Mudstone interlayer prediction method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a mudstone interlayer prediction method and device, electronic equipment and a storage medium. The method comprises the steps of performing high-resolution frequency extraction processing on pre-stack sub-angle gather data; pre-stack seismic elastic inversion of well logging constraint is carried out by using high longitudinal and transverse resolutions of well logging and earthquakes; determining independent and sensitive reservoir elastic parameters of the reservoir predicted by the mudstone interlayer; the selected reservoir elastic parameters serve as independent variables, a mudstone factor recognition function of the mudstone interlayer is constructed, and a calculation model of the mudstone factor recognition function of the mudstone interlayer is established; and according to the established mudstone factor identification function model and the selected reservoir elastic parameter inversion data as variable parameters, a division standard is established, and mudstone interlayer prediction is completed. According to the method, the earthquake identification capability of the mudstone interlayer can be improved, the accuracy of turbidite sandstone earthquake prediction can be improved, and the method has great application values for analyzing the connectivity of oil reservoir sand bodies, optimizing an oil reservoir development scheme and improving the oil reservoir recovery efficiency.

Description

Technical Field

[0001] The present application relates to the field of oil and gas exploration and development of deepwater turbidite reservoirs, and specifically to a method, device, electronic equipment and storage medium for predicting mudstone interlayers in deepwater turbidite channels based on prestack elastic parameters. Background Art

[0002] As an important research field in the field of deepwater oil and gas exploration and development, deepwater turbidite channels have always been a hot spot in the world's oil and gas exploration and development. This type of oil and gas reservoir has large reserves and high reservoir physical parameters, but the reservoir heterogeneity is strong, the internal mudstone interlayers are well developed, and the spatial distribution law of mudstone interlayers and the internal connectivity of turbidite sandstone reservoirs are not clearly understood, which restricts the efficient development of deepwater turbidite channel reservoirs. Therefore, it is necessary to study and establish a high-precision mudstone interlayer prediction method for deepwater turbidite channels, which can optimize the development plan of turbidite sandstone reservoirs and improve the recovery rate of turbidite oil fields.

[0003] At present, many scholars have conducted research on the prediction methods of mudstone interlayers in different reservoir types. Most of the literature has carried out phase-controlled random geological modeling, post-stack topological wave impedance inversion and other methods to predict mudstone interlayers based on post-stack seismic data. For example, Wang Min et al. proposed in "Study on the prediction method of interlayers in braided river reservoirs based on multi-information association: A case study of Fal block in P oilfield in South Sudan" (Earth Science Frontiers, Vol. 25(2), 2018) that the main types, locations and scales of interlayers in the study area can be determined by correlating multiple information data such as core, well logging and seismic data. Based on this, the geostatistical theory is used to finally establish a method that can A fine geological model reflecting the distribution of various types of interlayers can achieve reasonable prediction of interlayers; Zhou Zhan et al., in "Prediction Method of Thin Mudstone Interlayers in Gravity Flow in Deepwater Few-Well Areas" (Natural Gas Industry, Vol40(12), 2020), analyzed the rock electrical characteristics and seismic response characteristics of mudstone interlayers in the study area, clarified the mudstone genesis type, and used a combination of HFE topology technology and AIW wave impedance inversion technology to effectively improve the recognition accuracy of thin mudstones and effectively track the planar distribution of thin mudstone interlayers. The research results can provide technical support for the preparation of deepwater gas field development plans and the subsequent implementation of gas field development. However, this type of method does not conduct in-depth rock physics analysis and sensitive elastic parameter statistics on mudstone interlayers, and the prediction accuracy needs to be further improved.

[0004] Among the retrieved literature, there are also some technical achievements in the pre-stack attribute characteristics of reservoirs. Zhang Yan et al., in "High-precision Mudstone Interlayer Characterization Method Based on Pre-stack Sensitive Attribute Optimization" (China Earth Science Joint Academic Annual Conference 2020), based on the study of rock physical characteristics, proposed gradient (impedance) information as a sensitive seismic attribute for lithology identification, and broadened the seismic frequency band by performing high-resolution processing on the pre-stack seismic attribute; using the pre-stack reservoir inversion sand body prediction results as a constraint, the multi-solution nature of the identification is reduced, thereby achieving high-precision characterization of thin mudstone interlayers.

[0005] In the retrieved patent database, there are also some related patent reports, such as "Interlayer Seismic Prediction Method and Device" (CN110646850B, Chen Meng, 2018). However, this method mainly predicts interlayers based on the information constraints of post-stack seismic data volume, using seismic geostatistical inversion technology; it does not involve pre-stack reservoir elastic parameter inversion technology, nor does it involve the construction of mudstone factor identification function based on elastic parameters. It has not been deeply discussed in terms of the optimization of sensitive elastic parameters of interlayers.

[0006] In the retrieved literature, there is no report on the same research results as the method of identifying muddy interlayers in turbidite sandstone reservoirs using prestack reservoir elastic parameters. Summary of the invention

[0007] In view of this, the present application provides a mudstone interlayer prediction method, device, electronic device and storage medium, so as to solve the problems of difficulty in identifying mudstone interlayers in turbidite sandstone reservoirs and uncertainty in the connectivity of sand bodies in the reservoir during the development of deepwater turbidite sandstone reservoirs in the prior art, improve the accuracy of turbidite sandstone waterway prediction, optimize the development plan of turbidite sandstone reservoirs, and achieve the purpose of increasing oil field recovery.

[0008] In a first aspect, an embodiment of the present application provides a method for predicting mudstone interlayers, comprising:

[0009] Perform high-resolution frequency-boosting processing on pre-stack angle gather data to obtain amplitude-preserving, high-resolution pre-stack angle stack data volumes;

[0010] Using the high vertical and horizontal resolution of well logging and seismic data, we conduct pre-stack seismic elastic inversion constrained by well logging to obtain various high-resolution elastic parameter data volumes of the reservoir;

[0011] Based on the correlation coefficient between the reservoir elasticity parameters of turbidite sandstone reservoirs and the lithology discrimination parameters and reservoir elasticity parameters, the reservoir-independent and sensitive reservoir elasticity parameters for mudstone interlayer prediction are determined.

[0012] Taking the selected reservoir elastic parameters as independent variables, the mudstone factor identification function of mudstone interlayers is constructed, and the quantitative calculation model of the mudstone factor identification function of mudstone interlayers is established;

[0013] According to the established mudstone factor identification function model and the selected reservoir elastic parameter inversion data as variable parameters, the mudstone factor data body of the turbidite sandstone reservoir is obtained, and the data body is normalized to establish the division standard of mudstone identification factor of mudstone interlayers, and complete the prediction of mudstone interlayers.

[0014] In a possible implementation, the high-resolution frequency-enhancing processing is performed on the pre-stack angle-based gather data to obtain an amplitude-preserving, high-resolution pre-stack angle-based stack data volume, including:

[0015] The pre-stack angle-based gather data are processed with high resolution and frequency enhancement by adopting post-stack zero-phase deconvolution, post-stack inverse Q compensation and broadband expansion to obtain a pre-stack angle-based stack data volume with amplitude preservation and high resolution.

[0016] In a possible implementation, the reservoir elastic parameters of the turbidite sandstone reservoir are determined based on the correlation coefficient between the lithology discrimination parameter and the reservoir elastic parameters, and the reservoir independent and sensitive reservoir elastic parameters for mudstone interlayer prediction, including:

[0017] Formula (1) is used to obtain the reservoir elastic parameter to lithology discrimination parameter R:

[0018] R=|xy| / (x+y) (1)

[0019] Among them, R is the reservoir elastic parameter to mudstone discrimination parameter, x and y represent the elastic parameters of sand and mudstone respectively;

[0020] The correlation coefficient between the reservoir elastic parameters is obtained using formula (2):

[0021]

[0022] Where X is the correlation coefficient between reservoir elastic parameters; x i ,y i represent the elastic parameters of sand and mudstone, respectively; represent the average values ​​of elastic parameters of sand and mudstone, respectively; n is the number of samples;

[0023] Reservoir elastic parameters that are more sensitive to mudstone interlayers are preferred, and the selection principle of sensitive reservoir elastic parameters is mainly that the reservoir elastic parameter to lithology discrimination parameter R>0.1, and the correlation coefficient X between reservoir elastic parameters is<0.4.

[0024] In a possible implementation, the method of constructing a mudstone factor identification function of mudstone interlayers with the selected reservoir elastic parameters as independent variables and establishing a quantitative calculation model of the mudstone factor identification function of mudstone interlayers includes:

[0025] The quantitative calculation model of the mudstone factor identification function of the mudstone interlayer is shown in formula (3):

[0026]

[0027] Among them, G is the mud factor identification function of mudstone interlayer, S i is an independent and sensitive elastic parameter of the reservoir, k i is the unknown coefficient, k 0 is a constant, and n is the number of reservoir sensitive elastic parameters adopted.

[0028] In a possible implementation, the mudstone factor identification function model established and the selected reservoir elastic parameter inversion data are used as variable parameters to obtain a mudstone factor data body of a turbidite sandstone reservoir, and the data body is normalized to establish a classification standard for mudstone identification factors of mudstone interlayers, and complete mudstone interlayer prediction, including:

[0029] Based on the comparative analysis of actual drilling and prediction results, the identification and division criteria of mudstone interlayers are established as follows: when the G value is > 0.80, the mud content is the highest, which is a large set of mudstone interlayers; when the G value is between 0.80 and 0.50, the mud content is medium and the mudstone interlayers are most developed; when the G value is between 0 and 0.50, the mud content is low and sandstone is developed, thus achieving quantitative prediction of mudstone interlayers.

[0030] In a second aspect, an embodiment of the present application provides a mudstone interlayer prediction device, comprising:

[0031] Pre-stack angle-based gather data processing module is used to perform high-resolution frequency-enhancing processing on pre-stack angle-based gather data to obtain amplitude-preserving, high-resolution pre-stack angle-based stack data volumes;

[0032] High-precision pre-stack elastic parameter inversion module, which is used to carry out pre-stack seismic elastic inversion constrained by well logging using the high vertical and horizontal resolution of well logging and seismic, and obtain various high-resolution elastic parameter data bodies of the reservoir;

[0033] Reservoir elastic parameter acquisition module: used to determine the reservoir-independent and sensitive reservoir elastic parameters for mudstone interlayer prediction based on the correlation coefficient between the reservoir elastic parameters of turbidite sandstone reservoirs and the lithology discrimination parameters and reservoir elastic parameters;

[0034] A module for establishing a quantitative calculation model of a mudstone factor identification function is used to construct a mudstone factor identification function of mudstone interlayers with selected reservoir elastic parameters as independent variables, and to establish a quantitative calculation model of the mudstone factor identification function of mudstone interlayers;

[0035] Mudstone interlayer prediction module: used to obtain the mudstone factor data body of the turbidite sandstone reservoir based on the established mudstone factor identification function model and the selected reservoir elastic parameter inversion data as variable parameters, and normalize the data body to establish the division standard of mudstone identification factor of mudstone interlayer, and complete the prediction of mudstone interlayer.

[0036] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0037] processor;

[0038] Memory;

[0039] 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.

[0040] 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 the device where the computer-readable storage medium is located in the time and space where the program is executed executes any one of the methods described in the first aspect.

[0041] In the embodiments of the present application, the present application improves the seismic identification capability of mudstone interlayers in deep-water turbidite channels, improves the accuracy of seismic prediction of turbidite sandstones, and predicts the planar distribution characteristics of mudstone interlayers in deep-water turbidite channels; the prediction results of blind wells are consistent with the actual drilling conditions of the drilled wells; based on the present application, the reservoir development plan was adjusted, and two infill wells, PG and PT, were deployed, producing 16,000 barrels of crude oil per day, which can increase the recoverable reserves by 12 million barrels of crude oil, generating good economic benefits.

[0042] The method of the present invention has great application value in analyzing the connectivity of turbidite reservoir sand bodies, adjusting and optimizing turbidite reservoir development plans, and improving reservoir recovery rates, and has good promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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.

[0044] Figure 1 A flowchart of a mudstone interlayer prediction method provided in an embodiment of the present application;

[0045] Figure 2A classic sedimentary model diagram of the Tertiary turbidity current system in deep water of West Africa provided in the embodiments of the present application;

[0046] Figure 3 A comparison chart of the effects before and after high-resolution frequency boosting processing provided in the embodiment of the present application;

[0047] Figure 4 A cross-sectional diagram of shear wave impedance, density, shear modulus and Poisson's ratio of a typical well provided in an embodiment of the present application;

[0048] Figure 5 A statistical analysis chart of the 10 elastic parameters of the reservoir provided in the embodiment of the present application and the mudstone discrimination parameter R;

[0049] Figure 6 A cross plot of the mudstone factor G and the mud content Vsh of the mudstone interlayer provided in the embodiment of the present application;

[0050] Figure 7 A comparative analysis diagram of the measured mud content of the blind well B provided in the embodiment of the present application and the predicted section of the mudstone factor G of the mudstone interlayer passing through the B well;

[0051] Figure 8 A plane prediction distribution diagram of the mudstone factor G of the mudstone interlayer of the deepwater turbidite channel provided in the embodiment of the present application;

[0052] Fig. 9 A structural block diagram of a mudstone interlayer prediction device provided in an embodiment of the present application;

[0053] Fig.10 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] In the existing technology, there are problems such as the difficulty in identifying muddy interlayers in turbidite sandstone reservoirs during the development of deep-water turbidite sandstone reservoirs and the uncertainty of the connectivity of reservoir sand bodies. Because the existing technology cannot improve the oil field recovery rate, the success rate of deep-water oil and gas exploration is low.

[0059] In response to the above problems, the present application provides a mudstone interlayer prediction method, which makes full use of the rich lithology of prestack seismic and the high lateral resolution of seismic reservoirs to identify mudstone interlayers in deepwater turbidite channels, improve the accuracy of turbidite sandstone channel prediction, analyze the connectivity of turbidite sandstone reservoirs, optimize the development plan of turbidite sandstone reservoirs, and achieve the purpose of increasing oil field recovery.

[0060] In order to make the technical content and technical advantages of the present invention clearer, the present invention is further described below in conjunction with the accompanying drawings and an example of a deepwater oil field in West Africa. It should be pointed out that unless otherwise defined, the relevant terms used in the present invention should be technical terms that can be understood by technicians with general skills in the technical field to which the present invention belongs, and are described in detail below in conjunction with the accompanying drawings.

[0061] See also Figure 1 , is a schematic diagram of a flow chart of a mudstone interlayer prediction method provided in an embodiment of the present application. Figure 1 As shown, it mainly includes the following steps.

[0062] S101: Perform high-resolution frequency-enhancing processing on pre-stack angle-based gather data to obtain amplitude-preserving, high-resolution pre-stack angle-based stack data volumes;

[0063] In this embodiment, the reservoir sedimentary type of the Oligocene deepwater turbidite oil reservoir in a deepwater oil field offshore West Africa is the Tertiary deepwater channel turbidite system sedimentation, such as Figure 2 As shown. Turbidite channel sand and lobed sand bodies are widely distributed; vertically, the span of multiple reservoirs is 120-630m, the single-layer sand bodies are relatively thin, generally less than 10m, and thin interlayers are occasionally distributed. This type of oil and gas reservoir has strong heterogeneity, with well-developed internal mudstone interlayers, unclear spatial distribution patterns, and low seismic identification accuracy, which restricts the efficient development of deepwater turbidite channel reservoirs.

[0064] In the embodiments of the present application, post-stack zero-phase deconvolution, post-stack inverse Q compensation, broadband expansion and other technologies are used to perform high-resolution frequency-enhancing processing on pre-stack angle-based gather data to obtain an amplitude-preserving, high-resolution pre-stack angle-based stacking data volume.

[0065] During the specific implementation process, broadband extension technology was selected for high-resolution frequency processing. Broadband extension technology is an existing technology that uses short-time Fourier transform analysis to calculate the average spectrum of data, analyzes the design model, and then outputs the designed zero-phase time-varying operator, and finally obtains amplitude-preserving, high-resolution pre-stack angle gather data. According to the spectrum analysis, the pre-stack data after frequency extension has a 10Hz increase in main frequency compared to the original seismic data, and a 20Hz widening of the high frequency, achieving good application results, such as Figure 3 shown.

[0066] S102: Using the high vertical and horizontal resolution of well logging and seismic, carry out pre-stack seismic elastic inversion constrained by well logging to obtain various high-resolution elastic parameter data volumes of the reservoir;

[0067] In the specific implementation process of this application, the advantages of high vertical and horizontal resolution of well logging and seismic are fully utilized to carry out pre-stack seismic elastic parameter inversion constrained by well logging, and various high-resolution elastic parameter data bodies are obtained, mainly including shear modulus, Young's modulus, shear wave velocity, shear wave impedance, Poisson's ratio, longitudinal wave velocity, longitudinal and transverse wave velocity ratio, longitudinal wave impedance, density, Lame constant, etc., a total of 10 elastic parameter bodies, such as Figure 4 shown.

[0068] S103: Based on the correlation coefficient between the reservoir elasticity parameters of the turbidite sandstone reservoir and the lithology discrimination parameters and the reservoir elasticity parameters, the reservoir independent and sensitive reservoir elasticity parameters for mudstone interlayer prediction are determined;

[0069] According to the mud content measured in a single well and calculated by logging, the elastic parameter is calculated for the mudstone discrimination parameter R and the correlation coefficient X between the elastic parameters. The elastic parameters that are more sensitive to mudstone interlayers are selected by combining the two.

[0070] Formula (1) is used to obtain the reservoir elastic parameter to lithology discrimination parameter R:

[0071] R=|xy| / (x+y) (1)

[0072] Among them, R is the reservoir elastic parameter for mudstone differentiation parameter, x and y represent the elastic parameters of sand and mudstone respectively. The comparative analysis of 10 elastic parameters for sandstone differentiation parameter R shows that the elastic parameters that are more sensitive to mudstone interlayers are: shear modulus, Young's modulus, shear wave velocity, shear wave impedance, Poisson's ratio, longitudinal wave velocity, longitudinal and shear wave velocity ratio, longitudinal wave impedance, density, and Lame constant. Figure 5As shown in the figure, the statistical analysis chart of 10 elastic parameters of the reservoir and the mudstone discrimination parameter R.

[0073] The correlation coefficient between the reservoir elastic parameters is obtained using formula (2):

[0074]

[0075] Where X is the correlation coefficient between reservoir elastic parameters; x i ,y i represent the elastic parameters of sand and mudstone, respectively; represent the average values ​​of elastic parameters of sand and mudstone, respectively; n is the number of samples;

[0076] The analysis shows that the 10 elastic parameters are partially correlated with each other. For example, Young's modulus has a high correlation with shear wave impedance and shear modulus, with correlation coefficients X reaching 0.98 and 0.97 respectively; Poisson's ratio has a high correlation with the longitudinal and shear wave velocity ratio and Lame constant, with correlation coefficients X reaching 0.99 and 0.76 respectively, as shown in Table 5.

[0077] Table 5 Statistical analysis of correlation coefficients of 10 elastic parameters of reservoir

[0078]

[0079] The optimization principle for determining sensitive elastic parameters is mainly that the elastic parameter discrimination R for mud is greater than 0.1, and the correlation coefficient X between elastic parameters is less than 0.4. Based on this, it is determined that there are two sensitive elastic parameters of mudstone interlayers, namely shear modulus and Poisson's ratio.

[0080] S104: using the selected reservoir elastic parameters as independent variables, constructing a mudstone factor identification function for mudstone interlayers, and establishing a quantitative calculation model for the mudstone factor identification function for mudstone interlayers;

[0081] The quantitative calculation model of mudstone factor identification function of mudstone interlayer is shown in formula (3):

[0082]

[0083] Among them, G is the mud factor identification function of mudstone interlayer, S i is an independent and sensitive elastic parameter of the reservoir, k i is the unknown coefficient, k 0 is a constant, and n is the number of reservoir sensitive elastic parameters adopted.

[0084] In the implementation process, the shear modulus and Poisson's ratio, two sensitive elastic parameters of the reservoir, are used to conduct correlation cross-analysis with the mud content measured in a single well or calculated by logging, and a quantitative calculation model of the mudstone factor identification function G of the mudstone interlayer is established, as shown in formula (4):

[0085] G=3.16×Pr-8.6E-11×Mu (4)

[0086] Where G is the mudstone factor identification function of the defined mudstone interlayer, Pr is Poisson's ratio, and Mu is the shear modulus.

[0087] The intersection of the statistical mudstone interlayer identification factor function G and the actual well mud content Vsh shows that the two have a good correlation, with a correlation coefficient of 0.87. Therefore, the mudstone interlayer mudstone factor identification function G can be used to characterize the distribution characteristics of mudstone interlayers, such as Figure 6 As shown, the cross-plot of mudstone factor G and mud content Vsh of mudstone interlayer.

[0088] S105: Based on the established mudstone factor identification function model and the selected reservoir elastic parameter inversion data as variable parameters, a mudstone factor data body of the turbidite sandstone reservoir is obtained, and the data body is normalized to establish a division standard of mudstone identification factors of mudstone interlayers, and complete mudstone interlayer prediction.

[0089] Based on the comparative analysis of actual drilling and prediction results, the identification and division criteria of mudstone interlayers are established as follows: when the G value is > 0.80, the mud content is the highest, which is a large set of mudstone interlayers; when the G value is between 0.80 and 0.50, the mud content is medium and the mudstone interlayers are most developed; when the G value is between 0 and 0.50, the mud content is low and sandstone is developed, thus achieving quantitative prediction of mudstone interlayers.

[0090] In the specific implementation process, the shear modulus and Poisson's ratio data obtained by pre-stack inversion are substituted into the quantitative calculation model of the mudstone factor identification function of the mudstone interlayer, and the data body of the mudstone interlayer identification factor G can be obtained. Figure 7 The comparison and analysis of the measured mud content in the blind well B and the predicted mud factor G of the mudstone interlayer passing through the well B show that there is a good consistency between them. Figure 8 This is the plane prediction distribution diagram of the mudstone factor G. Its distribution characteristics are consistent with the mudstone interlayers encountered in the drilled wells, and can reflect the vertical and horizontal distribution rules of the mudstone interlayers. This example shows the effectiveness of this method and has good consistency.

[0091] At the same time, based on the comparative analysis of actual drilling and prediction results, the identification and division criteria of mudstone interlayers were established, namely: when the G value is greater than 0.80, the mud content is the highest, mainly large sets of mudstone; when the G value is between 0.8 and 0.5, the mud content is medium and the mudstone interlayers are most developed; when the G value is between 0 and 0.5, the mud content is low and sandstone is developed. Based on this standard, the quantitative prediction of mudstone interlayers in deepwater turbidite channels can be achieved.

[0092] Corresponding to the above-mentioned embodiments, the present application also provides a mudstone interlayer prediction device.

[0093] See also Fig. 9 , which is a structural block diagram of a mudstone interlayer prediction device provided in this application. Fig. 9 As shown, it mainly includes the following modules.

[0094] The pre-stack angle-based gather data processing module 901 is used to perform high-resolution frequency-enhancing processing on the pre-stack angle-based gather data to obtain amplitude-preserving, high-resolution pre-stack angle-based stack data volumes;

[0095] The high-precision pre-stack elastic parameter inversion module 902 is used to utilize the high vertical and horizontal resolutions of well logging and seismic to carry out pre-stack seismic elastic inversion constrained by well logging, and obtain various high-resolution elastic parameter data bodies of the reservoir;

[0096] Reservoir elastic parameter acquisition module 903: used to determine the reservoir-independent and sensitive reservoir elastic parameters for mudstone interlayer prediction based on the correlation coefficient between the reservoir elastic parameters of the turbidite sandstone reservoir and the lithology discrimination parameters and the reservoir elastic parameters;

[0097] A module 904 for establishing a quantitative calculation model of a mudstone factor identification function is used to construct a mudstone factor identification function of mudstone interlayers and establish a quantitative calculation model of the mudstone factor identification function of mudstone interlayers by taking the selected reservoir elastic parameters as independent variables;

[0098] Mudstone interlayer prediction module 905: is used to obtain the mudstone factor data body of the turbidite sandstone reservoir based on the established mudstone factor identification function model and the selected reservoir elastic parameter inversion data as variable parameters, and normalize the data body to establish the classification standard of the mudstone identification factor of the mudstone interlayer, and complete the mudstone interlayer prediction.

[0099] 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.

[0100] Corresponding to the above embodiment, an embodiment of the present application further provides an electronic device.

[0101] See also Fig.10 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Fig.10As shown, the electronic device 1000 may include: a processor 1001, a memory 1002 and a communication unit 1003. These components communicate via one or more buses. Those skilled in the art may understand that the electronic device structure shown in the figure does not constitute a limitation on 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.

[0102] The communication unit 1003 is used to establish a communication channel so that the electronic device can communicate with other devices.

[0103] The processor 1001 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 1002, 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 1001 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.

[0104] The memory 1002 is used to store the execution instructions of the processor 1001. The memory 1002 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.

[0105] When the execution instructions in the memory 1002 are executed by the processor 1001 , the electronic device 1000 is enabled to execute part or all of the steps in the above method embodiment.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 mudstone interlayers, characterized in that: include: Perform high-resolution frequency-boosting processing on pre-stack angle gather data to obtain amplitude-preserving, high-resolution pre-stack angle stack data volumes; Using the high vertical and horizontal resolution of well logging and seismic data, we conduct pre-stack seismic elastic inversion constrained by well logging to obtain various high-resolution elastic parameter data volumes of the reservoir; Based on the correlation coefficient between the reservoir elasticity parameters of turbidite sandstone reservoirs and the lithology discrimination parameters and reservoir elasticity parameters, the reservoir-independent and sensitive reservoir elasticity parameters for mudstone interlayer prediction are determined. Taking the selected reservoir elastic parameters as independent variables, the mudstone factor identification function of mudstone interlayers is constructed, and the quantitative calculation model of the mudstone factor identification function of mudstone interlayers is established; According to the established mudstone factor identification function model and the selected reservoir elastic parameter inversion data as variable parameters, the mudstone factor data body of the turbidite sandstone reservoir is obtained, and the data body is normalized to establish the division standard of mudstone identification factor of mudstone interlayers, and complete the prediction of mudstone interlayers.

2. A mudstone interlayer prediction method according to claim 1, characterized in that: The method of performing high-resolution frequency-enhancing processing on the pre-stack angle-based gather data to obtain an amplitude-preserving, high-resolution pre-stack angle-based stacking data volume includes: The pre-stack angle-based gather data are processed with high resolution and frequency enhancement by adopting post-stack zero-phase deconvolution, post-stack inverse Q compensation and broadband expansion to obtain a pre-stack angle-based stack data volume with amplitude preservation and high resolution.

3. A mudstone interlayer prediction method according to claim 1, characterized in that: The independent and sensitive reservoir elastic parameters of the turbidite sandstone reservoir are determined based on the correlation coefficient between the lithology discrimination parameter and the reservoir elastic parameters, including: Formula (1) is used to obtain the reservoir elastic parameter to lithology discrimination parameter R: R=|xy| / (x+y) (1) Among them, R is the reservoir elastic parameter to mudstone discrimination parameter, x and y represent the elastic parameters of sand and mudstone respectively; The correlation coefficient between the reservoir elastic parameters is obtained using formula (2): Where X is the correlation coefficient between reservoir elastic parameters; x i ,y i represent the elastic parameters of sand and mudstone respectively; represent the average values ​​of elastic parameters of sand and mudstone, respectively; n is the number of samples; Reservoir elastic parameters that are more sensitive to mudstone interlayers are preferred, and the selection principle of sensitive reservoir elastic parameters is mainly that the reservoir elastic parameter to lithology discrimination parameter R>0.1, and the correlation coefficient X between reservoir elastic parameters is<0.

4.

4. A mudstone interlayer prediction method according to claim 1, characterized in that: The method of using the selected reservoir elastic parameters as independent variables to construct a mudstone factor identification function for mudstone interlayers and establish a quantitative calculation model for the mudstone factor identification function for mudstone interlayers includes: The quantitative calculation model of the mudstone factor identification function of the mudstone interlayer is shown in formula (3): Among them, G is the mud factor identification function of mudstone interlayer, S i is an independent and sensitive elastic parameter of the reservoir, k i is the unknown coefficient, k0 is a constant, and n is the number of reservoir sensitive elastic parameters adopted.

5. A mudstone interlayer prediction method according to claim 1, characterized in that: The method uses the established mudstone factor identification function model and the selected reservoir elastic parameter inversion data as variable parameters to obtain a mudstone factor data body of a turbidite sandstone reservoir, normalize the data body, establish a classification standard for mudstone identification factors of mudstone interlayers, and complete mudstone interlayer prediction, including: Based on the comparative analysis of actual drilling and prediction results, the identification and division criteria of mudstone interlayers are established as follows: when the G value is > 0.80, the mud content is the highest, which is a large set of mudstone interlayers; when the G value is between 0.80 and 0.50, the mud content is medium and the mudstone interlayers are most developed; when the G value is between 0 and 0.50, the mud content is low and sandstone is developed, thus achieving quantitative prediction of mudstone interlayers.

6. A mudstone interlayer prediction device, characterized in that: include: Pre-stack angle-based gather data processing module is used to perform high-resolution frequency-enhancing processing on pre-stack angle-based gather data to obtain amplitude-preserving, high-resolution pre-stack angle-based stack data volumes; High-precision pre-stack elastic parameter inversion module, which is used to carry out pre-stack seismic elastic inversion constrained by well logging using the high vertical and horizontal resolution of well logging and seismic, and obtain various high-resolution elastic parameter data bodies of the reservoir; Reservoir elastic parameter acquisition module: used to determine the reservoir-independent and sensitive reservoir elastic parameters for mudstone interlayer prediction based on the correlation coefficient between the reservoir elastic parameters of turbidite sandstone reservoirs and the lithology discrimination parameters and reservoir elastic parameters; A module for establishing a quantitative calculation model of a mudstone factor identification function is used to construct a mudstone factor identification function of mudstone interlayers with selected reservoir elastic parameters as independent variables, and to establish a quantitative calculation model of the mudstone factor identification function of mudstone interlayers; Mudstone interlayer prediction module: used to obtain the mudstone factor data body of the turbidite sandstone reservoir based on the established mudstone factor identification function model and the selected reservoir elastic parameter inversion data as variable parameters, and normalize the data body to establish the division standard of mudstone identification factor of mudstone interlayer, and complete the prediction of mudstone interlayer.

7. 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 5.

8. 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 5.

Images