Method and device for predicting shale content of reservoir through pre-stack earthquake, electronic equipment and medium

Abstract

The invention provides a method and device for predicting the shale content of a reservoir through a pre-stack earthquake, electronic equipment and a medium, and the method comprises the steps: carrying out the pre-stack AVO processing, and obtaining the near-track and far-track superposition data; through well seismic calibration, seismic wavelets of near-trace and far-trace superposition data are extracted; wavelet shaping and zero-phasing processing are carried out; wave impedance and elastic impedance of the reservoir are calculated through colored inversion; calculating the shale content of the reservoir; carrying out standardization treatment on the shale content; and determining a drilling target according to the characteristics of the shale content plane distribution diagram of the reservoir. On the basis of reservoir rock physical parameter characteristic research, the nonlinear multivariate function relation between the reservoir shale content parameters and the reservoir pre-stacking elastic attributes is established, prediction of the reservoir shale content parameters is achieved, the accuracy of high-quality reservoir identification of the oil reservoir can be improved, the distribution characteristics of the high-quality reservoir can be determined, the drilling target can be optimized, and the method is suitable for the high-quality reservoir identification of the oil reservoir. The drilling risk is reduced.

Description

Technical Field

[0001] The present application relates to the field of petroleum exploration and development, and in particular to a method, device, electronic equipment and medium for predicting reservoir shale content by prestack seismic. Background Art

[0002] The mud content of the reservoir is one of the effective parameters for reservoir description and is also the main parameter model of the reservoir geological model. According to the survey of the China Journal Full-text Database, some relevant literature and research results were found. At present, there are two main methods for calculating the mud content of the reservoir based on seismic constraints.

[0003] One is the neural network shale content calculation method constrained by seismic attribute parameters, such as the reservoir physical property parameter prediction method based on LSTM recurrent neural network proposed by An Peng (2019 "Progress in Geophysics" VOL34, NO 5), the support vector machine reservoir parameter prediction method based on KICA attribute optimization proposed by Wang Weiqiang (2021 "Geophysical and Geochemical Exploration" VOL37, NO6), the multi-seismic attribute parameter prediction method based on probabilistic neural network proposed by Ji Yuxin (2003 "Petroleum Geophysical Exploration" VOL38, NO6), the multi-seismic attribute parameter prediction method based on radial Gaussian neural network proposed by Gao Meijuan et al. (2001 "Petroleum Geophysical Exploration" VOL36, NO5), and the genetic algorithm BP neural network reservoir parameter prediction method based on seismic attributes proposed by Wang Yonggang et al. (2002 "Petroleum Geophysical Exploration" VOL.45, NO.4).

[0004] The other type is the random inversion reservoir shale content calculation method based on geostatistics, such as the synchronous inversion method of reservoir physical property parameters based on prior probability information proposed by Yin Xingyao et al. (2014 "APPLIED GEOPHYSICS" VOL.11, NO.3), the elastic impedance reservoir physical property parameter prediction method based on prior probability information proposed by Yin Xingyao et al. (2014 "Chinese Journal of Geophysics" VOL.57, NO.12), the lithology identification method based on Markov random field proposed by Tian Yukun et al. (2013 "Chinese Journal of Geophysics" VOL.56, NO.4), the quantitative evaluation method of seismic lithofacies identification uncertainty based on probability statistics proposed by Yuan Cheng et al. (2015 "Chinese Journal of Geophysics" VOL.58, NO.10), and the new method of obtaining shale content using pseudo-acoustic constrained inversion proposed by Wang Wei et al. (2015 "Progress in Geophysics" VOL.32, NO.2).

[0005] There are also some relevant patents and research results in this field. For example, the patent of Li Shengjie et al. (application number: CN201510386046.6) discloses a seismic prediction method for the porosity and mud content of sandstone and mudstone reservoirs. This method mainly uses the research on the physical elastic parameter characteristics of reservoir rocks from logging data, and adopts seismic pre-stack inversion to obtain seismic longitudinal wave impedance data, transverse wave impedance data and density data; performs seismic porosity and mud content data inversion to obtain seismic predicted porosity data and mud content data; the patent of Yang Peijie et al. (application number: CN201610191787.3) discloses a new method for simultaneous inversion of reservoir porosity, water saturation and mud content parameters. This method combines rock physics and geological statistical analysis to establish an objective function for the inversion of three parameters of reservoir porosity, water saturation and mud content parameters, and solves the objective function to realize the prediction of reservoir mud content.

[0006] Whether it is the neural network method or the geostatistical random inversion method based on prior probability, both require a large amount of well information constraints and participation. Both have the problems of high computational cost, strong dependence on well constraints and limited work experience of oil and gas engineers, which brings greater risks to the exploration and development of oil reservoirs with few wells and fast sand body changes.

[0007] From the existing technology, although there are research reports on lithology parameters and fluid parameters based on pre-stack seismic, no relevant reports have been found on the technology of predicting reservoir shale content by directly applying acoustic impedance and elastic impedance parameters without the need for an initial parameter model. Summary of the invention

[0008] In view of this, in order to make full use of the rich lithology of prestack seismic and the high lateral resolution of seismic reservoirs in oil and gas exploration and development areas with sparse wells, and reduce the dependence on well reservoir parameter information and seismic inversion initial model parameters, this application proposes a reservoir shale content calculation method based on prestack seismic to predict the shale content of the reservoir, determine the high-quality distribution characteristics, reduce the risk of oil and gas drilling, and provide a more reliable basis for well site deployment and development plan optimization.

[0009] In a first aspect, an embodiment of the present application provides a method for predicting reservoir shale content by prestack seismic, comprising:

[0010] Perform pre-stack AVO processing to obtain near-track stacking data and far-track stacking data;

[0011] Through well-seismic calibration, seismic wavelets of the near-channel stacking data and the far-channel stacking data are respectively extracted;

[0012] Respectively performing wavelet shaping and zero-phase processing on the seismic wavelets of the near-path stacking data and the far-path stacking data to obtain a near-path stacking data volume and a far-path stacking data volume with zero phase after processing;

[0013] Performing colored inversion on the near-path stacked data volume and the far-path stacked data volume after the processing and zero-phase conversion, respectively, to obtain the wave impedance and elastic impedance of the reservoir;

[0014] By analyzing the correlation between the known well mud parameters and the extraction well bypass, the optimal parameters of the rotation angle θ are determined, and the mud content of the reservoir is calculated using the wave impedance and elastic impedance;

[0015] Standardizing the mud content;

[0016] By analyzing the planar distribution characteristics of the reservoir's mud content, the distribution characteristics of high-quality reservoirs are analyzed and the oil and gas drilling targets of high-quality reservoirs are determined.

[0017] In one possible implementation,

[0018] The wavelet shaping and zero-phase processing are respectively performed on the seismic wavelets of the near-path stacking data and the far-path stacking data to obtain the near-path stacking data volume and the far-path stacking data volume with zero phase after processing, specifically:

[0019] The seismic wavelets of the near-path stacking data and the far-path stacking data are shaped, spectrum equalized, and zero-phase processed, and the deconvolution operator of the data body is obtained to obtain the near-path stacking data body and the far-path stacking data body after processing and zero-phased.

[0020] In a possible implementation, the wave impedance calculation formula is:

[0021] AI=Vpρ

[0022] Where AI represents the wave impedance, V p represents the longitudinal wave velocity of the reservoir, and ρ represents the density of the reservoir.

[0023] In a possible implementation, the elastic impedance calculation formula is:

[0024]

[0025] Where EI is the elastic impedance, V p represents the P-wave velocity of the reservoir, V s represents the shear wave velocity of the reservoir, ρ represents the density of the reservoir, α represents the angle at which the seismic data is imaged.

[0026] In a possible implementation, the calculation formula for the mud content is as follows:

[0027] V clay =cosθ·AI+sinθ·Ei

[0028] Among them, Vclay represents the mud content, AI represents the acoustic impedance of the reservoir, EI represents the elastic impedance of the reservoir, and θ represents the rotation angle.

[0029] In a possible implementation, the standardization of the mud content is specifically performed as follows:

[0030] The time difference correction of the mud content is completed by calibrating the mud content of the key wells; and then the mud content is ensured to be within the reasonable value range of 0 to 100% through linear transformation.

[0031] In one possible implementation, the formula for the linear transformation is: y(x)=a+bx; wherein y(x) is a mud content function, x is a pseudo-mud content parameter obtained by prestack seismic attributes, and a and b are unknown coefficients determined by the least squares method.

[0032] In a second aspect, an embodiment of the present application provides a pre-stack seismic prediction device for reservoir shale content, comprising:

[0033] The stacking data acquisition module is used to perform pre-stack AVO processing to obtain near-track stacking data and far-track stacking data;

[0034] A wavelet extraction module, used to extract the seismic wavelets of the near-channel stacking data and the far-channel stacking data respectively through well-seismic calibration;

[0035] A data processing module, used for performing wavelet shaping and zero-phase processing on the seismic wavelets of the near-path stacking data and the far-path stacking data, respectively, to obtain a near-path stacking data volume and a far-path stacking data volume with zero phase after processing;

[0036] A colored inversion module is used to perform colored inversion on the near-path stacking data volume and the far-path stacking data volume after the processing and zero-phase conversion, respectively, to obtain the wave impedance and elastic impedance of the reservoir;

[0037] A shale content calculation module is used to determine the optimal parameter of the rotation angle θ by analyzing the correlation between the known well shale parameters and the extraction well bypass, and calculate the shale content of the reservoir using the wave impedance and elastic impedance;

[0038] A standardization processing module, used for performing standardization processing on the mud content;

[0039] The target determination module is used to analyze the distribution characteristics of high-quality reservoirs through the planar distribution map characteristics of the reservoir's mud content and determine the oil and gas drilling targets of the high-quality reservoirs.

[0040] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0041] processor;

[0042] Memory;

[0043] 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.

[0044] 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.

[0045] This application proposes a method for calculating reservoir shale content based on prestack seismic, which is an in-depth development and application of seismic prestack AVO oil and gas technology in the fine description of oil reservoirs. It utilizes the characteristics of rich lithology in prestack seismic amplitude and high lateral resolution of seismic reservoirs. On the basis of the study of reservoir rock physical parameter characteristics, a nonlinear multivariate functional relationship between reservoir shale content parameters and reservoir prestack elastic properties is established, and the prediction of reservoir shale content parameters is realized. This can improve the accuracy of oil reservoir description, determine the distribution characteristics of high-quality reservoirs, optimize drilling targets, and reduce drilling risks.

[0046] The present application proposes a reservoir shale content calculation method based on prestack seismic. It does not need to establish an inverse initial model of reservoir parameters, has little dependence on well reservoir information, and mainly relies on the rich lithology and oil and gas information of prestack seismic. It is more objective in describing reservoir parameters and can be used as reservoir geological model parameters for shale content parameter modeling. This prestack seismic shale content prediction method has achieved good application results in deepwater oil and gas drilling in West Africa and has great application value in the exploration and development of sandstone reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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.

[0048] Figure 1 A schematic flow chart of a method for predicting reservoir shale content by prestack seismic provided in an embodiment of the present application;

[0049] Figure 2 A schematic flowchart of zero-phasing processing in a method for predicting reservoir shale content in prestack seismic provided in an embodiment of the present application;

[0050] Figure 3A correlation diagram between the shale content of a reservoir and the wave impedance and elastic impedance in a method for predicting the shale content of a reservoir by prestack seismic provided in an embodiment of the present application;

[0051] Figure 4 A cross plot of wave impedance, Poisson's ratio and shale content of a reservoir in a method for predicting reservoir shale content in prestack seismic provided in an embodiment of the present application;

[0052] Figure 5 The seismic near-track and far-track seismic imaging amplitude profiles of a deepwater turbidite sandstone reservoir in West Africa provided in the embodiments of the present application;

[0053] Figure 6 for Figure 5 A shale content profile obtained by processing the seismic data of the medium seismic profile by a method for predicting reservoir shale content using prestack seismic provided in an embodiment of the present application;

[0054] Figure 7 A structural block diagram of a pre-stack seismic prediction device for reservoir shale content provided in an embodiment of the present application;

[0055] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] See also Figure 1 , is a flow chart of a method for predicting reservoir shale content using prestack seismic provided in an embodiment of the present application. Figure 1 As shown, it mainly includes the following steps.

[0061] Step S1: Perform pre-stack AVO processing to obtain near-track stacking data and far-track stacking data.

[0062] Step S2: extracting the seismic wavelets of the near-track stacking data and the far-track stacking data respectively through well-seismic calibration.

[0063] Step S3: performing wavelet shaping and zero-phase processing on the seismic wavelets of the near-path stacking data and the far-path stacking data respectively, to obtain a near-path stacking data volume and a far-path stacking data volume with zero phase after processing;

[0064] Specifically, the seismic wavelets of the near-track stacking data and the far-track stacking data are shaped, spectrum equalized, and zero-phase processed, and the deconvolution operator of the data volume is obtained to obtain the near-track stacking data volume and the far-track stacking data volume after processing with zero phase. Figure 2 , is a schematic flow chart of zero-phasing processing in a method for predicting reservoir shale content by prestack seismic provided in an embodiment of the present application.

[0065] Step S4: performing colored inversion on the near-path stacking data volume and the far-path stacking data volume after the processing and zero-phase conversion, respectively, to obtain the wave impedance and elastic impedance of the reservoir;

[0066] The calculation formula of the wave impedance AI is:

[0067] AI=Vpρ

[0068] The calculation formula of the elastic impedance EI is:

[0069]

[0070] Among them, V p represents the P-wave velocity of the reservoir, V s represents the shear wave velocity of the reservoir, ρ represents the density of the reservoir, α represents the angle at which the seismic data is imaged.

[0071] Step S5: By analyzing the correlation between the known well shale parameters and the extraction well bypass, the optimal parameters of the rotation angle θ are determined, and the shale content of the reservoir is calculated using the wave impedance and elastic impedance. Figure 3 , which is a correlation diagram between the mud content of the reservoir and the wave impedance and elastic impedance in a method for predicting the mud content of the reservoir by prestack seismic provided in an embodiment of the present application.

[0072] The clay content V clay The calculation formula is as follows:

[0073] V clay=cosθ·AI+sinθ·Ei

[0074] Wherein, AI represents the acoustic impedance of the reservoir, EI represents the elastic impedance of the reservoir, and θ represents the rotation angle.

[0075] Preferably, the parameters required for the correlation analysis include: wave impedance, elastic impedance, aspect ratio velocity parameter and Poisson's ratio. Figure 4 , which is a cross-plot of the wave impedance, Poisson's ratio and shale content of the reservoir in a method for predicting reservoir shale content by prestack seismic provided in an embodiment of the present application.

[0076] Step S6: standardizing the mud content.

[0077] Specifically, the mud content of key wells is calibrated to complete the time difference correction of the mud content; and the mud content is ensured to be within the reasonable value range of 0 to 100% through linear transformation y(x)=a+bx.

[0078] Among them, y(x) is the shale content function; x is the pseudo-shale content parameter obtained by prestack seismic attributes; a and b are unknown coefficients determined by the least squares method.

[0079] Step S7: Analyze the distribution characteristics of high-quality reservoirs through the planar distribution map characteristics of the reservoir's mud content, and determine the oil and gas drilling targets of the high-quality reservoirs.

[0080] The present embodiment of the application takes actual three-dimensional seismic data of a deep-water turbidite sandstone reservoir in West Africa as an example to illustrate the effectiveness of a pre-stack seismic method for predicting reservoir shale content provided by the present embodiment of the application.

[0081] See also Figure 5 , which is the seismic near-track and far-track seismic imaging amplitude profile of a deepwater turbidite sandstone reservoir in West Africa provided in the embodiment of the present application. Figure 5 It can be seen that the effective frequency band of the seismic data is narrow and the resolution is low. Multiple groups of strong seismic reflection wave groups appear in the target reservoir segment, which are the characteristics of strong reflection seismic wave groups. The main frequency of seismic reflection of the reservoir is about 15 Hz, and the effective frequency band is about 30 Hz. The boundary of the high-quality sandstone reservoir is not easy to identify. At the same time, the water depth of the target area is about 2000 meters. The turbidite sand bodies in the reservoir are intersected and overlapped, and the relationship is complex. The turbidite sandstone structure is loose, and the difference in physical characteristics between it and the surrounding rock is small. The detailed description of the reservoir and the internal connectivity of the oil reservoir face many challenges.

[0082] According to a method for predicting reservoir shale content by prestack seismic provided in an embodiment of the present application, near- and far-track imaging processing of seismic data is completed for the target layer segment, inversion processing of reservoir wave impedance and elastic impedance is completed, and on the basis of calibration of shale content of known wells, reservoir shale content body generation and standardization processing are completed, and an effective shale content data body is obtained. The result data improves the accuracy of effectively identifying high-quality sand bodies, provides reliable information for describing the internal connectivity of the reservoir, and achieves good application effects.

[0083] See also Figure 6 ,for Figure 5 The mud content profile obtained after the seismic data of the medium seismic profile is processed by a method for predicting reservoir mud content using a prestack seismic method provided in the embodiment of the present application is compared with the mud content curve of the actual well reservoir, and the prediction results have a good consistency. The mud content profile predicted by the prestack seismic elastic attribute parameters makes it very easy to identify deep-water turbidite sandstone targets, and can effectively determine the spatial distribution characteristics of the sand body, providing reliable reservoir parameter information for selecting drilling targets and optimizing oil field development plans, verifying the feasibility and effectiveness of a method for predicting reservoir mud content using a prestack seismic method provided in the embodiment of the present application.

[0084] In recent years, a method for predicting reservoir mud content by prestack seismic provided in the embodiment of the present application has been widely used in the deepwater specialty of the Angola project, and has participated in the deployment of 27 development wells. Good results have been seen in the identification of high-quality reservoirs of turbidite sandstone in the deepwater of Angola and the analysis of the connectivity of oil reservoir sand bodies. The actual drilling effect has reached the requirements of development wells, met the needs of deepwater oil and gas exploration and development, reduced drilling risks, achieved good economic benefits, and has been confirmed by subsequent exploration and development drilling practices, and has great application and promotion value.

[0085] In summary, the method for predicting reservoir mud content by prestack seismic proposed in the embodiment of the present application is an in-depth development and application of prestack seismic AVO oil and gas technology in the fine description of oil reservoirs. On the basis of AVO prediction processing, the rich lithology of prestack seismic and the high lateral resolution of seismic reservoirs are utilized to complete the superposition of near- and far-channel prestack data and the zero-phase processing of seismic data; the colored inversion technology is applied to complete the calculation of reservoir wave impedance and elastic impedance, and on the basis of the study of reservoir rock physical parameter characteristics, a nonlinear multivariate function relationship between reservoir mud content parameters and reservoir wave impedance and elastic impedance is established, and the prediction of reservoir mud content parameters is realized, which can improve the accuracy of reservoir identification and reservoir connectivity description, determine the distribution characteristics of high-quality reservoirs, optimize drilling targets, and reduce drilling risks.

[0086] A reservoir shale content calculation method based on prestack seismic proposed in the embodiment of the present application can improve the accuracy of identifying high-quality sandstone reservoirs, optimize drilling targets, and reduce drilling risks. Its effectiveness has been confirmed by subsequent exploration and development drilling, and its technology is feasible and applicable.

[0087] Corresponding to the above-mentioned embodiment, the present application also provides a pre-stack seismic prediction device for reservoir shale content.

[0088] See also Figure 7 , is a structural block diagram of a pre-stack seismic prediction device for reservoir shale content provided in an embodiment of the present application. Figure 7 As shown, it mainly includes the following modules.

[0089] The stacking data acquisition module 701 is used to perform pre-stack AVO processing to obtain near-track stacking data and far-track stacking data;

[0090] The wavelet extraction module 702 is used to extract the seismic wavelets of the near-channel stacking data and the far-channel stacking data respectively through well-seismic calibration;

[0091] The data processing module 703 is used to perform wavelet shaping and zero-phase processing on the seismic wavelets of the near-path stacking data and the far-path stacking data, respectively, to obtain a near-path stacking data volume and a far-path stacking data volume with zero phase after processing;

[0092] A colored inversion module 704 is used to perform colored inversion on the near-path stacking data volume and the far-path stacking data volume after the processing and zero-phase conversion, respectively, to obtain the wave impedance and elastic impedance of the reservoir;

[0093] The shale content calculation module 705 is used to determine the optimal parameter of the rotation angle θ by analyzing the correlation between the known well shale parameters and the extraction well bypass, and calculate the shale content of the reservoir using the wave impedance and elastic impedance;

[0094] A standardization processing module 706 is used to perform standardization processing on the mud content;

[0095] The target determination module 707 is used to analyze the distribution characteristics of high-quality reservoirs through the planar distribution characteristics of the mud content of the reservoirs, and determine the oil and gas drilling targets of the high-quality reservoirs.

[0096] 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.

[0097] Corresponding to the above embodiment, an embodiment of the present application further provides an electronic device.

[0098] See also Figure 8, is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 8 As shown, the electronic device 800 may include: a processor 801, a memory 802 and a communication unit 803. 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. 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.

[0099] The communication unit 803 is used to establish a communication channel so that the electronic device can communicate with other devices.

[0100] The processor 801 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 802, 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 801 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.

[0101] The memory 802 is used to store the execution instructions of the processor 801. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, 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.

[0102] When the execution instructions in the memory 802 are executed by the processor 801 , the electronic device 800 is enabled to execute part or all of the steps in the above method embodiment.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 reservoir shale content by prestack seismic, characterized in that: include: Perform pre-stack AVO processing to obtain near-track stacking data and far-track stacking data; Through well-seismic calibration, seismic wavelets of the near-channel stacking data and the far-channel stacking data are respectively extracted; Respectively performing wavelet shaping and zero-phase processing on the seismic wavelets of the near-path stacking data and the far-path stacking data to obtain a near-path stacking data volume and a far-path stacking data volume with zero phase after processing; Performing colored inversion on the near-path stacked data volume and the far-path stacked data volume after the processing and zero-phase conversion, respectively, to obtain the wave impedance and elastic impedance of the reservoir; By analyzing the correlation between the known well mud parameters and the extraction well bypass, the optimal parameters of the rotation angle θ are determined, and the mud content of the reservoir is calculated using the wave impedance and elastic impedance; Standardizing the mud content; By analyzing the planar distribution characteristics of the reservoir's mud content, the distribution characteristics of high-quality reservoirs are analyzed and the oil and gas drilling targets of high-quality reservoirs are determined.

2. The method for predicting reservoir shale content by prestack seismic analysis according to claim 1, characterized in that: The wavelet shaping and zero-phase processing are respectively performed on the seismic wavelets of the near-path stacking data and the far-path stacking data to obtain the near-path stacking data volume and the far-path stacking data volume with zero phase after processing, specifically: The seismic wavelets of the near-path stacking data and the far-path stacking data are shaped, spectrum equalized, and zero-phase processed, and the deconvolution operator of the data body is obtained to obtain the near-path stacking data body and the far-path stacking data body after processing and zero-phased.

3. The method for predicting reservoir shale content by prestack seismic according to claim 1, characterized in that: The wave impedance calculation formula is: AI=V p ρ Where AI represents the wave impedance, V p represents the longitudinal wave velocity of the reservoir, and ρ represents the density of the reservoir.

4. The method for predicting reservoir shale content by prestack seismic analysis according to claim 1, characterized in that: The elastic impedance calculation formula is: Where EI is the elastic impedance, V p represents the P-wave velocity of the reservoir, V s represents the shear wave velocity of the reservoir, ρ represents the density of the reservoir, α represents the angle at which the seismic data is imaged.

5. The method for predicting reservoir shale content by prestack seismic according to claim 1, characterized in that: The calculation formula of the mud content is as follows: V clay =cosθ·AI+sinθ·EI Among them, V clay represents the mud content, AI represents the acoustic impedance of the reservoir, EI represents the elastic impedance of the reservoir, and θ represents the rotation angle.

6. The method for predicting reservoir shale content by prestack seismic analysis according to claim 1, characterized in that: The standardization of the mud content is specifically as follows: The time difference correction of the mud content is completed by calibrating the mud content of the key wells; and then the mud content is ensured to be within the reasonable value range of 0 to 100% through linear transformation.

7. A method for predicting reservoir shale content by prestack seismic according to claim 6, characterized in that: The formula of the linear transformation is: y(x)=a+bx; wherein y(x) is a shale content function, x is a pseudo-shale content parameter obtained through prestack seismic attributes, and a and b are unknown coefficients determined by the least squares method.

8. A pre-stack seismic device for predicting reservoir shale content, characterized in that: include: The stacking data acquisition module is used to perform pre-stack AVO processing to obtain near-track stacking data and far-track stacking data; A wavelet extraction module, used to extract the seismic wavelets of the near-channel stacking data and the far-channel stacking data respectively through well-seismic calibration; The data processing module is used to perform wavelet shaping and zero-phase processing on the seismic wavelets of the near-channel stacking data and the far-channel stacking data, respectively, to obtain the near-channel stacking data volume and the far-channel stacking data volume after processing and zero-phased. A colored inversion module is used to perform colored inversion on the near-path stacking data volume and the far-path stacking data volume after the processing and zero-phase conversion, respectively, to obtain the wave impedance and elastic impedance of the reservoir; A shale content calculation module is used to determine the optimal parameter of the rotation angle θ by analyzing the correlation between the known well shale parameters and the extraction well bypass, and calculate the shale content of the reservoir using the wave impedance and elastic impedance; A standardization processing module, used for performing standardization processing on the mud content; The target determination module is used to analyze the distribution characteristics of high-quality reservoirs through the planar distribution map characteristics of the reservoir's mud content and determine the oil and gas drilling targets of the high-quality reservoirs.

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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