Gather data processing method and device and storage medium
By employing Radon transform, FK analysis, linear amplitude compensation, and reverse stretching processing, the problems of low signal-to-noise ratio, insufficient energy, and low resolution in pre-stack gather data were solved, enabling high-precision identification of reservoir fluids.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2024-10-08
- Publication Date
- 2026-04-10
AI Technical Summary
The low signal-to-noise ratio, insufficient energy, and low resolution of pre-stack gather data prevented effective recovery of the AVO effect, affecting the accuracy of reservoir fluid identification.
By removing multiple waves through Radon transform and FK analysis, and performing linear amplitude compensation and reverse stretching, the signal-to-noise ratio, amplitude energy, and resolution of the gather data are improved.
This improved the quality of gather data, enhanced the accuracy of reservoir fluid identification, and ensured the accuracy of AVO features.
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Figure CN121834145A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present disclosure relates to the technical field of data processing, in particular to a gather data processing method and device and storage medium. BACKGROUND
[0002] Unconventional oil and gas reservoirs have strong concealment and complex accumulation rules, which put forward higher requirements for seismic exploration technology. In order to improve the exploration accuracy, more fine and accurate gather data need to be used to obtain higher reliability of reservoir information.
[0003] In seismic exploration, gather data is mainly divided into two categories: pre-stack gather data and post-stack gather data. Pre-stack gather data is usually considered as key data for exploration because it can provide rich AVO (amplitude versus offset) information.
[0004] However, pre-stack gather data has problems of low signal-to-noise ratio, insufficient energy and low resolution, which leads to ineffective recovery of AVO effect and affects the identification accuracy of reservoir fluid. SUMMARY
[0005] The purpose of the embodiments of the present disclosure is to provide a device gather data processing method, device and storage medium, aiming to improve the quality of gather data and thus improve the identification accuracy of reservoir fluid.
[0006] To achieve the above purpose, in a first aspect, the embodiments of the present disclosure provide a gather data processing method, which comprises: performing first processing on original gather data to determine first gather data, the signal-to-noise ratio of the first gather data being greater than that of the original gather data; performing second processing on the first gather data to determine second gather data, the signal-to-noise ratio of the second gather data being greater than that of the original gather data, and the amplitude energy of the second gather data being greater than that of the original gather data; performing third processing on the second gather data to determine target gather data, the signal-to-noise ratio of the target gather data being greater than that of the original gather data, the amplitude energy of the target gather data being greater than that of the original gather data, and the resolution of the target gather data being greater than that of the original gather data.
[0007] In some embodiments, the first processing comprises Radon transform and FK analysis; the first processing on the original gather data to determine the first gather data comprises: according to a preset primary wave range, screening out gather data outside the primary wave range in the original gather data by Radon transform; according to a preset dip angle range, screening out gather data outside the dip angle range in the original gather data by FK analysis; and taking the screened original gather data as the first gather data.
[0008] In some embodiments, the preset primary wave range is -150 ms to 150 ms, and the dip angle range is -20° to 20°.
[0009] In some embodiments, the second processing includes linear amplitude compensation; the second processing on the first gather data to determine the second gather data includes: selecting an amplitude compensation coefficient based on the first gather data; performing amplitude compensation on the first gather data based on the amplitude compensation coefficient; and taking the compensated first gather data as the second gather data.
[0010] In some embodiments, the selected amplitude compensation coefficient is 1, 3, 5, or 7.
[0011] In some embodiments, the third processing includes inverse stretch processing; the third processing on the second gather data to determine the target gather data includes: determining the amplitude of the second gather data at a specific frequency and an incident angle based on the amplitude of the second gather data at a reference angle; adjusting the amplitude of the second gather data based on the amplitude of the second gather data at the specific frequency and the incident angle; and taking the adjusted second gather data as the target gather data.
[0012] In some embodiments, the reference angle is 5° to 10°.
[0013] In the second aspect, the embodiments of the present disclosure provide a method for identifying a target reservoir based on gather data, which includes: processing original gather data from an exploration area to obtain target gather data according to the gather data processing method provided in the first aspect or any of the embodiments of the first aspect, the exploration area being an identification area of the target reservoir; performing pre-stack inversion on the target gather data to determine the AVO characteristics of the target gather data; and identifying the target reservoir based on the AVO characteristics of the target gather data.
[0014] In the third aspect, the embodiments of the present disclosure provide a gather data processing device, which includes: a first processing module configured to perform first processing on original gather data to determine first gather data, the signal-to-noise ratio of the first gather data being greater than that of the original gather data; a second processing module configured to perform second processing on the first gather data to determine second gather data, the signal-to-noise ratio of the second gather data being greater than that of the original gather data, and the amplitude energy of the second gather data being greater than that of the original gather data; and a third processing module configured to perform third processing on the second gather data to determine target gather data, the signal-to-noise ratio of the target gather data being greater than that of the original gather data, the amplitude energy of the target gather data being greater than that of the original gather data, and the resolution of the target gather data being greater than that of the original gather data.
[0015] In a fourth aspect, the embodiments of the present disclosure provide a device for identifying a target reservoir based on gather data. The device comprises: a preprocessing unit configured to process original gather data to obtain target gather data according to the gather data processing method provided in the first aspect or any one of the embodiments of the first aspect; a determination unit configured to determine an AVO characteristic of the target gather data by using the target gather data for pre-stack inversion; and an identification unit configured to identify the target reservoir based on the AVO characteristic of the target gather data.
[0016] In a fifth aspect, the embodiments of the present disclosure provide a machine readable storage medium having instructions stored thereon for causing a machine to perform the gather data processing method provided in the first aspect or any one of the embodiments of the first aspect or the method for identifying a target reservoir based on gather data provided in the second aspect.
[0017] Through the above technical solution, the gather data processing method provided in the embodiments of the present disclosure can remove noise in original gather data, compensate for energy of the original gather data, and improve the problem of insufficient resolution of the original gather data, thereby effectively improving the quality of the gather data and improving the identification accuracy of reservoir fluid.
[0018] Other features and advantages of the embodiments of the present disclosure will be described in detail in the following specific implementation part. BRIEF DESCRIPTION OF DRAWINGS
[0019] The accompanying drawings are included to provide a further understanding of the embodiments of the present disclosure, and constitute a part of the specification, and are used to explain the embodiments of the present disclosure together with the following specific implementation, but do not constitute a limitation of the embodiments of the present disclosure. In the drawings:
[0020] Figure 1 is a flowchart provided according to the gather data processing method embodiment one of the present disclosure;
[0021] Figure 2 is a flowchart provided according to the gather data processing method embodiment two of the present disclosure;
[0022] Figure 2a is a gather diagram before multiple wave attenuation in the embodiment two of the present disclosure;
[0023] Figure 2b is a gather diagram after multiple wave attenuation in the embodiment two of the present disclosure;
[0024] Figure 2A is a gather diagram before noise attenuation in the embodiment two of the present disclosure;
[0025] Figure 2B is a gather diagram after noise attenuation in the embodiment two of the present disclosure;
[0026] Figure 3 is a flowchart diagram of a method for processing gather data according to an embodiment of the present disclosure;
[0027] Figure 3a is a gather diagram before amplitude compensation in the third embodiment of the present disclosure;
[0028] Figure 3b is a gather diagram after amplitude compensation in the third embodiment of the present disclosure;
[0029] Figure 4 is a flowchart diagram of a method for processing gather data according to an embodiment of the present disclosure;
[0030] Figure 4a is a gather diagram before inverse stretch processing in the fourth embodiment of the present disclosure;
[0031] Figure 4b is a gather diagram after inverse stretch processing in the fourth embodiment of the present disclosure;
[0032] Figure 5 is a flowchart diagram of a method for identifying a target reservoir based on gather data according to an embodiment of the present disclosure;
[0033] Figure 5a is a profile diagram before target processing in the embodiment of the present disclosure;
[0034] Figure 5b is a profile diagram after target processing in the embodiment of the present disclosure;
[0035] Figure 6 is a structural diagram of a gather data processing device according to an embodiment of the present disclosure;
[0036] Figure 7 is a structural diagram of a device for identifying a target reservoir based on gather data according to an embodiment of the present disclosure. DETAILED DESCRIPTION
[0037] The specific embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely intended to illustrate and explain the present disclosure, and are not intended to limit the present disclosure.
[0038] With the improvement of seismic exploration methods and seismic exploration equipment, previously restricted and undeveloped small structure complex oil and gas reservoirs and lithologic oil and gas reservoirs have become exploration targets. These unconventional oil and gas reservoirs are more concealed and have more complex reservoir forming rules, which requires higher precision of seismic exploration, more detailed and accurate description of underground medium, higher reliability of reservoir information obtained, and reduction of construction risk and exploitation cost. Due to the deficiency of previous computer level, post-stack seismic data is used. The size of post-stack data is smaller than that of pre-stack data, the signal-to-noise ratio is high, and the data quality is improved, but the post-stack seismic data cannot obtain the feature that the reflection coefficient changes with the change of offset, that is, the information (AVO, Amplitude versus Offset) that the reflection wave amplitude changes with the change of offset. The post-stack data can reflect the strength and continuity of the same phase axis, and it is difficult to describe whether the wave impedance difference of the reservoir is large. However, the pre-stack gather is not stacked, the data volume is large, and contains rich information, especially offset information, which is closely related to the characteristics of reservoir lithology and fluid. The principle of seismic pre-stack inversion is to find the relationship between observation data and underground medium attribute by using mathematical and physical methods, so as to obtain the change of medium attribute, and provide reliable basis for subsequent exploration direction and reservoir analysis. The accuracy of inversion result is affected by the quality of input pre-stack gather. The seismic pre-stack data obtained from the stage of seismic exploration acquisition contains random noise interference, coherent multiple, linear and parabolic interference, energy absorption of stratum, low resolution of deep layer, and AVO effect is not truly recovered, which further affects the accuracy of inversion result to a certain extent.
[0039] In the conventional gather optimization processing, the separation of effective signals and interference signals is usually performed in the Radon domain, which improves the signal-to-noise ratio to a certain extent, but the problems of insufficient energy and low resolution are not effectively solved.
[0040] Therefore, in a first aspect, the embodiments of the present disclosure provide a gather data processing method, which refers to Figure 1 , Figure 1 is a flowchart provided by the gather data processing method embodiment one of the present disclosure.
[0041] As shown in Figure 1 , the gather data processing method includes steps S101-S103.
[0042] Step S101, performing first processing on the original gather data to determine first gather data.
[0043] The original gather data can be pre-stack gather data obtained by seismic data acquisition in the exploration area. In the embodiments of the present disclosure, the original gather data can be obtained by using the existing technology, which is not limited here.
[0044] The signal-to-noise ratio of the first gather data is greater than that of the original gather data.
[0045] It should be noted that, in the process of seismic data acquisition, due to the complexity of geological structure, noise of acquisition instrument, non-standard operation of staff, action of tide and storm, and other factors, noise interference in pre-stack gather data is relatively serious, and therefore it is necessary to improve the signal-to-noise ratio of pre-stack gather data, thereby facilitating subsequent analysis work.
[0046] The first processing can include gather cutting processing, structure-oriented filtering method, Radon transform domain separation method, frequency-wave number domain denoising method, and globally optimized gather flattening algorithm, etc.
[0047] After the first processing is performed on the original gather data, the signal-to-noise ratio of the original gather data can be effectively improved.
[0048] In step S102, the first gather data is subjected to second processing to determine second gather data.
[0049] The first gather data is gather data after the first processing is performed on the original gather data.
[0050] The signal-to-noise ratio of the second gather data is greater than that of the original gather data, and the amplitude energy of the second gather data is greater than that of the original gather data.
[0051] It should be noted that, in the process of inversion analysis of AVO effect by using pre-stack gather data, the inversion result depends on the AVO effect of pre-stack gather data, and if the energy of pre-stack gather data is not compensated or is not compensated properly, the amplitude of deep and far offset gather cannot be truly recovered, which seriously affects the result of AVO technical analysis, and therefore it is necessary to compensate the energy of pre-stack gather data, thereby facilitating subsequent analysis work.
[0052] The second processing can include spherical divergence compensation in linear amplitude compensation, surface consistency amplitude compensation, and other processing methods for amplitude compensation of gather data.
[0053] The second processing is performed on the first gather data with improved signal-to-noise ratio, and the amplitude energy of the gather data can be further improved on the basis of improved signal-to-noise ratio.
[0054] In step S103, the second gather data is subjected to third processing to determine target gather data.
[0055] The second gather data is gather data after the first processing and the second processing are performed on the original gather data.
[0056] Among them, the signal-to-noise ratio of the target gather data is greater than that of the original gather data, the amplitude energy of the target gather data is greater than that of the original gather data, and the resolution of the target gather data is greater than that of the original gather data.
[0057] It should be noted that, in addition to the problems of low signal-to-noise ratio and insufficient energy, pre-stack gather data also suffers from insufficient resolution. This is mainly due to the absorption attenuation and stretching effect of the formation. Since the underground medium is usually viscoelastic and non-homogeneous, the amplitude of seismic waves attenuates and the phase is distorted during propagation in the medium. This results in insufficient deep amplitude energy in pre-stack gathers, and sometimes it can also disrupt the continuity of the phase axis. The attenuation is more severe at far offsets than at near offsets, which disrupts the AVO effect. Therefore, improving the resolution of pre-stack gather data is beneficial for subsequent analysis.
[0058] The third processing can include methods to improve the resolution of gather data, such as high-resolution τ-x transformation, non-rigid matching, and inverse stretching.
[0059] By processing the second gather data, which already has improved signal-to-noise ratio and amplitude energy, a third processing step can be performed to further improve the resolution of the gather data, building upon the already enhanced signal-to-noise ratio and amplitude energy.
[0060] The gather data processing method provided in this disclosure improves the signal-to-noise ratio, amplitude energy, and resolution of the gather data by processing it multiple times. This enables the effective analysis of AVO features from the gather data, thereby improving the identification accuracy of reservoir fluids.
[0061] In a first aspect, embodiments of this disclosure provide a method for processing gather data, with reference to Figure 2 , Figure 2 This is a flowchart provided according to Embodiment 2 of the data processing method disclosed herein.
[0062] In one feasible implementation, the first processing may include Radon transform and FK analysis, wherein Radon transform is a mathematical transformation used to transform a function (typically a function in two- or three-dimensional space) from its original domain (spatial domain) to a new domain (Radon domain), and frequency-wavenumber (FK) analysis is a signal processing technique widely used in seismology that helps researchers analyze the propagation characteristics of seismic waves by transforming seismic data from the spatiotemporal domain to the frequency-wavenumber domain.
[0063] like Figure 2 As shown, the first processing of the original gather data to determine the first gather data may include steps S201 to S203.
[0064] Step S201, according to a preset primary wave range, the original gather data outside the primary wave range is screened out through Radon transform.
[0065] It should be noted that in the seismic data collected in the field, in addition to random noise, it also contains some multiple waves, side interference waves and other linear noises. In the pre-stack gather, the multiple waves are not flattened after the moveout correction, which is manifested as the curvature of the same phase axis being not zero. Moreover, the pre-stack gather does not have the stacking link, and cannot suppress the effective wave and the linear interference wave through stacking, so the pre-stack suppression of the interference wave is to realize the signal-to-noise separation in other domains. The Radon transform can realize the separation of the effective signal and the interference signal in the Radon domain. Due to the diversity and flexibility of the transform form and the good amplitude preservation, the Radon transform is adopted to realize the multiple wave suppression in the embodiments of the present disclosure.
[0066] Specifically, the original gather data in the time-space domain is converted to the radon domain, the data range of the multiple waves is selected according to the velocity difference between the multiple waves and the primary waves, the selected multiple waves are subtracted from the original gather, and finally the gather data after the multiple wave removal is obtained by converting back to the time-space domain. In some embodiments, the preset primary wave range is-150ms150ms, and the range outside the range is considered to be a multiple wave.
[0067] The conversion formula 1 is as follows: In the formula, τ is the intercept time, p is the slowness (inverse of velocity), x is the inter-shot distance, m(τ, p) is the radon forward transform data, and d(t, x) represents the gather data which is the radon inverse transform data.
[0068] It can be understood that the seismic reflection event and the radon domain can be forward and inverse transformed. The difference between the hyperbolic characteristic seismic reflection event and the horizontal characteristic reflection event in the radon domain can well distinguish the primary wave and the multiple wave.
[0069] For example, the gather data before the multiple wave attenuation is as shown in Figure 2a The gather data after the multiple wave attenuation is as shown in Figure 2b After the multiple wave attenuation processing, the signal-to-noise ratio of the gather data is significantly improved. In the gather data before the attenuation, the existence of the multiple wave will cause the clarity of the main reflection event to be reduced, and will also increase the complexity of the seismic data processing. After the multiple wave attenuation processing, the main reflection event becomes clearer, and the reflection event is more prominent, thereby helping the geophysicists to more accurately interpret the distribution of the underground structure and the rock layer.
[0070] Step S202, according to a preset dip angle range, the original gather data outside the dip angle range is screened out through FK analysis.
[0071] It should be noted that after the multiple wave removal, there is still residual noise on the gather, and therefore noise suppression needs to be performed on the gather data.
[0072] Specifically, the space-time domain gather is converted to the FK domain, a suitable dip angle range is selected, the data within the range is considered as valid signal, and the data outside the range is removed, and the gather data after noise removal is obtained by inverse transformation back to the space-time domain. In some embodiments, the preset dip angle range is -20°-20°, and the data outside the range is considered as noise interference.
[0073] For example, the gather data before noise attenuation is as shown in Figure 2A The gather data after noise attenuation is as shown in Figure 2B It can be seen that in the gather data before noise attenuation, the seismic signal is covered by various noises, which can be caused by ground rolling, wind, rain, traffic vibration, etc., resulting in a decrease in the quality of seismic data, and the characteristics of reflection events are not obvious, making it difficult to accurately interpret and analyze the seismic data. In the gather data after noise attenuation, by using F-K analysis, the signal-to-noise ratio of the seismic signal can be significantly improved, the seismic reflection events are clearer, and the waveforms are smoother, thereby helping to more accurately identify and interpret the underground geological structure and fluid distribution.
[0074] In step S203, the screened original gather data is taken as the first gather data.
[0075] That is, the signal-to-noise ratio of the first gather data is greater than that of the original gather data.
[0076] The gather data processing method provided by the embodiments of the present disclosure converts the space-time domain gather data to the radon domain through multiple wave suppression, identifies and removes the multiple waves by using the speed difference between the multiple waves and the primary waves, thereby improving the accuracy of the data; converts the data to the frequency-wave number (FK) domain through noise suppression, and distinguishes the valid signal and the noise by selecting a suitable dip angle range, and then removes the noise to improve the data quality. The above method effectively improves the signal-to-noise ratio of the original gather data.
[0077] In a first aspect, the embodiments of the present disclosure provide a gather data processing method, which refers to Figure 3 , Figure 3 is a flowchart diagram provided by the third embodiment of the gather data processing method of the present disclosure.
[0078] In a feasible implementation, the second processing can include linear amplitude compensation, where the linear amplitude compensation is a seismic data processing technology mainly correcting the amplitude attenuation of seismic waves caused by factors such as stratum absorption and wavefront diffusion when the seismic waves propagate in the medium.
[0079] It should be noted that after noise removal, the gather signal-to-noise ratio is greatly improved, but the energy difference between shallow, medium and deep is large, and energy compensation is needed to reflect the true situation of the data. Linear amplitude compensation is usually used in this way, which does not destroy the relative relationship of the original gather amplitude.
[0080] As shown in Figure 3 , the second processing of the first gather data to determine the second gather data can include steps S301-S303.
[0081] Step S301, based on the first gather data, selecting an amplitude compensation coefficient.
[0082] Through the analysis of seismic data, the appropriate amplitude compensation coefficient is determined to compensate for the amplitude distortion caused by factors such as stratigraphic absorption attenuation.
[0083] In some embodiments, the selected amplitude compensation coefficient is 1, 3, 5, 7, which is determined according to the specific characteristics of the stratum and the propagation characteristics of the seismic wave.
[0084] Step S302, based on the amplitude compensation coefficient, amplitude compensation is performed on the first gather data.
[0085] The selected amplitude compensation coefficient is applied to the seismic gather data to adjust the amplitude so that it is closer to the true reflection coefficient of the underground reflection interface. This process can be implemented through various algorithms, such as spectral whitening, inverse Q filtering, etc. These methods can effectively compensate for the amplitude attenuation and phase distortion of the seismic wave during propagation.
[0086] Step S303, the compensated first gather data as the second gather data.
[0087] After amplitude compensation, the gather data obtained will have more accurate amplitude information, which will help subsequent analysis work.
[0088] Specifically, the gather data before amplitude compensation is as shown in Figure 3a , and the gather data after amplitude compensation is as shown in Figure 3b . It can be seen that the gather data before amplitude compensation may have a large difference in amplitude, which may be due to the influence of various factors on the propagation of seismic waves, such as stratigraphic absorption attenuation, wavefront diffusion, etc. The gather data after amplitude compensation is more balanced in amplitude, and the emission event is clearer, which helps to improve the lateral consistency of seismic data.
[0089] The gather data processing method provided by the embodiments of the present disclosure analyzes the energy characteristics of shallow, medium and deep layers through linear amplitude compensation, selects a compensation coefficient, and applies the compensation coefficient to the gather to compensate the shallow, medium and deep energy, so that the energy is consistent, while the relative relationship of the original gather amplitude is maintained.
[0090] In a first aspect, embodiments of this disclosure provide a method for processing gather data, with reference to Figure 4 , Figure 4 This is a flowchart provided according to Embodiment 4 of the data processing method disclosed herein.
[0091] In one feasible implementation, the third processing may include anti-stretching processing, which is a technique in seismic data processing primarily used to improve the resolution of seismic data.
[0092] It should be noted that the wavelet at the far offset in the gather exhibits a certain degree of stretching (lower frequency, larger amplitude), affecting the true amplitude variation characteristics. By using the inverse stretching method, with the near offset as a reference, the spectrum of the mid-to-far offset is modified according to the angle. This ensures that the frequency characteristics of the gather data at near, mid, and far distances are consistent, thereby improving data amplitude preservation and resolution.
[0093] like Figure 4 As shown, the third processing of the second gather data to determine the target gather data may include steps S401 to S403.
[0094] Step S401: Based on the amplitude of the near offset of the second gather data at the reference angle, determine the amplitude of the mid-to-far offset of the second gather data at a specific frequency and incident angle.
[0095] In the reverse stretching process, it is first necessary to determine the amplitude of the near-offset gather data at a reference angle. Then, using this as a benchmark, the amplitude of the mid-to-long-offset gather data at specific frequencies and incident angles is adjusted. This process can be implemented using specific algorithms, such as Equation 2, to calculate the amplitude of the offset at specific frequencies and incident angles. In some embodiments, the reference angle is 5° to 10°.
[0096] Specifically, Formula 2 is In the formula A j (f) represents the frequency domain amplitude, j represents the incident angle, and A0 represents the reference angle amplitude.
[0097] Step S403: Use the adjusted second gather data as the target gather data.
[0098] Specifically, the gather data after reverse stretching is as follows: Figure 4b As shown, the gather data before reverse stretching is as follows: Figure 4a As shown, it can be clearly seen that the low-frequency characteristics of the mid-to-far channels are reduced, and the waveform characteristics of the near-to-mid-to-far channels of the same phase axis are more consistent. This processing helps to eliminate the AVO (amplitude variation with angle) artifact caused by the stretching effect, thereby improving the accuracy of seismic data interpretation.
[0099] The method for processing gather data provided in the embodiments of the present disclosure uses near offset as a reference to modify the spectrum of middle and far offsets according to the angle, so that the frequency characteristics of near, middle and far gather data are consistent, and the data amplitude preservation and resolution are effectively improved.
[0100] Based on this, in a second aspect, the embodiments of the present disclosure provide a method for identifying a target reservoir based on gather data, which refers to Figure 5 , Figure 5 is a flowchart of the method for identifying a target reservoir based on gather data according to the embodiments of the present disclosure.
[0101] As Figure 5 shown, the method for identifying a target reservoir based on gather data includes steps S501-S503.
[0102] Step S501, processing the original gather data from the exploration area to obtain target gather data, the exploration area being the identification area of the target reservoir.
[0103] In the embodiments of the present disclosure, the original gather data is mainly processed by using the gather data processing method provided in the above embodiments to obtain the target gather data, and the specific steps can be referred to the detailed description of the gather data processing method above, which will not be repeated here.
[0104] Step S502, using the target gather data to perform pre-stack inversion to determine the AVO characteristics of the target gather data.
[0105] Pre-stack inversion is a seismic data processing technique that uses the reflection and transmission characteristics of seismic waves in different media to infer the physical properties of underground rocks. AVO (amplitude versus offset) characteristic analysis can help identify different geological bodies, such as oil and gas reservoirs, water layers, and dry layers.
[0106] Step S503, identifying the target reservoir based on the AVO characteristics of the target gather data.
[0107] According to the AVO characteristics obtained in the previous step, combined with geological knowledge and experience, the abnormal characteristics in the seismic gather are identified to determine the possible reservoir location.
[0108] Through the AVO characteristic rules of the pre-stack gather of the drilled well key sand body, it is found that pre-stack inversion can be applied for fluid identification, gas layers show III type AVO anomaly; dense layers show strong amplitude and no AVO anomaly, and mudstone shows weak amplitude and no AVO anomaly. The gather flatness of the target layer segment before processing is poor, the middle and far gathers are obviously pulled down, and the AVO characteristics show IV type. After the target processing, the gather flatness of the target layer segment is obviously improved, and the AVO characteristics show III type, which is consistent with the understanding of the drilled well.
[0109] Specifically, the profile feature before target processing is as shown in Figure 5a The profile after target processing is as shown in Figure 5b It can be seen that the target layer section before target processing has no obvious difference from the surrounding rock features, and the highlight features are more prominent after target processing, which fully demonstrates the effectiveness of target processing.
[0110] The method for identifying a target reservoir based on gather data provided by the embodiments of the present disclosure can effectively identify a target reservoir with specific physical properties by using the processed target gather data for pre-stack inversion and AVO feature analysis.
[0111] Therefore, in a third aspect, the embodiments of the present disclosure provide a gather data processing apparatus, which refers to Figure 6 , Figure 6 is a structural schematic diagram of the gather data processing apparatus provided by the embodiments of the present disclosure.
[0112] As shown in Figure 6 , the gather data processing apparatus 100 includes a first processing module 110, a second processing module 120, and a third processing module 130.
[0113] The first processing module 110 is configured to perform first processing on the original gather data to determine first gather data, and the signal-to-noise ratio of the first gather data is greater than that of the original gather data.
[0114] The second processing module 120 is configured to perform second processing on the first gather data to determine second gather data, and the signal-to-noise ratio of the second gather data is greater than that of the original gather data, and the amplitude energy of the second gather data is greater than that of the original gather data.
[0115] The third processing module 130 is configured to perform third processing on the second gather data to determine target gather data, and the signal-to-noise ratio of the target gather data is greater than that of the original gather data, the amplitude energy of the target gather data is greater than that of the original gather data, and the resolution of the target gather data is greater than that of the original gather data.
[0116] In some embodiments, the first processing includes Radon transform and FK analysis; the first processing module 110 performs first processing on the original gather data to determine the first gather data in the following manner: according to a preset primary wave range, the gather data outside the primary wave range in the original gather data is screened out through Radon transform; according to a preset dip angle range, the gather data outside the dip angle range in the original gather data is screened out through FK analysis; and the screened original gather data is taken as the first gather data.
[0117] In some embodiments, the preset primary wave range is -150ms-150ms, and the dip angle range is -20°-20°.
[0118] In some embodiments, the second processing includes linear amplitude compensation; the second processing module 120 performs the second processing on the first gather data in the following manner to determine the second gather data: based on the first gather data, an amplitude compensation coefficient is selected; based on the amplitude compensation coefficient, amplitude compensation is performed on the first gather data; and the compensated first gather data is used as the second gather data.
[0119] In some embodiments, the selected amplitude compensation coefficients are 1, 3, 5, and 7.
[0120] In some embodiments, the third processing includes a reverse stretching process; the third processing module performs the third processing on the second gather data in the following manner to determine the target gather data: based on the amplitude of the near offset of the second gather data at a reference angle, determines the amplitude of the mid-to-far offset of the second gather data at a specific frequency and incident angle; based on the amplitude of the mid-to-far offset of the second gather data at the specific frequency and incident angle, adjusts the amplitude of the second gather data; and uses the adjusted second gather data as the target gather data.
[0121] In some embodiments, the reference angle is 5° to 10°.
[0122] The Daoji data processing apparatus provided in this disclosure, employing the Daoji data processing method in the above embodiments, can solve the technical problems mentioned in the background art.
[0123] The beneficial effects of the Daoji data processing device provided in this disclosure are the same as those of the Daoji data processing method provided in the above embodiments, and other technical features in the Daoji data processing device are the same as those disclosed in the Daoji data processing method, and will not be repeated here.
[0124] Based on this, in a fourth aspect, embodiments of this disclosure provide an apparatus for identifying target reservoirs based on gather data, referring to... Figure 7 , Figure 7 This is a schematic diagram of a device for identifying target reservoirs based on gather data, according to an embodiment of this disclosure.
[0125] like Figure 7 As shown, the apparatus 200 for identifying target reservoirs based on gather data includes a preprocessing unit 210, a determination unit 220, and an identification unit 230.
[0126] The preprocessing unit 210 is used to process the original gather data according to the gather data processing method provided in the first aspect or any embodiment of the first aspect to obtain the target gather data.
[0127] The determination unit 220 is used to perform pre-stack inversion using the target gather data to determine the AVO characteristics of the target gather data.
[0128] The identifying unit 230 is configured to identify the target reservoir based on the AVO characteristics of the target gather data.
[0129] The device for identifying a target reservoir based on gather data provided by the embodiments of the present disclosure adopts the method for identifying a target reservoir based on gather data provided by the above embodiments, and can solve the technical problems in the background art.
[0130] The device for identifying a target reservoir based on gather data provided by the present disclosure has the same beneficial effects as the method for identifying a target reservoir based on gather data provided by the above embodiments, and other technical features of the device for identifying a target reservoir based on gather data are the same as those of the method for identifying a target reservoir based on gather data, which will not be repeated here.
[0131] Therefore, in a fifth aspect, the embodiments of the present disclosure provide a machine readable storage medium having instructions stored thereon for causing a machine to perform the gather data processing method provided by the first aspect or any of the embodiments of the first aspect, or the method for identifying a target reservoir based on gather data provided by the second aspect.
[0132] The machine readable storage medium provided by the present disclosure has the same beneficial effects as the gather data processing method provided by the above embodiments, or the method for identifying a target reservoir based on gather data provided by the above embodiments, which will not be repeated here.
[0133] The embodiments of the present disclosure also provide a device, which includes a memory and a processor configured to perform the gather data processing method provided by the above embodiments, or the method for identifying a target reservoir based on gather data provided by the above embodiments.
[0134] In a typical configuration, the device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.
[0135] The memory can include non-persistent memory in computer readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer readable media.
[0136] Computer-readable media includes permanent and non-permanent, removable and non-removable media implemented by any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media 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 disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.
[0137] The device provided by the embodiments of the present disclosure adopts the gather data processing method provided by the above embodiments or the method for identifying a target reservoir based on gather data provided by the above embodiments, and can solve the technical problems in the background art.
[0138] The device provided by the embodiments of the present disclosure has the same beneficial effects as the gather data processing method provided by the above embodiments or the method for identifying a target reservoir based on gather data provided by the above embodiments, and other technical features in the device are the same as the features disclosed in the gather data processing method provided by the above embodiments or the method for identifying a target reservoir based on gather data provided by the above embodiments, which will not be repeated here.
[0139] The embodiments of the present disclosure also provide a computer program product, which includes a computer program that is executed by a processor to implement the steps of the gather data processing method provided by the above embodiments or the method for identifying a target reservoir based on gather data provided by the above embodiments.
[0140] The computer program product provided by the embodiments of the present disclosure has the same beneficial effects as the gather data processing method provided by the above embodiments or the method for identifying a target reservoir based on gather data provided by the above embodiments, which will not be repeated here.
[0141] Those skilled in the art will appreciate that embodiments of the disclosure can be supplied as a method, a system, or a computer program product. Accordingly, the disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the disclosure can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, and the like) embodying computer-readable program code.
[0142] The disclosure is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the disclosure. It should be understood that each flow and / or block in the flowchart and / or block diagrams, and a combination of flows and / or blocks in the flowchart and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart and / or block diagrams block or blocks. Figure 1 one or more flows and / or blocks Figure 1 an apparatus to perform the functions specified in the flowchart and / or block diagram block or blocks.
[0143] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more flows and / or blocks Figure 1 an apparatus to perform the functions specified in the flowchart and / or block diagram block or blocks.
[0144] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more flows and / or blocks Figure 1 an apparatus to perform the functions specified in the flowchart and / or block diagram block or blocks.
[0145] It should be noted that although the terms "first", "second", etc. are used herein to describe different modules, steps and data of the embodiments of the disclosure, the terms "first", "second", etc. are only used to distinguish between different modules, steps and data, and do not indicate a specific order or importance. In fact, the terms "first", "second", etc. can be used interchangeably.
[0146] Although the operations are described in a particular, sequential order, this should not be understood as a requirement and is not intended herein to suggest that any particular order is critical, that it is necessary to perform all operations, or that the techniques described herein must be performed in the order presented. Many other operations that are not depicted can also be performed or events can occur without performing all of the operations or events depicted and some operations or events can be performed more than depicted. Concurrent performance of operations or events can be possible, and certain operations or events can be performed in any order, including concurrently. The inclusion of certain operations or events does not exclude others.
[0147] The acquisition, transmission, storage, use, processing, etc. of data in the embodiments of the present disclosure comply with relevant provisions of national laws and regulations.
[0148] It should be noted that in the embodiments of the present disclosure, some industry existing solutions, components, models, etc. can be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility of the implementation of the technical solutions of the present disclosure, but does not mean that the applicant has or will necessarily use the solution.
[0149] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles, or equipment including a series of elements not only include those elements, but also include other elements not explicitly listed, or further include elements inherent to such processes, methods, articles, or equipment. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article, or equipment including the element.
[0150] The above is only an embodiment of the present disclosure, and is not intended to limit the present disclosure. Those skilled in the art can make various modifications and changes to the present disclosure. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present disclosure shall be included in the scope of claims of the present disclosure.
Claims
1. A method for processing collection data, characterized in that, The method includes: The original gather data is first processed to determine the first gather data, wherein the signal-to-noise ratio of the first gather data is greater than the signal-to-noise ratio of the original gather data. The first gather data is processed in a second way to determine the second gather data, wherein the signal-to-noise ratio of the second gather data is greater than that of the original gather data, and the amplitude energy of the second gather data is greater than that of the original gather data. The second trace data is subjected to a third processing step to determine target trace data, wherein the signal-to-noise ratio of the target trace data is greater than that of the original trace data, the amplitude energy of the target trace data is greater than that of the original trace data, and the resolution of the target trace data is greater than that of the original trace data.
2. The method according to claim 1, characterized in that, The first processing includes Radon transform and FK analysis; the first processing of the original gather data to determine the first gather data includes: Based on a preset primary wave range, Radon transform is used to filter out the original gather data that are outside the primary wave range; Based on a preset inclination angle range, FK analysis is used to filter out gather data from the original gather data that are outside the inclination angle range; The original gather data after filtering is used as the first gather data.
3. The method according to claim 2, characterized in that, The preset primary wave range is -150ms to 150ms, and the tilt angle range is -20° to 20°.
4. The method according to claim 1, characterized in that, The second processing includes linear amplitude compensation; the second processing of the first gather data to determine the second gather data includes: Based on the first gather data, select the amplitude compensation coefficient; Based on the amplitude compensation coefficient, amplitude compensation is performed on the first gather data; The compensated first gather data is used as the second gather data.
5. The method according to claim 4, characterized in that, The selected amplitude compensation coefficients are 1, 3, 5, and 7.
6. The method according to claim 1, characterized in that, The third processing includes reverse stretching; the third processing of the second gather data to determine the target gather data includes: Based on the amplitude of the near offset of the second gather data at the reference angle, determine the amplitude of the mid-to-far offset of the second gather data at a specific frequency and incident angle. Based on the amplitude of the mid-to-long offset of the second gather data at a specific frequency and incident angle, adjust the amplitude of the second gather data; The adjusted second gather data is used as the target gather data.
7. The method according to claim 6, characterized in that, The reference angle is 5° to 10°.
8. A method for identifying target reservoirs based on gather data, characterized in that, The method includes: According to any one of claims 1-7, the raw gather data from the exploration area is processed to obtain target gather data, wherein the exploration area is the identification area of the target reservoir; Pre-stack inversion is performed using the target gather data to determine the AVO features of the target gather data; The target reservoir is identified based on the AVO features of the target gather data.
9. A data processing device for a collection of documents, characterized in that, The gather data processing device includes: The first processing module is used to perform a first processing on the original gather data to determine the first gather data, wherein the signal-to-noise ratio of the first gather data is greater than the signal-to-noise ratio of the original gather data. The second processing module is used to perform a second processing on the first gather data to determine the second gather data, wherein the signal-to-noise ratio of the second gather data is greater than the signal-to-noise ratio of the original gather data, and the amplitude energy of the second gather data is greater than the amplitude energy of the original gather data. The third processing module is used to perform a third processing on the second trace data to determine the target trace data, wherein the signal-to-noise ratio of the target trace data is greater than that of the original trace data, the amplitude energy of the target trace data is greater than that of the original trace data, and the resolution of the target trace data is greater than that of the original trace data.
10. A device for identifying target reservoirs based on gather data, characterized in that, The device includes: A preprocessing unit is used to process raw gather data from an exploration area according to any one of claims 1-7, to obtain target gather data, wherein the exploration area is the identification area of the target reservoir. A determination unit is used to perform pre-stack inversion using the target gather data to determine the AVO features of the target gather data; The identification unit is used to identify the target reservoir based on the AVO characteristics of the target gather data.
11. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the gather data processing method of any one of claims 1-7 or the method for identifying target reservoirs based on gather data as described in claim 8.