Method, device and equipment for identifying passive continental margin deep water sedimentary reservoir and storage medium

Abstract

This application relates to the field of geophysical exploration technology, and in particular to a method, apparatus, device, and storage medium for identifying passive continental margin deep-water sedimentary reservoirs. The method includes: preprocessing raw seismic waveform data from each zone of a target area to obtain target waveform data; establishing a seismic attribute plane map based on the target waveform data; determining sedimentary facies zones based on the seismic amplitude data; calculating amplitude ratio data based on the target waveform data corresponding to the sedimentary facies zones; and obtaining the identification result of the passive continental margin deep-water sedimentary reservoir based on the amplitude ratio data and the target waveform data of the target area. This application facilitates improving the accuracy of identifying the spatial distribution of passive continental margin deep-water sedimentary reservoirs.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, and in particular to a method, apparatus, equipment and storage medium for passive identification of deep-water sedimentary reservoirs on continental margins. Background Technology

[0002] Global marine oil and gas resources are abundant, and passive continental margin deepwater exploration areas have become the main battleground for offshore oil and gas exploration and development in recent years. Passive continental margins are widely distributed in western America, East Africa, West Africa, southern Australia and India, as well as northern Europe, Asia and North America. Thick deepwater sedimentary layers, namely passive continental margin deepwater sedimentary reservoirs, are deposited on passive continental margins. In order to identify whether passive continental margin deepwater sedimentary reservoirs contain relatively rich oil and gas resources, it is necessary to identify the spatial distribution of passive continental margin deepwater sedimentary reservoirs.

[0003] Currently, the common method for identifying the spatial distribution of passive continental margin deep-water sedimentary reservoirs is to obtain the seismic attribute characteristics of the area where the passive continental margin deep-water sedimentary reservoirs are located or to obtain the elastic parameters obtained by seismic inversion of the area to predict the spatial distribution of the passive continental margin deep-water sedimentary reservoirs.

[0004] However, in practice, the above-mentioned common identification methods have been found to have poor accuracy in identifying the spatial distribution of passive continental margin deep-water sedimentary reservoirs due to the problem of multiple solutions. Summary of the Invention

[0005] To improve the accuracy of identifying the spatial distribution of passive continental margin deep-water sedimentary reservoirs, this application provides a method, apparatus, equipment, and storage medium for identifying passive continental margin deep-water sedimentary reservoirs.

[0006] In a first aspect, this application provides a method for identifying passive continental margin deep-water sedimentary reservoirs, including:

[0007] The target waveform data is obtained by preprocessing the raw seismic waveform data of each zone in the target area;

[0008] A seismic attribute plane map is established based on the target waveform data, and sedimentary facies zones are determined based on the seismic amplitude data;

[0009] Amplitude ratio data are calculated based on the target waveform data corresponding to the depositional phasor;

[0010] The passive continental margin deep-water sedimentary reservoir identification result is obtained based on the amplitude ratio data and the target waveform data of the target region.

[0011] Secondly, this application provides a passive continental margin deep-water sedimentary reservoir identification device, comprising:

[0012] The preprocessing module is used to preprocess the raw seismic waveform data of each zone in the target area to obtain the target waveform data;

[0013] The facies zone determination module is used to establish a seismic attribute plane map based on the target waveform data and to determine sedimentary facies zones based on the seismic amplitude data.

[0014] An amplitude ratio calculation module is used to calculate amplitude ratio data based on the target waveform data corresponding to the depositional facies zone.

[0015] The result recognition module is used to obtain the passive continental margin deep-water sedimentary reservoir recognition result based on the amplitude ratio data and the target waveform data of the target area.

[0016] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the method described above.

[0017] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method.

[0018] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0019] The aforementioned method, apparatus, equipment, and storage medium for identifying passive continental margin deep-water sedimentary reservoirs involve preprocessing raw seismic waveform data from each zone within a target area to obtain target waveform data; establishing a seismic attribute plane map based on the target waveform data; determining sedimentary facies zones based on the seismic amplitude data; calculating amplitude ratio data based on the target waveform data corresponding to the sedimentary facies zones; and obtaining the identification result of passive continental margin deep-water sedimentary reservoirs based on the amplitude ratio data and the target waveform data of the target area. Through this implementation, by processing the raw seismic waveform data from each zone within the target area, a unique and intuitive identification result of passive continental margin deep-water sedimentary reservoirs can be formed, avoiding the problem of multiple solutions. This facilitates improving the accuracy of identifying the spatial distribution of passive continental margin deep-water sedimentary reservoirs.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a passive continental margin deep-water sedimentary reservoir identification method provided in the embodiments of this application;

[0023] Figure 2 This is a seismic attribute plan view provided in the embodiments of this application;

[0024] Figure 3 This is a schematic diagram of the passive continental margin deep-water sedimentary reservoir identification results provided in the embodiments of this application;

[0025] Figure 4 This is a schematic diagram of a waterway sedimentary facies zone provided in an embodiment of this application;

[0026] Figure 5 This is a spectral diagram of a waterway sedimentary facies zone provided in an embodiment of this application;

[0027] Figure 6 This is a schematic diagram of a fan-shaped sedimentary facies zone provided in an embodiment of this application;

[0028] Figure 7 This is a spectral diagram of a fan-shaped sedimentary phase zone provided in an embodiment of this application;

[0029] Figure 8 This is a schematic diagram of a sedimentary facies zone of constant-depth current deposition provided in the embodiments of this application;

[0030] Figure 9 This is a spectral diagram of a sedimentary facies zone of constant-depth current deposition provided in the embodiments of this application;

[0031] Figure 10 This is a flowchart of another passive continental margin deep-water sedimentary reservoir identification method provided in the embodiments of this application;

[0032] Figure 11 This application provides an embodiment of obtaining an amplitude ratio data graph by rendering amplitude ratio data;

[0033] Figure 12 This is a schematic diagram of the structure of a passive continental margin deep-water sedimentary reservoir identification device provided in the embodiments of this application;

[0034] Figure 13This is a schematic diagram of another passive continental margin deep-water sedimentary reservoir identification device provided in the embodiments of this application;

[0035] Figure 14 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application;

[0036] Figure 15 This is an internal structural diagram of a computer-readable storage medium provided in an embodiment of this application. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this disclosure.

[0038] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings herein are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0039] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0040] Example 1

[0041] Figure 1 A flowchart of a passive continental margin deep-water sedimentary reservoir identification method provided in Embodiment 1 of this application is shown below. Figure 1 The method can be executed by a device that performs the method, which can be implemented in software and / or hardware, and the method includes:

[0042] S110. Preprocess the original seismic waveform data of each zone in the target area to obtain the target waveform data.

[0043] The target area is the region containing passive continental margin deep-water sedimentary reservoirs within a certain geographical range, and includes multiple pre-divided zones. By setting up seismic monitoring devices in the target area, seismic data corresponding to each zone within the target area can be acquired. The seismic data includes raw seismic waveform data, which is the original seismic waveforms detected by the seismic monitoring devices corresponding to that zone. Preprocessing involves optimizing the raw seismic waveforms to meet the needs of subsequent analysis. Furthermore, preprocessing the raw seismic waveforms can improve the accuracy of the subsequently calculated passive continental margin deep-water sedimentary reservoir identification results. The target waveform data is the seismic waveform data obtained after preprocessing the raw seismic waveform data.

[0044] Specifically, the raw waveform data of earthquakes in each zone within the target area is obtained by setting up earthquake monitoring devices in the target area, and then the raw waveform data is preprocessed to obtain the target waveform data.

[0045] S120. Establish a seismic attribute plane map based on the target waveform data, and determine the sedimentary facies zone based on the seismic amplitude data.

[0046] Among these methods, by performing appropriate mathematical processing on the target waveform data, a seismic attribute plane map can be established from the data obtained after mathematical processing. The seismic attribute plane map is shown below. Figure 2 As shown, a seismic attribute plane map is used to represent sedimentary facies zones within a given area. These sedimentary facies zones are regions with specific geographical characteristics within the area represented by the seismic attribute plane map. Various sedimentary facies zones can be identified through image analysis of the seismic attribute plane map. For example, a seismic attribute plane map may contain sedimentary facies zones such as... Figure 2 As shown, the area includes channels and fans. The geographical characteristics of the channels are as follows: the grain size of the rocks filling the deep-water turbidite channels typically decreases from bottom to top and from the axis to the edge, advancing from the nearshore source on the east side to the deep-water environment on the west side, and undergoing multiple channel changes. This provides the conditions for the development of passive continental margin deep-water sedimentary reservoirs. Figure 2 The area features narrow, elongated, and winding reddish-green bands. The geographical characteristics of the fan-shaped rock are: it forms a lobed fan at the end of a turbidite channel due to overflow; the rock grain size typically transitions from coarse to fine from the fan root to the fan tip, possessing the conditions for the development of passive continental margin deep-water sedimentary reservoirs. Figure 2 Medium-yellowish-green lobes; in addition, the seismic attribute plan may include sedimentary facies zones such as: plain facies and isobathrous deposits. Among them, the geographical characteristics of the plain facies are: dominated by fine-grained muddy deposits, which is the main background of deep-water sedimentary environments and does not have the conditions for the development of passive continental margin deep-water sedimentary reservoirs. Figure 2 The blue area in the image; the geographical characteristics of isobathry deposits are: deposits formed by isobathry, possessing the conditions for the development of passive continental margin deep-water sedimentary reservoirs, corresponding to... Figure 2 Red stripe on the lower side of the central waterway.

[0047] Specifically, the target waveform data is subjected to corresponding mathematical processing, and the processed data is used to establish a seismic attribute plane map. Then, image analysis is performed on the seismic attribute plane map, and various sedimentary facies zones in the seismic attribute plane map are determined based on the geographical characteristics of different regions in the seismic attribute plane map.

[0048] S130. Calculate the amplitude ratio data based on the target waveform data corresponding to the deposition phasor.

[0049] Each sedimentary facies zone has its corresponding target waveform data. The amplitude ratio data is derived from the target waveform data. The amplitude ratio data is used to characterize the energy attenuation of different frequency components when seismic waves pass through the two-phase medium in the corresponding sedimentary facies zone. Calculating the amplitude ratio data facilitates the subsequent calculation of the identification results of passive continental margin deep-water sedimentary reservoirs.

[0050] Specifically, taking one sedimentary facies zone as an example, the target waveform data corresponding to the sedimentary facies zone is determined from the target waveform data corresponding to the target area; then, the amplitude ratio data corresponding to the sedimentary facies zone is calculated based on the target waveform data; the amplitude ratio data corresponding to each sedimentary facies zone is calculated through the above steps.

[0051] S140. Based on the amplitude ratio data and the target waveform data of the target region, the passive continental margin deep-water sedimentary reservoir identification result is obtained.

[0052] The passive continental margin deep-water sedimentary reservoir identification results are composed of the amplitude ratio data of each sedimentary facies zone and the target waveform data of each partition in the target area. These results are used to represent the spatial distribution of passive continental margin deep-water sedimentary reservoirs. The passive continental margin deep-water sedimentary reservoir identification results are as follows: Figure 3 As shown.

[0053] Specifically, the amplitude ratio data of each sedimentary facies zone is superimposed with the target waveform data of each partition in the target area to obtain the passive continental margin deep-water sedimentary reservoir identification results.

[0054] It should be noted that this embodiment obtains target waveform data by preprocessing the raw seismic waveform data of each zone in the target area; a seismic attribute plane map is established based on the target waveform data; sedimentary facies zones are determined based on the seismic amplitude data; amplitude ratio data is calculated based on the target waveform data corresponding to the sedimentary facies zones; and the identification result of passive continental margin deep-water sedimentary reservoirs is obtained based on the amplitude ratio data and the target waveform data of the target area. Through the above implementation, by processing the raw seismic waveform data of each zone in the target area, a unique and intuitive identification result of passive continental margin deep-water sedimentary reservoirs can be formed, avoiding the problem of multiple solutions, thus facilitating the improvement of the accuracy of identifying the spatial distribution of passive continental margin deep-water sedimentary reservoirs.

[0055] Example 2

[0056] This application provides a passive continental margin deep-water sedimentary reservoir identification method in Embodiment 2. This method optimizes the "preprocessing of the original seismic waveform data of each zone in the target area to obtain the target waveform data" in Embodiment 1. It should be noted that for parts not described in detail in this embodiment, please refer to the descriptions in other embodiments. The method includes:

[0057] S211. Perform anomaly processing on the original seismic waveform data of each partition in the target area to obtain primary waveform data.

[0058] Anomaly handling includes, but is not limited to, one or more of the following: deduplication, outlier correction, and missing value filling of the original seismic waveform data. It should be noted that the original seismic waveform data is the initial seismic waveform data acquired by the seismic monitoring device. This seismic waveform data may contain anomalies such as data duplication, data anomalies, and data missingness. Therefore, it is necessary to perform anomaly handling on the original seismic waveform data to address these anomalies. The primary waveform data is the data obtained after anomaly handling of the original seismic waveform data.

[0059] Specifically, the raw seismic waveform data of each zone in the target area is obtained. Taking the raw seismic waveform data of one zone as an example, the abnormalities in the raw seismic waveform data are analyzed. The raw seismic waveform data is then processed to address these abnormalities, thereby obtaining the primary waveform data.

[0060] S212. The primary waveform data is truncated based on a preset time window to obtain the target waveform data.

[0061] The primary waveform data consists of seismic waveform data over a relatively long period. Therefore, the amount of primary waveform data is large. Using only the primary waveform data to calculate the final passive continental margin deep-water sedimentary reservoir identification result would lead to low computational efficiency. To improve efficiency, a portion of the primary waveform data can be extracted and used to calculate the final passive continental margin deep-water sedimentary reservoir identification result. Furthermore, not every time period of the primary waveform data has a positive effect on the final passive continental margin deep-water sedimentary reservoir identification result. By extracting data from a specific time period that has a positive effect on the final passive continental margin deep-water sedimentary reservoir identification result, the accuracy of the final calculated result can be improved. To enhance both the efficiency and accuracy of the passive continental margin deep-water sedimentary reservoir identification result calculation, this embodiment includes a preset time window. This time window is used to extract data from the primary waveform data within a certain period; the extracted data is the target waveform data.

[0062] Specifically, the primary waveform data is extracted using a preset time window to obtain the target waveform data.

[0063] S220. Establish a seismic attribute plane map based on the target waveform data, and determine the sedimentary facies zone based on the seismic amplitude data.

[0064] S230. Calculate the amplitude ratio data based on the target waveform data corresponding to the deposition phasor.

[0065] S240. Based on the amplitude ratio data and the target waveform data of the target region, the passive continental margin deep-water sedimentary reservoir identification result is obtained.

[0066] Example 3

[0067] This application provides a passive continental margin deep-water sedimentary reservoir identification method in Embodiment 3, which optimizes the "establishment of a seismic attribute plane map based on the target waveform data" in Embodiment 1. It should be noted that for parts not detailed in this embodiment, please refer to the descriptions in other embodiments. The method includes:

[0068] S310. Preprocess the original seismic waveform data of each zone in the target area to obtain the target waveform data.

[0069] S321. Obtain the amplitude data corresponding to each sampling point of the target waveform data to obtain the amplitude dataset.

[0070] Among them, the earthquake monitoring equipment collects earthquake waveform data at a certain sampling frequency to form the initial earthquake waveform data. That is, each data point in the initial earthquake waveform data has its corresponding sampling point (or sampling time point). The target waveform data is generated based on the initial earthquake waveform data. Therefore, each data point in the target waveform data also has its corresponding sampling point. The amplitude data is the amplitude data that represents the target waveform data at the corresponding sampling point. This amplitude data is the earthquake vibration intensity. The larger the amplitude data, the greater the earthquake vibration intensity. The amplitude dataset is the set of amplitude data corresponding to each sampling point of the target waveform data.

[0071] Specifically, the amplitude data of the target waveform data at each sampling point is obtained, and the amplitude data is collected to obtain the amplitude dataset.

[0072] S322. Calculate the root mean square amplitude based on each amplitude data in the amplitude dataset.

[0073] The amplitude dataset includes multiple amplitude data points. The number of amplitude data points in the amplitude dataset is denoted as the amplitude count. The sum of squares of each amplitude data point in the amplitude dataset is denoted as the amplitude sum of squares. The ratio of the amplitude sum of squares to the amplitude count is denoted as the mean of the sum of squares. The root mean square amplitude is the result obtained by taking the square root of the mean of the sum of squares.

[0074] Specifically, the number of amplitude data points in the amplitude dataset is determined to obtain the amplitude count. The sum of squares of each amplitude data point in the amplitude dataset is calculated to obtain the amplitude sum of squares. The ratio of this amplitude sum of squares to the number of amplitudes is calculated to obtain the mean of the sum of squares. Furthermore, the square root of the mean of the sum of squares is taken to obtain the root mean square amplitude.

[0075] S323. Establish a seismic attribute plane map based on the root mean square amplitude corresponding to each partition.

[0076] The root mean square amplitude is calculated based on the original seismic waveform data of the corresponding partition, so each partition has its own corresponding root mean square amplitude; the root mean square amplitude corresponding to each partition is used to generate the waveform after rendering. Figure 2 The image shows a planar diagram of earthquake attributes.

[0077] Specifically, the root mean square amplitude corresponding to each partition of the target area is rendered to obtain the seismic attribute plane map.

[0078] It should be noted that generating a seismic attribute plane map helps to determine the sedimentary facies zones within it through image analysis of the seismic attribute plane map.

[0079] S324. Determine the sedimentary facies zone based on the earthquake amplitude data.

[0080] S330. Calculate the amplitude ratio data based on the target waveform data corresponding to the deposition phasor.

[0081] S340. Based on the amplitude ratio data and the target waveform data of the target region, the passive continental margin deep-water sedimentary reservoir identification result is obtained.

[0082] Example 4

[0083] This application provides a passive continental margin deep-water sedimentary reservoir identification method in Embodiment 4, which optimizes the "calculation of amplitude ratio data based on the target waveform data corresponding to the sedimentary facies zone" in Embodiment 1. It should be noted that for parts not detailed in this embodiment, please refer to the descriptions in other embodiments. The method includes:

[0084] S410. Preprocess the original seismic waveform data of each zone in the target area to obtain the target waveform data.

[0085] S420. Establish a seismic attribute plane map based on the target waveform data, and determine the sedimentary facies zone based on the seismic amplitude data.

[0086] S431. Generate a phase band spectrum diagram based on the target waveform data corresponding to the deposition phase band.

[0087] In this embodiment, since plain facies sedimentary facies do not possess the conditions for the development of passive continental margin deep-water sedimentary reservoirs, this embodiment does not generate a facies spectrum map corresponding to the plain facies sedimentary facies to improve the efficiency of calculating the identification results of passive continental margin deep-water sedimentary reservoirs. In addition to plain facies, the sedimentary facies involved in this embodiment also include channels, fans, and isobaths. Each sedimentary facies contains multiple partitions, and each partition has corresponding target waveform data. Therefore, each sedimentary facies has corresponding target waveform data. The facies spectrum map is the spectrum data obtained by performing a Fourier transform on the target waveform data of the corresponding sedimentary facies, and this spectrum data is recorded as the facies spectrum. The facies spectrum map is also a diagram of the spectrum of the facies.

[0088] Specifically, to obtain such Figure 4 The target waveform data of the sedimentary facies zone shown is analyzed. A Fourier transform of this target waveform data yields the corresponding facies spectrum, as illustrated in the figure. Figure 5 The phase band spectrum diagram; obtain as follows Figure 6 The target waveform data of the fan-shaped depositional facies zone shown is used to perform a Fourier transform on the target waveform data to obtain the corresponding phase band spectrum, as illustrated in the figure. Figure 7 The phase band spectrum diagram; obtain as follows Figure 8 The target waveform data of the sedimentary facies zone shown iso-deep current deposition is used to perform a Fourier transform on the target waveform data to obtain the corresponding facies zone spectrum. The spectrum of this facies zone is shown in the figure. Figure 9The phase band spectrum diagrams are shown above. The horizontal axis of each phase band spectrum diagram represents frequency, and the vertical axis represents amplitude.

[0089] S432. Calculate the amplitude ratio data based on the phase band spectrum diagram.

[0090] The amplitude ratio data is calculated based on the corresponding phase band spectrum. The amplitude ratio data is then used to overlay the target data of the target area to generate the passive continental margin deep-water sedimentary reservoir identification results.

[0091] Specifically, calculate the amplitude ratio corresponding to the spectrum of each phase band.

[0092] S440. Based on the amplitude ratio data and the target waveform data of the target region, the passive continental margin deep-water sedimentary reservoir identification result is obtained.

[0093] Example 5

[0094] This application provides a passive continental margin deep-water sedimentary reservoir identification method in Embodiment 5, which optimizes the "calculation of amplitude ratio data based on the phase band spectrum" in Embodiment 4. It should be noted that for parts not detailed in this embodiment, please refer to the descriptions in other embodiments. The method includes:

[0095] S510. Preprocess the original seismic waveform data of each zone in the target area to obtain the target waveform data.

[0096] S520. Establish a seismic attribute plane map based on the target waveform data, and determine the sedimentary facies zone based on the seismic amplitude data.

[0097] S531. Generate a phase band spectrum diagram based on the target waveform data corresponding to the deposition phase band.

[0098] S532A. Determine the dominant frequency of the phase band spectrum diagram and determine the amplitude of the dominant frequency.

[0099] In this embodiment, the dominant frequency is the frequency corresponding to the peak point of the spectrum curve in the phase band spectrum diagram, and the dominant frequency amplitude is the amplitude corresponding to the dominant frequency in the spectrum curve.

[0100] Specifically, the frequency corresponding to the peak point of the spectrum curve in the phase band spectrum diagram is determined as the main frequency, and then the amplitude corresponding to the spectrum curve of the main frequency is determined, and this amplitude is taken as the main frequency amplitude.

[0101] S532B: Determine the frequency divider from the phase band spectrum diagram based on the main frequency, and calculate the average amplitude of the frequency divider.

[0102] In this process, the frequency spectrum curve typically has several secondary peak points near the main frequency, with amplitudes smaller than the main frequency. The frequency corresponding to these secondary peak points is determined as the frequency division. A preset frequency range is then extended to the left and right of the frequency division axis, centered on the frequency division, to obtain the left and right frequency ranges. Combining the left and right frequency ranges yields the frequency division range. Each frequency point in the frequency division range corresponds to multiple amplitudes on the frequency spectrum curve, which are the frequency division bodies. The average amplitude of the frequency division is the average of the amplitudes in the frequency division body.

[0103] Specifically, determine the frequency division near the main frequency. Taking one of the frequency divisions as an example, further determine the frequency range of the frequency division. Then, determine the multiple amplitudes corresponding to the frequency division range on the spectrum curve to obtain the frequency division body. After that, calculate the mean of each amplitude in the frequency division body to obtain the average amplitude of the frequency division.

[0104] S532C: Based on the main frequency amplitude and the average amplitude of the frequency division, obtain amplitude ratio data.

[0105] Each frequency division has its corresponding average amplitude. Taking one frequency division average amplitude as an example, the amplitude ratio is the ratio of the average amplitude of the frequency division to the amplitude of the main frequency. Each frequency division can be calculated to have its corresponding amplitude ratio. The amplitude ratio data includes the amplitude ratio corresponding to each frequency division.

[0106] Specifically, taking one frequency division as an example, the ratio of the average amplitude of the frequency division to the amplitude of the main frequency is calculated to obtain the amplitude ratio of the frequency division. The amplitude ratio of each frequency division is collectively referred to as the amplitude ratio data.

[0107] S540. Based on the amplitude ratio data and the target waveform data of the target region, the passive continental margin deep-water sedimentary reservoir identification result is obtained.

[0108] Example 6

[0109] This application provides a passive continental margin deep-water sedimentary reservoir identification method in Embodiment Six, which is referenced... Figure 10 The method can be executed by a device that performs the method, which can be implemented in software and / or hardware, and the method includes:

[0110] S610. Preprocess the original seismic waveform data of each zone in the target area to obtain the target waveform data.

[0111] S620. Establish a seismic attribute plane map based on the target waveform data, and determine the sedimentary facies zone based on the seismic amplitude data.

[0112] S630. Calculate the amplitude ratio data based on the target waveform data corresponding to the deposition phasor.

[0113] S640. Based on the amplitude ratio data and the target waveform data of the target region, the passive continental margin deep-water sedimentary reservoir identification result is obtained.

[0114] S650. Determine the oil and gas content difference information in the target area based on the amplitude ratio data.

[0115] Among them, the differences in hydrocarbon content, fluid properties, and fluid types in different areas of the passive continental margin deep-water sedimentary reservoir in the target area; since the amplitude ratio data is used to characterize the energy attenuation of different frequency components when seismic waves pass through the two-phase medium in the corresponding sedimentary facies zone, the differences in hydrocarbon content in the target area can be determined by the energy attenuation.

[0116] Specifically, according to, for example Figure 11 The image shown is an amplitude ratio data map obtained by rendering amplitude ratio data, which determines the differences in oil and gas content in the target area.

[0117] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0118] Example 7

[0119] Based on the same inventive concept, this embodiment also provides a passive continental margin deep-water sedimentary reservoir identification device for implementing the passive continental margin deep-water sedimentary reservoir identification method described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more passive continental margin deep-water sedimentary reservoir identification device embodiments provided below can be found in the limitations of the passive continental margin deep-water sedimentary reservoir identification method described above, and will not be repeated here.

[0120] In this embodiment, as Figure 12 As shown, a passive continental margin deep-water sedimentary reservoir identification device is provided, comprising:

[0121] The preprocessing module is used to preprocess the raw seismic waveform data of each zone in the target area to obtain the target waveform data;

[0122] The facies zone determination module is used to establish a seismic attribute plane map based on the target waveform data and to determine sedimentary facies zones based on the seismic amplitude data.

[0123] An amplitude ratio calculation module is used to calculate amplitude ratio data based on the target waveform data corresponding to the depositional facies zone.

[0124] The result recognition module is used to obtain the passive continental margin deep-water sedimentary reservoir recognition result based on the amplitude ratio data and the target waveform data of the target area.

[0125] Example 8

[0126] Based on the same inventive concept, this embodiment also provides a passive continental margin deep-water sedimentary reservoir identification device for implementing the passive continental margin deep-water sedimentary reservoir identification method described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more passive continental margin deep-water sedimentary reservoir identification device embodiments provided below can be found in the limitations of the passive continental margin deep-water sedimentary reservoir identification method described above, and will not be repeated here.

[0127] In this embodiment, as Figure 13 As shown, a passive continental margin deep-water sedimentary reservoir identification device is provided, comprising:

[0128] The preprocessing module is used to preprocess the raw seismic waveform data of each zone in the target area to obtain the target waveform data;

[0129] The facies zone determination module is used to establish a seismic attribute plane map based on the target waveform data and to determine sedimentary facies zones based on the seismic amplitude data.

[0130] An amplitude ratio calculation module is used to calculate amplitude ratio data based on the target waveform data corresponding to the depositional facies zone.

[0131] The result recognition module is used to obtain the passive continental margin deep-water sedimentary reservoir recognition result based on the amplitude ratio data and the target waveform data of the target area;

[0132] The information determination module is used to determine the differences in oil and gas content in the target area based on the amplitude ratio data.

[0133] Each module in the aforementioned passive continental margin deep-water sedimentary reservoir identification device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0134] It should be noted that this embodiment obtains target waveform data by preprocessing the raw seismic waveform data of each zone in the target area; a seismic attribute plane map is established based on the target waveform data; sedimentary facies zones are determined based on the seismic amplitude data; amplitude ratio data is calculated based on the target waveform data corresponding to the sedimentary facies zones; and the identification result of passive continental margin deep-water sedimentary reservoirs is obtained based on the amplitude ratio data and the target waveform data of the target area. Through the above implementation, by processing the raw seismic waveform data of each zone in the target area, a unique and intuitive identification result of passive continental margin deep-water sedimentary reservoirs can be formed, avoiding the problem of multiple solutions, thus facilitating the improvement of the accuracy of identifying the spatial distribution of passive continental margin deep-water sedimentary reservoirs.

[0135] Example 9

[0136] In this embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows. Figure 14 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a passive continental margin deep-water sedimentary reservoir identification method.

[0137] Those skilled in the art will understand that Figure 14 The structure shown is merely a block diagram of a portion of the structure related to the present disclosure and does not constitute a limitation on the computer device to which the present disclosure is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0138] Example 10

[0139] In this embodiment, a computer-readable storage medium is provided, such as... Figure 15 As shown, a computer program is stored thereon, and when the computer program is executed by the processor, it implements the steps in the above-described method embodiments.

[0140] Example 11

[0141] In this embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0142] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0143] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this disclosure can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this disclosure may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this disclosure may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0144] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0145] The embodiments described above are merely illustrative of several implementations of this disclosure, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent disclosure. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this disclosure, and these all fall within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the appended claims.

Claims

1. A method for identifying passive continental margin deep-water sedimentary reservoirs, characterized in that, include: The target waveform data is obtained by preprocessing the raw seismic waveform data of each zone in the target area; A seismic attribute plane map is established based on the target waveform data, and sedimentary facies zones are determined based on the seismic amplitude data; Amplitude ratio data are calculated based on the target waveform data corresponding to the depositional phasor; The passive continental margin deep-water sedimentary reservoir identification result is obtained based on the amplitude ratio data and the target waveform data of the target region.

2. The method according to claim 1, characterized in that, The process of preprocessing the raw seismic waveform data of each partition in the target area to obtain the target waveform data includes: Anomaly processing is performed on the original seismic waveform data of each partition in the target area to obtain primary waveform data; The primary waveform data is extracted based on a preset time window to obtain the target waveform data.

3. The method according to claim 1, characterized in that, The process of establishing a seismic attribute plane map based on the target waveform data includes: Obtain the amplitude data corresponding to each sampling point of the target waveform data to obtain the amplitude dataset; The root mean square amplitude is calculated based on each amplitude data in the amplitude dataset; A seismic attribute plane map is established based on the root mean square amplitude corresponding to each partition.

4. The method according to claim 1, characterized in that, The calculation of amplitude ratio data based on the target waveform data corresponding to the deposition facies zone includes: A phase band spectrum diagram is generated based on the target waveform data corresponding to the depositional phase band; The amplitude ratio data is calculated based on the phase band spectrum diagram.

5. The method according to claim 4, characterized in that, The calculation of amplitude ratio data based on the phase band spectrum includes: Determine the dominant frequency of the phase band spectrum diagram, and determine the amplitude of the dominant frequency; Based on the main frequency, the frequency divider is determined from the phase band spectrum diagram, and the average amplitude of the frequency divider is calculated. The amplitude ratio data is obtained based on the main frequency amplitude and the average amplitude of the frequency division.

6. The method according to claim 1, characterized in that, Also includes: Based on the amplitude ratio data, the differences in oil and gas content in the target area are determined.

7. A passive continental margin deep-water sedimentary reservoir identification device, characterized in that, The device includes: The preprocessing module is used to preprocess the raw seismic waveform data of each zone in the target area to obtain the target waveform data; The facies zone determination module is used to establish a seismic attribute plane map based on the target waveform data and to determine sedimentary facies zones based on the seismic amplitude data. An amplitude ratio calculation module is used to calculate amplitude ratio data based on the target waveform data corresponding to the depositional facies zone. The result recognition module is used to obtain the passive continental margin deep-water sedimentary reservoir recognition result based on the amplitude ratio data and the target waveform data of the target area.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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