Series deconvolution processing method and device, equipment and storage medium

Abstract

The invention provides a series deconvolution processing method, device and equipment and a storage medium, and belongs to the technical field of geophysics. The method comprises the following steps: performing earth surface consistency deconvolution on to-be-processed seismic data to obtain first seismic data; performing prediction deconvolution processing on the first seismic data based on the virtual fixed surface to obtain second seismic data; and performing reverse static correction on the second seismic data, and correcting the second seismic data to a trend floating surface corresponding to the stable stratum to obtain series deconvolution data. By adopting a series deconvolution method based on geosteering, the resolution of data is improved, periodic multiples are suppressed, the hierarchical relationship of wave groups is improved, the amplitude and phase consistency is improved, and the problems of phase inversion and stratification in the traditional series deconvolution processing process are solved; powerful technical support is provided for deconvolution processing of seismic data of complex geological structures, and the requirement for fine processing of the seismic data is met.

Description

Technical Field

[0001] The present application relates to the field of geophysical technology, and in particular to a serial deconvolution processing method, device, equipment and storage medium. Background Art

[0002] In seismic data processing, deconvolution is a technique that compresses seismic wavelets and broadens the frequency band of seismic data, which can improve the resolution of seismic data and thus improve the contact relationship and characteristic morphology of stratum wave groups. Since the deconvolution process sometimes causes operator instability, a certain assumption of signal-to-noise ratio data is required. Therefore, if there are gypsum or igneous rocks with uneven lateral distribution and large changes in dip and occurrence, the signal-to-noise ratio of seismic data will decrease, and the amplitude and frequency will also change significantly. In addition, the layered media encountered in seismic data acquisition can produce reflections and transmissions at any level, which inevitably produces multiple reflections between layers. These layers are usually thin, and the two-way travel time in each layer is much smaller than the continuation length of the wavelet. Therefore, multiple reflections between layers change the shape of the wavelet and reduce the resolution of the wavelet.

[0003] At present, technicians usually use the surface consistency deconvolution method to solve the problem of inconsistent lateral sub-waves of different recording channels caused by the lateral changes in surface thickness and velocity, and then use series prediction deconvolution to improve resolution, suppress periodic interlayer multiple waves, and improve the morphology of formation wave groups. However, the above two methods in series cannot completely eliminate the influence of deconvolution on the stable operator caused by the large lateral changes in underground lithology and dip angles and the interference effect of multiple waves, which will lead to the phenomenon of formation phase reversal and layer stringing, and the accuracy of the deconvolution results is low. Summary of the invention

[0004] The present application provides a serial deconvolution processing method, device, equipment and storage medium, which can solve the problems of phase inversion and stringing in the traditional serial deconvolution processing process. The technical solution is as follows:

[0005] On the one hand, an embodiment of the present application provides a serial deconvolution processing method, comprising:

[0006] Performing surface consistency deconvolution on the seismic data to be processed to obtain first seismic data;

[0007] Performing predictive deconvolution processing on the first seismic data based on a virtual fixed plane to obtain second seismic data, wherein the virtual fixed plane is a plane obtained by correcting a stable formation, and the stable formation refers to a formation whose reflection coefficient is greater than a coefficient threshold and whose reflection interface is continuous;

[0008] Performing reverse static correction on the second seismic data, correcting the second seismic data to the trend floating surface corresponding to the stable formation, and obtaining serial deconvolution data.

[0009] On the other hand, an embodiment of the present application provides a serial deconvolution processing device, comprising:

[0010] A first processing module, used for performing surface consistency deconvolution on the seismic data to be processed to obtain first seismic data;

[0011] A second processing module is used to perform predictive deconvolution processing on the first seismic data based on a virtual fixed plane to obtain second seismic data, wherein the virtual fixed plane is a plane obtained by correcting a stable formation, and the stable formation refers to a formation whose reflection coefficient is greater than a coefficient threshold and whose reflection interface is continuous;

[0012] The correction module is used to perform reverse static correction on the second seismic data, correct the second seismic data to the trend floating surface corresponding to the stable formation, and obtain serial deconvolution data.

[0013] On the other hand, an embodiment of the present application provides an electronic device, which includes a memory and a processor; a computer program is stored in the memory, and when the computer program is executed by the processor, the method described in the above aspects is implemented.

[0014] On the other hand, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is loaded and executed by a processor to implement the method as described in the above aspects.

[0015] The technical solution provided by this application includes at least the following beneficial effects:

[0016] The serial deconvolution processing method, device, equipment and storage medium provided in the present application improve the resolution of data, suppress periodic multiple waves, improve the hierarchical relationship of wave groups, improve the amplitude and phase consistency, solve the phase inversion and layer-crossing problems in the traditional serial deconvolution processing process, provide strong technical support for the deconvolution processing of seismic data of complex geological structures, and meet the needs of fine processing of seismic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments.

[0018] Figure 1 is a flow chart of a serial deconvolution processing method provided by an exemplary embodiment of the present application;

[0019] Figure 2 is a flow chart of a serial deconvolution processing method provided by another exemplary embodiment of the present application;

[0020] Figure 3 is a schematic diagram of a smooth trend floating surface and a virtual fixed surface provided by an exemplary embodiment of the present application;

[0021] Figure 4 is a schematic diagram of a deconvolution analysis time window provided by an exemplary embodiment of the present application;

[0022] Figure 5 It is a comparison diagram of seismic data effects provided by an exemplary embodiment of the present application;

[0023] Figure 6 is a schematic diagram of a north-south survey line provided by an exemplary embodiment of the present application;

[0024] Figure 7 is a schematic diagram of an east-west survey line provided by an exemplary embodiment of the present application;

[0025] Figure 8 is a flow chart of a serial deconvolution processing method provided by another exemplary embodiment of the present application;

[0026] Fig. 9 is a wavelet curve quality control diagram provided by an exemplary embodiment of the present application;

[0027] Fig.10 is a curve comparison diagram of an amplitude spectrum and a phase spectrum provided by an exemplary embodiment of the present application;

[0028] Fig.11 is a structural block diagram of a serial deconvolution processing device provided by an exemplary embodiment of the present application;

[0029] Fig.12 It is a structural block diagram of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0031] Embodiment 1

[0032] Please refer to Figure 1 , which shows a flow chart of a serial deconvolution processing method provided by an exemplary embodiment of the present application. The method comprises the following steps:

[0033] Step 101, performing surface consistency deconvolution on the seismic data to be processed to obtain first seismic data.

[0034] Deconvolution, also known as inverse filtering or deconvolution, is a processing method that eliminates the previous filtering effect. This method improves the vertical resolution of seismic data by compressing the basic wavelet. Surface consistency deconvolution is a deconvolution process that eliminates the influence of surface factors such as shot points and receiving points on seismic records during seismic data processing. This method is one of the more commonly used deconvolution methods in seismic data processing in complex surface areas.

[0035] The equipment first performs surface consistency deconvolution on the seismic data to be processed to eliminate the influence of the near surface on the deconvolution operator, ensure that the data resolution is appropriately improved without reducing the data signal-to-noise ratio, and obtain the first seismic data.

[0036] Step 102, predictive deconvolution processing is performed on the first seismic data based on a virtual fixed plane to obtain second seismic data, where the virtual fixed plane is a plane obtained by correcting a stable formation, and a stable formation refers to a formation whose reflection coefficient is greater than a coefficient threshold and whose reflection interface is continuous.

[0037] Predictive deconvolution is the process of filtering seismic traces using a prediction error filter. Since the prediction step size can be appropriately selected to suppress and eliminate repetitive vibrations at this scale, it is often used to eliminate predictable interference waves such as offshore reverberation and multiple waves.

[0038] Since improving resolution processing is a process of multiple trials and gradual improvement, technicians usually use serial combination deconvolution to process seismic data. Surface consistency deconvolution and predictive deconvolution are a set of common serial deconvolution combinations. Surface consistency deconvolution can solve the lateral sub-wave inconsistency problem of different recording channels caused by the lateral changes in surface thickness and velocity, and then serial predictive deconvolution to improve resolution, while suppressing periodic interlayer multiple waves and improving the morphology of formation wave groups. However, serializing surface consistency deconvolution and predictive deconvolution cannot completely eliminate the influence of deconvolution on the stable operator caused by the large lateral changes in underground lithology and dip angles, as well as the interference effect of multiple waves, which leads to the phenomenon of formation phase inversion and stringing.

[0039] In a possible implementation, the electronic device of the embodiment of the present application does not directly perform predictive deconvolution processing after performing surface consistency deconvolution on the seismic data, but instead determines a virtual fixed surface corresponding to the stable formation, and then performs predictive deconvolution processing on the first seismic data based on the virtual fixed surface.

[0040] Among them, a stable formation refers to a formation with a reflection coefficient greater than the coefficient threshold and a continuous reflection interface, and the virtual fixed surface is a virtual plane obtained after the stable formation is corrected for height difference. The virtual fixed surface has a consistent height, which can make the deconvolution operator more stable in the laterally and eliminate the problem of instability of the deconvolution operator caused by large changes in the formation inclination.

[0041] Step 103, performing reverse static correction on the second seismic data, correcting the second seismic data to the trend floating surface corresponding to the stable formation, and obtaining serial deconvolution data.

[0042] Static correction, also known as terrain correction, refers to the process of correcting seismic data to a uniform horizontal reference plane. After the electronic equipment performs predictive deconvolution based on the virtual fixed surface, it performs reverse static correction on the second seismic data obtained, correcting the second seismic data from the horizontal fixed virtual surface back to the trend floating surface corresponding to the stable stratum, thereby obtaining seismic data after tandem deconvolution based on geosteering.

[0043] In summary, the present application provides a serial deconvolution processing method, which improves the resolution of data, suppresses periodic multiple waves, improves the hierarchical relationship of wave groups, improves the amplitude and phase consistency, and solves the phase inversion and layer-crossing problems in the traditional serial deconvolution processing process by adopting a serial deconvolution method based on geological guidance. It provides strong technical support for the deconvolution processing of seismic data with complex geological structures and meets the needs of fine processing of seismic data.

[0044] Embodiment 2

[0045] Please refer to Figure 2 , which shows a flow chart of a serial deconvolution processing method provided by another exemplary embodiment of the present application. The method comprises the following steps:

[0046] Step 201, performing surface consistency deconvolution on the seismic data to be processed to obtain first seismic data.

[0047] The specific implementation of step 201 can refer to the above step 101, and the embodiment of the present application will not be repeated here.

[0048] Step 202, determining the time correction amount corresponding to the virtual fixed surface and the trend floating surface, the time correction amount is used to indicate the propagation time difference of the seismic wave reaching the ground.

[0049] The trend floating surface is a smooth curved surface that can reflect the fluctuation trend of stable strata, and the virtual fixed surface is a highly consistent horizontal surface. The information of each point on the trend floating surface can be expressed as (X, Y, T z ), where X and Y represent coordinates, T z It is the time it takes for the seismic wave to propagate from that point to the ground. The trend floating surface is uneven, so the corresponding time of each point is different, while the height of each point on the virtual fixed surface is consistent, so the corresponding time is the same, which can be expressed as (X, Y, T avg ). The time correction is the propagation time difference of the seismic wave reaching the ground ΔT = T z -T avg, which can reflect the height difference between the trend floating surface and the virtual fixed surface. The electronic device performs static correction on the first seismic data by determining the time correction amount between the virtual fixed surface and the trend floating surface, controls the analysis time window of the prediction deconvolution on the virtual fixed surface, and then performs prediction deconvolution processing.

[0050] In a possible implementation, the electronic device first needs to determine the trend floating surface and define the virtual fixed surface. Before step 202, the method of the embodiment of the present application further includes the following steps:

[0051] Step 1: determine the stable formation after well calibration based on the well data and perform stratigraphic interpretation on the stack corresponding to the first seismic data.

[0052] Step 2: Based on the smoothing size parameter, the stable strata after strata interpretation are smoothed to obtain the trend floating surface.

[0053] Step 3: Define a virtual fixed surface based on the trend floating surface. The propagation time of each point on the virtual fixed surface is the average propagation time of each point on the trend floating surface.

[0054] After performing surface consistency deconvolution on the seismic data to be processed, the electronic device outputs the first seismic data and the stacking body. The electronic device obtains the well data, determines a set of stable formations H01 after well calibration, and performs stratigraphic interpretation on the stacking body after surface consistency deconvolution. A stable formation is a formation whose reflection coefficient is greater than the coefficient threshold and whose reflection interface is continuous. The electronic device can determine whether the formation is stable by analyzing the reflection coefficient in the well data and whether the reflection interface (phase axis) of the formation in the lateral direction is continuous. Schematically, the coefficient threshold is 0.18. The electronic device can track and interpret the stable formation based on a grid of a preset size (for example, 200m×200m), and grid it.

[0055] Stratigraphic smoothing refers to smoothing the gridded stable strata in the interpretation system to eliminate the influence of local structural anomalies on the subsequent data processing process. The smoothing size parameter can be determined by parameter testing. Indicatively, the smoothing size parameter is selected from the range of 2000m to 4000m. The electronic equipment smoothes the stable strata after the stratum interpretation to obtain a smooth trend floating surface.

[0056] The electronic device defines a virtual fixed surface based on the trend floating surface. The propagation time of each point on the virtual fixed surface is the average propagation time of each point on the trend floating surface. That is, the information of the trend floating surface in space is (X, Y, T z ), X and Y represent coordinates, T z is time, so each X and Y corresponds to a time T z(Time), assuming that X has 1000 sample points and Y has 1000 sample points, there are 1 million time sample points on the trend floating surface. The average of 1 million time sample points is the average time T. avg The average time is a fixed constant, which is the time corresponding to each point on the virtual fixed surface, that is, 1 million points (X, Y, T avg )The surface formed by the above is a virtual fixed surface. Figure 3 A smooth trend floating surface and a virtual fixed surface corresponding to a stable formation are shown.

[0057] Step 202 specifically includes the following steps:

[0058] Step 4: Determine the time correction amount based on the propagation time of each point on the trend floating surface and the propagation time of the corresponding point on the virtual fixed surface.

[0059] The correction amount ΔT is the height difference between the smooth trend floating surface and the virtual fixed surface, that is, ΔT = T z -T avg .

[0060] Step 203, based on the time correction amount, static correction is performed on the first seismic data, and the analysis time window of the prediction deconvolution is controlled on a virtual fixed surface to obtain third seismic data.

[0061] The time correction value ΔT is used to perform static correction on the seismic data after surface consistent deconvolution in order to ensure that the predicted deconvolution analysis time window is controlled on the virtual surface, so that the calculated deconvolution operator is more stable in the lateral direction. Figure 4 The analysis time windows of the deconvolution on different planes are shown.

[0062] Step 204: perform predictive deconvolution processing on the third seismic data to obtain second seismic data.

[0063] The third seismic data is subjected to predictive deconvolution processing, which, on the one hand, eliminates the influence of the interlayer multiple wave interference effect on the change of the wavelet morphology and suppresses the periodic multiple waves, and on the other hand, eliminates the problem of instability of the deconvolution operator caused by the large change of the formation dip angle. The specific implementation process can refer to the above step 102.

[0064] Step 205: Perform inverse static correction on the second seismic data based on the time correction amount to obtain serial deconvolution data.

[0065] The electronic device reuses the time correction amount calculated by the above process to perform reverse static correction on the second seismic data, so that the second seismic data is corrected to a smooth trend floating surface, and finally obtains seismic data and profiles after serial deconvolution processing based on geological guidance.

[0066] Figure 5The comparison of the effects of three types of seismic data is shown: before serial deconvolution, after serial deconvolution based on the traditional method, and after serial deconvolution using the method provided in the embodiment of the present application. Figure 6 and Figure 7 They are comparison diagrams of profiles processed by survey lines in different directions. It can be seen that in the east and south directions of the profile, the traditional serial deconvolution has the problem of inconsistent phase and amplitude. After the serial deconvolution processing is performed using the method of the embodiment of the present application, the stratigraphic hierarchy relationship in the southeast direction is clearer, and the above-mentioned problem is better solved.

[0067] In the embodiment of the present application, a surface consistency deconvolution method is firstly adopted to solve the influence of factors such as near-surface and static correction on the deconvolution operator, and improve the lateral consistency of the surface wavelet. Then, a series prediction deconvolution method is used in a geological structure-guided manner to eliminate the influence of the amplitude and phase caused by the large lateral change of the underground dip angle and the multiple waves generated by special lithology bodies, thereby ensuring that the prediction deconvolution analysis time window is more accurate, the deconvolution operator obtained is more stable, and the problems of stratum phase inversion and layer stringing after series deconvolution are solved.

[0068] Embodiment 3

[0069] Please refer to Figure 8 , which shows a flow chart of a serial deconvolution processing method provided by another exemplary embodiment of the present application. The method comprises the following steps:

[0070] Step 801 , preprocessing the seismic data to be processed, the preprocessing including static correction processing and prestack noise suppression processing.

[0071] In a possible implementation, the seismic data to be processed is single shot data. The electronic device performs a static correction process and a pre-stack noise suppression process on the seismic data to be processed, thereby improving the signal-to-noise ratio of the pre-stack data and the accuracy of the seismic data.

[0072] Step 802 , performing velocity analysis and stacking processing on the pre-processed seismic data to be processed, and performing quality control on the seismic data to be processed based on the obtained stacking volume.

[0073] The electronic equipment performs velocity analysis and stacking processing on the pre-processed seismic data to be processed, sorts the single-shot data into CMP gathers, performs dynamic correction, and finally stacks and outputs a stacking volume, and performs quality control on the single-shot data based on the stacking volume.

[0074] Step 803: Perform surface consistency deconvolution on the seismic data to be processed to obtain first seismic data.

[0075] Step 804, performing predictive deconvolution processing on the first seismic data based on a virtual fixed plane to obtain second seismic data, wherein the virtual fixed plane is a plane obtained by correcting a stable formation, and a stable formation refers to a formation having a reflection coefficient greater than a coefficient threshold and a continuous reflection interface.

[0076] The specific implementation of steps 803 to 804 may refer to the above steps 101 to 102, and the embodiments of the present application will not be repeated here.

[0077] Step 805: Perform quality control based on the relationship between the wavelet curve corresponding to the second seismic data and the envelope to determine the rationality of the deconvolution operator.

[0078] Step 806: In response to the rationality determination result indicating that the deconvolution operator is rational, inverse static correction is performed on the second seismic data to obtain serial deconvolution data.

[0079] Electronic equipment can use amplitude spectrum and phase spectrum curves for quality control to determine the rationality of processing effects. Fig. 9 The wavelet curve quality control diagrams are shown before deconvolution, after traditional serial deconvolution, and after geosteering serial deconvolution. It can be seen that the wavelet curve after serial processing using the method of the embodiment of the present application presents a normal distribution and the wavelet morphology is stable. Fig.10 A curve comparison of the amplitude spectrum and phase spectrum after two series deconvolutions is shown.

[0080] In the embodiment of the present application, qualitative analysis and evaluation are performed through wavelet curves, amplitude spectrum curves and phase spectrum curves to ensure the reliability and accuracy of the application effect, provide reliable basic data for velocity modeling and migration imaging, and have good application prospects in seismic data processing of complex underground geological structures.

[0081] Embodiment 4

[0082] In summary of the above embodiments, a serial deconvolution process provided by an exemplary embodiment of the present application is as follows:

[0083] 1. Input the seismic data DATA1 after static correction and pre-stack noise suppression, perform a velocity analysis and stacking process, output the stacking volume STKVOL1, and perform quality control on the seismic data DATA1 based on the stacking volume STKVOL1.

[0084] 2. Perform surface consistency deconvolution on the seismic data DATA1 to eliminate the influence of the near surface on the deconvolution operator, ensure that the data resolution is appropriately improved without reducing the data signal-to-noise ratio, and output the stacking volume STKVOL2 and seismic data DATA2.

[0085] 3. Using the well data, a set of stable formations H01 after well calibration is determined, and stratigraphic interpretation is performed on the stacked body STKVOL2 after surface consistency deconvolution.

[0086] 4. Smooth the interpreted stratum H01 to obtain a smooth trend floating surface Surface 1 , the average time T of the statistical trend floating surface avg , and define it as a virtual fixed surface Surface 2 , and then smooth the trend floating surface Surface 1 Correct to the virtual fixed surface Surface 2 , and generates a correction amount ΔT.

[0087] 5. Using the correction value ΔT, the seismic data DATA2 after surface consistency deconvolution is subjected to static correction processing, in order to ensure that the predicted deconvolution analysis time window is controlled on the virtual surface, so that the calculated deconvolution operator is more stable in the lateral direction, and the seismic data DATA3 is output.

[0088] 6. The seismic data DATA3 is processed by predictive deconvolution. On the one hand, the influence of the inter-layer multiple wave interference effect on the change of the wavelet morphology is eliminated and the periodic multiple waves are suppressed. On the other hand, the problem of unstable deconvolution operator caused by large changes in stratum inclination is eliminated to produce seismic data DATA4. The relationship between the wavelet curve and the envelope line is used for quality control to determine the rationality of the deconvolution operator.

[0089] 7. Reuse the correction value ΔT to perform reverse static correction on DATA4 so that DATA4 is corrected to the smooth trend floating surface Surface 1 Finally, the seismic data DATA5 and profiles after serial deconvolution processing based on geosteering are obtained, and the amplitude spectrum and phase spectrum curves are used for quality control to judge the rationality of the processing effect.

[0090] Embodiment 5

[0091] Please refer to Fig.11 , which shows a structural block diagram of a serial deconvolution processing device provided by an exemplary embodiment of the present application, the device comprising:

[0092] The first processing module 1101 is used to perform surface consistency deconvolution on the seismic data to be processed to obtain first seismic data;

[0093] A second processing module 1102 is configured to perform predictive deconvolution processing on the first seismic data based on a virtual fixed plane to obtain second seismic data, wherein the virtual fixed plane is a plane obtained by correcting a stable formation, and the stable formation refers to a formation having a reflection coefficient greater than a coefficient threshold and a continuous reflection interface;

[0094] The correction module 1103 is used to perform reverse static correction on the second seismic data, correct the second seismic data to the trend floating surface corresponding to the stable formation, and obtain serial deconvolution data.

[0095] Optionally, the second processing module 1102 is further configured to:

[0096] Determine a time correction amount corresponding to the virtual fixed surface and the trend floating surface, wherein the time correction amount is used to indicate a propagation time difference of a seismic wave reaching the ground;

[0097] Based on the time correction amount, static correction is performed on the first seismic data, and the analysis time window of the prediction deconvolution is controlled on the virtual fixed plane to obtain third seismic data;

[0098] The third seismic data is subjected to predictive deconvolution processing to obtain the second seismic data.

[0099] Optionally, the correction module 1103 is further used to:

[0100] The second seismic data is subjected to inverse static correction based on the time correction amount to obtain the series deconvolution data.

[0101] Optionally, the device further includes a determining module, configured to:

[0102] Determine the stable formation after well calibration based on the well data and perform stratigraphic interpretation on the stack corresponding to the first seismic data;

[0103] Performing formation smoothing processing on the stable formation after formation interpretation based on smoothing size parameters to obtain the trend floating surface;

[0104] The virtual fixed surface is defined based on the trend floating surface, and the propagation time of each point on the virtual fixed surface is the average propagation time of each point on the trend floating surface.

[0105] Optionally, the second processing module 1102 is further configured to:

[0106] The time correction amount is determined based on the propagation time of each point on the trend floating surface and the propagation time of the corresponding point on the virtual fixed surface.

[0107] Optionally, the determining module is further used to:

[0108] Performing quality control based on the relationship between the wavelet curve and the envelope corresponding to the second seismic data to determine the rationality of the deconvolution operator;

[0109] The correction module 1103 is further used for:

[0110] In response to the rationality determination result indicating that the deconvolution operator is rational, an inverse static correction is performed on the second seismic data to obtain the series deconvolution data.

[0111] Optionally, the device further comprises a preprocessing module, which is used to:

[0112] Preprocessing the seismic data to be processed, wherein the preprocessing includes static correction processing and prestack noise suppression processing;

[0113] Velocity analysis and stacking processing are performed on the pre-processed seismic data to be processed, and quality control is performed on the seismic data to be processed based on the obtained stacking volume.

[0114] Embodiment 6

[0115] An embodiment of the present application provides an electronic device; Fig.12 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Fig.12 As shown, the electronic device 1200 includes: a processor 1201, at least one communication bus 1202, a user interface 1203, at least one external communication interface 1204, and a memory 1205. The communication bus 1202 is configured to realize the connection and communication between these components. The user interface 1203 may include a display screen, and the external communication interface 1204 may include a standard wired interface and a wireless interface. The processor 1201 is configured to execute the program of the serial deconvolution processing method stored in the memory to implement the steps in the method provided in the above embodiment.

[0116] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. The computer program is loaded and executed by a processor to implement the method described in the above embodiment.

[0117] The embodiment of the present application further provides a computer program product, which runs on a processor of a computer device, so that the computer device executes the method described in the above embodiment.

[0118] It should be noted here that the description of the above storage medium, electronic device, and remote control embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0119] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and 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 of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0120] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, object or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, object or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, object or device including the element.

[0121] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0122] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0123] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0124] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), disks or optical disks, etc. Various media that can store program codes.

[0125] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0126] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A serial deconvolution processing method, characterized in that: include: Performing surface consistency deconvolution on the seismic data to be processed to obtain first seismic data; Performing predictive deconvolution processing on the first seismic data based on a virtual fixed plane to obtain second seismic data, wherein the virtual fixed plane is a plane obtained by correcting a stable formation, and the stable formation refers to a formation whose reflection coefficient is greater than a coefficient threshold and whose reflection interface is continuous; Performing reverse static correction on the second seismic data, correcting the second seismic data to the trend floating surface corresponding to the stable formation, and obtaining serial deconvolution data.

2. The method according to claim 1, characterized in that The performing predictive deconvolution processing on the first seismic data based on the virtual fixed surface to obtain the second seismic data comprises: Determine a time correction amount corresponding to the virtual fixed surface and the trend floating surface, wherein the time correction amount is used to indicate a propagation time difference of a seismic wave reaching the ground; Based on the time correction amount, static correction is performed on the first seismic data, and the analysis time window of the prediction deconvolution is controlled on the virtual fixed plane to obtain third seismic data; The third seismic data is subjected to predictive deconvolution processing to obtain the second seismic data.

3. The method according to claim 2, characterized in that The step of performing reverse static correction on the second seismic data to correct the second seismic data to the trend floating surface corresponding to the stable formation to obtain serial deconvolution data includes: The second seismic data is subjected to inverse static correction based on the time correction amount to obtain the series deconvolution data.

4. The method according to claim 2, characterized in that: Before determining the time correction amount corresponding to the virtual fixed surface and the trend floating surface, the method further includes: Determine the stable formation after well calibration based on the well data and perform stratigraphic interpretation on the stack corresponding to the first seismic data; Performing formation smoothing processing on the stable formation after formation interpretation based on smoothing size parameters to obtain the trend floating surface; The virtual fixed surface is defined based on the trend floating surface, and the propagation time of each point on the virtual fixed surface is the average propagation time of each point on the trend floating surface.

5. The method according to claim 4, characterized in that The determining of the time correction amount corresponding to the virtual fixed surface and the trend floating surface includes: The time correction amount is determined based on the propagation time of each point on the trend floating surface and the propagation time of the corresponding point on the virtual fixed surface.

6. The method according to any one of claims 1 to 5, characterized in that: Before performing reverse static correction on the second seismic data to correct the second seismic data to the trend floating surface corresponding to the stable formation to obtain the serial deconvolution data, the method further includes: Performing quality control based on the relationship between the wavelet curve and the envelope corresponding to the second seismic data to determine the rationality of the deconvolution operator; The step of performing reverse static correction on the second seismic data to correct the second seismic data to the trend floating surface corresponding to the stable formation to obtain serial deconvolution data includes: In response to the rationality determination result indicating that the deconvolution operator is rational, an inverse static correction is performed on the second seismic data to obtain the series deconvolution data.

7. The method according to any one of claims 1 to 5, characterized in that: Before performing surface consistency deconvolution on the seismic data to be processed to obtain the first seismic data, the method further includes: Preprocessing the seismic data to be processed, wherein the preprocessing includes static correction processing and prestack noise suppression processing; Velocity analysis and stacking processing are performed on the pre-processed seismic data to be processed, and quality control is performed on the seismic data to be processed based on the obtained stacking volume.

8. A serial deconvolution processing device, characterized in that: include: A first processing module, used for performing surface consistency deconvolution on the seismic data to be processed to obtain first seismic data; A second processing module is used to perform predictive deconvolution processing on the first seismic data based on a virtual fixed plane to obtain second seismic data, wherein the virtual fixed plane is a plane obtained by correcting a stable formation, and the stable formation refers to a formation whose reflection coefficient is greater than a coefficient threshold and whose reflection interface is continuous; The correction module is used to perform reverse static correction on the second seismic data, correct the second seismic data to the trend floating surface corresponding to the stable formation, and obtain serial deconvolution data.

9. An electronic device, characterized in that: The method comprises a memory and a processor; a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored, and the computer program is loaded and executed by a processor to implement the method according to any one of claims 1 to 7.

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