Quantitative analysis method and system based on well-seismic combination seismic result gather frequency-broadening potential

Abstract

The invention discloses a seismic result gather frequency-broadening potential quantitative analysis method and system based on well-seismic combination, and relates to the technical field of seismic data processing, and the method comprises the steps: making synthetic records corresponding to different dominant frequencies based on the logging data of a target interval; based on a linear prediction method, estimating an actual seismic channel spectrum and spectrums of synthetic records corresponding to different dominant frequencies; calculating the Pearson's correlation coefficient of the actual seismic trace spectrum and the spectrum of the synthetic record corresponding to different dominant frequencies; and determining the dominant frequency corresponding to the frequency spectrum of the synthetic record with the Pearson's correlation coefficient exceeding a preset threshold value as a frequency expansion potential value of the existing seismic result gather data of the target interval. According to the method, the technical problem of lack of quantitative analysis of the seismic data frequency-expanding potential in the prior art is relieved.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data processing, and in particular to a method and system for quantitatively analyzing the potential of frequency expansion of well-seismic combined seismic results gathers. Background Art

[0002] Analyzing the potential of frequency extension of existing seismic data is a question that seems simple but is actually difficult to answer. The difficulty lies in that if we only start from the perspective of signal processing, there are many processing methods that can increase the main frequency of seismic signals and widen the frequency band, but are the additional reflection events after the simple increase of the main frequency and widening of the frequency band still a true reflection of the characteristics of the underground stratum reflection interface? In other words, is there an intrinsic relationship between the number and amplitude of reflection events and the size of the underground stratum reflection interface and reflection coefficient? This situation cannot be defined from the perspective of pure signal frequency extension processing. Most of the existing frequency extension potential analyses are based on some qualitative processing cognitions, such as: 1) the original reflection wave group characteristics cannot be destroyed after frequency extension processing; 2) the low-frequency information of the signal cannot be lost after frequency extension processing; 3) the signal-to-noise ratio cannot be reduced after frequency extension processing, etc.; but there is a lack of quantitative analysis of frequency extension potential. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for quantitatively analyzing the potential of frequency expansion based on well-seismic combined with seismic results in order to solve at least one of the above technical problems.

[0004] In the first aspect, an embodiment of the present invention provides a quantitative analysis method for the frequency extension potential based on well-seismic combined with seismic results track gathers, including: based on the logging data of the target layer segment, preparing synthetic records corresponding to different main frequencies; based on the linear prediction method, estimating the actual seismic track spectrum and the spectrum of the synthetic records corresponding to the different main frequencies; calculating the Pearson correlation coefficient between the actual seismic track spectrum and the spectrum of the synthetic records corresponding to the different main frequencies; determining the main frequency corresponding to the spectrum of the synthetic record whose Pearson correlation coefficient exceeds a preset threshold as the frequency extension potential value of the existing seismic results track gather data of the target layer segment.

[0005] Furthermore, based on the logging data of the target layer segment, synthetic records corresponding to different main frequencies are produced, including: based on the convolution theory, the logging data is combined with seismic data, and Ricker wavelets with different main frequencies are used to produce synthetic records of the target layer segment.

[0006] Furthermore, based on a linear prediction method, estimating the spectrum of the actual seismic trace and the spectrum of the synthetic record corresponding to the different main frequencies includes:

[0007]

[0008] Where S(f) is the spectrum of the actual seismic trace or the spectrum of the synthetic record corresponding to the different main frequencies, E p is the minimum prediction error energy, T is the seismic record sampling period, a k is the AR parameter model coefficient estimated based on the linear prediction method, and P is the AR parameter model order.

[0009] Furthermore, the method further comprises: determining the order of the AR parameter model based on an autoregressive transfer function criterion.

[0010] In the second aspect, an embodiment of the present invention further provides a quantitative analysis system for the frequency extension potential based on well-seismic combined with seismic results track gathers, comprising: a synthesis module, an estimation module, a calculation module and a determination module; wherein the synthesis module is used to produce synthetic records corresponding to different main frequencies based on the logging data of the target layer segment; the estimation module is used to estimate the actual seismic trace spectrum and the spectrum of the synthetic records corresponding to the different main frequencies based on the linear prediction method; the calculation module is used to calculate the Pearson correlation coefficient between the actual seismic trace spectrum and the spectrum of the synthetic records corresponding to the different main frequencies; the determination module is used to determine the main frequency corresponding to the spectrum of the synthetic record whose Pearson correlation coefficient exceeds a preset threshold as the frequency extension potential value of the existing seismic results track gather data of the target layer segment.

[0011] Furthermore, the synthesis module is also used to: utilize the logging data in combination with seismic data based on convolution theory, and use Ricker wavelets with different main frequencies to produce a synthetic record of the target layer segment.

[0012] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the embodiment of the present invention when executing the computer program.

[0013] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method provided in the embodiment of the present invention is implemented.

[0014] The present invention provides a method and system for quantitative analysis of the frequency extension potential of well-seismic combined seismic gathers, which estimates the frequency spectrum of actual seismic trace matching and synthetic records with different main frequencies through a linear prediction method, and calculates the Pearson correlation coefficient. The frequency extension potential value of existing seismic gather data is determined through the Pearson correlation coefficient, thereby realizing quantitative analysis of the frequency extension potential of well-seismic combined seismic gathers, and alleviating the technical problem of the lack of quantitative analysis of the frequency extension potential of seismic data in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 A flowchart of a quantitative analysis method for the potential of frequency extension based on well-seismic combined with seismic results gathers provided in an embodiment of the present invention;

[0017] Figure 2 A schematic diagram of a frequency spectrum of an existing seismic stacking target layer segment estimated by a linear prediction method provided in an embodiment of the present invention;

[0018] Figure 3 A correlation analysis diagram of a target layer segment actual seismic gather record spectrum and a different main frequency synthetic record spectrum provided by an embodiment of the present invention;

[0019] Figure 4 A schematic diagram of comparison between actual seismic gather recorded spectrum of a target layer segment before and after spectrum spreading processing and synthesized recorded spectrum with different main frequencies provided by an embodiment of the present invention;

[0020] Figure 5 A schematic diagram of a result of spectrum spreading processing provided by an embodiment of the present invention;

[0021] Figure 6 A schematic diagram of a quantitative analysis system for the potential of frequency expansion based on well-seismic combined with seismic results gathers provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] 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 are within the scope of protection of the present invention.

[0023] Embodiment 1

[0024] Figure 1 1 is a flow chart of a quantitative analysis method of the potential of frequency expansion based on well-seismic combined seismic results gathers provided according to an embodiment of the present invention. Figure 1 As shown, the method specifically comprises the following steps:

[0025] Step S102: Based on the logging data of the target layer, synthetic records corresponding to different main frequencies are produced.

[0026] Step S104, based on the linear prediction method, the spectrum of the actual seismic trace and the spectrum of the synthetic records corresponding to different main frequencies are estimated.

[0027] Step S106, calculating the Pearson correlation coefficient between the actual seismic trace spectrum and the spectrum of the synthetic record corresponding to different main frequencies.

[0028] Step S108, determining the main frequency corresponding to the spectrum of the synthetic record whose Pearson correlation coefficient exceeds the preset threshold as the frequency extension potential value of the existing seismic gather data of the target layer segment.

[0029] Specifically, in step S102, the existing seismic gather data and stacked data volume are first loaded and analyzed, and then based on the convolution theory, the logging data are combined with the seismic data, and Ricker wavelets with different main frequencies are used to produce synthetic records of the target layer segment.

[0030] Specifically, the convolution theory of transmitting earthquakes includes:

[0031] For vertically incident seismic waves, the reflection coefficient is:

[0032] R=(Z2―Z1) / (Z2+Z1) (1)

[0033] Where Z1 is the wave group impedance (the product of velocity and density) of the first layer, and Z2 is the wave group impedance (the product of velocity and density) of the second layer;

[0034] From formula (1), we can see that the reflection coefficient reflects the difference in the anti-interface of the formation wave group.

[0035] According to the convolution model theory, the seismic trace can be expressed as follows when multiple reflections and noise are ignored:

[0036] s(t)=w(t)*ξ(t) (2)

[0037] Where s(t) is the amplitude of the seismic record in the time domain, w(t) is the wavelet in the time domain, and ξ(t) is the reflection coefficient in the time domain;

[0038] Considering the influence of absorption and attenuation on seismic waves during propagation, this influence can be regarded as the ground filtering effect of the source wavelet, and w(t) can be written as:

[0039] w(t)=b(t)*g(t) (3)

[0040] Where b(t) is the source wavelet, g(t) is the ground filter;

[0041] Substituting formula (3) into formula (2), we have

[0042] s(t)=b(t)*g(t)*ξ(t) (4)

[0043] From formula (4), it can be seen that the amplitude of the actual seismic reflection wave is the result of the convolution and superposition of a single reflection coefficient and the source wavelet after attenuation by the earth absorption. The amplitude at each moment is formed by the superposition of many reflection coefficients. The phase information at each moment is superimposed on each other. The strength of the reflection wave amplitude depends on the size of the reflection coefficient and the combination of the reflection coefficients. However, the amplitude of a single reflection seismic record can be regarded as a reflection coefficient with a limited bandwidth. This provides a theoretical basis for the present invention to use well logging data to make synthetic records to quantitatively describe the topology of existing seismic data.

[0044] Specifically, according to the convolution theory, the reflection record of the target layer can be regarded as the result of the convolution of the source wavelet and the reflection coefficient of the underground stratum interface during this period after the earth filtering, while the synthetic record made with logging data can be regarded as the result of the convolution of the source wavelet and the reflection coefficient of the underground stratum interface without the earth filtering. Under the premise that the previous processing work is amplitude preservation, the spectral characteristics of the two should have a high similarity. Based on this, the potential of frequency extension of seismic data can be quantitatively evaluated by combining well and seismic data.

[0045] According to the logging data, the synthetic record of the target layer is made with Ricker wavelets of different main frequencies. The main frequency of the seismic data of the target layer of the well bypass channel of the existing seismic results is analyzed, and compared with the synthetic record of the same main frequency for calibration. Under the premise of maintaining the amplitude, the strength relationship and wave group characteristics of the two wave groups should have a high similarity.

[0046] Specifically, the spectrum calculation formula in step S104 includes:

[0047]

[0048] Where S(f) is the spectrum of the actual seismic trace or the spectrum of the synthetic record corresponding to different main frequencies, E p is the minimum prediction error energy, T is the seismic record sampling period, a k is the AR parameter model coefficient estimated based on the linear prediction method, and P is the AR parameter model order.

[0049] Specifically, the spectrum analysis of signals is one of the important means to study signal characteristics. Data spectrum analysis usually uses Fourier transform. This method implicitly assumes that the data outside the data working area is zero, which is equivalent to windowing the data. Only when the signal to be analyzed is a stationary random signal and has a certain observation length can the frequency resolution, the stability of the spectrum estimation and a sufficient number of frequency points be guaranteed. The seismic signals intercepted by the target layer and the synthetic record data made from logging data are non-stationary signals, and the data length is generally short. The Fourier transform is used to provide sufficient frequency points and spectrum stability for subsequent analysis. Therefore, it is more appropriate to use the AR parameter model method, which is the most widely used in modern spectrum estimation. Linear prediction analysis is a commonly used method in model spectrum estimation. The reflection coefficient is regarded as an all-pole AR model H(z), and the seismic record s(n) is the output generated by the wavelet w(n) to excite the model. The transfer function of the model is:

[0050]

[0051] In the above formula: G is the gain constant, P is the model order, a i is the model coefficient. The model coefficient can be used to construct a P-order prediction system, using the first P data of the earthquake record to predict the value of the current data s(n):

[0052]

[0053] The predicted mean square error is:

[0054]

[0055] Linear prediction analysis starts from the observed data and estimates the model coefficient a according to the minimum prediction mean square error criterion. i , these coefficients are called linear prediction coefficients (LPC coefficients). Calculate the LPC coefficients a i and the minimum prediction error energy E p Commonly used methods include the Levinson-Durbin recursive algorithm based on autocorrelation.

[0056] In solving the model coefficient a i After that, the frequency spectrum of the earthquake record can be calculated according to the following formula:

[0057]

[0058] Where T is the sampling period of earthquake records, E P is the minimum prediction error energy when the model order is P. The frequency f can be arbitrarily set within the Nyquist sampling quantitative range (0≤f≤1 / (2T)), thereby providing sufficient frequency points for cepstrum domain analysis.

[0059] Specifically, the method provided by the embodiment of the present invention further includes: determining the order of the AR parameter model based on the autoregressive transfer function criterion.

[0060] Specifically, since the poles of the seismic recording signal are unknown, the order P of the model is not known a priori. When the model order is too low, the spectrum estimation of the seismic recording is too smooth and cannot provide sufficient resolution; when the model order is too high, the spectrum estimation result will produce oscillations and spectrum splitting will occur. In order to eliminate spectrum splitting and improve the resolution of spectrum estimation, it is necessary to select a suitable model order. The present invention adopts the autoregressive transfer function criterion (CAT) to select the model order:

[0061]

[0062] Where N is the length of the earthquake record, E k is the minimum prediction error energy when the model order is k. The order P that makes equation (9) the minimum value can be taken as the optimal model order.

[0063] Figure 2 FIG. 1 is a schematic diagram of a spectrum of an existing seismic stacking target layer segment estimated by a linear prediction method according to an embodiment of the present invention. Figure 2 As shown in the figure, the amplitude of the reflected seismic signal can be regarded as the reflection coefficient of limited bandwidth, and the magnitude of the seismic amplitude depends on the magnitude of the reflection coefficient and the combination of the reflection coefficient. This provides a theoretical basis for quantitatively analyzing the potential of frequency extension of existing seismic data by the similarity and attenuation slope of the actual seismic trace reflection amplitude spectrum and the synthetic record reflection amplitude spectrum. The estimation process is: the amplitude spectrum of the actual seismic signal target layer and the amplitude spectrum of the synthetic record with different main frequencies are regarded as two digital series, and the Pearson correlation coefficient is used to measure the similarity of the two series, and its value is between 0 and 1. The intuitive expression of this linear correlation is whether Y increases at the same time as X increases, or whether Y decreases at the same time as X decreases; when the two are distributed on a straight line, the Pearson correlation coefficient is equal to 1; if there is no linear relationship between the two variables, the Pearson correlation coefficient is 0, and r is usually used to represent the Pearson correlation coefficient. The correlation coefficient of the two digital series is:

[0064]

[0065] Where:

[0066]

[0067]

[0068] After calculating the Pearson correlation coefficient, the range of correlation strength values ​​in the table below can be used to quantitatively estimate the increase in the main frequency of the target layer of the existing seismic data set.

[0069] Pearson correlation coefficient |r| Correlation strength (dominant frequency increased) 0.8-1.0 Very strong correlation 0.6-0.8 Strong correlation 0.4-0.6 Moderately related 0.2-0.4 Weak correlation 0.0–0.2 Not relevant

[0070] Therefore, the process of quantitatively estimating the frequency extension potential of existing seismic gather data is:

[0071] a. Calculate the Pearson correlation coefficient between the actual seismic record spectrum series of the target layer and the spectrum series of synthetic records with different main frequencies according to formula (10);

[0072] b. Measure the Pearson correlation coefficient value of the actual seismic record spectrum series of the target layer segment and the spectrum series of synthetic records with different main frequencies. Take the Pearson correlation coefficient value of 0.5 as the critical value, and determine the main frequency of the synthetic record corresponding to the degree of similarity between the actual seismic record target layer reflection amplitude spectrum and the synthetic record reflection amplitude spectrum greater than 0.5 as the main frequency improvement potential value after the existing seismic data is processed by spectrum extension.

[0073] From the above description, it can be seen that an embodiment of the present invention provides a quantitative analysis method for the frequency extension potential based on well-seismic combined with seismic results gathers, estimates the frequency spectrum of actual seismic trace matching and different main frequency synthetic records through a linear prediction method, and calculates the Pearson correlation coefficient. The frequency extension potential value of the existing seismic results gather data is determined through the Pearson correlation coefficient, thereby realizing the quantitative analysis of the frequency extension potential based on well-seismic combined with seismic results gathers, and alleviating the technical problem of the lack of quantitative analysis of the frequency extension potential of seismic data in the prior art.

[0074] Embodiment 2

[0075] Figure 3 : is a correlation analysis diagram of the actual seismic gather recording spectrum of the target layer segment and the spectrum of the synthetic recordings with different main frequencies provided according to an embodiment of the present invention, such as Figure 3 As shown in the figure, the ordinate is the Pearson correlation coefficient calculated according to formula (10), and the abscissa is the frequency. When the Pearson correlation coefficient is 0.5, the main frequency value of the corresponding synthetic record is about 28 Hz, while the main frequency of the target layer spectrum of the existing seismic results gather is about 12 Hz. Therefore, under the premise of maintaining the amplitude, the main frequency of the target layer can be increased by 16 Hz to about 28 Hz.

[0076] Figure 4 1 is a schematic diagram of a comparison between the actual seismic gather recorded spectrum of the target layer segment before and after spectrum spreading processing and the synthesized recorded spectrum with different main frequencies according to an embodiment of the present invention. Figure 4 As shown in the figure, the spectrum of the actual seismic record of the target layer after the frequency extension processing is most similar to the spectrum of the synthetic record with a main frequency of 30 Hz, and the amplitude preservation of the frequency extension processing result is good.

[0077] Figure 5 FIG. 1 is a schematic diagram of a result of spectrum spreading processing provided according to an embodiment of the present invention. Figure 5As shown, the effectiveness of the method provided by the embodiment of the present invention can be well demonstrated.

[0078] Embodiment 3

[0079] Figure 6 Schematic diagram of a quantitative analysis system for the potential of frequency expansion based on well-seismic combined seismic results gathers provided according to an embodiment of the present invention. Figure 6 As shown, the system includes: a synthesis module 10, an estimation module 20, a calculation module 30 and a determination module 40.

[0080] Specifically, the synthesis module 10 is used to produce synthetic records corresponding to different main frequencies based on the logging data of the target layer section;

[0081] An estimation module 20, for estimating the spectrum of the actual seismic trace and the spectrum of the synthetic records corresponding to different main frequencies based on a linear prediction method;

[0082] A calculation module 30, for calculating the Pearson correlation coefficient between the spectrum of the actual seismic trace and the spectrum of the synthetic record corresponding to different main frequencies;

[0083] The determination module 40 is used to determine the main frequency corresponding to the spectrum of the synthetic record whose Pearson correlation coefficient exceeds the preset threshold as the frequency extension potential value of the existing seismic gather data of the target layer segment.

[0084] Specifically, the synthesis module 10 is also used to: utilize logging data in combination with seismic data based on the convolution theory, and use Ricker wavelets with different main frequencies to produce synthetic records of the target layer segment.

[0085] The present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the embodiment of the present invention when executing the computer program.

[0086] The present invention also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the method provided in the embodiment of the present invention is implemented.

[0087] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0088] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A quantitative analysis method for the potential of frequency expansion based on well-seismic combined with seismic results gathers, characterized in that: include: Based on the logging data of the target layer, synthetic records corresponding to different main frequencies are produced; Based on a linear prediction method, estimating the spectrum of the actual seismic trace and the spectrum of the synthetic record corresponding to the different main frequencies; Calculating the Pearson correlation coefficient between the actual seismic trace spectrum and the spectrum of the synthetic record corresponding to the different main frequencies; The main frequency corresponding to the spectrum of the synthetic record whose Pearson correlation coefficient exceeds a preset threshold is determined as the frequency extension potential value of the existing seismic gather data of the target layer segment.

2. The method according to claim 1, characterized in that: Based on the logging data of the target layer segment, synthetic records corresponding to different main frequencies are produced, including: based on the convolution theory, the logging data is combined with seismic data, and Ricker wavelets with different main frequencies are used to produce synthetic records of the target layer segment.

3. The method according to claim 1, characterized in that: Based on a linear prediction method, the actual seismic trace spectrum and the spectrum of the synthetic record corresponding to the different main frequencies are estimated, including: Where S(f) is the spectrum of the actual seismic trace or the spectrum of the synthetic record corresponding to the different main frequencies, E p is the minimum prediction error energy, T is the seismic record sampling period, a k is the AR parameter model coefficient estimated based on the linear prediction method, and P is the AR parameter model order.

4. The method according to claim 3, characterized in that: The method further includes determining the AR parameter model order based on an autoregressive transfer function criterion.

5. A quantitative analysis system for the potential of frequency expansion based on well-seismic combined with seismic results, characterized in that: include: A synthesis module, an estimation module, a calculation module and a determination module; wherein, The synthesis module is used to produce synthetic records corresponding to different main frequencies based on the logging data of the target layer segment; The estimation module is used to estimate the spectrum of the actual seismic trace and the spectrum of the synthetic record corresponding to the different main frequencies based on the linear prediction method; The calculation module is used to calculate the Pearson correlation coefficient between the actual seismic trace spectrum and the spectrum of the synthetic record corresponding to the different main frequencies; The determination module is used to determine the main frequency corresponding to the spectrum of the synthetic record whose Pearson correlation coefficient exceeds a preset threshold as the spectrum extension potential value of the existing seismic result gather data of the target layer segment.

6. The system according to claim 5, characterized in that: The synthesis module is also used to: utilize the logging data in combination with seismic data based on convolution theory, and use Ricker wavelets with different main frequencies to produce a synthetic record of the target layer segment.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 4 when executing the computer program.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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