Reservoir space frequency dispersion detection method, device, equipment and medium

Abstract

The invention discloses a frequency dispersion detection method and device for a reservoir space, equipment and a medium. According to the method, a low-frequency value and a high-frequency value of effective bandwidth of seismic data obtained at a target well point are used as reference frequencies, and velocity frequency dispersion correction processing is performed on logging data obtained at the target well point, so that a low-frequency synthetic seismic record and a high-frequency synthetic seismic record are obtained; performing time-frequency decomposition on the seismic data to obtain a full-frequency gather; determining low-frequency seismic data according to the low-frequency synthetic seismic record and the full-frequency gather, and determining high-frequency seismic data according to the high-frequency synthetic seismic record and the full-frequency gather; and explaining the target reservoir section according to the low-frequency seismic data and the high-frequency seismic data, and determining the frequency dispersion degree of the target reservoir section. According to the technical scheme, the propagation characteristics of the seismic waves in the reservoir at different frequencies are explained, so that the seismic frequency dispersion characteristic detection facing the reservoir space is realized, and data support is provided for fine seismic exploration.

Description

Technical Field

[0001] The present application relates to the field of seismic exploration technology, and in particular to a method, device, equipment and medium for detecting frequency dispersion in a reservoir space. Background Art

[0002] When seismic waves propagate through underground media, high-frequency components typically travel faster than low-frequency components. This phenomenon of velocity varying with frequency is called dispersion. The fundamental cause of dispersion lies in differences in measurement scale—that is, differences in propagation frequency. In seismic exploration, the measurement frequency of well logging data ranges from 2 to 20 kHz, while the dominant frequency of seismic data is generally 10 to 100 Hz. Because the frequency of well logging data is much higher than that of seismic data, in dispersive formations, the formation velocities obtained from well logging are often higher than those obtained from seismic data, resulting in inconsistencies when matching the two data.

[0003] Existing technical solutions, when both well logging and seismic data are free of other interference, can exploit this well-to-seismic inconsistency to detect the dispersion of the formation. Then, through dispersion correction, the well logging velocities are corrected to the seismic velocities, improving the consistency between well logging and seismic data. Due to the significant difference in measurement frequency between well logging and seismic, velocity dispersion between well logging and seismic data is relatively pronounced. However, this dispersion detection method has a limited range, only capturing the dispersion at the well point and failing to extend it to inter-well areas, making it difficult to detect dispersion within the reservoir space.

[0004] Therefore, how to provide a technical solution that can detect the dispersion degree of the reservoir space is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0005] The present application provides a method, apparatus, device and medium for detecting dispersion in reservoir space. By interpreting the propagation characteristics of seismic waves at different frequencies in the reservoir, it realizes the detection of seismic dispersion characteristics for reservoir space and provides data support for fine seismic exploration.

[0006] According to one aspect of the present application, a method for detecting dispersion in a reservoir space is provided, the method comprising:

[0007] The low-frequency value and the high-frequency value of the effective bandwidth of the seismic data obtained at the target well point are respectively used as reference frequencies, and the velocity dispersion correction processing is performed on the logging data obtained at the target well point to obtain a low-frequency synthetic seismic record and a high-frequency synthetic seismic record;

[0008] Performing time-frequency decomposition on the seismic data to obtain full-frequency gathers;

[0009] Determining low-frequency seismic data based on the low-frequency synthetic seismic record and the full-frequency gather, and determining high-frequency seismic data based on the high-frequency synthetic seismic record and the full-frequency gather;

[0010] The target reservoir segment is interpreted based on the low-frequency seismic data and the high-frequency seismic data respectively to determine the dispersion degree of the target reservoir segment.

[0011] According to another aspect of the present application, a device for detecting frequency dispersion in a reservoir space is provided, the device comprising:

[0012] a dispersion correction module for performing velocity dispersion correction processing on the logging data obtained at the target well point by using the low-frequency value and the high-frequency value of the effective bandwidth of the seismic data obtained at the target well point as reference frequencies, thereby obtaining a low-frequency synthetic seismic record and a high-frequency synthetic seismic record;

[0013] A time-frequency decomposition module, used for performing time-frequency decomposition on the seismic data to obtain full-frequency gathers;

[0014] a seismic data determination module, configured to determine low-frequency seismic data based on the low-frequency synthetic seismic record and the full-frequency gather, and to determine high-frequency seismic data based on the high-frequency synthetic seismic record and the full-frequency gather;

[0015] The dispersion detection module is used to interpret the target reservoir segment based on the low-frequency seismic data and the high-frequency seismic data, and determine the dispersion degree of the target reservoir segment.

[0016] According to another aspect of the present application, a dispersion detection device for a reservoir space is provided, the device comprising:

[0017] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method for detecting the dispersion of the reservoir space described in any embodiment of the present application.

[0018] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for detecting the dispersion of a reservoir space described in any embodiment of the present application when executed.

[0019] The technical solution provided in this application uses the low-frequency and high-frequency values ​​of the effective bandwidth of the seismic data acquired at the target well point as reference frequencies, performs velocity dispersion correction processing on the logging data acquired at the target well point, and obtains low-frequency and high-frequency synthetic seismic records. The seismic data is then subjected to time-frequency decomposition to obtain full-frequency gathers. Low-frequency seismic data are determined based on the low-frequency and full-frequency gathers, and high-frequency seismic data are determined based on the high-frequency and full-frequency gathers. The target reservoir segment is interpreted based on the low-frequency and high-frequency seismic data to determine the dispersion level of the target reservoir segment. This technical solution interprets the propagation characteristics of seismic waves in the reservoir at different frequencies to achieve reservoir-specific seismic dispersion feature detection, providing data support for fine seismic exploration.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 This is a flow chart of a method for detecting frequency dispersion in a reservoir space provided in Example 1 of the present application;

[0023] Figure 2 A flow chart of a method for detecting frequency dispersion in a reservoir space provided in Example 2 of the present application;

[0024] Figure 3 A schematic structural diagram of a frequency dispersion detection device for a reservoir space provided in Example 3 of the present application;

[0025] Figure 4 It is a structural schematic diagram of an apparatus for implementing a method for detecting frequency dispersion in a reservoir space according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0027] It should be noted that the terms "first", "second", "third", "fourth", "fifth", "sixth", "candidate", "target", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Example 1

[0029] Figure 1 This is a flow chart of a method for detecting dispersion of a reservoir space provided in Example 1 of the present application. This embodiment is applicable to the case of performing dispersion detection on a reservoir space. The method can be performed by a dispersion detection device for a reservoir space. The dispersion detection device for a reservoir space can be implemented in the form of hardware and / or software. The dispersion detection device for a reservoir space can be configured in a device with data processing capabilities. Figure 1 As shown, the method includes:

[0030] S110, using the low-frequency value and the high-frequency value of the effective bandwidth of the seismic data obtained at the target well point as reference frequencies, performing velocity dispersion correction processing on the logging data obtained at the target well point, and obtaining low-frequency synthetic seismic records and high-frequency synthetic seismic records.

[0031] The effective bandwidth can be defined as the portion of the seismic data where the signal-to-noise ratio is greater than a certain threshold, such as a signal-to-noise ratio greater than 1. Using the dominant frequency of the earthquake as the dividing line, frequencies above the dominant frequency are considered high-frequency values, while frequencies below the dominant frequency are considered low-frequency values. The dominant frequency of the earthquake can be defined as the midpoint between the low-frequency cutoff and the high-frequency cutoff in the effective bandwidth. In this approach, any frequency value below the dominant frequency can be selected as the low-frequency value, and any frequency value above the dominant frequency can be selected as the high-frequency value.

[0032] Synthetic seismograms are artificially synthesized from acoustic logging or vertical seismic profile data. Synthetic seismograms are produced through a simplified one-dimensional forward modeling process, resulting from the convolution of seismic wavelets with reflection coefficients. Specifically, the reflection coefficients are calculated from acoustic and density logging curves. These reflection coefficients are then convolved with the extracted seismic wavelets to produce the initial synthetic seismogram. This initial synthetic seismogram is then corrected using a more accurate velocity field and then aligned with the wellbore seismic traces to produce the final synthetic seismogram.

[0033] Velocity dispersion correction involves converting velocities from the well logging frequency band to the seismic frequency band. Specifically, a resonant quality factor (Q) model is used to perform velocity dispersion correction on the well logging data acquired at the target well point, using the low-frequency and high-frequency values ​​of the effective bandwidth of the seismic data acquired at the target well point as reference frequencies.

[0034] In this scheme, by using the low-frequency value as the reference frequency to perform velocity dispersion correction on the logging data obtained at the target well point, a low-frequency synthetic seismic record can be obtained; by using the high-frequency value as the reference frequency to perform velocity dispersion correction on the logging data obtained at the target well point, a high-frequency synthetic seismic record can be obtained.

[0035] Optionally, the low-frequency value of the seismic data obtained at the target well point is used as the reference frequency, and the velocity dispersion correction processing is performed on the logging data obtained at the target well point to obtain a low-frequency synthetic seismic record, including: using the low-frequency value of the seismic data obtained at the target well point as the reference frequency, and establishing a first dispersion correction coefficient equation based on the reference frequency, the resonant frequency, the logging frequency, the resonant quality factor, and the reference quality factor at the reference frequency; taking the reference quality factor at a preset interval within a preset range to obtain a first preset number of reference quality factors, and substituting each of the reference quality factors into to the first dispersion correction coefficient equation, thereby obtaining a first preset number of candidate dispersion correction coefficients; determining first candidate phase velocities at the seismic scale corresponding to each dispersion correction coefficient based on each dispersion correction coefficient and the phase velocity at the logging scale; generating first candidate synthetic seismic records corresponding to each first candidate phase velocity based on each first candidate phase velocity and the seismic data; matching and screening each first candidate synthetic seismic record with the seismic data to obtain a first correction velocity, and using the first candidate synthetic seismic record corresponding to the first correction velocity as a low-frequency synthetic seismic record.

[0036] Specifically, according to the resonant quality factor Q model, when Q>>1, the phase velocity can be expressed by the following formula:

[0037]

[0038] Where C(ω) represents the phase velocity corresponding to the logging frequency ω, C c Represents the reference frequency ω c Phase velocity, Q c Represents the reference frequency ω c The quality factor under τ0 is the reciprocal of the resonant frequency ω0, and Q0 is the resonant quality factor.

[0039] Among them, the resonant frequency ω0 and the resonant quality factor Q0 can be obtained through core experiment measurement.

[0040] In the above formula, substituting the low frequency value as the reference frequency, we get the following expression for correcting the logging velocity to seismic velocity:

[0041] C(ω)=C c ·f -1 ;

[0042] Where, f -1 represents the dispersion correction coefficient, and its corresponding first dispersion correction coefficient equation is:

[0043]

[0044] According to the above expression, for the reference frequency ω c Quality factor Q under c In the value range [Q min ,Q max ], a series of candidate dispersion correction coefficients f can be obtained by taking values ​​at equal intervals ΔQ. -1 and the first candidate phase velocity C c .

[0045] On this basis, first candidate synthetic seismic records corresponding to each first candidate phase velocity are generated according to each first candidate phase velocity and seismic data, and are calibrated and matched with the wellside seismic trace to determine the first corrected velocity.

[0046] It should be noted that since seismic velocity is usually lower than logging velocity, the dispersion correction factor f -1 The value of is generally less than 1, so in order to simplify the calculation process, the dispersion correction coefficient f -1 Starting from 1 and gradually decreasing with a certain step size, calculate the first candidate phase velocity C c , and then carry out calibration matching of synthetic seismic records to determine the first correction velocity.

[0047] Optionally, each of the first candidate synthetic seismic records and the seismic data is matched and screened to obtain a first correction velocity, including: performing well-seismic matching on each of the first candidate synthetic seismic records and the seismic data to determine a first correlation coefficient corresponding to each of the first candidate synthetic seismic records; and using the first candidate phase velocity corresponding to the maximum value of each of the first correlation coefficients as the first correction velocity.

[0048] Well-to-seismic matching can be used to determine the correlation between candidate synthetic seismograms and seismic data. In this solution, well-to-seismic matching can be performed on each first candidate synthetic seismogram and the seismic data, and the correlation coefficient can be calculated. The first candidate phase velocity corresponding to the maximum correlation coefficient is used as the dispersion-corrected seismic velocity, i.e., the first corrected velocity.

[0049] Accordingly, the high-frequency value of the seismic data obtained at the target well point is used as the reference frequency, and the velocity dispersion correction processing is performed on the logging data obtained at the target well point to obtain a high-frequency synthetic seismic record, including: using the high-frequency value of the seismic data obtained at the target well point as the reference frequency, and establishing a second dispersion correction coefficient equation based on the reference frequency, the resonant frequency, the logging frequency, the resonant quality factor, and the reference quality factor at the reference frequency; taking values ​​of the reference quality factor at a preset interval within a preset range to obtain a second preset number of reference quality factors, and substituting each of the reference quality factors into to the second dispersion correction coefficient equation, thereby obtaining a second preset number of candidate dispersion correction coefficients; determining, respectively, based on each dispersion correction coefficient and the phase velocity at the logging scale, a second candidate phase velocity at the seismic scale corresponding to each dispersion correction coefficient; generating, respectively, based on each second candidate phase velocity and the seismic data, a second candidate synthetic seismic record corresponding to each second candidate phase velocity; matching and screening each second candidate synthetic seismic record with the seismic data, respectively, to obtain a second correction velocity, and using the second candidate synthetic seismic record corresponding to the second correction velocity as a high-frequency synthetic seismic record.

[0050] Correspondingly, each of the second candidate synthetic seismic records and the seismic data is matched and screened to obtain a second corrected velocity, including: performing well-seismic matching on each of the second candidate synthetic seismic records and the seismic data to determine a second correlation coefficient corresponding to each of the second candidate synthetic seismic records; and using the second candidate phase velocity corresponding to the maximum value of each of the second correlation coefficients as the second corrected velocity.

[0051] In this solution, the process of determining the high-frequency synthetic seismic record and the second correction velocity may refer to the process of determining the low-frequency synthetic seismic record and the first correction velocity.

[0052] S120. Perform time-frequency decomposition on the seismic data to obtain full-frequency gathers.

[0053] Time-frequency decomposition can be the process of converting seismic data from the time domain to the frequency domain. For example, time-frequency decomposition can be performed using methods such as Gabor transform and wavelet transform.

[0054] The full-frequency channel gather may be a collection of seismic channels corresponding to each frequency within the effective bandwidth.

[0055] S130. Determine low-frequency seismic data based on the low-frequency synthetic seismic record and the full-frequency gather, and determine high-frequency seismic data based on the high-frequency synthetic seismic record and the full-frequency gather.

[0056] The low-frequency seismic data may be the seismic data that best matches the low-frequency synthetic seismic record in the full-frequency gather, and the high-frequency seismic data may be the seismic data that best matches the high-frequency synthetic seismic record in the full-frequency gather.

[0057] Specifically, it can be determined through waveform matching, attribute matching, etc.

[0058] Optionally, low-frequency seismic data is determined based on the low-frequency synthetic seismic record and the full-frequency channel gather, including: performing well-seismic matching on the seismic channels corresponding to each frequency in the low-frequency synthetic seismic record and the full-frequency channel gather, and determining the third correlation coefficient corresponding to each frequency; and taking the seismic channel corresponding to the maximum value of each third correlation coefficient as the low-frequency seismic data.

[0059] The third correlation coefficient represents the cross-correlation between the low-frequency synthetic seismic record and the seismic traces corresponding to each frequency. A larger correlation coefficient indicates a closer match between the low-frequency synthetic seismic record and the seismic traces at that frequency. In this approach, the maximum value of the third correlation coefficient is used to extract the seismic traces at the corresponding frequency from the full-frequency gather to generate low-frequency seismic data.

[0060] For example, the initially selected low-frequency value is 15 Hz. After calculating the third correlation coefficients of its low-frequency synthetic seismic record and the seismic traces corresponding to each frequency, the seismic trace with a frequency of 16 Hz in the full-frequency channel set has the largest correlation coefficient with the low-frequency synthetic seismic record. The seismic data corresponding to 16 Hz is then used as the low-frequency seismic data.

[0061] Correspondingly, high-frequency seismic data is determined based on the high-frequency synthetic seismic record and the full-frequency channel gather, including: performing well-seismic calibration on the seismic traces corresponding to each frequency in the high-frequency synthetic seismic record and the full-frequency channel gather, and determining the fourth correlation coefficient corresponding to each frequency; and taking the seismic trace corresponding to the maximum value of each fourth correlation coefficient as high-frequency seismic data.

[0062] For details, please refer to the above-mentioned method for determining low-frequency seismic data.

[0063] S140 , interpreting the target reservoir segment based on the low-frequency seismic data and the high-frequency seismic data, respectively, to determine the degree of dispersion of the target reservoir segment.

[0064] Because seismic waves exhibit significant differences when propagating underground, especially in fluid-bearing reservoirs, this approach interprets the target reservoir segment in low-frequency and high-frequency seismic data to determine the differences within the target reservoir segment and, in turn, the degree of dispersion within the target reservoir segment.

[0065] An embodiment of the present invention provides a method for detecting dispersion in reservoir space. The method uses the low-frequency and high-frequency values ​​of the effective bandwidth of the seismic data acquired at the target well point as reference frequencies, performs velocity dispersion correction processing on the logging data acquired at the target well point, and obtains low-frequency and high-frequency synthetic seismic records. The seismic data is then subjected to time-frequency decomposition to obtain a full-frequency gather. Low-frequency seismic data is determined based on the low-frequency and full-frequency gathers, and high-frequency seismic data is determined based on the high-frequency and full-frequency gathers. The target reservoir segment is interpreted based on the low-frequency and high-frequency seismic data to determine the dispersion level of the target reservoir segment. This technical solution interprets the propagation characteristics of seismic waves in the reservoir at different frequencies to achieve reservoir-specific seismic dispersion feature detection, providing data support for fine seismic exploration.

[0066] Example 2

[0067] Figure 2 This is a flow chart of a method for detecting the dispersion of a reservoir space provided in Example 2 of this application. This example is optimized based on the above example. Figure 2 As shown, the method of this embodiment specifically includes the following steps:

[0068] S210, using the low-frequency value and the high-frequency value of the effective bandwidth of the seismic data obtained at the target well point as reference frequencies, and performing velocity dispersion correction processing on the logging data obtained at the target well point to obtain low-frequency synthetic seismic records and high-frequency synthetic seismic records.

[0069] S220. Perform time-frequency decomposition on the seismic data to obtain full-frequency gathers.

[0070] S230. Determine low-frequency seismic data based on the low-frequency synthetic seismic record and the full-frequency gather, and determine high-frequency seismic data based on the high-frequency synthetic seismic record and the full-frequency gather.

[0071] S240. Interpret the target reservoir segment based on the low-frequency seismic data and the high-frequency seismic data, respectively, to obtain a first reflection time difference of the target reservoir segment in the low-frequency seismic data and a second reflection time difference of the target reservoir segment in the high-frequency seismic data.

[0072] The first reflection time difference may be the time difference between the seismic wave entering the target reservoir segment and exiting the target reservoir segment in the low-frequency seismic data, and the second reflection time difference may be the time difference between the seismic wave entering the target reservoir segment and exiting the target reservoir segment in the high-frequency seismic data.

[0073] Specifically, by finely interpreting the low-frequency and high-frequency seismic data to obtain stable and comparable phase components, the time differences of seismic waves in different frequency bands can be detected to analyze the dispersion characteristics in different seismic frequency bands.

[0074] Optionally, interpreting the target reservoir segment according to the low-frequency seismic data to obtain a first reflection time difference of the target reservoir segment in the low-frequency seismic data includes: interpreting the target reservoir segment according to the low-frequency seismic data to obtain a first top position value and a first bottom position value of each seismic reflection point of the target reservoir segment in the low-frequency seismic data; and determining the first reflection time difference of each seismic reflection point of the target reservoir segment in the low-frequency seismic data according to each first top position value and each first bottom position value.

[0075] The first top-layer position value may be the time when the seismic wave in the low-frequency seismic data enters the target reservoir segment, and the first bottom-layer position value may be the time when the seismic wave in the low-frequency seismic data exits the target reservoir segment. The first reflection time difference may be the difference between the first bottom-layer position value and the first top-layer position value.

[0076] For example, the first top-layer bit value is 1120 ms, and the first bottom-layer bit value is 1150 ms, and the first reflection time difference is 30 ms.

[0077] Accordingly, interpreting the target reservoir segment according to the high-frequency seismic data to obtain a second reflection time difference of the target reservoir segment in the high-frequency seismic data includes: interpreting the target reservoir segment according to the high-frequency seismic data to obtain a second top layer position value and a second bottom layer position value of each seismic reflection point of the target reservoir segment in the high-frequency seismic data; and determining the second reflection time difference of each seismic reflection point of the target reservoir segment in the high-frequency seismic data according to each second top layer position value and each second bottom layer position value.

[0078] Specifically, it can be determined by referring to the above-mentioned process of determining the first reflection time difference.

[0079] The beneficial effect of the above technical solution is that, by respectively performing detailed interpretation of the target reservoir segments in the low-frequency seismic data and the high-frequency seismic data, the reflection time difference of the seismic waves in the low frequency and high frequency are determined, thereby reflecting the propagation speed of the seismic waves in the low frequency and the propagation speed in the high frequency.

[0080] S250: Determine the dispersion degree of the target reservoir segment according to the first reflection time difference and the second reflection time difference.

[0081] Since the propagation speed of seismic waves in low frequencies is often slower than that in high frequencies, the first reflection time difference is greater than the second reflection time difference. The dispersion time difference of the target reservoir segment can be obtained by subtracting the second reflection time difference from the first reflection time difference to determine the dispersion degree of the target reservoir segment.

[0082] Specifically, the smaller the dispersion time difference, the weaker the reservoir dispersion; the larger the dispersion time difference, the stronger the reservoir dispersion.

[0083] An embodiment of the present invention provides a method for detecting dispersion in reservoir space. The method uses the low-frequency and high-frequency values ​​of the effective bandwidth of seismic data acquired at a target well point as reference frequencies, performs velocity dispersion correction processing on the logging data acquired at the target well point, and obtains low-frequency and high-frequency synthetic seismic records. The seismic data is then subjected to time-frequency decomposition to obtain full-frequency gathers. Low-frequency seismic data are determined based on the low-frequency and full-frequency synthesized seismic records, and high-frequency seismic data are determined based on the high-frequency and full-frequency synthesized seismic records. The target reservoir segment is interpreted based on the low-frequency and high-frequency seismic data to obtain a first reflection time difference of the target reservoir segment in the low-frequency seismic data and a second reflection time difference of the target reservoir segment in the high-frequency seismic data. The dispersion degree of the target reservoir segment is determined based on the first and second reflection time differences. This technical solution interprets the target reservoir segment in the low-frequency and high-frequency seismic data to determine the propagation velocities of seismic waves at low and high frequencies, and then detects the dispersion degree of the reservoir based on the difference in propagation velocities.

[0084] Based on the above embodiments, optionally, before using the low-frequency value and the high-frequency value of the effective bandwidth of the seismic data acquired at the target well point as reference frequencies, performing velocity dispersion correction processing on the logging data acquired at the target well point, and obtaining low-frequency synthetic seismic records and high-frequency synthetic seismic records, the method further includes: performing well-seismic calibration on the logging data and seismic data at the target well point, and determining a fifth correlation coefficient between the synthetic seismic record generated by the logging data and the seismic data; if the fourth correlation coefficient is lower than a first preset threshold, using the seismic main frequency of the seismic data as the reference frequency, performing velocity dispersion correction processing on the logging data, and performing well-seismic matching on the synthetic seismic record generated after the correction and the seismic data to determine a sixth correlation coefficient; if the sixth correlation coefficient is higher than the fifth correlation coefficient, it is determined that dispersion phenomenon exists at the target well point, and using the low-frequency value and the high-frequency value of the effective bandwidth of the seismic data acquired at the target well point as reference frequencies, performing velocity dispersion correction processing on the logging data acquired at the target well point, and obtaining low-frequency synthetic seismic records and high-frequency synthetic seismic records.

[0085] At a single seismic scale, the limited bandwidth of seismic data makes the dispersion characteristics relatively weak and difficult to detect. However, at both the logging frequency and seismic frequency measurement scales, this dispersion characteristic is relatively obvious. Therefore, before testing the dispersion degree of the reservoir space, the dispersion degree at the well point can be tested first. If the dispersion degree at the well point is large, further testing of the reservoir space dispersion degree can be carried out. If the dispersion degree at the well point is small, further testing of the reservoir space dispersion degree is not necessary.

[0086] Specifically, based on the velocity and density curves of the well logging data, wave impedance curves and reflection coefficient series are generated. Seismic wavelets are extracted using statistical or deterministic methods to create synthetic seismic records. For the target reservoir section, the synthetic traces are matched with the near-well seismic traces, and the fifth correlation coefficient is calculated.

[0087] If the fifth correlation coefficient is high, it means that the seismic velocity is relatively consistent with the logging velocity, and the reservoir dispersion at the current well point is low; if the fifth correlation coefficient is low, the seismic main frequency is used as the reference frequency to perform velocity dispersion correction to correct the logging velocity to the seismic velocity, and obtain the corrected and optimized sixth correlation coefficient.

[0088] Comparing the fifth and sixth correlation coefficients, if the corrected sixth correlation coefficient fails to improve, it indicates that the quality or consistency of the original seismic and logging data needs to be further improved; if the corrected sixth correlation coefficient improves, it indicates that there is a difference between the logging velocity and the seismic velocity before dispersion correction, and it can be determined that the well-seismic dispersion degree of the reservoir at the current well point is high.

[0089] The beneficial effect of the above technical solution is that whether to detect the dispersion degree of the reservoir space is determined according to the dispersion degree at the well point, thereby improving the accuracy of the dispersion degree of the reservoir space.

[0090] Example 3

[0091] Figure 3 This is a schematic diagram of the structure of a frequency dispersion detection device for a reservoir space provided in Example 3 of this application. Figure 3 As shown, the device includes:

[0092] The dispersion correction module 310 is used to perform velocity dispersion correction processing on the well logging data obtained at the target well point by using the low-frequency value and the high-frequency value of the effective bandwidth of the seismic data obtained at the target well point as reference frequencies, thereby obtaining a low-frequency synthetic seismic record and a high-frequency synthetic seismic record;

[0093] A time-frequency decomposition module 320 is used to perform time-frequency decomposition on the seismic data to obtain full-frequency gathers;

[0094] a seismic data determination module 330 for determining low-frequency seismic data based on the low-frequency synthetic seismic record and the full-frequency gather, and determining high-frequency seismic data based on the high-frequency synthetic seismic record and the full-frequency gather;

[0095] The dispersion detection module 340 is used to interpret the target reservoir segment based on the low-frequency seismic data and the high-frequency seismic data, and determine the dispersion degree of the target reservoir segment.

[0096] An embodiment of the present invention provides a reservoir space dispersion detection device. The device uses the low-frequency and high-frequency values ​​of the effective bandwidth of the seismic data acquired at the target well point as reference frequencies, performs velocity dispersion correction processing on the logging data acquired at the target well point, and obtains low-frequency and high-frequency synthetic seismic records. The device also performs time-frequency decomposition on the seismic data to obtain a full-frequency gather. The device determines low-frequency seismic data based on the low-frequency synthetic seismic records and the full-frequency gather, and determines high-frequency seismic data based on the high-frequency synthetic seismic records and the full-frequency gather. The device then interprets the target reservoir segment based on the low-frequency and high-frequency seismic data to determine the dispersion level of the target reservoir segment. This technical solution interprets the propagation characteristics of seismic waves in the reservoir at different frequencies to achieve reservoir-space-oriented seismic dispersion feature detection, providing data support for precise seismic exploration.

[0097] Furthermore, the dispersion correction module 310 includes:

[0098] a first dispersion correction coefficient equation establishing unit, configured to use a low-frequency value of seismic data acquired at a target well point as a reference frequency, and establish a first dispersion correction coefficient equation based on the reference frequency, the resonant frequency, the logging frequency, the resonant quality factor, and the reference quality factor at the reference frequency;

[0099] a first candidate dispersion correction coefficient determination unit, configured to take values ​​of the reference quality factor at preset intervals within a preset range to obtain a first preset number of reference quality factors, and substitute each of the reference quality factors into the first dispersion correction coefficient equation to obtain a first preset number of candidate dispersion correction coefficients;

[0100] a first candidate phase velocity determining unit, configured to determine, based on each dispersion correction coefficient and the phase velocity at the logging scale, first candidate phase velocities at the seismic scale corresponding to each dispersion correction coefficient;

[0101] A first candidate synthetic seismic record generating unit is configured to generate a first candidate synthetic seismic record corresponding to each of the candidate phase velocities according to each of the first candidate phase velocities and the seismic data;

[0102] a low-frequency synthetic seismic record determining unit, configured to match and screen each of the first candidate synthetic seismic records with the seismic data to obtain a first corrected velocity, and use the candidate synthetic seismic record corresponding to the first corrected velocity as the low-frequency synthetic seismic record;

[0103] Accordingly, the dispersion correction module 310 further includes:

[0104] a second dispersion correction coefficient equation establishing unit, configured to use a high frequency value of seismic data acquired at a target well point as a reference frequency, and establish a second dispersion correction coefficient equation based on the reference frequency, the resonant frequency, the logging frequency, the resonant quality factor, and the reference quality factor at the reference frequency;

[0105] a second candidate dispersion correction coefficient determination unit, configured to take values ​​of the reference quality factor at preset intervals within a preset range to obtain a second preset number of reference quality factors, and substitute each of the reference quality factors into the second dispersion correction coefficient equation to obtain a second preset number of candidate dispersion correction coefficients;

[0106] a second candidate phase velocity determining unit, configured to determine, based on each dispersion correction coefficient and the phase velocity at the logging scale, second candidate phase velocities at the seismic scale corresponding to each dispersion correction coefficient;

[0107] a second candidate synthetic seismic record generating unit, configured to generate second candidate synthetic seismic records corresponding to each of the candidate phase velocities according to each of the second candidate phase velocities and the seismic data;

[0108] The high-frequency synthetic seismic record determination unit is used to match and screen each of the second candidate synthetic seismic records and the seismic data respectively to obtain a second corrected velocity, and use the second candidate synthetic seismic record corresponding to the second corrected velocity as the high-frequency synthetic seismic record.

[0109] Furthermore, the low-frequency synthetic seismic record determination unit includes:

[0110] a first correlation coefficient determination subunit, configured to perform well-seismic matching on each of the first candidate synthetic seismic records and the seismic data, and determine a first correlation coefficient corresponding to each of the candidate synthetic seismic records;

[0111] a first correction velocity determination subunit, configured to use the candidate phase velocity corresponding to the maximum value among the first correlation coefficients as a first correction velocity;

[0112] Accordingly, the high-frequency synthetic seismic record determination unit includes:

[0113] a second correlation coefficient determination subunit, configured to perform well-seismic matching on each of the second candidate synthetic seismic records and the seismic data, and determine a second correlation coefficient corresponding to each of the second candidate synthetic seismic records;

[0114] The second correction velocity determining subunit is configured to use the second candidate phase velocity corresponding to the maximum value of each of the second correlation coefficients as the second correction velocity.

[0115] Furthermore, the seismic data determination module 330 includes:

[0116] a second correlation coefficient determination unit, configured to perform well-seismic matching on the low-frequency synthetic seismic record and the seismic trace corresponding to each frequency in the full-frequency gather, respectively, to determine a third correlation coefficient corresponding to each frequency;

[0117] a low-frequency seismic data determining unit, configured to use the seismic trace corresponding to the maximum value of each of the third correlation coefficients as the low-frequency seismic data;

[0118] Accordingly, the seismic data determination module 330 further includes:

[0119] A fourth correlation coefficient determination unit is configured to perform well-seismic calibration on the high-frequency synthetic seismic record and the seismic trace corresponding to each frequency in the full-frequency gather, respectively, to determine a fourth correlation coefficient corresponding to each frequency;

[0120] The high-frequency seismic data determining unit is configured to use the seismic trace corresponding to the maximum value of each of the fourth correlation coefficients as the high-frequency seismic data.

[0121] Furthermore, the dispersion detection module 340 includes:

[0122] a reflection time difference determining unit, configured to interpret a target reservoir segment based on the low-frequency seismic data and the high-frequency seismic data, respectively, to obtain a first reflection time difference of the target reservoir segment in the low-frequency seismic data and a second reflection time difference of the target reservoir segment in the high-frequency seismic data;

[0123] A dispersion degree determining unit is configured to determine the dispersion degree of the target reservoir segment according to the first reflection time difference and the second reflection time difference.

[0124] Furthermore, the reflection time difference determining unit includes:

[0125] a low-frequency seismic data interpretation subunit, configured to interpret a target reservoir segment based on the low-frequency seismic data, and obtain a first top layer position value and a first bottom layer position value of each seismic reflection point in the low-frequency seismic data of the target reservoir segment;

[0126] a first reflection time difference determining subunit, configured to determine a first reflection time difference of each seismic reflection point in the low-frequency seismic data of the target reservoir segment according to each of the first top layer position values ​​and each of the first bottom layer position values;

[0127] Accordingly, interpreting the target reservoir segment based on the high-frequency seismic data to obtain the second reflection time difference of the target reservoir segment in the high-frequency seismic data includes:

[0128] a high-frequency seismic data interpretation subunit, configured to interpret a target reservoir segment based on the high-frequency seismic data, and obtain a second top layer position value and a second bottom layer position value of each seismic reflection point in the high-frequency seismic data of the target reservoir segment;

[0129] The second reflection time difference determining subunit is configured to determine the second reflection time difference of each seismic reflection point in the high-frequency seismic data of the target reservoir segment according to each second top layer position value and each second bottom layer position value.

[0130] Furthermore, the device further comprises:

[0131] a fifth correlation coefficient determination module for performing well-seismic calibration on the well logging data and the seismic data at the target well point, using the low-frequency value and the high-frequency value of the effective bandwidth of the seismic data acquired at the target well point as reference frequencies, before performing velocity dispersion correction processing on the well logging data acquired at the target well point to obtain low-frequency synthetic seismic records and high-frequency synthetic seismic records, and determining a fifth correlation coefficient between the synthetic seismic records generated from the well logging data and the seismic data;

[0132] a sixth correlation coefficient determination module, configured to, if the fourth correlation coefficient is lower than a first preset threshold, use the seismic main frequency of the seismic data as a reference frequency, perform velocity dispersion correction processing on the well logging data, and perform well-seismic matching between the synthetic seismic record generated after the correction processing and the seismic data to determine a sixth correlation coefficient;

[0133] A well point dispersion detection module is used to determine that a dispersion phenomenon exists at the target well point if the sixth correlation coefficient is higher than the fifth correlation coefficient, and to perform velocity dispersion correction processing on the logging data obtained at the target well point by using the low-frequency value and the high-frequency value of the effective bandwidth of the seismic data obtained at the target well point as reference frequencies, thereby obtaining a low-frequency synthetic seismic record and a high-frequency synthetic seismic record.

[0134] A frequency dispersion detection device for a reservoir space provided in an embodiment of the present application can execute a frequency dispersion detection method for a reservoir space provided in any embodiment of the present application, and has functional modules and beneficial effects corresponding to the execution method.

[0135] Example 4

[0136] Figure 4 A schematic diagram of the structure of an apparatus 10 that can be used to implement an embodiment of the present application is shown. The apparatus is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The apparatus can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0137] like Figure 4As shown, device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 and a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores a computer program executable by the at least one processor, and processor 11 can perform various appropriate actions and processes based on the computer program stored in the read-only memory (ROM) 12 or loaded from storage unit 18 into the random access memory (RAM) 13. RAM 13 can also store various programs and data required for the operation of device 10. Processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to bus 14.

[0138] Various components in device 10 are connected to I / O interface 15, including an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless communication transceiver, etc. Communication unit 19 allows device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0139] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as the method for detecting dispersion in reservoir space.

[0140] In some embodiments, the reservoir-space dispersion detection method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the reservoir-space dispersion detection method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the reservoir-space dispersion detection method in any other suitable manner (e.g., via firmware).

[0141] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0142] Computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0143] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0144] To provide interaction with a user, the systems and techniques described herein can be implemented on a device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0145] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0146] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0147] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this application can be achieved. This is not limited herein.

[0148] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A method for detecting dispersion in a reservoir space, characterized in that: The method comprises: The low-frequency value and the high-frequency value of the effective bandwidth of the seismic data obtained at the target well point are respectively used as reference frequencies, and the velocity dispersion correction processing is performed on the logging data obtained at the target well point to obtain a low-frequency synthetic seismic record and a high-frequency synthetic seismic record; Performing time-frequency decomposition on the seismic data to obtain full-frequency gathers; Determining low-frequency seismic data based on the low-frequency synthetic seismic record and the full-frequency gather, and determining high-frequency seismic data based on the high-frequency synthetic seismic record and the full-frequency gather; The target reservoir segment is interpreted based on the low-frequency seismic data and the high-frequency seismic data respectively to determine the dispersion degree of the target reservoir segment.

2. The method according to claim 1, characterized in that The low-frequency value of the seismic data obtained at the target well point is used as the reference frequency, and the velocity dispersion correction processing is performed on the logging data obtained at the target well point to obtain a low-frequency synthetic seismic record, including: Using a low-frequency value of seismic data acquired at a target well point as a reference frequency, and establishing a first dispersion correction coefficient equation based on the reference frequency, the resonant frequency, the logging frequency, the resonant quality factor, and the reference quality factor at the reference frequency; Taking values ​​of the reference quality factor at preset intervals within a preset range to obtain a first preset number of reference quality factors, and substituting each of the reference quality factors into the first dispersion correction coefficient equation to obtain a first preset number of candidate dispersion correction coefficients; Determining first candidate phase velocities at the seismic scale corresponding to the dispersion correction coefficients according to the dispersion correction coefficients and the phase velocity at the logging scale; generating first candidate synthetic seismic records corresponding to each first candidate phase velocity according to each first candidate phase velocity and the seismic data; Matching and screening each of the first candidate synthetic seismic records with the seismic data to obtain a first corrected velocity, and using the first candidate synthetic seismic record corresponding to the first corrected velocity as a low-frequency synthetic seismic record; Accordingly, the high frequency value of the seismic data obtained at the target well point is used as the reference frequency, and the velocity dispersion correction processing is performed on the logging data obtained at the target well point to obtain a high frequency synthetic seismic record, including: Using the high frequency value of the seismic data acquired at the target well point as a reference frequency, and establishing a second dispersion correction coefficient equation based on the reference frequency, the resonant frequency, the logging frequency, the resonant quality factor, and the reference quality factor at the reference frequency; Taking values ​​of the reference quality factor at preset intervals within a preset range to obtain a second preset number of reference quality factors, and substituting each of the reference quality factors into the second dispersion correction coefficient equation to obtain a second preset number of candidate dispersion correction coefficients; Determining second candidate phase velocities at the seismic scale corresponding to the dispersion correction coefficients based on the dispersion correction coefficients and the phase velocity at the logging scale; generating second candidate synthetic seismic records corresponding to each second candidate phase velocity according to each second candidate phase velocity and the seismic data; Matching and screening are performed on each of the second candidate synthetic seismic records and the seismic data to obtain a second corrected velocity, and the second candidate synthetic seismic record corresponding to the second corrected velocity is used as a high-frequency synthetic seismic record.

3. The method according to claim 2, characterized in that Matching and screening each of the first candidate synthetic seismic records and the seismic data to obtain a first corrected velocity includes: performing well-seismic matching on each of the first candidate synthetic seismic records and the seismic data, respectively, to determine a first correlation coefficient corresponding to each of the first candidate synthetic seismic records; using the first candidate phase velocity corresponding to the maximum value of each of the first correlation coefficients as the first correction velocity; Accordingly, matching and screening each of the second candidate synthetic seismic records and the seismic data is performed to obtain a second corrected velocity, including: performing well-seismic matching on each of the second candidate synthetic seismic records and the seismic data, respectively, to determine a second correlation coefficient corresponding to each of the second candidate synthetic seismic records; The second candidate phase velocity corresponding to the maximum value of each of the second correlation coefficients is used as the second correction velocity.

4. The method according to claim 1, wherein Determining low-frequency seismic data based on the low-frequency synthetic seismic record and the full-frequency gather includes: Perform well-seismic matching on the low-frequency synthetic seismic record and the seismic trace corresponding to each frequency in the full-frequency gather, respectively, to determine the third correlation coefficient corresponding to each frequency; The seismic trace corresponding to the maximum value of each third correlation coefficient is used as low-frequency seismic data; Accordingly, determining high-frequency seismic data based on the high-frequency synthetic seismic record and the full-frequency gather includes: performing well-seismic calibration on the high-frequency synthetic seismic record and the seismic trace corresponding to each frequency in the full-frequency gather, respectively, to determine a fourth correlation coefficient corresponding to each frequency; The seismic trace corresponding to the maximum value of each of the fourth correlation coefficients is used as high-frequency seismic data.

5. The method according to claim 1, wherein Interpreting the target reservoir segment based on the low-frequency seismic data and the high-frequency seismic data respectively to determine the dispersion degree of the target reservoir segment includes: interpreting the target reservoir segment based on the low-frequency seismic data and the high-frequency seismic data, respectively, to obtain a first reflection time difference of the target reservoir segment in the low-frequency seismic data and a second reflection time difference of the target reservoir segment in the high-frequency seismic data; The dispersion degree of the target reservoir segment is determined according to the first reflection time difference and the second reflection time difference.

6. The method according to claim 5, characterized in that Interpreting a target reservoir segment based on the low-frequency seismic data to obtain a first reflection time difference of the target reservoir segment in the low-frequency seismic data includes: Interpreting the target reservoir segment according to the low-frequency seismic data to obtain a first top layer position value and a first bottom layer position value of each seismic reflection point in the target reservoir segment in the low-frequency seismic data; determining a first reflection time difference of each seismic reflection point of the target reservoir segment in the low-frequency seismic data according to each of the first top layer position values ​​and each of the first bottom layer position values; Accordingly, interpreting the target reservoir segment based on the high-frequency seismic data to obtain the second reflection time difference of the target reservoir segment in the high-frequency seismic data includes: Interpreting the target reservoir segment according to the high-frequency seismic data to obtain a second top layer position value and a second bottom layer position value of each seismic reflection point in the target reservoir segment in the high-frequency seismic data; The second reflection time difference of each seismic reflection point in the high-frequency seismic data of the target reservoir segment is determined according to each second top layer position value and each second bottom layer position value.

7. The method according to claim 1, characterized in that Before performing velocity dispersion correction processing on the well logging data acquired at the target well point by using the low-frequency value and the high-frequency value of the effective bandwidth of the seismic data acquired at the target well point as reference frequencies to obtain low-frequency synthetic seismic records and high-frequency synthetic seismic records, the method further includes: Performing well-seismic calibration on the well logging data and seismic data at the target well point, and determining a fifth correlation coefficient between the synthetic seismic record generated by the well logging data and the seismic data; If the fourth correlation coefficient is lower than a first preset threshold, the seismic main frequency of the seismic data is used as a reference frequency, velocity dispersion correction processing is performed on the well logging data, and well-seismic matching is performed on the synthetic seismic record generated after the correction processing and the seismic data to determine a sixth correlation coefficient; If the sixth correlation coefficient is higher than the fifth correlation coefficient, it is determined that there is a dispersion phenomenon at the target well point, and the low-frequency value and the high-frequency value of the effective bandwidth of the seismic data obtained at the target well point are used as reference frequencies respectively, and the velocity dispersion correction processing is performed on the logging data obtained at the target well point to obtain low-frequency synthetic seismic records and high-frequency synthetic seismic records.

8. A frequency dispersion detection device for a reservoir space, characterized in that: The device comprises: a dispersion correction module for performing velocity dispersion correction processing on the logging data obtained at the target well point by using the low-frequency value and the high-frequency value of the effective bandwidth of the seismic data obtained at the target well point as reference frequencies, thereby obtaining a low-frequency synthetic seismic record and a high-frequency synthetic seismic record; A time-frequency decomposition module, used for performing time-frequency decomposition on the seismic data to obtain full-frequency gathers; a seismic data determination module, configured to determine low-frequency seismic data based on the low-frequency synthetic seismic record and the full-frequency gather, and to determine high-frequency seismic data based on the high-frequency synthetic seismic record and the full-frequency gather; The dispersion detection module is used to interpret the target reservoir segment based on the low-frequency seismic data and the high-frequency seismic data, and determine the dispersion degree of the target reservoir segment.

9. An electronic device, characterized in that: The device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the frequency dispersion detection method for a reservoir space according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the frequency dispersion detection method for a reservoir space according to any one of claims 1 to 7 when the instructions are executed.

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