A method for seismic prediction of thin reservoirs based on frequency set

Abstract

The invention discloses a thin reservoir earthquake prediction method based on frequency division set, comprising the following steps: S1, obtaining a seismic gather data body; S2, performing frequency division scanning on the seismic gather data body, and extracting a high frequency set data body as basic gather data; S3, carrying out angle-divided stacking, and respectively obtaining near-, middle- and far-angle high frequency set stacking sections; S4, carrying out angle-divided extraction of near-, middle- and far-angle high frequency set stacking seismic wavelets; S5, taking the top and bottom positions of the target layer as control layers, carrying out synthetic record calibration, and determining the calibration quality of the synthetic record by a correlation coefficient; S6, carrying out standard wavelet calculation on the near-, middle- and far-angle high frequency set stacking seismic wavelets, and determining the standard wavelet; S7, carrying out normalization processing on the standard wavelet, and obtaining a comprehensive wavelet; S8, taking the comprehensive wavelet as an inversion wavelet, and taking the near-, middle- and far-angle high frequency set stacking sections as seismic pre-stack inversion, and obtaining an inversion data body.

Description

Technical Field

[0001] The invention relates to the field of earthquake prediction, and in particular to a thin reservoir earthquake prediction method based on a frequency sub-channel set. Background Art

[0002] At present, there are many seismic identification and prediction evaluation technologies for thin interbedded sedimentary geological bodies. The main method is to carry out frequency enhancement processing based on post-stack seismic data, and describe the high-frequency response geological body by obtaining high-frequency information. The second method is to use acoustic wave curve reconstruction to add high-frequency signals for inversion prediction.

[0003] Interpretive frequency enhancement is relatively simple and easy to implement, but the effect of interpretative frequency enhancement is mainly affected by the quality of the original post-stack seismic data. The high-frequency energy response of post-stack seismic data itself is weak, so the high-frequency information obtained by frequency enhancement on this basis is multi-solution, and the effect of describing thin interbedded geological bodies is not ideal. The acoustic wave curve reconstruction method reconstructs the acoustic wave curve by weighting the high-frequency information, but this method changes the characteristics of the original logging acoustic wave curve, and the prediction is multi-solution and the prediction effect is not good. Therefore, it is very necessary to study a method for thin reservoir seismic prediction using a frequency set. Summary of the invention

[0004] The purpose of the present invention is to solve the above problems and provide a thin reservoir seismic prediction method for a sub-channel set of thin interbedded geological sediments.

[0005] In order to achieve the above object, the technical solution of the present invention is:

[0006] (1) Obtain seismic gather data volume.

[0007] (2) Perform frequency division scanning on the gather to obtain frequency division channel set data bodies of different frequencies, and extract the high frequency set as the basic gather data. The frequency division principle is:

[0008]

[0009] Where: m is the sampling number in the frequency domain; n is the sampling number in the time domain; N is the total number of seismic sampling in the time domain; Δf is the sampling interval in the frequency domain; Δt is the sampling interval in the time domain.

[0010] (3) Use the obtained high-frequency channel set to carry out angle stacking to obtain the near-angle high-frequency channel set stacking section, the medium-angle high-frequency channel set stacking section, and the far-angle high-frequency channel set stacking section.

[0011] (4) Extracting the near-angle high-frequency channel set and stacking the seismic wavelet, the medium-angle high-frequency channel set and stacking the seismic wavelet, and the far-angle high-frequency channel set from the seismic records of multiple wells in the work area.

[0012] (5) Strata-controlled seismic synthetic record calibration. The top and bottom layers of the target layer are used as control layers to carry out synthetic record calibration. The correlation coefficient determination method is used to determine the quality of synthetic record calibration. The correlation coefficient determination method formula is as follows:

[0013] Calculate the sample mean With statistical error

[0014] Next, calculate the standard error

[0015] Then, the correlation coefficient

[0016] Wells with single-well synthetic record correlation greater than 0.7 are used as inversion participating wells, and their wavelets are saved as calculation factors for multi-well wavelet normalization.

[0017] (6) Multi-well joint wavelet normalization processing: perform standard wavelet calculation on all wavelets obtained in step 4 based on high-frequency near, medium and far stacked gather seismic data, and select the wavelet R with the largest correlation coefficient as the standard wavelet. The formula is as follows:

[0018]

[0019] For sub-waves at different shot points, the sub-waves that do not meet the correlation coefficient conditions are eliminated using formula (2-8).

[0020] remove(r x,y <αR max )(0.6<α<0.8)(5.1)

[0021] After selecting the standard wavelet, the filter factor is obtained in the frequency domain. The expression is shown in formula (2-7):

[0022]

[0023] After eliminating the sub-waves that do not meet the conditions, each shot sub-wave is shaped through the filter factor formula (2-7) to obtain a sub-wave set that meets the requirements. After superposition, it becomes the average wavelet, which is the seismic wavelet required for subsequent inversion.

[0024] (7) The obtained near, middle and far normalized wavelets of the high-frequency channel set are used as inversion wavelets, and the pre-stack inversion is carried out for the earthquake using the stacked sections of the near, middle and far trace sets of the high-frequency channel set, and the results are output.

[0025] Compared with the prior art, the present invention has the following advantages and positive effects:

[0026] The present invention starts with pre-stack seismic trace gathers, uses the trace gathers for frequency division processing, obtains frequency division channel sets in different frequency domains, uses high frequency channel sets to carry out angle stacking, and obtains high-frequency near-angle stacking sections, high-frequency medium-angle stacking sections, and high-frequency far-angle stacking sections. Compared with post-stack frequency extension to improve resolution, the stacked seismic profile based on the frequency division channel set has a stronger high-frequency seismic response, which is more conducive to identifying thin interlayer geological bodies. On the basis of high-frequency near-angle stacking sections, high-frequency medium-angle stacking sections, and high-frequency far-angle stacking sections, pre-stack inversion is carried out using acoustic wave curves, and the original acoustic logging information is not changed, while the acoustic wave curve reconstruction method changes the information of the logging acoustic wave, so the technical reliability of this method is stronger. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0028] Figure 1 Schematic diagram of the implementation process of the present invention;

[0029] Figure 2 This is a schematic diagram of the comparison between the original full-band gather and the high-frequency gather after frequency division;

[0030] Figure 3a is the original seismic profile, Figure 3b Overlay seismic profiles for high-frequency channel sets;

[0031] Figure 4 It is the calibration of synthetic records and the correlation coefficient determination diagram;

[0032] Figure 5 Determine the integrated wavelet schematic for wavelet normalization;

[0033] Figure 6 Output graph for inversion results;

[0034] Figure 7 This is a comparison chart of the results of sparse pulse inversion and channel set inversion;

[0035] Figure 8 Comparison of the plane predictions of the channel set inversion (left) and the sparse pulse inversion (right). DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work, any modifications, equivalent substitutions, improvements, etc., should be included in the protection scope of the present invention.

[0037] Seismic prediction of thin interbedded geological sedimentary bodies usually uses seismic data frequency enhancement or acoustic wave curve reconstruction methods. However, after multiple iterations, the high-frequency energy signal of the seismic data stacking profile itself is weak, and the prediction effect based on frequency enhancement is not ideal. The acoustic wave curve reconstruction method changes the original characteristics of the acoustic wave curve, and the prediction effect has strong multi-solution. A thin reservoir seismic prediction method based on frequency division set carries out frequency division processing based on seismic trace sets. By superimposing high-frequency channel sets, the reflection energy of the high-frequency signal of the stacked seismic profile is increased compared with the full-channel set stacked profile, thereby enhancing the longitudinal and lateral seismic response of the thin reservoir geological body. Pre-stack inversion operation is performed by matching wavelet extraction with near, medium and far high-frequency angle stacked seismic data to achieve seismic identification and prediction evaluation of thin reservoir geological sedimentary bodies.

[0038] like Figures 1 to 8 As shown, this study applied the actual data of XX block in XX oilfield. After the original channel gathers were frequency-divided, the high-frequency band 20HZ-50HZ sub-channel gathers were selected as the basis for seismic data superposition. The data body obtained by superposition of near-, medium- and far-angle sub-channel gathers formed by high-frequency band channel gather data was used for pre-stack inversion prediction through precise synthetic record calibration and wavelet extraction, thereby improving the recognition accuracy of thin interbedded geological sedimentary bodies and realizing the thin reservoir seismic prediction method based on sub-channel gathers.

[0039] First, according to step 1, obtain the seismic data gather data.

[0040] Secondly, according to step 2, the seismic gather data is processed by frequency division, and the high-frequency seismic gather 20HZ-50HZ is selected as the basis of the angle stacking gather.

[0041]

[0042] Where: m is the sampling number in the frequency domain; n is the sampling number in the time domain; N is the total number of seismic sampling in the time domain; Δf is the sampling interval in the frequency domain; Δt is the sampling interval in the time domain.

[0043] Then, according to step 3, the 20Hz-50Hz gathers are stacked to obtain the stacked profiles of the near, middle and far frequency gathers.

[0044] Step 4: Extract sub-waves at different angles on the superimposed sections of the frequency sub-channel sets at different angles.

[0045] Step 5: Through layer control, carry out synthetic record calibration based on the structural model to enhance the correlation between the synthetic earthquake record and the original earthquake. The synthetic record calibration quality is determined by the synthetic record correlation coefficient determination method. If the correlation coefficient is <0.7, return to step 4. If the correlation coefficient is >0.7, go to step 6.

[0046] Step 6: Determine the multi-well wavelets through synthetic record calibration and perform wavelet normalization to determine the integrated wavelet.

[0047] Step 7, through the pre-stack inversion method, use the normalized integrated wavelet and high-frequency angle stacking seismic data to carry out pre-stack seismic inversion; obtain the inversion data volume and identify the thin interbedded sedimentary geological body.

Claims

1. A thin reservoir seismic prediction method based on a frequency set, characterized in that: The following steps are involved: S1. Obtaining seismic gather data volume; S2, frequency scanning the seismic gather data volume to obtain frequency-divided channel gather data volumes of different frequencies, and extracting the high-frequency channel gather data volume as basic gather data; S3, using the obtained high-frequency channel set data volume to carry out angle stacking, and respectively obtain a near-angle high-frequency channel set stacking section, a medium-angle high-frequency channel set stacking section, and a far-angle high-frequency channel set stacking section; S4, extracting near-angle high-channel set stacked seismic wavelets, medium-angle high-channel set stacked seismic wavelets, and far-angle high-channel set stacked seismic wavelets from the near-angle high-channel set stacked section, the medium-angle high-channel set stacked section, and the far-angle high-channel set stacked section respectively; S5. Take the top and bottom layers of the target layer as the control layers, carry out synthetic record calibration, and determine the calibration quality of the synthetic record by the correlation coefficient; S6, performing standard wavelet calculation on the near-angle high-channel set stacked seismic wavelet, the medium-angle high-channel set stacked seismic wavelet, and the far-angle high-channel set stacked seismic wavelet obtained in step S4, and selecting the wavelet with the largest correlation coefficient as the standard wavelet; S7, eliminating the wavelets that do not meet the correlation coefficient condition in the standard wavelets, and normalizing them to obtain a wavelet set that meets the requirements, and superimposing them to obtain a comprehensive wavelet; S8. Using the comprehensive wavelet as the inversion wavelet, pre-stack inversion is performed for seismic data by using the near-angle high-channel set stacked section, the medium-angle high-channel set stacked section, and the far-angle high-channel set stacked section to obtain the inversion data volume and identify the thin interbedded sedimentary geological body; The frequency division formula in step S2 is: Among them, m is the number of frequency domain samples; n is the number of time domain samples; N is the total number of time domain seismic samples; Δf is the frequency domain sampling interval; Δt is the time domain sampling interval; The method for calculating the correlation coefficient in step S5 comprises the following steps: S51. Calculate sample mean With statistical error Among them, X i (i=1,2,3…,n) is the error of the sampling number; is the average error; e i is the statistical error; S52. Calculate standard error Among them, e i is the statistical error, n is the number of samples; S53, its correlation coefficient The calculation formula of the standard wavelet in step S6 is: Among them, r is the sub-wave of different shot points, and n is the x,y sequence number of different shot points; The formula for eliminating the wavelets that do not meet the correlation coefficient condition in step S7 is: remove(r x,y <αR max )(0.6<α<0.8) Among them, r x,y is the wavelet of the spatial shot point, R max is the standard wavelet, α is the correlation coefficient; The formula for normalization processing in step S7 is: Among them, Y is the spectrum of the standard sub-wave, X is the spectrum of each sub-wave, and a is the white noise factor.

Images