Strong reflecting layer separation method, device, equipment, medium and program

Abstract

The invention relates to a seismic interpretation processing technology, and discloses a strong reflection layer separation method, device, equipment, medium and program, and the method comprises the steps: carrying out the seismic forward modeling through a pre-constructed geologic model, and obtaining a seismic section; calculating frequency space change characteristics of an overlying stratum in the seismic section according to the original seismic data of the seismic section to obtain matched wavelets; and performing strong reflection separation on the original seismic data based on the matched wavelet to obtain a separated seismic record. According to the method, frequency parameters can be optimized, the strong reflection separation effect can be improved, and the reflection characteristics of the underlying small-scale fracture-cavity body can be enhanced, so that the strong reflection event can be represented and separated more accurately, and the separation precision can be improved.

Description

Technical Field

[0001] The present invention relates to the field of seismic interpretation processing technology, and in particular to a method, device, equipment, medium and program for separating a strong reflection layer. Background Art

[0002] This section is intended to provide a background or context to the embodiments recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] For the T74 unconformity interface in the northwest exploration area, there is a large difference in wave impedance between the strata above and below the interface, forming an obvious strong reflection event on the seismic profile. The strong reflection energy will shield the weak reflection characteristics of the underlying small-scale fracture-cavity body, making the accurate identification of the fracture-cavity body extremely difficult. Therefore, by separating the T74 strong reflection event, the weak reflection information of the small-scale fracture-cavity reservoir can be highlighted and the prediction accuracy can be improved. It can be seen that the accurate identification and separation of the strong reflection event is the key. The matching pursuit algorithm is a commonly used method for separating strong reflection layers. It takes the instantaneous frequency of the strong reflection event as the frequency parameter, and obtains the matching wavelet characterizing the strong reflection event through the optimization calculation of the wavelet control frequency parameter. The matching wavelet obtained by this method not only contains the frequency characteristics of the strong reflection layer itself, but also contains the frequency change characteristics caused by the underlying small-scale fracture-cavity body, but the prediction error is also large. In summary, how to optimize the frequency parameters to more accurately characterize and separate the strong reflection event, improve the separation accuracy and highlight the weak reflection characteristics has become an urgent problem to be solved. Summary of the invention

[0004] In view of the above problems, embodiments of the present invention provide a method, device, equipment, medium and program for separating a strong reflective layer.

[0005] In a first aspect, an embodiment of the present invention provides a method for separating a strong reflection layer, comprising:

[0006] Use the pre-built geological model to perform earthquake forward simulation and obtain the seismic profile;

[0007] Calculating the frequency spatial variation characteristics of the overlying strata in the seismic section according to the original seismic data of the seismic section to obtain a matching wavelet;

[0008] The original seismic data is subjected to strong reflection separation based on the matching wavelet to obtain separated seismic records.

[0009] According to an embodiment of the present invention, the method of performing seismic forward modeling using a pre-built geological model to obtain a seismic profile includes:

[0010] Using a preset wavelet function to perform self-excitation and self-reception forward modeling on the geological model, and obtaining the wavelet shape and frequency characteristics;

[0011] A seismic profile is generated according to the wavelet morphology and the frequency characteristics.

[0012] According to an embodiment of the present invention, the step of calculating the frequency spatial variation characteristics of the overlying strata in the seismic section according to the original seismic data of the seismic section to obtain the matching wavelet includes:

[0013] Calculating three-moment attributes based on the original seismic data of the seismic profile using a preset complex seismic trace analysis technology;

[0014] Calculating an initial matching wavelet according to the three instantaneous attributes and the pre-acquired scale factor, and optimizing the initial matching wavelet based on the formation frequency of the overlying formation to obtain an optimized initial matching wavelet;

[0015] Calculating the wavelet amplitude according to the optimized initial matching wavelet and the target signal in the original seismic data;

[0016] The wavelet amplitude is calculated using the following formula:

[0017]

[0018] Wherein, a represents the wavelet amplitude, g represents the initial matching wavelet, and R represents the target signal in the original seismic data;

[0019] Performing a product operation on the wavelet amplitude and the optimized initial matching wavelet to obtain a matching wavelet;

[0020] The matching wavelet is calculated using the following formula:

[0021] g=a×g

[0022] Wherein, g′ represents the matching wavelet, a represents the wavelet amplitude, and g represents the initial matching wavelet.

[0023] According to an embodiment of the present invention, the calculating of the initial matching wavelet according to the three instant attributes and the pre-acquired scale factor includes:

[0024] The initial matching wavelet is calculated using the following formula:

[0025]

[0026] Among them, g represents the initial matching wavelet, f represents the frequency in the three instant attributes, u represents the central delay time in the three instant attributes, φ represents the phase in the three instant attributes, k represents the scale factor, t represents the preset time parameter, and π represents the preset calculation parameter.

[0027] According to an embodiment of the present invention, the optimizing process of the initial matching wavelet based on the formation frequency of the overlying formation to obtain the optimized initial matching wavelet includes:

[0028] Setting the disturbance amount of the initial matching wavelet, and determining the search range in a preset search dictionary according to the disturbance amount;

[0029] The parameters of the initial matching wavelet are updated according to the search range and the formation frequency to obtain an optimized initial matching wavelet.

[0030] According to an embodiment of the present invention, the strong reflection separation of the original seismic data based on the matching wavelet to obtain separated seismic records includes:

[0031] Using a preset matching pursuit strong reflection separation method to perform separation calculations based on the original seismic data and the matching wavelet to obtain separated seismic records;

[0032] The matching pursuit strong reflection separation method is expressed as:

[0033] S=S0-g′

[0034] Wherein, S represents the separated seismic record, S0 represents the original seismic record, and g′ represents the matching wavelet.

[0035] In a second aspect, an embodiment of the present invention provides a strong reflection layer separation device, characterized in that it includes:

[0036] The earthquake forward modeling module is used to perform earthquake forward modeling using a pre-built geological model to obtain earthquake profiles;

[0037] A matching wavelet calculation module, used to calculate the frequency spatial variation characteristics of the overlying strata in the seismic section according to the original seismic data of the seismic section to obtain a matching wavelet;

[0038] A strong reflection separation module is used to perform strong reflection separation on the original seismic data based on the matching wavelet to obtain separated seismic records.

[0039] In a third aspect, an embodiment of the present invention provides an electronic device, comprising:

[0040] processor;

[0041] a memory for storing instructions executable by the processor;

[0042] Wherein, the processor is configured to execute the instructions to implement a strong reflection layer separation method as described in the first aspect.

[0043] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a strong reflection layer separation method as described in the first aspect.

[0044] In a fifth aspect, an embodiment of the present invention provides a computer program, characterized in that when the program is executed by a processor, a strong reflection layer separation method as described in the first aspect is implemented.

[0045] Compared with the prior art, the above technical solution of the present invention has the following beneficial effects:

[0046] The embodiments of the present invention perform seismic forward modeling through a pre-constructed geological model, and can accurately obtain seismic profiles, and verify the reliability and necessity of the overlying strata frequency-constrained strong reflection separation method; the frequency-space variation characteristics of the seismic profile are calculated through the original seismic data of the seismic profile, and the matching sub-waves can be accurately obtained, highlighting and improving the weak reflection characteristics of small-scale fractures and caves; the original seismic data is separated by strong reflections through matching sub-waves, and separated seismic records can be accurately obtained, and the strong reflection event axes can be more accurately characterized and separated, thereby improving the separation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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 work.

[0048] Figure 1 The flowchart of the strong reflection layer separation method according to the first embodiment of the present invention is shown;

[0049] Figure 2 A schematic diagram showing the specific structure of the geological model of the first embodiment of the present invention is shown;

[0050] Figure 3 A structural schematic diagram showing the wavelet morphology and frequency characteristics after forward modeling of the geological model according to the first embodiment of the present invention;

[0051] Figure 4 A schematic diagram showing the structure of the forward modeling records of the geological model of the first embodiment of the present invention for the development of strong reflection layers and the development of non-strong reflection layers;

[0052] Figure 5 The instantaneous frequency of the strong reflection event axis and the schematic diagram of the strong reflection separation cross-sectional structure corresponding to the conventional method of the first embodiment of the present invention are shown;

[0053] Figure 6A schematic diagram showing the structure of the frequency parameters of the overlying strata and the strong reflection separation profile after the strong reflection event axis is matched and pursued to separate the strong reflection layer according to the first embodiment of the present invention;

[0054] Figure 7 A comparison diagram showing the effects of separating the seismic waveform, the standard reservoir reflection waveform and the conventional reflection waveform according to the first embodiment of the present invention;

[0055] Figure 8 A schematic diagram showing the structure of the instantaneous frequency plane of T74 and the frequency plane of the overlying formation according to the first embodiment of the present invention is shown;

[0056] Fig. 9 A comparison diagram of the root mean square amplitude of the results before and after the improvement of the first embodiment of the present invention is shown;

[0057] Fig.10 The functional module diagram of the strong reflection layer separation device of the third embodiment of the present invention is shown;

[0058] Fig.11 A schematic diagram of the composition structure of an electronic device for implementing the strong reflection layer separation method according to the fourth embodiment of the present invention is shown. DETAILED DESCRIPTION

[0059] The present disclosure is further described below in conjunction with the embodiments shown in the accompanying drawings.

[0060] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0061] The present invention proposes a strong reflection layer separation method, which is based on a pre-constructed geological model, uses seismic forward simulation to obtain a seismic profile, calculates the frequency spatial variation characteristics of the seismic profile according to the original seismic data of the seismic profile, obtains a matching wavelet, and performs strong reflection separation on the original seismic data based on the matching wavelet to obtain a separated seismic record. Compared with the traditional method, the strong reflection layer separation method can accurately identify the T74 strong reflection phase axis, optimize the frequency parameters, improve the effect of strong reflection separation, and enhance the reflection characteristics of the underlying small-scale fracture-cavity body, thereby more accurately characterizing and separating the strong reflection phase axis and improving the separation accuracy.

[0062] Embodiment 1

[0063] like Figure 1 As shown, the present invention proposes a strong reflection layer separation method, comprising the following steps:

[0064] S1. Use the pre-built geological model to perform earthquake forward simulation and obtain the seismic profile.

[0065] In the embodiment of the present invention, the Ordovician carbonate fracture-cave reservoir in the Tarim Basin can be studied. There will be a more obvious influence of the T74 strong reflection layer. Since the strong reflection shields the weak reflection signal of the underlying small-scale fracture-cave body, it brings great difficulties to reservoir prediction. Therefore, it is necessary to carry out research on the strong reflection separation method to highlight the underlying weak reflection signal.

[0066] Furthermore, the geological model refers to an observation system, such as Figure 2 As shown in FIG. 1 , it is a schematic diagram of the specific structure of the geological model, wherein the length of the geological model is set to 800 m, the shot spacing is 10 m, and the detector spacing is 10 m. The geological model contains 4 sets of strata, and the reflection interfaces from top to bottom are interface A, interface B, and interface C, respectively. The velocity and density parameters of the interfaces increase and the reflection coefficients are all positive. Reflection interface B is a set of strong reflection interfaces. At the lower position of reflection interface B, a certain scale of reservoir is simulated and developed, with a maximum thickness of 20 m and an impedance greater than the surrounding rock impedance. Compared with reflection interface B, the reservoir reflection is relatively weak. The specific reservoir parameters are as follows: Figure 2 shown.

[0067] In the embodiment of the present invention, the method of performing earthquake forward modeling using a pre-constructed geological model to obtain a seismic profile includes:

[0068] Using a preset wavelet function to perform self-excitation and self-reception forward modeling on the geological model, and obtaining the wavelet shape and frequency characteristics;

[0069] A seismic profile is generated according to the wavelet morphology and the frequency characteristics.

[0070] In the embodiment of the present invention, the wavelet function refers to the Morlet wavelet function, the frequency of the wavelet function is set to 35 Hz, a detector is placed at the shot point of the geological model, and the corresponding signal data is received by using the 35 Hz Morlet wavelet function; a forward simulation algorithm is used, and the corresponding wavelet morphology and frequency characteristics are analyzed according to the signal data, wherein the signal data includes signal, amplitude, frequency and channel number data, etc.; specifically, Figure 3 a, which shows the wavelet shape after the forward modeling of the geological model, such as Figure 3 As shown in b, it represents the frequency characteristics of the geological model after forward modeling, wherein the lateral main frequency is 35 Hz.

[0071] Furthermore, generating a seismic profile according to the wavelet morphology and the frequency characteristics refers to extracting features from the wavelet morphology to obtain wavelet change features, and identifying strong reflection features based on the wavelet change features and the frequency characteristics to obtain strong reflection features; acquiring a seismic interface, removing the strong reflection features of the seismic interface to obtain a target seismic interface, and forward modeling the target seismic interface to obtain a seismic profile.

[0072] Specifically, after forward modeling the A interface, B interface, and C interface using the geological model, three wave crest reflection interfaces are obtained, such as Figure 4 As shown in a, it is a schematic diagram of the structure of the forward record of the geological model for the development of strong reflection layers, wherein the phase axis B shows obvious waveform change characteristics at the reservoir development site, but because the phase axis B is a set of strong reflections and the reservoir is a weak reflection, the weak reflection characteristics of the reservoir are covered by the strong reflections, and the feature identification is more difficult; therefore, it is necessary to use the geological model to remove the strong reflection characteristics of the B interface, and then perform forward simulation to obtain the seismic profile, as shown in FIG. Figure 4 As shown in b, it is a schematic diagram showing the structure of the forward record of the geological model for the undeveloped strong reflection layer. In this case, the reservoir characteristics are significantly enhanced, that is, the effect of ideal strong reflection separation (weak reflection characteristics of the reservoir).

[0073] In the embodiment of the present invention, by using the geological model to perform earthquake forward simulation, the seismic profile can be accurately obtained, so that the original seismic data corresponding to the seismic profile can be accurately obtained, thereby improving the accuracy of data processing.

[0074] S2. Calculate the frequency spatial variation characteristics of the overlying strata in the seismic section according to the original seismic data of the seismic section to obtain a matching wavelet.

[0075] In the embodiment of the present invention, the original seismic data includes signal data, waveform data, time data, scale data and the like.

[0076] In the embodiment of the present invention, the frequency spatial variation characteristics of the overlying strata in the seismic section are calculated according to the original seismic data of the seismic section to obtain the matching wavelet, including:

[0077] Calculating three-moment attributes based on the original seismic data of the seismic profile using a preset complex seismic trace analysis technology;

[0078] Calculating an initial matching wavelet according to the three instantaneous attributes and the pre-acquired scale factor, and optimizing the initial matching wavelet based on the formation frequency of the overlying formation to obtain an optimized initial matching wavelet;

[0079] Calculating the wavelet amplitude according to the optimized initial matching wavelet and the target signal in the original seismic data;

[0080] A product operation is performed according to the wavelet amplitude and the optimized initial matching wavelet to obtain a matching wavelet.

[0081] In an embodiment of the present invention, the complex seismic trace analysis technology refers to converting the real seismic trace corresponding to the seismic profile into a complex seismic trace through Hilbert transform, and then separating three instantaneous attributes from the complex signal in the seismic data corresponding to the complex seismic trace, wherein the three instantaneous attributes include frequency, center delay time and phase; the scale factor refers to a pre-set standard scaling factor.

[0082] In the embodiment of the present invention, the following formula is used to calculate the initial matching wavelet:

[0083]

[0084] Among them, g represents the initial matching wavelet, f represents the frequency in the three instant attributes, u represents the central delay time in the three instant attributes, φ represents the phase in the three instant attributes, k represents the scale factor, t represents the preset time parameter, and π represents the preset calculation parameter.

[0085] In the embodiment of the present invention, the optimizing process of the initial matching wavelet based on the formation frequency of the overlying formation to obtain the optimized initial matching wavelet includes:

[0086] Setting the disturbance amount of the initial matching wavelet, and determining the search range in a preset search dictionary according to the disturbance amount;

[0087] The parameters of the initial matching wavelet are updated according to the search range and the formation frequency to obtain an optimized initial matching wavelet.

[0088] In an embodiment of the present invention, a disturbance amount is set according to the initial value of the initial matching wavelet calculated in the above steps, wherein the disturbance amount refers to a number of influencing factors; based on the influence value of the disturbance amount on the three instantaneous attributes and the scale factor in the initial matching wavelet, the fluctuation range of the three instantaneous attributes and the scale factor, that is, the search range, is found in the search dictionary. For example, the search range of the central delay time u in the three instantaneous attributes is [u-Δu, u+Δu], Δu represents the time interval, wherein the search dictionary contains the interval sizes affected by a number of parameters and corresponding influencing factors.

[0089] In an embodiment of the present invention, the frequency in the initial matching wavelet is replaced by the formation frequency, and within the search range, the optimal attribute values ​​and the optimal scale values ​​of the three instantaneous attributes and the scale factor are found, and the initial matching wavelet is recalculated based on the formation frequency, the optimal attribute values ​​and the optimal scale values ​​to achieve the update of the initial matching wavelet and obtain the optimized initial matching wavelet.

[0090] In the embodiment of the present invention, the wavelet amplitude is calculated using the following formula:

[0091]

[0092] Wherein, a represents the wavelet amplitude, g represents the initial matching wavelet, and R represents the target signal in the original seismic data.

[0093] In the embodiment of the present invention, the matching wavelet is calculated using the following formula:

[0094] g′=a×g

[0095] Wherein, g′ represents the matching wavelet, a represents the wavelet amplitude, and g represents the initial matching wavelet.

[0096] In the embodiment of the present invention, the frequency spatial variation characteristics of the seismic profile can be accurately calculated based on the original seismic data of the seismic profile, so as to accurately obtain the matching wavelet, and further accurately realize the strong reflection separation.

[0097] S3. Perform strong reflection separation on the original seismic data based on the matching wavelet to obtain separated seismic records.

[0098] In the embodiment of the present invention, the strong reflection separation of the original seismic data based on the matching wavelet to obtain the separated seismic records includes:

[0099] Using a preset matching pursuit strong reflection separation method to perform separation calculations based on the original seismic data and the matching wavelet to obtain separated seismic records;

[0100] The matching pursuit strong reflection separation method is expressed as:

[0101] S=S0-g′

[0102] Wherein, S represents the separated seismic record, S0 represents the original seismic record, and g′ represents the matching wavelet.

[0103] In the embodiment of the present invention, the matching pursuit strong reflection separation method is used to identify and remove the strong reflection event B in the post-stack section. The conventional method is to use the calculated instantaneous frequency of the strong reflection event as the initial frequency input to perform matching pursuit to obtain the strong reflection event matching wavelet, and then perform strong reflection separation to obtain a conventional result, such as Figure 5 a shows the instantaneous frequency of the strong reflection phase axis B in the conventional method. It can be seen that the frequency has obvious lateral changes. This change comes from the superposition of the strong reflection interface B and the reservoir characteristics, rather than just the frequency characteristics of the strong reflection interface B. The strong reflection separation profile in the conventional method is as follows: Figure 5As shown in (b), due to the influence of the frequency parameter, the whole set of strong reflection phase axis B (including the single reflection interface B and the reservoir response characteristics) is completely separated, and some weak reservoir response characteristics remain in the processed section, but they are different from the theoretical results ( Figure 4 b) The difference is too great, and the purpose of high-precision strong reflection identification and separation is not achieved, and the weak reflection characteristics of the reservoir cannot be well restored.

[0104] Furthermore, in order to avoid the interference of the reservoir on the frequency calculation, the strong reflection event B is not directly calculated, but the event A overlying the event B is calculated. According to the seismic wave propagation theory, the frequency before the seismic wave reaches the reflection interface B is the true frequency characteristic of the strong reflection event B (that is, it is not affected by the reservoir under the reflection interface B). For example, the instantaneous frequency of the event A is as follows: Figure 6 As shown in a, the horizontal direction is 35Hz. Using the frequency of 35Hz as a constraint, matching pursuit strong reflection layer identification and separation are carried out. The results are as follows Figure 6 As shown in b, it shows the result of matching pursuit separation of strong reflection layer by strong reflection event axis B, where the strong reflection separation result has a high matching degree with the weak reflection characteristics of theoretical reservoir.

[0105] In the embodiment of the present invention, after performing strong reflection separation on the original seismic data based on the matching wavelet to obtain separated seismic records, the method further includes:

[0106] Performing waveform conversion on the separated seismic record to obtain a separated seismic waveform;

[0107] Acquire a standard reservoir reflection waveform and a conventional reflection waveform, perform separation effect analysis according to the standard reservoir reflection waveform, the conventional reflection waveform and the separated seismic waveform, and determine whether the result of the separation effect analysis is within a preset target variation range;

[0108] When the result is not within the target variation range, determining that the separated seismic record is unqualified;

[0109] When the result is within the target variation range, the separated seismic record is determined to be qualified.

[0110] In an embodiment of the present invention, the waveform conversion of the separated seismic record can be achieved by inversion based on the separated seismic record to obtain a separated seismic waveform; the standard reservoir reflection waveform refers to a theoretical weak reflection waveform, for example, it can be the 43rd (reservoir position) seismic waveform, and the conventional reflection waveform refers to a general reflection waveform.

[0111] Specifically, the seismic waveform of track 43 (reservoir location) is extracted for comparison, such as Figure 7 As shown, it can be found that at the reservoir location, the conventional results Figure 7 b and theoretical weak reflection waveform Figure 7 a has obvious differences, the separation effect is poor, and the reflection characteristics of the reservoir cannot be effectively characterized. The separation results are constrained by the frequency of the overlying strata Figure 7 c has a better similarity with the theoretical weak reflection waveform, which can effectively characterize the reflection characteristics of the reservoir, verifying the adaptability and effectiveness of the method. Therefore, the strong reflection separation method based on the frequency constraint of the overlying strata can better identify the strong reflection event axis through the model, and the separation effect is better, which is helpful for the subsequent identification and prediction of the weak reflection characteristics of the reservoir.

[0112] In the embodiment of the present invention, the study area is located in the Tarim Basin, and the research target is the Ordovician carbonate fracture-cave reservoir. This type of reservoir has strong lateral heterogeneity, large differences in reservoir size, and small-scale fracture-cave bodies are difficult to identify. Affected by the overlying strata, T74 is a strong reflection feature, and the reflection characteristics of the lower fracture-cave reservoir are easily disturbed.

[0113] Furthermore, the matching pursuit strong reflection separation method was studied to optimize the frequency selection. First, the matching frequency of the T74 strong reflection event axis was calculated, such as Figure 8 As shown in (a), it can be seen from the figure that the matching frequency and lateral changes of the T74 strong reflection event axis are more dramatic, and have a certain correlation with the fault characteristics and fracture-cavity development characteristics, which affects the calculation of the true matching frequency and reduces the accuracy of subsequent matching and tracking strong reflection separation.

[0114] Specifically, the study extracts the frequency characteristics of seismic data within 200ms above T74 to characterize the spatial frequency characteristics before the seismic wave reaches the T74 reflection interface, such as Figure 8 As shown in (b), the actual frequency characteristics are relatively smooth, which effectively eliminates the influence of the fractures and fractures near T74. The result is closer to the actual spatial frequency characteristics, thereby better characterizing the frequency characteristics of the T74 strong reflection event axis itself. Therefore, the frequency is used as a constraint to carry out matching pursuit strong reflection separation to improve the separation result.

[0115] In the embodiment of the present invention, Fig. 9 To improve the RMS amplitude comparison (T74 down 40ms) before and after results, conventional methods (such as Fig. 9 a) The strong reflection layer of T74 was separated, and the influence of strong reflection was significantly weakened, but at the same time, it also caused certain damage to the reservoir reflection signal, especially for the weak reflection characteristics; improved methods (such as Fig. 9 b) It can more accurately separate the T74 strong reflection phase axis, better preserve the weak reflection characteristics of the fracture-cavity body, make the northern river channel characteristics (shown by the black arrow) more obvious, and the plane prediction effect is significantly better than the conventional method, and the application effect is significantly improved.

[0116] Embodiment 2

[0117] In order to more clearly understand the present invention, a second embodiment is used below to further explain the situation in which the present invention uses a pre-built geological model to perform earthquake forward simulation to obtain a seismic profile.

[0118] In the embodiment of the present invention, the method of performing earthquake forward modeling using a pre-constructed geological model to obtain a seismic profile includes:

[0119] Performing numerical simulation according to the geological model to obtain simulation records;

[0120] Performing velocity analysis on the simulation record to obtain a simulation velocity, and performing correction and stacking offset processing on the simulation record to obtain imaging data;

[0121] Forward modeling is performed based on the simulated velocity and the imaging data to obtain seismic data, and a seismic profile is generated based on the seismic data.

[0122] In the embodiment of the present invention, the seismic response of the geological model under the action of the assumed excitation source is utilized, and for a preset exploration block, the physical equations corresponding to the parameters are applied to calculate and solve the simulation record according to the corresponding parameters collected by the geological model; a series of identical time intervals are set to perform velocity scanning based on the simulation record, and the stacking energy or similarity coefficient is used as a criterion for velocity analysis to obtain the simulated velocity.

[0123] Furthermore, the simulation record contains data such as frequency, amplitude and time, and the simulation record is corrected and overlaid with offset processing using different screening ranges. For example, the frequency data in the simulation record is corrected using standard frequency data to obtain corrected data; the data corresponding to the simulation record after correction and overlay offset processing is used as imaging data; the simulation velocity and the imaging data are calculated using the Fourier transform method to obtain seismic data, and a profile conversion is performed based on the seismic data to obtain a seismic profile.

[0124] In the embodiment of the present invention, the seismic forward modeling is performed using the geological model, so that the seismic profile can be accurately obtained, thereby further accurately analyzing and processing the seismic profile.

[0125] Embodiment 3

[0126] like Fig.10 As shown, this embodiment also provides a functional module diagram of a strong reflection layer separation device.

[0127] The strong reflection layer separation device 1000 described in this embodiment can be installed in an electronic device. According to the functions to be implemented, the strong reflection layer separation device 1000 can include a seismic forward modeling module 1001, a matching wavelet calculation module 1002 and a strong reflection separation module 1003. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0128] In this embodiment, the functions of each module / unit are as follows:

[0129] The earthquake forward modeling module 1001 is used to perform earthquake forward modeling using a pre-built geological model to obtain an earthquake profile;

[0130] The matching wavelet calculation module 1002 is used to calculate the frequency spatial variation characteristics of the overlying strata in the seismic section according to the original seismic data of the seismic section to obtain the matching wavelet;

[0131] The strong reflection separation module 1003 is used to perform strong reflection separation on the original seismic data based on the matching wavelet to obtain separated seismic records.

[0132] In detail, each module described in the strong reflection layer separation device 1000 described in the embodiment of the present invention adopts the same technical means as the strong reflection layer separation method described in Example 1 and Example 2 when in use, and can produce the same technical effect, which will not be repeated here.

[0133] Embodiment 4

[0134] like Fig.11 As shown, this embodiment also provides a computer electronic device, and the electronic device 1100 may include a processor 1101, a memory 1102, a communication bus 1103 and a communication interface 1104, and may also include a computer program stored in the memory 1102 and executable on the processor 1101, such as a strong reflection layer separation program.

[0135] In some embodiments, the processor 1101 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 1101 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 1102 (for example, executing a strong reflective layer separation program, etc.), and calls data stored in the memory 1102 to execute various functions of the electronic device and process data.

[0136] The memory 1102 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. The memory 1102 may be an internal storage unit of an electronic device in some embodiments, such as a mobile hard disk of the electronic device. The memory 1102 may also be an external storage device of an electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 1102 may also include both an internal storage unit of the electronic device and an external storage device. The memory 1102 may not only be used to store application software and various types of data installed in the electronic device, such as the code of the strong reflective layer separation program, but also be used to temporarily store data that has been output or is to be output.

[0137] The communication bus 1103 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 1102 and at least one processor 1101, etc.

[0138] The communication interface 1104 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0139] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0140] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 1101 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0141] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0142] The strong reflection layer separation program stored in the memory 1102 in the electronic device is a combination of multiple instructions. When running in the processor 1101, it can achieve:

[0143] Use the pre-built geological model to perform earthquake forward simulation and obtain the seismic profile;

[0144] Calculating the frequency spatial variation characteristics of the overlying strata in the seismic section according to the original seismic data of the seismic section to obtain a matching wavelet;

[0145] The original seismic data is subjected to strong reflection separation based on the matching wavelet to obtain separated seismic records.

[0146] Specifically, the specific implementation method of the processor 1101 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0147] Furthermore, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0148] Embodiment 5

[0149] This embodiment provides a storage medium storing a computer program. When the computer program is executed by a processor, the steps of the strong reflection layer separation method described above are implemented.

[0150] These program codes can also be loaded onto a computer or other programmable data processing device so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions executed on the computer or other programmable device for implementing the process. Figure 1 The steps of a specified function in a process or multiple processes.

[0151] Storage media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, modules of programs or other data. Examples of storage media can include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0152] Embodiment 6

[0153] A computer program, characterized in that when the computer program is executed by a processor of an electronic device, it can achieve:

[0154] Use the pre-built geological model to perform earthquake forward simulation and obtain the seismic profile;

[0155] Calculating the frequency spatial variation characteristics of the overlying strata in the seismic section according to the original seismic data of the seismic section to obtain a matching wavelet;

[0156] The original seismic data is subjected to strong reflection separation based on the matching wavelet to obtain separated seismic records.

[0157] A computer program is a set of instructions that a computer can recognize and execute, and runs on an electronic computer. It is written in a programming language and runs on a target architecture. In order for a computer program to run, the computer needs to load code and data. From the bottom layer of the computer, this is a high-level language (such as Java, C / C++, C#, etc.) code translated into machine language and understood by the CPU for loading. If on a computer that meets most standards, an operating system such as Windows, Linux, etc., loads and executes many programs, in this case, each program is a separate mapping, not all executable programs on the computer. A computer program refers to a coded instruction sequence that can be executed by a computer or other device with information processing capabilities in order to obtain a certain result, or a symbolic instruction sequence or symbolic statement sequence that can be automatically converted into a coded instruction sequence.

[0158] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. When the terms "include" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0159] It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or described herein.

[0160] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0161] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0162] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0163] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0164] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0165] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0166] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for separating a strong reflection layer, characterized in that: The method comprises: Use the pre-built geological model to perform earthquake forward simulation and obtain the seismic profile; Calculating the frequency spatial variation characteristics of the overlying strata in the seismic section according to the original seismic data of the seismic section to obtain a matching wavelet; The original seismic data is subjected to strong reflection separation based on the matching wavelet to obtain separated seismic records.

2. The method for separating a strong reflective layer according to claim 1, characterized in that: The method of using the pre-built geological model to perform earthquake forward simulation to obtain an earthquake profile includes: Using a preset wavelet function to perform self-excitation and self-reception forward modeling on the geological model, and obtaining the wavelet shape and frequency characteristics; A seismic profile is generated according to the wavelet morphology and the frequency characteristics.

3. The strong reflection layer separation method according to claim 1, characterized in that: The step of calculating the frequency spatial variation characteristics of the overlying strata in the seismic section according to the original seismic data of the seismic section to obtain a matching wavelet includes: Calculating three-moment attributes based on the original seismic data of the seismic profile using a preset complex seismic trace analysis technology; Calculating an initial matching wavelet according to the three instantaneous attributes and the pre-acquired scale factor, and optimizing the initial matching wavelet based on the formation frequency of the overlying formation to obtain an optimized initial matching wavelet; Calculating the wavelet amplitude according to the optimized initial matching wavelet and the target signal in the original seismic data; The wavelet amplitude is calculated using the following formula: Wherein, a represents the wavelet amplitude, g represents the initial matching wavelet, and R represents the target signal in the original seismic data; Performing a product operation on the wavelet amplitude and the optimized initial matching wavelet to obtain a matching wavelet; The matching wavelet is calculated using the following formula: g′=a×g Wherein, g′ represents the matching wavelet, a represents the wavelet amplitude, and g represents the initial matching wavelet.

4. The strong reflection layer separation method according to claim 3, characterized in that: The calculating of the initial matching wavelet according to the three instantaneous attributes and the pre-acquired scale factor comprises: The initial matching wavelet is calculated using the following formula: Among them, g represents the initial matching wavelet, f represents the frequency in the three instant attributes, u represents the central delay time in the three instant attributes, φ represents the phase in the three instant attributes, k represents the scale factor, t represents the preset time parameter, and π represents the preset calculation parameter.

5. The strong reflection layer separation method according to claim 3, characterized in that: The step of optimizing the initial matching wavelet based on the formation frequency of the overlying formation to obtain an optimized initial matching wavelet includes: Setting the disturbance amount of the initial matching wavelet, and determining the search range in a preset search dictionary according to the disturbance amount; The parameters of the initial matching wavelet are updated according to the search range and the formation frequency to obtain an optimized initial matching wavelet.

6. The strong reflection layer separation method according to claim 1, characterized in that: The step of performing strong reflection separation on the original seismic data based on the matching wavelet to obtain separated seismic records comprises: Using a preset matching pursuit strong reflection separation method to perform separation calculations based on the original seismic data and the matching wavelet to obtain separated seismic records; The matching pursuit strong reflection separation method is expressed as: S=S0-g′ Wherein, S represents the separated seismic record, S0 represents the original seismic record, and g′ represents the matching wavelet.

7. A strong reflection layer separation device, characterized in that: The device comprises: The earthquake forward modeling module is used to perform earthquake forward modeling using a pre-built geological model to obtain earthquake profiles; A matching wavelet calculation module, used to calculate the frequency spatial variation characteristics of the overlying strata in the seismic section according to the original seismic data of the seismic section to obtain a matching wavelet; A strong reflection separation module is used to perform strong reflection separation on the original seismic data based on the matching wavelet to obtain separated seismic records.

8. An electronic device, comprising: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the strong reflection layer separation method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the method for separating a strong reflection layer as claimed in any one of claims 1 to 6 is implemented.

10. A computer program, characterized in that When the program is executed by a processor, the strong reflection layer separation method according to any one of claims 1 to 6 is implemented.

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