Processing method based on Fresnel zone constraint shale oil non-reflection weak signal enhancement

Abstract

The invention relates to the technical field of non-reflection signal processing, in particular to a processing method for shale oil non-reflection weak signal enhancement based on Fresnel zone constraint, which comprises the following steps: collecting well seismic geological data of a shale oil field, processing to obtain a CRP gather data vector, analyzing the frequency domain characteristics of each modal component of the CRP gather data vector, and obtaining a CRP gather signal; determining an amplitude attenuation stability coefficient and a non-reflection wave characteristic value of each modal component, analyzing the difference between each modal component and other modal components about the non-reflection wave characteristic value to obtain reflection signal identification of each modal component so as to extract a reflection signal, and performing wavelet fusion processing to obtain reflection wave field CRP gather data; obtaining subtraction CRP gather data by adopting a self-adaptive subtraction filtering algorithm; and through coherence consistency testing, mixed wave superposition processing and quality control processing, a result after non-reflection weak signal enhancement is obtained. According to the invention, the enhanced processing effect of the shale oil non-reflection signal can be provided.

Description

Technical Field

[0001] The present application relates to the technical field of non-reflection signal processing, and in particular to a processing method for enhancing non-reflection weak signals of shale oil based on Fresnel band constraints. Background Art

[0002] During the exploration of continental shale oil, basins are subject to the influence and transformation of multiple phases of tectonic movement, resulting in the widespread development of strike-slip faults with small throws. Low-order faults and small faults, which are crucial for shale oil exploration, often develop along strike-slip fault zones. Strike-slip faults and small faults have unique seismic response mechanisms. Among the various types of information received by reflection seismic, which is primarily based on single reflections, only weak signals such as non-reflection signals and diffraction wavefields near small faults can be useful for imaging strike-slip faults and fractures. Therefore, most current methods choose to enhance the processing of shale oil non-reflection weak signals to improve the response characteristics of shale oil reservoir non-reflection seismic weak signals, thereby increasing the success rate of shale oil reservoir exploration.

[0003] Currently, research methods for enhancing weak, non-reflective shale oil signals use adaptive subtraction filtering to extract them from full-wavefield data, aiming to more accurately characterize the response characteristics of weak, non-reflective seismic signals from shale oil reservoirs. However, traditional adaptive subtraction filtering uses a predictive approach to characterize noise interference data, subtracting it from the full-wavefield data to obtain the suppressed, non-reflective shale oil signal. However, in practice, the noise interference data predicted by traditional adaptive subtraction filtering cannot accurately describe the data characteristics of the reflected waves, making the adaptive subtraction results ineffective in suppressing the reflected waves in the reflection wavefield. This results in poor accuracy in extracting weak, non-reflective shale oil signals and, consequently, ineffective enhancement of weak, non-reflective seismic signals from shale oil reservoirs. Summary of the Invention

[0004] In order to solve the above technical problems, the present application provides a processing method for enhancing non-reflective weak signals of shale oil based on Fresnel band constraints to solve the existing problems.

[0005] The processing method of the present application for enhancing the non-reflective weak signal of shale oil based on Fresnel zone constraint adopts the following technical solutions:

[0006] One embodiment of the present application provides a method for enhancing non-reflective weak signals of shale oil based on Fresnel band constraints, comprising the following steps:

[0007] Collect well seismic geological data from shale oil fields to obtain CMP gather data Data5;

[0008] The Fresnel zone constraint and dip gather generation technology are used to perform prestack migration processing on the CMP gather data Data5 under steady phase control to obtain the CRP gather data Data6, which is converted into vector form and recorded as CRP gather data vector, and the modal components of the CRP gather data vector are extracted.

[0009] Analyze the frequency domain characteristics of each modal component of the CRP gather data vector. According to the change of the frequency amplitude and amplitude difference in the frequency domain of each modal component, determine the amplitude attenuation stability coefficient of each modal component. Combined with the downward trend of the frequency amplitude of each modal component, obtain the non-reflected wave characteristic value of each modal component. Analyze the degree of difference between each modal component and other modal components with respect to the non-reflected wave characteristic value to obtain the reflection signal recognition degree of each modal component.

[0010] The reflection signal is extracted by the reflection signal recognition of each modal component of the CRP gather data vector, and the weak reflection signal is processed by wavelet fusion to obtain the reflection wave field CRP gather data. Then, the reflection wave field CRP gather data is filtered out by the adaptive subtraction filtering algorithm to obtain the subtraction CRP gather data.

[0011] The subtraction CRP gather data were subjected to coherence consistency test, mixing and superposition processing, and quality control processing to obtain the result after the non-reflection weak signal was enhanced.

[0012] Preferably, the IMF modal components obtained by performing variational modal decomposition on the CRP gather data vector are used as the modal components of the CRP gather data vector.

[0013] Preferably, the amplitude attenuation stability coefficient of each modal component is calculated as follows:

[0014] In the formula, Gu j is the amplitude attenuation stability coefficient of the jth modal component, exp() is an exponential function with a natural constant as the base, R is the number of elements in the second-order difference vector of the amplitude vector of the jth modal component, Fu j,t and fu j,t-1 They are respectively the tth and t-1th elements in the second-order difference vector of the amplitude vector of the jth modal component.

[0015] Preferably, the process of constructing the amplitude vector further includes: arranging the amplitudes in the spectrum diagram of each modal component in ascending order of frequency to form a vector, which is recorded as the amplitude vector of each modal component.

[0016] Preferably, the calculation method of the non-reflected wave characteristic value of each modal component is:

[0017] De j =Gu j / (Xuj +∈), where De j is the non-reflected wave eigenvalue of the jth modal component, ∈ is a constant to avoid the denominator being 0, Xu j is the amplitude attenuation of the j-th modal component, which is obtained by the amplitude change degree in the amplitude vector.

[0018] Preferably, the amplitude attenuation is the absolute value of the sum of all negative elements in the first-order difference vector of the amplitude vector.

[0019] Preferably, the calculation method of the recognition degree of the reflected signal of each modal component is:

[0020] Where, Hg j is the recognition degree of the reflection signal of the jth modal component, K is the number of modal components of the CRP gather data vector, De j 、De k are the non-reflected wave eigenvalues ​​of the j-th and k-th modal components, respectively.

[0021] Preferably, the extraction of the reflection signal further includes: performing threshold segmentation on the reflection signal recognition degrees of all modal components of the CRP gather data vector, and taking the modal component corresponding to the reflection signal recognition degree higher than the segmentation threshold as the reflection signal.

[0022] Preferably, the acquisition of the reflection wavefield CRP gather data includes: performing fusion processing on the reflection signal by using a wavelet fusion algorithm to obtain the reflection wavefield CRP gather data.

[0023] Preferably, the acquisition of the subtracted CRP gather data further comprises:

[0024] After the seismic data is migrated, the migrated CRP gather data are collected and recorded as the full-wavefield CRP gather data;

[0025] The full-wavefield CRP gather data and the reflected-wavefield CRP gather data are input into the adaptive subtraction filtering algorithm, the reflected-wavefield CRP gather data are filtered out from the full-wavefield CRP gather data, and the subtraction CRP gather data are output.

[0026] This application has at least the following beneficial effects:

[0027] This application distinguishes between signals that contribute to imaging and those that do not, based on Fresnel zone constraints and dip gather technology, effectively separating reflection signals from non-reflection signals, and enhancing the characterization of shale oil reservoir fault and fracture responses.

[0028] At the same time, based on the analysis of the frequency domain characteristics of each modal component of the CRP gather data Data6, a differential technique was used to construct an amplitude attenuation stability coefficient. The amplitude attenuation stability coefficient reflects the rate of change of the energy attenuation of the non-reflection signal. The non-reflection wave characteristic value was also constructed based on the amplitude attenuation stability coefficient. The non-reflection wave characteristic value reflects the characteristics of the non-reflection wave signal in the CRP gather data Data6, which helps to subsequently eliminate the non-reflection wave component of the CRP gather data Data6. Then, the adaptive subtraction technique was used to accurately extract the weak non-reflection wave signal of shale oil. Based on the non-reflection wave characteristic value and its variation characteristics, a reflection signal recognition degree was constructed to enhance the reflection wave data characteristics of the reflection wave field in the CRP gather data Data6 and eliminate the interference of the non-reflection signal. The adaptive subtraction filtering algorithm was used to perform adaptive subtraction processing on the full-wavefield CRP gather data based on the reflection wavefield CRP gather data, thereby accurately extracting the weak non-reflection wave signal of shale oil in the full wavefield. Then, weighted mixing testing and processing were used to enhance the weak non-reflection signal without changing the fidelity of the original data.

[0029] Therefore, on the first hand, the present application accurately describes the data characteristics of the reflected waves in the full wave field, so that the result of adaptive subtraction can effectively suppress the reflected waves of the reflected wave field, improve the accuracy of the extraction of non-reflective weak signals of shale oil, and thus effectively strengthen the non-reflective seismic weak signals of shale oil reservoirs; on the second hand, the present application can highlight the non-reflective weak signals on the basis of fidelity, realize "enhancement" on the basis of "fidelity", and play an important supporting role in shale oil seismic exploration; on the third hand, the present application realizes the efficient rapid separation of reflected wave field signals and non-reflective wave field signals, and determines the processing parameters through two quality controls of preliminary coherent body quality control and fine imaging logging quality control, and finally improves the resolution of non-reflective wave field weak signals through wavelet transform, and efficiently highlights non-reflective weak signals to the greatest extent, improves the accuracy of seismic prediction of fractured reservoirs, and improves the exploration success rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0031] Figure 1 A flowchart of the steps of the processing method for enhancing non-reflective weak signals of shale oil based on Fresnel zone constraints provided in this application;

[0032] Figure 2Schematic diagram of the five tilt angle ranges A, B, C, D, and E and the spatial constraint weights provided for this application;

[0033] Figure 3 This is a schematic diagram of an actual seismic section before the non-reflection weak signal enhancement of the present application is applied;

[0034] Figure 4 Schematic diagram of an actual seismic profile after the non-reflection weak signal of the present application is enhanced;

[0035] Figure 5 Schematic diagram of the distribution of small fracture planes detected without applying the non-reflection weak signal enhancement processing of the present application;

[0036] Figure 6 This is a schematic diagram of the distribution of small fracture planes detected after the non-reflective weak signal enhancement processing of this application;

[0037] Figure 7 Schematic diagram of the seismic imaging cross section of Well X1 without applying the non-reflection weak signal enhancement processing method of the present application;

[0038] Figure 8 This is a schematic diagram of the seismic imaging cross-section of Well X1 after applying the non-reflection weak signal enhancement processing method of the present application. DETAILED DESCRIPTION

[0039] To further illustrate the technical means and effectiveness of this application's implementation of the intended invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of the Fresnel-zone-constrained shale oil non-reflection weak signal enhancement method proposed in this application. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0040] Unless otherwise defined, terms such as "comprises," "comprising," or any other variants thereof are intended to encompass non-exclusive inclusion, such that a circuit structure, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such article or device. In the absence of further restrictions, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the article or device comprising the element. In addition, the term "and\or" as used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains.

[0041] The specific scheme of the processing method for enhancing non-reflective weak signals of shale oil based on Fresnel band constraints provided by the present application is described in detail below with reference to the accompanying drawings.

[0042] An embodiment of the present application provides a method for enhancing the non-reflective weak signal of shale oil based on Fresnel band constraint. For details, please refer to Figure 1 , including the following steps:

[0043] Step 1: Collect well seismic geological data from shale oil fields to obtain CMP gather data Data5.

[0044] This embodiment aims to employ Fresnel zone constraints and dip gather technology to distinguish between signals that contribute to imaging and those that do not, effectively separating reflection and non-reflection signals and enhancing the characterization of shale oil reservoir fracture and crack responses. Furthermore, by enhancing the data characteristics of reflection signals, the reflection wave data in the full wavefield data is more accurately characterized. Adaptive subtraction filtering is then used to filter out the reflection wave data in the full wavefield data, enabling more accurate extraction of weak non-reflection shale oil signals and effectively enhancing these weak non-reflection seismic signals from shale oil reservoirs.

[0045] During the seismic exploration process of shale oil fields in the target work area, in this embodiment, basic well-seismic geological data of the unconventional reservoir study area with shale oil as the main research object type are collected. The basic well-seismic geological data include well logging data, seismic post-stack data volume Data1, and seismic pre-stack CMP gather data (Common Middle Point).

[0046] In this embodiment, the Hechuan area of ​​the Sichuan Basin in the Daqing Oilfield mining area is used as the target work area. The target work area can be selected according to actual needs and is not specifically limited in this embodiment. The well logging data includes oil well location data, P-wave velocity data, S-wave velocity data, and density data.

[0047] Among them, the storage form of the seismic post-stack data volume is vector form, and the seismic post-stack data volume Data1 is input into the structural guided filtering processing algorithm. The seismic post-stack data volume is directionally smoothed by the structural guided filtering processing algorithm, and the output of the structural guided filtering processing algorithm is recorded as the guided filtering data volume Data2. The guided filtering data volume eliminates the background interference caused by the steeply inclined strata, making the fault distribution clearer and more conducive to the detailed interpretation of the structural target. Among them, the structural guided filtering processing algorithm is a well-known technology, and the specific process is not repeated here.

[0048] In traditional technology, the guided filter data volume processed by structural guided filtering can eliminate the background interference caused by steeply inclined strata, better maintain the edge characteristics, and is more conducive to the construction of local dip features, providing a favorable data basis for the precise calculation of subsequent pseudo-dip gathers.

[0049] To accurately extract pseudo-dip gathers, existing formation dip estimation techniques are used to identify the dip angle of the formation and construct a formation dip volume header, Data3. Seismic interpretation results are used to analyze the steering filter data volume, Data2. Two-dimensional complex traces are used to analyze the formation dip information. This information is extracted from the target formation and a formation dip volume is constructed. The formation dip volume is a three-dimensional dataset that depicts the formation dip angle of the target formation and accurately reveals the inclination and orientation characteristics of the geological structure in the target formation.

[0050] At the same time, in order to facilitate the subsequent extraction and interpretation of pseudo-dip gathers, the maximum formation dip of each imaging point in the formation dip body is calculated. The maximum formation dip reflects the maximum degree of inclination at different formation positions. The maximum formation dip of all imaging points in the formation dip body is written into the data body header to obtain the formation dip body header Data3.

[0051] The use of two-dimensional complex channels to analyze the dip information of the strata and the calculation of the maximum tilt are both well-known technologies, and the specific processes will not be described in detail.

[0052] Furthermore, Fresnel zone constraints and dip gather technology are used to distinguish signals that contribute to imaging from those that do not, effectively separating reflected and non-reflected signals and enhancing the characterization of shale oil reservoir faults and fracture responses.

[0053] The dip gather data is calculated by seismic prestack CMP gather data, and the calculation results are recorded as pseudo-dip gather data Data4; at the same time, the formation dip angles of all imaging points in the formation dip body trace head Data3 are divided into five dip ranges: A, B, C, D, and E. Among them, the formation dip angle of category A is less than 5 degrees, the pseudo-dip gather maximum dip field is ±5, and the spatial constraint weight is 0.2; the formation dip angle of category B is greater than or equal to 5 degrees and less than 10 degrees, and the pseudo-dip gather maximum dip field is ±5, and the spatial constraint weight is 0.2. The maximum inclination field of the pseudo-dip gather is ±10, and the spatial constraint weight is 0.4; the inclination of the formation of type C is greater than or equal to 10 degrees and less than 20 degrees, the maximum inclination field of the pseudo-dip gather is ±20, and the spatial constraint weight is 0.6; the inclination of the formation of type D is greater than or equal to 20 degrees and less than 30 degrees, the maximum inclination field of the pseudo-dip gather is ±30, and the spatial constraint weight is 0.8; the inclination of the formation of type E is greater than or equal to 30 degrees and less than 50 degrees, the maximum inclination field of the pseudo-dip gather is ±50, and the spatial constraint weight is 0.85. Specifically, in this embodiment, the schematic diagram of the five types of inclination ranges of A, B, C, D, and E and the spatial constraint weights is as follows: Figure 2 shown.

[0054] Furthermore, the pseudo-dip gather data Data4 is subjected to external excision processing of the pseudo-dip gather data through the five inclination ranges of A, B, C, D, and E, that is, the formation inclination data in the pseudo-dip gather data Data4 that is greater than 50 degrees and outside the maximum inclination field of ±50 of the pseudo-dip gather is excised, and the remaining results after the excision of the pseudo-dip gather are converted to the CMP domain to obtain the excised CMP gather data Data5, that is, the common middle point gather (Common Middle Point), wherein the calculation of the dip gather data and the conversion of the CMP domain are both well-known technologies, and the specific process will not be repeated.

[0055] Step 2: Use Fresnel zone constraint and dip gather generation technology to perform prestack migration processing on the CMP gather data Data5 under steady-phase control to obtain CRP gather data Data6, which is converted into vector form and recorded as CRP gather data vector, and the modal components of the CRP gather data vector are extracted.

[0056] Furthermore, for the CMP gather data Data5, the Fresnel zone constraint is used to perform pre-stack migration processing under steady-phase control to obtain the CRP gather data Data6. The Fresnel zone in the CMP gather data Data5 can be displayed vividly. The Fresnel zone boundary of each imaging point is determined by calculating the inclination of the reflection point, and the weight coefficient of each input seismic trace at the imaging point is calculated based on the Fresnel zone boundary of each imaging point. The CMP gather data Data5 is subjected to steady-phase migration calculation in combination with the weight coefficient of each input seismic trace at the imaging point. The results of the steady-phase migration calculation are organized according to the common reflection point to generate the CRP gather data Data6. The CRP gather data Data6 can reflect the reflection wave data characteristics of the reflection wave field, which is helpful for subsequent efficient wave field separation processing, thereby extracting the data characteristics of the weak signal of the non-reflected wave of shale oil. The confirmation of the Fresnel zone boundary and the steady-phase migration processing calculation are both well-known technologies, and the specific process will not be repeated.

[0057] In order to use the results of adaptive subtraction filtering technology to effectively suppress the reflected waves of the reflected wave field and improve the accuracy of extracting non-reflective weak signals of shale oil, it is necessary to enhance the reflected wave data characteristics of the reflected wave field in the CRP gather data Data6 and eliminate the non-reflective weak signal characteristics in the CRP gather data Data6.

[0058] The vector storage technology is used to convert the CRP gather data Data6 into a vector form for storage, which is recorded as a CRP gather data vector, and the CRP gather data vector is input into the VMD variational modal decomposition algorithm (Variational Modal Decomposition, VMD). The preset mode number is 20, and the VMD variational modal decomposition algorithm outputs the IMF modal components of the CRP gather data vector. Among them, the vector storage technology and the VMD variational modal decomposition algorithm are both well-known technologies. The specific process is existing technology and will not be repeated in this embodiment.

[0059] Step 3: Analyze the frequency domain characteristics of each modal component of the CRP gather data vector. According to the change of the frequency amplitude and amplitude difference in the frequency domain of each modal component, determine the amplitude attenuation stability coefficient of each modal component. Combined with the downward trend of the frequency amplitude of each modal component, obtain the non-reflected wave characteristic value of each modal component. Analyze the difference between each modal component and other modal components in terms of the non-reflected wave characteristic value to obtain the reflection signal recognition degree of each modal component.

[0060] Generally speaking, non-reflective weak signals of shale oil refer to seismic waves directly received by the detector in addition to the downlink wave field reflected at the wave impedance interface, such as scattered waves and surface waves. Compared with the reflection signals in the reflection wave field, non-reflective weak signals have the characteristics of irregular energy attenuation, complex frequency components and uneven frequency distribution. The various modal components of the CRP data vector contain the various reflection wave signals of the reflection wave field and the weak interference signals of the non-reflective waves. In order to enhance the reflection wave data characteristics in the CRP data set Data6, so that the adaptive subtraction filtering technology can more accurately extract the non-reflective weak signals of shale oil, it is necessary to analyze the frequency domain characteristics of each modal component.

[0061] Each modal component is input into the fast Fourier transform to obtain the frequency domain data of the unilateral frequency range, and the frequency spectrum of each modal component is constructed through the frequency domain data of the unilateral frequency range. In the frequency spectrum, the horizontal axis represents the frequency and the vertical axis represents the amplitude. The fast Fourier transform and the construction of the frequency spectrum are both well-known technologies, and the specific process will not be repeated here.

[0062] At the same time, the vector composed of the amplitudes in the spectrum diagram of each modal component arranged in ascending order of frequency is recorded as the amplitude vector of each modal component. The amplitude vector reflects the change of signal amplitude in the full wave field. If the amplitude attenuation in the amplitude vector changes faster, the faster the energy attenuation in the reflected wave field can be reflected; conversely, if the amplitude attenuation in the amplitude vector changes slower, the slower the energy attenuation in the non-reflected wave weak signal can be reflected.

[0063] In order to eliminate the non-reflective weak signal in the CRP data set Data6, the first-order difference vector and the second-order difference vector of the amplitude vector of the j-th modal component are calculated. The size of the elements in the first-order difference vector of the amplitude vector represents the attenuation change characteristics of the amplitude in the spectrum diagram, and the size of the elements in the second-order difference vector of the amplitude vector represents the change rate characteristics of the amplitude attenuation in the spectrum diagram. If the attenuation change characteristics of the amplitude in the spectrum diagram are smaller and the change rate of the amplitude attenuation is more stable, it can more accurately illustrate that the modal component actually represents the non-reflective signal in the full wave field of the shale oil environment. The calculation of the first-order difference vector and the second-order difference vector are both well-known technologies, and the specific process will not be repeated here.

[0064] Based on the above analysis, the amplitude attenuation stability coefficient of each modal component is calculated according to the degree of change of the amplitude within the amplitude vector of each modal component and the amplitude difference. In this embodiment, the calculation formula is:

[0065] In the formula, Gu j is the amplitude attenuation stability coefficient of the jth modal component, exp() is an exponential function with a natural constant as the base, R is the number of elements in the second-order difference vector of the amplitude vector of the jth modal component, Fu j,t and fu j,t-1 They are respectively the tth and t-1th elements in the second-order difference vector of the amplitude vector of the jth modal component.

[0066] It can be understood that in the above formula, the greater the difference between the elements in the second-order difference vector of the amplitude vector, the more unstable the rate of change of the amplitude attenuation in the spectrum diagram representing the modal component, and the more consistent it is with the characteristics of the seismic reflection signal in the full wavefield environment of shale oil. The smaller the stability of the attenuation change of the frequency domain amplitude of the modal component, the smaller the amplitude attenuation stability coefficient, that is, the more accurately it can be explained that the modal component actually represents the reflection signal in the full wavefield of the shale oil environment.

[0067] Furthermore, the amplitude attenuation of each modal component is calculated by analyzing the downward trend characteristics of the frequency amplitude of each modal component, and the non-reflected wave characteristic value of each modal component is calculated based on the amplitude attenuation stability coefficient of each modal component. In this embodiment, the specific calculation formula is:

[0068] De j =Gu j / (Xu j +∈), where De j is the non-reflected wave eigenvalue of the jth modal component, Xu jis the amplitude attenuation of the j-th modal component, which is the absolute value of the sum of all negative elements in the first-order difference vector of the amplitude vector of the j-th modal component. ∈ is a constant to avoid the denominator being 0, and its value range is 0 to 0.1. In this embodiment, it is 0.001.

[0069] It can be understood that the greater the amplitude attenuation of the modal component, the more significant the attenuation change characteristics of the frequency domain amplitude representing the modal component. At the same time, the amplitude attenuation stability coefficient Gu j The smaller it is, the more it can reflect the frequency domain characteristics of the reflected signal in the full wave field, but cannot reflect the amplitude attenuation characteristics of the non-reflected weak signal. That is, the smaller the characteristics of the non-reflected signal in the full wave field, the smaller the non-reflected wave characteristic value.

[0070] Generally speaking, the reflected wave energy of the full-aperture migration results is concentrated within the spatial Fresnel zone. The modal components of the CRP gather data vector extracted using the Fresnel zone constraint mainly contain reflected wave signals and very few non-reflected weak signals. At this time, the smaller the difference between the non-reflected wave eigenvalues ​​between the modal components and the smaller the eigenvalue of the non-reflected wave, the more likely it is that the modal component represented by the reflected signal in all modal components can be identified.

[0071] Based on the above analysis, the difference between each modal component and other modal components regarding the non-reflected wave characteristic value is analyzed, and the reflection signal recognition degree of each modal component is calculated. In this embodiment, the specific calculation formula is:

[0072] Where, Hg j is the recognition degree of the reflection signal of the jth modal component, K is the number of modal components of the CRP gather data vector, De j 、De k are the non-reflected wave eigenvalues ​​of the j-th and k-th modal components, respectively.

[0073] It can be understood that when the non-reflected wave characteristic value corresponding to the modal component is smaller and the difference in the non-reflected wave characteristic value between the modal component and other modal components is smaller, the characteristics of the reflected signal reflected by the modal component are more significant, the modal component represented by the reflected signal can be more accurately identified, and the recognition degree of the reflected signal is greater.

[0074] Step 4: Extract the reflection signal through the reflection signal recognition of each modal component of the CRP gather data vector, and perform wavelet fusion processing on the weak reflection signal to obtain the reflection wave field CRP gather data. Then, use the adaptive subtraction filtering algorithm to filter out the reflection wave field CRP gather data to obtain the subtraction CRP gather data.

[0075] Furthermore, all modal components representing non-reflection signals of the CRP gather data vector are eliminated to enhance the reflected wave data characteristics of the reflected wave field reflected in the CRP gather data Data6. In this embodiment, the reflection signal recognition of all modal components of the CRP gather data vector is input into the maximum inter-class variance algorithm, and the maximum inter-class variance algorithm outputs a segmentation threshold. The maximum inter-class variance algorithm is a well-known technology, and the specific process will not be repeated here.

[0076] The maximum inter-class variance technique can clearly derive the segmentation threshold for distinguishing reflection signals from non-reflection signals. Modal components corresponding to reflection signal recognition levels exceeding the segmentation threshold better reflect information about the reflection signal within the full wavefield environment of shale oil. Therefore, in this embodiment, modal components corresponding to reflection signal recognition levels exceeding the segmentation threshold are considered reflection signals. Furthermore, wavelet fusion technology is used to enhance the reflection wave data characteristics of the CRP gather data Data6, eliminating the interference effects of non-reflection signals. Modal components corresponding to reflection signal recognition levels exceeding the segmentation threshold are input into the wavelet fusion algorithm. The wavelet fusion algorithm outputs the signal fusion result, which is recorded as the reflection wavefield CRP gather data. The wavelet fusion algorithm is a well-known technique, and the specific process is not further described.

[0077] In order to more accurately extract the signal of the non-reflection wavefield for identification, after the seismic data migration processing, the CRP gather data corresponding to the velocity and gather migration are collected and recorded as the full-wavefield CRP gather data. The full-wavefield CRP gather data reflects the data characteristics of the full wavefield, while the reflected wavefield CRP gather data after signal enhancement specifically reflects the reflected wave data characteristics of the reflected wavefield.

[0078] Furthermore, in order to suppress the reflected waves of the reflected wavefield, the full-wavefield CRP gather data and the reflected wavefield CRP gather data are input into an adaptive subtraction filtering algorithm. The reflected wavefield CRP gather data is used as noise interference data, and the noise interference data is filtered out from the full-wavefield CRP gather data. The output of the adaptive subtraction filtering algorithm is the subtraction CRP gather data. The subtraction CRP gather data specifically reflects the characteristics of the non-reflection wavefield signal in the full-wavefield environment of shale oil. Among them, the adaptive subtraction filtering algorithm is a well-known technology, and the specific process will not be repeated.

[0079] Step 5: Perform coherence consistency test, mixing and superposition processing, and quality control processing on the subtracted CRP gather data to obtain the result after the non-reflection weak signal is enhanced.

[0080] Furthermore, through coherence consistency testing, mixed wave superposition processing, and quality control processing, the non-reflection wavefield signal is enhanced without changing the fidelity of the original data. Specifically, the non-reflection wavefield signal of the subtracted CRP gather data is enhanced according to the following process.

[0081] Preferably, in this embodiment, the subtracted CRP gather data is cut and stacked to form a post-stack data volume 2, and the target layer coherence volume is extracted from the seismic post-stack data volume Data1 and the post-stack data volume 2. The coherence of the post-stack data volume 2 is analyzed to determine whether there is distribution consistency in the coherence of the quality control seismic post-stack data volume Data1 in the area where the coherence is less than 0.5, so as to obtain the coherence consistency result, wherein the purpose is to examine whether the coherence of the post-stack data volume 2 is consistent in the area where the coherence of the seismic post-stack data volume Data1 is poor. It should be noted that the coherence consistency result is obtained through statistics. In actual application scenarios, the implementer performs statistics on his own, and there is no special limitation on this in this embodiment.

[0082] If the coherence consistency result is less than 80%, it fails quality control and the above process needs to be repeated. The maximum absolute value of the maximum dip field of the pseudo-dip gather is readjusted, and the maximum absolute value of each type of maximum dip field is reduced by 1. Then, the pseudo-dip gather external excision process is re-performed and repeated until the coherence consistency result is greater than or equal to 80%. It should be noted that the maximum absolute value of the maximum dip field of the pseudo-dip gather refers to the maximum value after taking the absolute value of the maximum dip field of the pseudo-dip gather.

[0083] Post-stack data volume 2, which has passed quality control, is multiplied by spatial constraint weights to obtain post-stack data volume 4. Seismic post-stack data volumes Data1 and 4 are then subjected to amplitude-weighted mixing processing. The mixing weight for seismic post-stack data volume Data1 is Weight1, and the mixing weight for post-stack data volume 4 is Weight2. After mixing processing, post-stack data volume 5 is obtained. Weight1 and Weight2 are obtained through testing, and Weight1 + Weight2 = 1. Weight1 and Weight2 are determined through multi-well synthetic record calibration. Implementers can also set their own mixing weights in actual application scenarios, and this is not limited in this embodiment.

[0084] The target layer amplitude consistency compensation processing is performed on the post-stack data 5 after the mixing process to obtain the post-stack data volume 6 after weak signal compensation.

[0085] Furthermore, the structural tensor attributes of the post-stack data volume 6 are extracted, and the wells where the imaging logging of the target layer is measured are selected as the well set for subsequent quality control, and the imaging logging data of the target layer are extracted.

[0086] Based on the imaging logging data from the well set, the wells are quality-controlled using the extracted structural tensor attributes. Preferably, in this embodiment, if the regional conformity of the structural tensor attributes of the wells with developed fractures in the target layer is greater than or equal to 80%, the wells pass quality control. Simultaneously, for the wells in the target layer, during the synthetic seismic process, the number of wells with a well-seismic correlation greater than 80% is counted. If this number reaches 80% of the total number of wells, the wells pass quality control. If they fail quality control, Weight 1 and Weight 2 are reset, and the above process is repeated until the number of wells with a regional conformity greater than or equal to 80% or a well-seismic correlation greater than 80% reaches 80% of the total number of wells. In seismic exploration and reservoir characterization, regional conformity measures the consistency between the geological model and the actual observation data, i.e., the degree of similarity between the geological model and the actual observation data. Well-seismic correlation measures the degree of match between the seismic data and the downhole measurement data, i.e., the degree of similarity between the seismic data and the downhole measurement data. The specific methods for obtaining regional conformity and well-seismic correlation in seismic exploration and reservoir characterization are well-known in practice and will not be described in detail in this embodiment.

[0087] According to the above process of this embodiment, a post-stack data volume 6 that meets quality control can be output, that is, the result after the non-reflection weak signal is enhanced. The result after the non-reflection weak signal is enhanced can ensure that the interpretation of the seismic data matches the actual geological conditions, which helps to improve the accuracy of seismic exploration.

[0088] Based on the Fresnel zone constraint, the processing method for enhancing the non-reflective weak signal of shale oil can achieve rapid and efficient separation of the reflected wave field signal on the pseudo-dip gather, extract the non-reflective weak signal through adaptive subtraction, and determine the optimal dip angle, spatial weight and other parameters through iterative testing of well-seismic geological quality control. The non-reflective weak signal is enhanced without destroying the overall fidelity of the data. The fidelity is enhanced through resolution improvement and amplitude consistency processing. Good results have been achieved in the actual data application practice of shale oil exploration and development in the Sichuan Basin, and it has good application and promotion prospects.

[0089] Specifically, in this embodiment, the actual seismic profile diagram before the non-reflection weak signal is enhanced is not used, such as Figure 3 As shown; the actual seismic profile diagram after the non-reflection weak signal is strengthened in this embodiment is as follows: Figure 4 As shown, through Figure 3 and Figure 4 Compare the areas indicated by the blue and green arrows and the areas marked in the red circle. Figure 4 The middle one is the non-reflection weak signal after enhancement. The signal characteristics are more obvious, highlighting the seismic response of the non-reflection weak signal.

[0090] In this embodiment, the schematic diagram of the coherence volume property comparison of small fractures and faults before and after the non-reflection weak signal enhancement processing in this embodiment is shown as follows: Figure 5 、 6 As shown, it includes wells X1, X2, X3 and X4, wherein the small fracture plane distribution diagram detected without the non-reflection weak signal enhancement processing in this embodiment is shown in FIG. Figure 5 As shown in FIG. 1 , the distribution diagram of the small fracture plane detected after the non-reflection weak signal enhancement processing in this embodiment is shown in FIG. Figure 6 As shown, the red circle is the comparison window, through the comparison, it can be seen that Figure 6 More possible small faults can be identified in the middle red circle, and the fault identification accuracy is high.

[0091] In the example of a 3D seismic work area, a schematic diagram of a comparison of a seismic imaging cross section of Well X1 with and without the non-reflection weak signal enhancement processing method of this embodiment is shown in FIG. Figure 7 、 8 The schematic diagram of the cross section of the seismic imaging through the X1 well without using the non-reflection weak signal enhancement processing method of this embodiment is shown in FIG. Figure 7 As shown, the schematic diagram of the seismic imaging cross section of the X1 well after the non-reflection weak signal enhancement processing method of this embodiment is shown in FIG. Figure 8 As shown, by comparison we can see Figure 8 The imaging of non-reflective weak signals such as small faults and fractures pointed by the yellow arrows in the middle is significantly enhanced, making it easier to identify the exact locations of cracks and faults, which is conducive to the accurate exploration and efficient development of shale oil.

[0092] It is understood that references to "one embodiment" or "some embodiments" in the present specification mean that one or more embodiments of the present application include a particular feature, structure, or characteristic described in conjunction with that embodiment. Thus, if "in one embodiment," "in some embodiments," "in other embodiments," or "in other embodiments" appear in different places in this specification, they do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0093] It should be noted that the above-mentioned sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above description is of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous. At the same time, the size of the sequence number of each step in the embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments in this specification.

[0094] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A processing method for enhancing non-reflective weak signals of shale oil based on Fresnel band constraints, characterized in that: The following steps are involved: Collect well seismic geological data from shale oil fields to obtain CMP gather data Data5; The Fresnel zone constraint and dip gather generation technology are used to perform prestack migration processing on the CMP gather data Data5 under steady phase control to obtain the CRP gather data Data6, which is converted into vector form and recorded as CRP gather data vector, and the modal components of the CRP gather data vector are extracted. Analyze the frequency domain characteristics of each modal component of the CRP gather data vector. According to the change of the frequency amplitude and amplitude difference in the frequency domain of each modal component, determine the amplitude attenuation stability coefficient of each modal component. Combined with the downward trend of the frequency amplitude of each modal component, obtain the non-reflected wave characteristic value of each modal component. Analyze the degree of difference between each modal component and other modal components with respect to the non-reflected wave characteristic value to obtain the reflection signal recognition degree of each modal component. The reflection signal is extracted by the reflection signal recognition of each modal component of the CRP gather data vector, and the weak reflection signal is processed by wavelet fusion to obtain the reflection wave field CRP gather data. Then, the reflection wave field CRP gather data is filtered out by the adaptive subtraction filtering algorithm to obtain the subtraction CRP gather data. The subtraction CRP gather data were subjected to coherence consistency test, mixing and superposition processing, and quality control processing to obtain the result after the non-reflection weak signal was enhanced.

2. The method for enhancing non-reflective weak signals of shale oil based on Fresnel band constraints according to claim 1, characterized in that: The IMF modal components obtained by performing variational modal decomposition on the CRP gather data vector are used as the modal components of the CRP gather data vector.

3. The method for enhancing non-reflective weak signals of shale oil based on Fresnel band constraints according to claim 1, characterized in that: The calculation method of the amplitude attenuation stability coefficient of each modal component is: In the formula, Gu j is the amplitude attenuation stability coefficient of the jth modal component, exp() is an exponential function with a natural constant as the base, R is the number of elements in the second-order difference vector of the amplitude vector of the jth modal component, Fu j,t and fu j,t-1 They are respectively the tth and t-1th elements in the second-order difference vector of the amplitude vector of the jth modal component.

4. The method for enhancing non-reflective weak signals of shale oil based on Fresnel band constraints according to claim 3, characterized in that: The process of constructing the amplitude vector further includes: arranging the amplitudes in the spectrum diagram of each modal component in ascending order of frequency to form a vector, which is recorded as the amplitude vector of each modal component.

5. The method for enhancing non-reflective weak signals of shale oil based on Fresnel band constraints according to claim 4, characterized in that: The calculation method of the non-reflected wave eigenvalue of each modal component is: De j =Gu j / (Xu j +∈), where De j is the non-reflected wave eigenvalue of the jth modal component, ∈ is a constant to avoid the denominator being 0, Xu j is the amplitude attenuation of the j-th modal component, which is obtained by the amplitude change degree in the amplitude vector.

6. The method for enhancing non-reflective weak signals of shale oil based on Fresnel band constraints according to claim 1, characterized in that: The amplitude attenuation is the absolute value of the sum of all negative elements in the first-order difference vector of the amplitude vector.

7. The method for enhancing non-reflective weak signals of shale oil based on Fresnel band constraints according to claim 1, characterized in that: The calculation method of the reflection signal recognition degree of each modal component is: Where, Hg j is the recognition degree of the reflection signal of the jth modal component, K is the number of modal components of the CRP gather data vector, De j 、De k are the non-reflected wave eigenvalues ​​of the j-th and k-th modal components, respectively.

8. The method for enhancing non-reflective weak signals of shale oil based on Fresnel band constraints according to claim 1, characterized in that: The extraction of the reflection signal further includes: performing threshold segmentation on the reflection signal recognition degrees of all modal components of the CRP gather data vector, and taking the modal component corresponding to the reflection signal recognition degree higher than the segmentation threshold as the reflection signal.

9. The method for enhancing non-reflective weak signals of shale oil based on Fresnel band constraints according to claim 1, characterized in that: The acquisition of the reflection wave field CRP gather data includes: using a wavelet fusion algorithm to perform fusion processing on the reflection signal to obtain the reflection wave field CRP gather data.

10. The method for enhancing non-reflective weak signals of shale oil based on Fresnel band constraints according to claim 1, characterized in that: The acquisition of the subtracted CRP gather data further comprises: After the seismic data is migrated, the migrated CRP gather data are collected and recorded as the full-wavefield CRP gather data; The full-wavefield CRP gather data and the reflected-wavefield CRP gather data are input into the adaptive subtraction filtering algorithm, the reflected-wavefield CRP gather data are filtered out from the full-wavefield CRP gather data, and the subtraction CRP gather data are output.

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