to the characteristics of the target area data includes:

[0018] Obtain well data for the target area, synthesize seismic records from the well data, and perform correlation analysis with seismic data to determine the dominant frequency that matches the characteristics of the data in the target area.

[0019] In some optional embodiments, the relationship between the desired main frequency FH, the frequency attribute FA, and the frequency attribute FB is as follows:

[0020] FH = x*FA + y*FB

[0021] Where x and y are the weighting coefficients of the frequency attribute FA and the frequency attribute FB, respectively.

[0022] In some optional embodiments, the window used to extract the frequency attribute FA is the same as the window used to extract the frequency attribute FB.

[0023] In some optional embodiments, applying the weighting coefficients x and y to the channel set A and the channel set B respectively to obtain the desired main channel set C includes:

[0024] Multiply the trace set A by the weight coefficient x to obtain the first trace set;

[0025] Multiply the path set B by the weight coefficient y to obtain the second path set;

[0026] Add the first set of channels and the second set of channels to obtain the desired main channel set C.

[0027] At least one embodiment of the present invention also provides a frequency consistency processing device for the Loess Plateau transition zone, characterized in that it comprises:

[0028] The gather acquisition module is used to acquire gather A of common center points / common reflection points of the target area;

[0029] The first frequency extraction module is used to obtain the corresponding stacking / offset profile based on the common center point / common reflection point gather A, and extract the frequency attribute FA along the target layer on the stacking / offset profile.

[0030] The second frequency extraction module is used to process the common center point / common reflection point domain gather A to obtain a high-resolution gather B, and to superimpose the high-resolution gather B to extract the frequency attribute FB along the target layer on the cross-section obtained by superposition.

[0031] The expected frequency determination module is used to determine the expected frequency FH ​​that conforms to the characteristics of the target area data;

[0032] The coefficient calculation module is used to determine the weight coefficients x and y of the frequency attribute FA and the frequency attribute FB respectively based on the relationship between the expected main frequency FH, the frequency attribute FA and the frequency attribute FB;

[0033] A channel set assembly module is used to apply the weight coefficients x and y to channel set A and channel set B respectively to obtain the desired main channel set C;

[0034] The results generation module is used to overlay the expected main channel set C to obtain the corresponding results profile.

[0035] At least one embodiment of the present invention also provides an electronic device, characterized in that it comprises:

[0036] At least one processor; and,

[0037] A memory communicatively connected to the at least one processor; wherein,

[0038] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the frequency consistency processing method for the Loess Plateau transition zone as described above.

[0039] At least one embodiment of the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the frequency consistency processing method for the Loess Plateau transition zone as described above.

[0040] At least one embodiment of the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the frequency consistency processing method for the Loess Plateau transition zone as described above.

[0041] Compared with the prior art, the frequency consistency processing method for the Loess Plateau transition zone provided by the embodiments of the present invention has the following beneficial effects:

[0042] The frequency consistency processing method provided in this paper for the Loess Plateau transition zone reduces frequency differences caused by non-geological factors by making the dominant frequency of the target layer in the whole area more consistent, thereby reducing the ambiguity of seismic data and providing basic data for fluvial sand body identification and seismic attribute analysis. Attached Figure Description

[0043] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0044] Figure 1 This is a flowchart of the steps of the frequency consistency processing method in the Loess Plateau transition zone used in Embodiment 1 of the present invention;

[0045] Figure 2 This is a schematic diagram of the main frequency of the superimposed profile corresponding to the A gather obtained in Embodiment 1 of the present invention;

[0046] Figure 3 This is a schematic diagram of the superimposed profile dominant frequency corresponding to the B gather obtained in Embodiment 1 of the present invention;

[0047] Figure 4 This is a schematic diagram of the superposition profile main frequency corresponding to the C-gather obtained in Embodiment 1 of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the embodiments of the present invention to facilitate a better understanding of the invention. However, the technical solutions claimed in the present invention can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0049] As mentioned earlier, for the Loess Plateau transition zone, frequency differences caused by excitation and reception factors in the Loess Plateau, grassland, and deep gullies cannot be eliminated by wavelet shaping, near-surface Q compensation, or surface-consistent deconvolution. This invention proposes a frequency consistency processing method for the Loess Plateau transition zone. This method can highlight frequency attributes related to geological characteristics, reduce the influence of information unrelated to underground geological reflections, and decrease the ambiguity of regional interpretation.

[0050] The implementation details of the above method are described in detail below through examples. The following content is only for the convenience of understanding the implementation details and is not necessary for implementing this solution.

[0051] Example 1:

[0052] like Figure 1 As shown in the figure, this embodiment provides a method for frequency consistency processing in the Loess Plateau transition zone. The method mainly includes the following steps:

[0053] (1) Common midpoint / common reflection point gathers (A) were obtained after conventional seismic data processing:

[0054] Using pre-processed (static correction, pre-stack noise attenuation, deconvolution) common middle point gathers, suitable imaging conditions and model parameters, including velocity models, are determined. Velocity analysis / reflection grid tomography inversion is typically performed to obtain more accurate velocity information. Pre-stack migration is then performed on CMP multichannel seismic data to obtain common reflection point (CRP) or common imaging point (CIP) gathers. The process typically includes the following steps:

[0055] Seismic data preprocessing includes tasks such as reference plane correction, residual static correction, pre-stack noise attenuation, and deconvolution to ensure that static correction problems of seismic data are solved, the signal-to-noise ratio of the data is significantly improved, and high-resolution data is obtained through wavelet compression.

[0056] Based on this data, an initial velocity model is established. The time-domain offset velocity is obtained through the update and iteration of the target linear velocity. The depth-domain velocity model is usually obtained by combining time-domain data and well data. This model will be used for subsequent ray tracing and travel time calculations. Inversion equations are established and solved to correct the depth-domain velocity.

[0057] Pre-stack time / depth migration: Pre-stack migration can provide more accurate imaging results in complex geological structures and can more effectively reflect information on wavefield and geological structure.

[0058] (2) Obtaining stacked / offset profiles by stacking gathers from common center points / common reflection points;

[0059] In the optimization and stacking process of the CRP gathers, the original CRP gathers are cleaned, noise on far-offset gathers is removed, and multiple wave attenuation is used to eliminate the influence of multiple waves. The gather quality is improved through optimization, while effectively preserving frequency and reflection characteristics. The optimized CRP / CIP gathers are then stacked to obtain the offset stacking result (e.g., ...). Figure 2 (As shown), this highlights stratigraphic features and improves the imaging quality and accuracy of seismic data interpretation.

[0060] (3) Pick the target stratigraphic horizon in the superimposed / offset profile;

[0061] Based on well data and previous geological understanding, the research object is selected, and the target stratigraphic position is identified. This can be achieved through manual interpretation or automated stratigraphic tracking algorithms. The accuracy of stratigraphic tracking directly affects the quality of subsequent amplitude extraction and the reliability of interpretation, ensuring that no part of the area is missing.

[0062] (4) Extract frequency attributes (FA) along the layer in the stacking / offset profile;

[0063] Seismic frequency attributes reflect the frequency variations of seismic waves as they propagate through strata. The selection of the statistical time window size is crucial for extracting seismic profile amplitude attributes along strata. The time window size directly affects the accuracy of frequency attributes and typically includes only one set of strata. In practical applications, optimization is necessary. The optimal time window size can be selected by comparing and analyzing the frequency attribute results under different time window sizes. Furthermore, an adaptive time window size method can be used, dynamically adjusting the time window size based on the local characteristics of the seismic signal to achieve optimal frequency attribute extraction.

[0064] (5) High-resolution gathers (B) are obtained by tuning deconvolution and spectral whitening of the common center point / common reflection point domain gathers;

[0065] By utilizing tuned deconvolution and spectral whitening to extend the frequency band and improve resolution, the overall resolution of seismic data and the dominant frequency of seismic data are improved.

[0066] (6) Stack the gathers after tuned deconvolution and frequency division compensation processing;

[0067] The CRP gather after resolution improvement is cleaned, and the cleaned CRP / CIP are superimposed to obtain the superimposed profile after resolution improvement (e.g.) Figure 3 (As shown).

[0068] (7) Extract frequency attributes (FB) along the layers of the superimposed data after tuning deconvolution and frequency division compensation processing;

[0069] Repeat process (4), using the same layer and window as (3), to extract the frequency attribute (FB).

[0070] (8) Determine the expected dominant frequency (FH) that matches the characteristics of the data through conventional and high-resolution frequency attribute analysis;

[0071] By comparing well data and performing correlation analysis between the synthesized seismic record from the well data and the seismic data, the dominant frequency that matches the characteristics of the seismic data is determined, and the FH is identified.

[0072] (9) Find the coefficients of x and y in FH = x*FA + y*FB;

[0073] Use FA, FB and FH to construct a linear equation in two variables to find the corresponding x and y, where x and y represent the weight coefficients of FA and FB, respectively.

[0074] (10) This coefficient is applied to the collection to obtain the desired main channel set (C), C = x*A + y*B;

[0075] The final set corresponding to FH is obtained by multiplying the set corresponding to FA by the weight coefficient x and the set corresponding to FB by the weight coefficient y.

[0076] (11) Overlay the corresponding gathers (C) to obtain the corresponding result profile, and output the result profile (e.g., Figure 4 (As shown).

[0077] Example 2

[0078] Another embodiment of the present invention relates to a frequency consistency processing device for the Loess Plateau transition zone, comprising:

[0079] The gather acquisition module is used to acquire gather A of common center points / common reflection points of the target area;

[0080] The first frequency extraction module is used to obtain the corresponding stacking / offset profile based on the common center point / common reflection point gather A, and extract the frequency attribute FA along the target layer on the stacking / offset profile.

[0081] The second frequency extraction module is used to process the common center point / common reflection point domain gather A to obtain a high-resolution gather B, and to superimpose the high-resolution gather B to extract the frequency attribute FB along the target layer on the cross-section obtained by superposition.

[0082] The expected frequency determination module is used to determine the expected frequency FH ​​that conforms to the characteristics of the target area data;

[0083] The coefficient calculation module is used to determine the weight coefficients x and y of the frequency attribute FA and the frequency attribute FB respectively based on the relationship between the expected main frequency FH, the frequency attribute FA and the frequency attribute FB;

[0084] A channel set assembly module is used to apply the weight coefficients x and y to channel set A and channel set B respectively to obtain the desired main channel set C;

[0085] The results generation module is used to overlay the expected main channel set C to obtain the corresponding results profile.

[0086] Example 3

[0087] Another embodiment of the present invention relates to an electronic device, comprising: 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 frequency consistency processing method for the Loess Plateau transition zone in the above embodiments.

[0088] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0089] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0090] Example 4

[0091] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the frequency consistency processing method for the loess plateau transition zone described in the above embodiments.

[0092] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0093] Example 5

[0094] Another embodiment of the present invention relates to a computer program product, including a computer program that, when executed by a processor, implements the steps of the frequency consistency processing method for the Loess Plateau transition zone described in the above embodiments.

[0095] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of the present invention.

Claims

1. A method for frequency consistency processing in the Loess Plateau transition zone, characterized in that, include: Obtain the common center point / common reflection point gather A of the target area; Based on the common center point / common reflection point gather A, the corresponding stacking / offset profile is obtained, and the frequency attribute FA is extracted along the target layer on the stacking / offset profile. The common center point / common reflection point domain gather A is processed to obtain a high-resolution gather B. The high-resolution gather B is then stacked, and the frequency attribute FB is extracted along the target layer on the cross-section obtained by stacking. Determine the expected dominant frequency FH ​​that matches the characteristics of the data in the target area; The weight coefficients x and y of frequency attribute FA and frequency attribute FB are determined based on the relationship between the expected main frequency FH, the frequency attribute FA and the frequency attribute FB. The weighting coefficients x and y are applied to channel set A and channel set B respectively to obtain the desired main channel set C; The desired main channel set C is overlaid to obtain the corresponding result profile.

2. The method for frequency consistency processing in the Loess Plateau transition zone according to claim 1, characterized in that, The process of processing the common centroid / common reflection point domain gather A to obtain high-resolution gather B includes: The common center point / common reflection point domain gather A is subjected to tuned deconvolution and spectral whitening to obtain high-resolution gather B.

3. The method for frequency consistency processing in the Loess Plateau transition zone according to claim 1, characterized in that, The step of stacking the high-resolution gather B and extracting the frequency attribute FB along the target layer on the profile obtained by stacking includes: The high-resolution gather B is cleaned, and the cleaned gathers are superimposed to obtain a superimposed profile with improved resolution. Frequency attribute FB is extracted along the target layer on the overlay profile after resolution improvement.

4. The method for frequency consistency processing in the Loess Plateau transition zone according to claim 1, characterized in that, The determination of the expected dominant frequency FH ​​that conforms to the characteristics of the target area data includes: Obtain well data for the target area, synthesize seismic records from the well data, and perform correlation analysis with seismic data to determine the dominant frequency that matches the characteristics of the data in the target area.

5. The method for frequency consistency processing in the Loess Plateau transition zone according to claim 1, characterized in that, The relationship between the desired main frequency FH, the frequency attribute FA, and the frequency attribute FB is as follows: FH = x*FA + y*FB Where x and y are the weighting coefficients of the frequency attribute FA and the frequency attribute FB, respectively.

6. The method for frequency consistency processing in the Loess Plateau transition zone according to claim 1, characterized in that, The window used to extract the frequency attribute FA is the same as the window used to extract the frequency attribute FB.

7. The method for frequency consistency processing in the Loess Plateau transition zone according to claim 1, characterized in that, The step of applying the weighting coefficients x and y to channel set A and channel set B respectively to obtain the desired main channel set C includes: Multiply the trace set A by the weight coefficient x to obtain the first trace set; Multiply the path set B by the weight coefficient y to obtain the second path set; Add the first set of channels and the second set of channels to obtain the desired main channel set C.

8. A frequency consistency processing device for the Loess Plateau transition zone, characterized in that, include: The gather acquisition module is used to acquire gather A of common center points / common reflection points of the target area; The first frequency extraction module is used to obtain the corresponding stacking / offset profile based on the common center point / common reflection point gather A, and extract the frequency attribute FA along the target layer on the stacking / offset profile. The second frequency extraction module is used to process the common center point / common reflection point domain gather A to obtain a high-resolution gather B, and to superimpose the high-resolution gather B to extract the frequency attribute FB along the target layer on the cross-section obtained by superposition. The expected frequency determination module is used to determine the expected frequency FH ​​that conforms to the characteristics of the target area data; The coefficient calculation module is used to determine the weight coefficients x and y of the frequency attribute FA and the frequency attribute FB respectively based on the relationship between the expected main frequency FH, the frequency attribute FA and the frequency attribute FB; A channel set assembly module is used to apply the weight coefficients x and y to channel set A and channel set B respectively to obtain the desired main channel set C; The results generation module is used to overlay the expected main channel set C to obtain the corresponding results profile.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the frequency consistency processing method for the Loess Plateau transition zone as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the frequency consistency processing method for the Loess Plateau transition zone as described in any one of claims 1 to 7.

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