Reservoir favorable area prediction method and device, electronic equipment and storage medium

Abstract

The invention provides a reservoir favorable area prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: extracting a root-mean-square amplitude attribute of a pre-stack bright spot seismic parameter of a target layer; extracting root-mean-square amplitude attributes of the post-stack bright spot seismic parameters of the target layer; performing weighted fusion on the root-mean-square amplitude attribute of the pre-stack bright spot seismic parameter and the root-mean-square amplitude attribute of the post-stack bright spot seismic parameter to obtain the comprehensive attribute of the target stratum; and according to the comprehensive attribute of the target layer, predicting a reservoir favorable area. According to the method for predicting the favorable area of the reef-beach facies carbonate reservoir through attribute fusion based on pre-stack / post-stack multiple bright spot seismic parameters in the well-free area, pre-stack sensitive parameter information related to the oil and gas height is fully excavated, attribute weighted fusion is carried out, the fused attributes can further highlight the reservoir and oil and gas-bearing information, and the reservoir and oil and gas-bearing information can be further predicted. The precision of predicting the carbonate reservoir and the oil-gas possibility is obviously improved, and the multiplicity is obviously reduced.

Description

Technical Field

[0001] The present application relates to the field of seismic theory and technology of oil and gas exploration engineering, and specifically to a method, device, electronic equipment and storage medium for predicting favorable reservoir areas. Background Art

[0002] Generally speaking, in areas without wells, the prediction of favorable areas for reef-beach carbonate reservoirs is mainly carried out from a geological perspective, relying on the analysis of reservoir-forming conditions and paleo-geomorphological restoration. It is qualitatively believed that the paleo-geomorphological high-potential areas are favorable areas for the development of reef-beach reservoirs.

[0003] From a geophysical perspective, post-stack seismic data is mainly relied on, and wave impedance (Ip) is used to qualitatively reflect the distribution of favorable areas of reef-beach reservoirs. However, post-stack seismic data has its own limitations, which are mainly reflected in the following aspects: (1) Field acquisition of seismic data is a multi-shot and multi-channel observation system. Different channels recorded at each common depth point (CDP) or common center point (CMP) have different shot offsets, and the reflection amplitude on each channel varies with the shot offset. Post-stack attributes include post-stack inversion based on conventionally processed horizontal stacking data, with self-excitation and self-collection as the assumption, that is, after each CDP or CMP gather is subjected to dynamic correction, the recorded channels with different shot offsets are dynamic corrected to zero shot offset position, and then horizontal stacking is performed. In this way, the post-stack profile cannot reflect the characteristics of the amplitude recorded by field acquisition changing with the shot offset, and a large amount of shear wave information closely related to the shot offset is lost. (2) Post-stack wave impedance inversion does not change with the incident angle, but is only related to the P-wave velocity (Vp) and density (DEN). Due to the lack of drilling calibration, the failure to consider the influence of complex lithology, and the limitations of post-stack seismic data, there is great uncertainty in using the wave impedance of post-stack seismic data to characterize the distribution of favorable areas of reef-beach carbonate reservoirs.

[0004] The wave impedance of pre-stack inversion is closely related to the incident angle and is related to parameters such as P-wave velocity (Vp), S-wave velocity (Vs), and density (DEN). Since P-wave and S-wave velocity information are used at the same time, the elastic parameters calculated are much richer than those of post-stack inversion, and can distinguish lithology and hydrocarbon content. Therefore, it is urgent to use pre-stack / post-stack seismic data together, optimize sensitive attributes and parameters, and carry out multi-attribute fusion analysis and prediction to further reduce prediction risks and multiple solutions. Summary of the invention

[0005] In view of this, the present application provides a reservoir favorable area prediction method, device, electronic device and storage medium, so as to solve the technical problem of the difficulty in predicting and evaluating sweet spots in tight sandstone reservoirs in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for predicting a favorable reservoir area, the method comprising:

[0007] Extract the root mean square amplitude attributes of the prestack bright spot seismic parameters of the target layer;

[0008] Extract the root mean square amplitude attributes of the post-stack bright spot seismic parameters of the target layer;

[0009] Performing weighted fusion on the root mean square amplitude attribute of the pre-stack bright spot seismic parameter and the root mean square amplitude attribute of the post-stack bright spot seismic parameter to obtain the comprehensive attribute of the target layer;

[0010] Based on the comprehensive properties of the target layer, the favorable reservoir area is predicted.

[0011] Furthermore, the extracting of the root mean square amplitude attribute of the prestack bright spot seismic parameter of the target layer includes:

[0012] Using prestack seismic data, prestack density reflectivity volume solution is carried out to extract the root mean square amplitude attribute of the density reflectivity volume of the target layer;

[0013] Using pre-stack seismic data, pre-stack Poisson's ratio reflectivity volume solution is carried out to extract the root mean square amplitude attribute of the Poisson's ratio reflectivity volume of the target layer;

[0014] The prestack Lame modulus reflectivity volume solution is carried out using prestack seismic data to extract the root mean square amplitude attributes of the Lame modulus reflectivity volume of the target layer.

[0015] Furthermore, it is characterized in that the pre-stack seismic data is pre-stack CRP gather data.

[0016] Furthermore, the extracting of the root mean square amplitude attribute of the post-stack bright spot seismic parameter of the target layer includes:

[0017] The root mean square amplitude attributes of the wave impedance reflectivity volume of the target layer are extracted using post-stack seismic data.

[0018] Furthermore, the weighted fusion of the root mean square amplitude attribute of the pre-stack bright spot seismic parameter and the root mean square amplitude attribute of the post-stack bright spot seismic parameter to obtain the comprehensive attribute of the target layer includes:

[0019] Normalizing the root mean square amplitude attributes of the pre-stack bright spot seismic parameters and the root mean square amplitude attributes of the post-stack bright spot seismic parameters to obtain normalized root mean square amplitude attributes of the pre-stack bright spot seismic parameters and normalized root mean square amplitude attributes of the post-stack bright spot seismic parameters;

[0020] The root mean square amplitude attribute of the normalized pre-stack bright spot seismic parameter and the root mean square amplitude attribute of the normalized post-stack bright spot seismic parameter are weightedly fused to obtain the comprehensive attribute of the target layer.

[0021] Furthermore, after predicting the favorable reservoir area according to the comprehensive attributes of the target layer, the method further includes:

[0022] The favorable reservoir area is compared with the main controlling factors of reservoir geological accumulation to verify the reliability of the prediction results.

[0023] Furthermore, the main controlling factors of geological accumulation include paleo-geomorphology, seismic phase or current structural characteristics.

[0024] In a second aspect, an embodiment of the present application provides a reservoir favorable area prediction device, the device comprising:

[0025] Prestack seismic attribute extraction module, extracting the root mean square amplitude attributes of the prestack bright spot seismic parameters of the target layer;

[0026] Post-stack seismic attribute extraction module, extracting the root mean square amplitude attributes of post-stack bright spot seismic parameters of the target layer;

[0027] An attribute fusion module performs weighted fusion on the root mean square amplitude attribute of the pre-stack bright spot seismic parameter and the root mean square amplitude attribute of the post-stack bright spot seismic parameter to obtain a comprehensive attribute of the target layer;

[0028] The reservoir prediction module predicts favorable reservoir areas according to the comprehensive attributes of the target layer.

[0029] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0030] processor;

[0031] Memory;

[0032] And a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, and when the instructions are executed by the processor, the electronic device executes any one of the methods described in the first aspect.

[0033] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described in the first aspect.

[0034] The present application aims at the technical problem that the traditional geophysical prediction means mainly rely on post-stack seismic data, such as post-stack attributes and post-stack wave impedance inversion technology such as root mean square amplitude rms, amplitude change rate, layer velocity, absorption / attenuation, and spectrum decomposition, but these parameters and attributes are not closely and sensitively related to oil and gas, and reservoir prediction, especially reservoir oil and gas prediction, has strong multi-solution. A method for predicting favorable areas of reef-beach carbonate reservoirs by using pre-stack / post-stack seismic data in well-free areas and fusing attributes based on multiple pre-stack / post-stack bright spot seismic parameters is proposed. The method fully mines the pre-stack sensitive parameter information highly related to oil and gas, focuses on three pre-stack bright spot seismic parameters of density (DEN), Poisson's ratio (POIS), and Lambda, and combines the wave impedance (Ip) post-stack bright spot seismic parameters to carry out weighted fusion of attributes. The fused attributes can further highlight the reservoir and oil and gas information. Compared with the amplitude or wave impedance prediction that only relies on traditional post-stack seismic data, the accuracy of carbonate reservoir and oil and gas prediction is significantly improved, and the multi-solution is significantly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative labor.

[0036] Figure 1 A schematic diagram of a flow chart of a reservoir favorable area prediction method provided in an embodiment of the present application;

[0037] Figure 2 Provide a pre-stack / post-stack bright spot seismic parameter inversion profile of a P target layer in a wellless area for the embodiment of the present application;

[0038] Figure 3 A comparison diagram of the root mean square amplitude attributes of the pre-stack bright spot seismic parameters and the root mean square amplitude attributes of the post-stack bright spot seismic parameters before and after weighted fusion of the target layer P in the well-free area provided in the embodiment of the present application;

[0039] Figure 4 A comparison chart of the comprehensive properties and paleo-geomorphology of the target layer P in the well-free area provided in the embodiment of the present application;

[0040] Figure 5 A structural block diagram of a reservoir favorable area prediction device provided in an embodiment of the present application;

[0041] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0043] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0044] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0045] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0046] See also Figure 1 , is a flow chart of a method for predicting favorable reservoir areas provided in an embodiment of the present application, the method is mainly used to predict favorable areas of reef-beach carbonate reservoirs, especially in areas without wells, to predict favorable areas of reef-beach carbonate reservoirs. Figure 1 As shown, the method mainly includes the following steps.

[0047] S101: Extract the root mean square amplitude attribute of the pre-stack bright spot seismic parameters of the target layer.

[0048] In one embodiment, in addition to using post-stack bright spot seismic parameters to participate in reservoir prediction, the bright spot seismic parameters provided by pre-stack seismic data are also focused on for reservoir prediction. The pre-stack bright spot seismic parameters include density (DEN), Poisson's ratio (POIS), and Lambda.

[0049] The analysis of rock physical sensitive parameters based on a large number of carbonate reservoirs and hydrocarbon content shows that wave impedance (Ip), density (DEN), Poisson's ratio (POIS), and Lamme modulus (Lambda) are four very critical parameters reflecting carbonate reservoirs and hydrocarbon content. Among them, wave impedance (Ip) and density (DEN) are more sensitive to reflecting the difference between reservoirs and non-reservoirs, while Poisson's ratio (POIS) and Lamme modulus (Lambda) are more sensitive to reflecting the hydrocarbon content of reservoirs. There are differences in Poisson's ratio (POIS) between different rocks and the same rock containing different fluids. Compared with parameters such as velocity (Vp) and density (DEN), Poisson's ratio (POIS) has a more obvious difference and clear physical meaning. Therefore, Poisson's ratio (POIS) parameter is more conducive to identifying fluids. At the same time, Lamme modulus (Lambda) parameter has a more prominent response ability to fluids in carbonate formations than Poisson's ratio (POIS) parameter. Therefore, in addition to focusing on the amplitude and wave impedance (Ip) information that post-stack seismic data can provide, we also further focus on the pre-stack information of density (DEN), Poisson's ratio (POIS), and Lamé modulus (Lambda) that pre-stack seismic data can provide.

[0050] As one of the three-porosity logging series, the prestack density (DEN) parameter can reflect the reservoir porosity from the mechanism itself, that is, it can reflect the physical properties of the reservoir; while the prestack Poisson's ratio (POIS) parameter and the prestack Lamé modulus (Lambda) parameter can be determined and calculated in seismology through the relationship function of the longitudinal wave velocity (Vp), the shear wave velocity (Vs) and the density (DEN). In oil and gas seismic exploration, the Poisson's ratio (POIS) and the Lamé modulus (Lambda) are usually obtained by the prestack seismic inversion method based on the AVO theory.

[0051] The specific steps of extracting the root mean square amplitude attribute of the prestack bright spot seismic parameters of the target layer are as follows:

[0052] Using prestack seismic data, we can solve the prestack density reflectivity volume, obtain the density inversion profile, and extract the root mean square amplitude attribute of the density reflectivity volume of the target layer.

[0053] Using pre-stack seismic data, we can solve the pre-stack Poisson's ratio reflectivity volume, obtain the Poisson's ratio inversion profile, and extract the root mean square amplitude attribute of the Poisson's ratio reflectivity volume of the target layer;

[0054] Using prestack seismic data, the prestack Lame modulus reflectivity volume solution is carried out to obtain the Lame modulus inversion profile and extract the root mean square amplitude attribute of the Lame modulus reflectivity volume of the target layer.

[0055] In one embodiment, the pre-stack seismic data is pre-stack CRP gather data.

[0056] S102: Extract the root mean square amplitude attribute of the post-stack bright spot seismic parameters of the target layer.

[0057] The post-stack bright spot seismic parameters include wave impedance (Ip).

[0058] Specifically, the root mean square amplitude attribute of the wave impedance reflectivity volume of the target layer is extracted using the post-stack seismic data, wherein the post-stack seismic data is equivalent to the wave impedance reflectivity volume, and the post-stack seismic profile is equivalent to the wave impedance inversion profile.

[0059] Figure 2 These are the pre-stack / post-stack bright spot seismic parameter inversion sections of the P target layer in the wellless area, which, from top to bottom, are the wave impedance inversion section, density inversion section, Poisson's ratio inversion section, and Lame modulus inversion section.

[0060] S103: performing weighted fusion on the root mean square amplitude attributes of the pre-stack bright spot seismic parameters and the root mean square amplitude attributes of the post-stack bright spot seismic parameters to obtain comprehensive attributes of the target layer.

[0061] In one embodiment, before weighted fusion is performed, the root mean square amplitude attribute of the pre-stack bright spot seismic parameter and the root mean square amplitude attribute of the post-stack bright spot seismic parameter need to be normalized to obtain the normalized root mean square amplitude attribute of the pre-stack bright spot seismic parameter and the normalized root mean square amplitude attribute of the post-stack bright spot seismic parameter;

[0062] Then, the root mean square amplitude attribute of the normalized pre-stack bright spot seismic parameter and the root mean square amplitude attribute of the normalized post-stack bright spot seismic parameter are weightedly fused to obtain the comprehensive attribute of the target layer.

[0063] Figure 3 The following is a comparison of the root mean square amplitude attributes of the pre-stack bright spot seismic parameters of the P target layer in the wellless area and the root mean square amplitude attributes of the post-stack bright spot seismic parameters before and after weighted fusion. The left side shows the root mean square amplitude attributes of the density reflectivity body, Poisson's ratio reflectivity body, Lame modulus reflectivity body and wave impedance reflectivity body, and the right side shows the comprehensive attributes after weighted fusion. Figure 3 It can be seen that in the weighted fusion comprehensive attribute map, the attributes of the reservoir / favorable oil and gas areas are more prominent.

[0064] S104: Predicting favorable reservoir areas based on the comprehensive attributes of the target layer.

[0065] Specifically, on the comprehensive attribute map, the reservoir / oil-bearing favorable area is identified. The favorable reservoir area is compared with the main controlling factors of reservoir geological accumulation to verify the reliability of the prediction results. The main controlling factors of geological accumulation include paleo-geomorphology, seismic phase or current structural characteristics. Geologically, it is generally believed that paleo-geomorphology has a good control effect on reef-beach bodies, and the high potential area of ​​paleo-geomorphology is the favorable development area of ​​reef-beach reservoirs. If the predicted favorable reservoir area after weighted fusion of seismic attributes has a good match with the paleo-geomorphology, it can further prove the reliability of the prediction results of the favorable reservoir area.

[0066] Figure 4 The figure below is a comparison of the comprehensive attributes and paleo-geomorphology of the P target layer in the wellless area. The left side is a plane diagram of the comprehensive (fusion) attributes, and the right side is a three-dimensional diagram of the approximate paleo-geomorphology. Figure 4 It can be seen that the reservoir / oil-bearing favorable area ( Figure 4 The area within the left frame) and the paleo-geomorphic high potential area ( Figure 4 The area within the frame in the right figure) is highly matched, proving that the favorable reservoir area predicted by the method of this application is extremely reliable.

[0067] This application aims at the technical problem that the traditional geophysical prediction means mainly rely on post-stack seismic data, such as post-stack attributes and post-stack wave impedance inversion technology such as root mean square amplitude rms, amplitude change rate, layer velocity, absorption / attenuation, spectrum decomposition, etc., but these parameters and attributes are not closely and sensitively related to oil and gas, and reservoir prediction, especially reservoir oil and gas prediction, has strong multi-solution characteristics. A method for predicting favorable areas of reef-beach carbonate reservoirs by combining pre-stack / post-stack seismic data in well-free areas and fusing attributes based on multiple pre-stack / post-stack bright spot seismic parameters is proposed. The technical effects are specifically reflected in the following aspects:

[0068] (1) This application is not limited to post-stack seismic data, but also utilizes pre-stack CRP gather data to fully tap the potential of pre-stack data's AVO and hydrocarbon information, and improve the response information of reservoir / hydrocarbon properties;

[0069] (2) This application uses pre-stack CRP gather data to highlight and target the pre-stack seismic parameters POIS and Lambda, which are most sensitive to carbonate oil and gas, and conducts pre-stack POIS and Lambda reflectivity volume solutions, providing highly sensitive basic data for subsequent attribute weighted fusion;

[0070] (3) This application does not rely on independent single attributes to carry out predictions, but instead carries out weighted fusion of multiple pre-stack / post-stack attributes to further highlight reservoir and hydrocarbon information;

[0071] (4) This application strengthens the integration of geology and geophysics, comparing the fused attribute anomaly area with the paleo-geomorphology, seismic facies or present-day structural features of the reef-beach reservoir, reducing the ambiguity and risk of prediction;

[0072] (5) This application provides a new technology and method for predicting favorable areas of reef-beach carbonate reservoirs in areas without wells, and the technology and method are applicable to a large number of reef-beach carbonate reservoir areas and have very strong applicability and extension potential.

[0073] Corresponding to the above-mentioned embodiments, the present application also provides a reservoir favorable area prediction device.

[0074] See also Figure 5 , is a structural block diagram of a reservoir favorable area prediction device provided in an embodiment of the present application. Figure 5 As shown, it mainly includes the following modules.

[0075] A pre-stack seismic attribute extraction module 501 extracts the root mean square amplitude attribute of the pre-stack bright spot seismic parameters of the target layer;

[0076] A post-stack seismic attribute extraction module 502 extracts the root mean square amplitude attribute of the post-stack bright spot seismic parameters of the target layer;

[0077] The attribute fusion module 503 performs weighted fusion on the root mean square amplitude attribute of the pre-stack bright spot seismic parameter and the root mean square amplitude attribute of the post-stack bright spot seismic parameter to obtain the comprehensive attribute of the target layer;

[0078] The reservoir prediction module 504 predicts favorable reservoir areas according to the comprehensive attributes of the target layer.

[0079] It should be pointed out that the specific contents involved in the embodiments of the present application can be found in the description of the above method embodiments, and for the sake of brevity, they will not be repeated here.

[0080] Corresponding to the above embodiment, an embodiment of the present application further provides an electronic device.

[0081] See also Figure 6 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device 600 may include: a processor 601, a memory 602 and a communication unit 603. These components communicate via one or more buses. Those skilled in the art will appreciate that the electronic device structure shown in the figure does not limit the embodiments of the present application, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.

[0082] The communication unit 603 is used to establish a communication channel so that the electronic device can communicate with other devices.

[0083] The processor 601 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It runs or executes software programs and / or modules stored in the memory 602, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 601 can include only a central processing unit (CPU). In the embodiment of the present application, the CPU can be a single computing core or multiple computing cores.

[0084] Memory 602 is used to store execution instructions of processor 601. Memory 602 can be implemented by any type of volatile or non-volatile storage device or a combination of them, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0085] When the execution instructions in the memory 602 are executed by the processor 601 , the electronic device 600 is enabled to execute part or all of the steps in the above method embodiment.

[0086] Corresponding to the above embodiment, the embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium may store a program, wherein when the program is running, the device where the computer-readable storage medium is located may be controlled to perform some or all of the steps in the above method embodiment. In a specific implementation, the computer-readable storage medium may be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0087] Corresponding to the above-mentioned embodiment, the embodiment of the present application further provides a computer program product, which includes executable instructions. When the executable instructions are executed on a computer, the computer executes some or all of the steps in the above-mentioned method embodiment.

[0088] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and subsequent associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0089] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0090] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0091] In several embodiments provided in the present application, any function can be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., and other media that can store program codes.

[0092] The above is only a specific implementation of the present application. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. The protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for predicting favorable reservoir areas, characterized in that: include: Extract the root mean square amplitude attributes of the prestack bright spot seismic parameters of the target layer; Extract the root mean square amplitude attributes of the post-stack bright spot seismic parameters of the target layer; Performing weighted fusion on the root mean square amplitude attribute of the pre-stack bright spot seismic parameter and the root mean square amplitude attribute of the post-stack bright spot seismic parameter to obtain the comprehensive attribute of the target layer; Based on the comprehensive properties of the target layer, the favorable reservoir area is predicted.

2. The method according to claim 1, characterized in that The extracting of the root mean square amplitude attribute of the prestack bright spot seismic parameter of the target layer includes: Using prestack seismic data, prestack density reflectivity volume solution is carried out to extract the root mean square amplitude attribute of the density reflectivity volume of the target layer; Using pre-stack seismic data, pre-stack Poisson's ratio reflectivity volume solution is carried out to extract the root mean square amplitude attribute of the Poisson's ratio reflectivity volume of the target layer; The prestack Lame modulus reflectivity volume solution is carried out using prestack seismic data to extract the root mean square amplitude attributes of the Lame modulus reflectivity volume of the target layer.

3. The method according to claim 2, characterized in that The pre-stack seismic data are pre-stack CRP gather data.

4. The method according to claim 1, characterized in that The extracting of the root mean square amplitude attribute of the post-stack bright spot seismic parameter of the target layer includes: The root mean square amplitude attributes of the wave impedance reflectivity volume of the target layer are extracted using post-stack seismic data.

5. The method according to claim 1, characterized in that The weighted fusion of the root mean square amplitude attribute of the pre-stack bright spot seismic parameter and the root mean square amplitude attribute of the post-stack bright spot seismic parameter to obtain the comprehensive attribute of the target layer includes: Normalizing the root mean square amplitude attributes of the pre-stack bright spot seismic parameters and the root mean square amplitude attributes of the post-stack bright spot seismic parameters to obtain normalized root mean square amplitude attributes of the pre-stack bright spot seismic parameters and normalized root mean square amplitude attributes of the post-stack bright spot seismic parameters; The root mean square amplitude attribute of the normalized pre-stack bright spot seismic parameter and the root mean square amplitude attribute of the normalized post-stack bright spot seismic parameter are weightedly fused to obtain the comprehensive attribute of the target layer.

6. The method according to claim 1, characterized in that After predicting the favorable reservoir area according to the comprehensive attributes of the target layer, the method further includes: The favorable reservoir area is compared with the main controlling factors of reservoir geological accumulation to verify the reliability of the prediction results.

7. The method according to claim 6, characterized in that The main controlling factors of geological accumulation include paleo-geomorphology, seismic phase or current structural characteristics.

8. A reservoir favorable area prediction device, characterized in that: include: Prestack seismic attribute extraction module, extracting the root mean square amplitude attributes of the prestack bright spot seismic parameters of the target layer; Post-stack seismic attribute extraction module, extracting the root mean square amplitude attributes of post-stack bright spot seismic parameters of the target layer; An attribute fusion module performs weighted fusion on the root mean square amplitude attribute of the pre-stack bright spot seismic parameter and the root mean square amplitude attribute of the post-stack bright spot seismic parameter to obtain a comprehensive attribute of the target layer; The reservoir prediction module predicts favorable reservoir areas according to the comprehensive attributes of the target layer.

9. An electronic device, characterized in that: include: processor; Memory; And a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, and when the instructions are executed by the processor, the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

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