Turbid lobe configuration characterization method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a turbidite lobe configuration characterization method and device, electronic equipment and a storage medium, and the method mainly comprises the steps: recognizing the boundary of a single lobe through the flat section distribution feature of a deep-sea turbidite lobe, obtaining the deposition form parameters of the single lobe, fitting the quantitative relation of length-thickness and width-thickness, and obtaining the deposition form parameters of the single lobe; obtaining empirical formulas of length / thickness and width / thickness of a single lobe body, and taking the empirical formulas as constraint parameters of lobe body configuration simulation; extracting an optimal attribute body based on a high-resolution shallow earthquake, and realizing a three-dimensional training image of a single leaf body through an earthquake body engraving technology; configuration modeling of a single lobe level is realized through simulation, and verification is carried out through comparison with a real drilling well. The deep water turbidite lobes quantitative characterization method based on multi-point statistics is formed, and the method has an important supporting effect on oil and gas reservoir reserve accurate evaluation, development scheme design guidance, development risk evaluation and efficient management.

Description

Technical Field

[0001] This application belongs to the technical field of oil and gas geological analysis, and specifically relates to a method, device, electronic device and storage medium for quantitatively characterizing the configuration of deep-sea turbidite lobes based on multi-point statistics. Background Art

[0002] Deep-water turbidite lobes are an important part of the deep-water sedimentary system and an important area of current exploration and development focus. The sedimentary environment and process of deep-water turbidite lobes are complex, and the reservoir heterogeneity is strong. Achieving fine dissection and quantitative simulation of the internal structure of lobes plays an important supporting role in the accurate assessment of oil and gas reserves, the guidance of development plan design, the evaluation of development risks, and efficient management. After consulting the relevant literature on the quantitative simulation of deep-water turbidite lobe configuration, the technical content covers two major aspects:

[0003] (1) Quantitative research on deep-sea turbidite lobe configuration models and sediment geometric shapes. (Lin Yu et al., 2014) Taking the lobe reservoir in a deep-water area of the lower continental slope of Niger in West Africa and the outcrops of deep-water lobes in fault basins in the Western Qinling area as prototypes, a 7-level configuration model was established, clarifying the development pattern of compound lobes and the internal filling configuration model of single lobes. It is considered that a single lobe is vertically accreted by multiple plate-like sandstones constrained by parallel and sub-parallel interfaces, and can be further divided into the proximal lobe end, the middle lobe end, and the distal lobe end. The proximal lobe end is mainly composed of massive medium-coarse sandstones, the middle lobe end is mainly composed of massive medium sandstones intercalated with thin layers of silt and mudstone, and the distal lobe end is mainly composed of sand-mud interbeds. (Zhang Jiajia et al., 2019) Studied the quantitative parameters of deep-water slope fan / restricted lobe and basin fan / restricted lobe sediments in West Africa, and considered that there are certain differences in the distribution laws, compound styles, and quantitative scales of submarine fan lobes under different backgrounds. The single lobe of the slope fan / restricted type has a larger thickness and a smaller planar area, while the single lobe of the basin floor fan has a smaller thickness and a larger planar area. (Duan Kairui et al., 2022) Taking a deep-water oilfield in the Niger Delta Basin in West Africa as an example, the division of the configuration units of the single lobe edge and lobe center of the turbidite lobe reservoir was realized, and it was considered that there are sedimentary patterns of edge-edge stacking, body-body stacking, upper body-lower edge stacking, and upper edge-lower body stacking in a single lobe.

[0004] (2) Research on the quantitative characterization of deep-sea turbidite lobe configuration. (Zhao Xiaoming et al., 2020) proposed a method for characterizing the configuration of deep-sea lobe reservoirs based on well-seismic model fitting. It mainly forms identification samples of the configuration levels of deep-sea lobes based on core-log-seismic calibration, and gradually conducts boundary identification and internal stacking pattern characterization of lobe system levels, compound lobe series levels, compound lobe levels, etc. Combining the correction of production dynamics and four-dimensional seismic data, the characterization of the planar flow path, morphology, width, and their mutual stacking and connectivity relationships of the highest level of compound lobes is realized.

[0005] In summary, the current technical status in this field is as follows: As one of the important sedimentary units in the deep-sea sedimentary system, the deep-sea lobe has been studied more for its configuration pattern. Due to the influence of the development location and origin, the development scale of deep-sea turbidite lobes varies greatly. Relevant scholars have conducted certain quantitative research on the geometric morphology of turbidite lobes and established a characterization method for the composite lobe level. However, there is no relevant report on the quantitative characterization simulation of the configuration of a single lobe, and the relevant technical methods still need further research. Summary of the Invention

[0006] In view of this, the present application provides a method, device, electronic device, and storage medium for characterizing the configuration of turbidite lobes, which helps to solve the problem of the lack of a quantitative characterization method for the configuration pattern of a single lobe level in the prior art and provides a basis and support for the quantitative characterization and efficient development of deep-sea turbidite lobe reservoirs.

[0007] In a first aspect, an embodiment of the present application provides a method for characterizing the configuration of turbidite lobes, including:

[0008] Identifying the boundary of a single lobe through the planar and cross-sectional distribution characteristics of deep-sea turbidite lobes, obtaining the sedimentary morphological parameters of the single lobe, fitting the quantitative relationships of "length-thickness" and "width-thickness", obtaining the empirical formulas of the length / thickness and width / thickness of the single lobe, and using the empirical formulas as the constraint parameters for lobe configuration simulation;

[0009] Extracting and optimizing the attribute volume based on high-resolution shallow seismic data, and realizing the three-dimensional training image of a single lobe through seismic volume carving technology;

[0010] Realizing the configuration modeling at the single lobe level through multiple-point statistical simulation and verifying it by comparing with the actual drilled wells.

[0011] In a possible implementation manner, the identifying the boundary of a single lobe through the planar and cross-sectional distribution characteristics of deep-sea turbidite lobes, obtaining the sedimentary morphological parameters of the single lobe, fitting the quantitative relationships of "length-thickness" and "width-thickness", and obtaining the length / thickness and width / thickness of the single lobe includes:

[0012] Selecting a typical block of deep-water turbidite lobe deposition and three-dimensional seismic attributes that can reflect the deposition characteristics of the lobe;

[0013] Extracting through isochronous stratigraphic slices to provide slices for identifying the planar distribution of turbidite lobes;

[0014] Interpret the boundary of a single lobe according to the continuity and reflection intensity of the same-phase axis, pick up samples of the single lobe from the seismic reflection profile, and quantify the sedimentary parameters of the single lobe;

[0015] Select the slice with the largest distribution range of lobes among multiple formation slices, intercept the seismic profile along the longest axis of lobe extension and the widest axis perpendicular to the lobe, and measure the width, length, and thickness of a single lobe;

[0016] By analyzing the distribution ranges of the length, width, and thickness of the lobes and fitting the correlation relationship, obtain the length / thickness and width / thickness of a single lobe.

[0017] In a possible implementation, the preferred attribute volume is extracted based on high-resolution shallow seismic, and a three-dimensional training image of a single lobe is realized through seismic volume carving technology, including:

[0018] Extract a three-dimensional target volume of compound lobes through seismic volume carving technology;

[0019] Extract the attribute volume based on the preferred sensitive attributes of seismic data;

[0020] For each configuration unit, determine the distribution range and upper and lower threshold values of the attribute parameters respectively, and perform preliminary truncation and division on the lobe edge and lobe center with the attribute threshold;

[0021] Based on the quantitative knowledge base of turbidite lobes, further finely characterize the internal stacking characteristics of the target geological body, and establish a quantitative three-dimensional training image of turbidite sandstone sediment bodies with the help of human-computer interaction.

[0022] In a possible implementation, the configuration modeling at the single lobe level is realized through multiple-point statistical simulation and verified by comparing with the actual drilled wells, including:

[0023] Based on the obtained sedimentary morphological parameters and training images of a single lobe, and based on the hard data of well logging interpretation, with the distribution of Miocene lobes in seismic interpretation as the collaborative constraint, use the generated training images to simulate the distribution of lobes in the study area and establish a sedimentary facies model of the Miocene reservoir in the study area;

[0024] Use the established sedimentary facies model for simulation. The simulation process mainly includes data preparation, training image scanning and stable search tree construction, matching parameter setting, obtaining the cumulative conditional probability distribution function of each simulated point data event in the preset order, and sampling to obtain the simulation realization;

[0025] Examine the simulation results to determine whether the simulation results reflect the sedimentation pattern of a single lobe and the degree of combination of the sedimentary facies model and the well.

[0026] In a second aspect, an embodiment of the present application provides a turbidite lobe configuration characterization device, including:

[0027] A sedimentary morphology parameter acquisition device is used to identify the boundary of a single lobe through the planar profile distribution characteristics of a deep-sea turbidite lobe, acquire the sedimentary morphology parameters of the single lobe, fit the quantitative relationships of "length-thickness" and "width-thickness", obtain the empirical formulas of the length / thickness and width / thickness of the single lobe, and use the empirical formulas as the constraint parameters for lobe configuration simulation;

[0028] A three-dimensional training image acquisition device is used to extract an optimized attribute volume based on high-resolution shallow seismic data and realize a three-dimensional training image of a single lobe through seismic volume carving technology;

[0029] A comparison and verification device is used to realize the configuration modeling of a single lobe level through multiple-point statistical simulation and verify it by comparing with actual drilled wells.

[0030] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0031] A processor;

[0032] A memory;

[0033] And a computer program, where the computer program is stored in the memory, and the computer program includes instructions that, when executed by the processor, cause the electronic device to execute the method described in any item of the first aspect.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, the device where the computer-readable storage medium is located executes the method described in any item of the first aspect.

[0035] In the embodiments of the present application, the present application quantitatively characterizes deep-sea turbidite lobes based on multiple-point statistical simulation, quantitatively obtains the sedimentary configuration parameters of a single lobe in a specific area, and obtains a three-dimensional training image based on high-resolution shallow seismic data, realizing multiple-point statistical simulation of a single lobe level of deep-sea turbidite lobes, which has an important supporting role in the accurate assessment of oil and gas reservoir reserves, the guidance of development plan design, the evaluation of development risks, and efficient management. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] 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 those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0037] Figure 1Schematic flow chart of a method for characterizing turbidite lobe configuration provided by an embodiment of the present application;

[0038] Figure 2 Specific quantitative characterization flow chart of a method for characterizing turbidite lobe configuration provided by an embodiment of the present application;

[0039] Figure 3(a) is a schematic diagram showing the planar distribution pattern of the RMS attribute volume of the deep-sea lobe along the layer slice provided by an embodiment of the present application;

[0040] Figure 3(b) is a schematic diagram of the stacking pattern of the deep-sea turbidite lobe provided by an embodiment of the present application;

[0041] Figure 4(a) is a schematic diagram (one) of measuring the sedimentation quantitative parameters of the deep-sea turbidite lobe provided by an embodiment of the present application;

[0042] Figure 4(b) is a schematic diagram (two) of measuring the sedimentation quantitative parameters of the deep-sea turbidite lobe provided by an embodiment of the present application;

[0043] Figure 5(a) is a schematic diagram showing the distribution range of the thickness of a single lobe of the deep-sea lobe sedimentation provided by an embodiment of the present application;

[0044] Figure 5(b) is a schematic diagram showing the distribution range of the width of a single lobe of the deep-sea lobe sedimentation provided by an embodiment of the present application;

[0045] Figure 5(c) is a schematic diagram showing the distribution range of the length and width of a single lobe of the deep-sea lobe sedimentation provided by an embodiment of the present application;

[0046] Figure 5(d) is a schematic diagram showing the correlation between the width / thickness and length / thickness of a single lobe of the deep-sea lobe sedimentation provided by an embodiment of the present application;

[0047] Figure 6(a) is a three-dimensional training image of a single lobe provided by an embodiment of the present application;

[0048] Figure 6(b) is a schematic diagram of the lobe cross-section of the training image along the extension direction of the lobe provided by an embodiment of the present application;

[0049] Figure 6(c) is a cross-section of the training image perpendicular to the extension direction of the lobe provided by an embodiment of the present application;

[0050] Figure 7(a) is a schematic diagram of the simulation model with K = 1 of the deep-sea lobe provided by an embodiment of the present application;

[0051] Figure 7(b) is a schematic diagram of the simulation model with K = 16 of the deep-sea lobe provided by an embodiment of the present application;

[0052] Figure 7(c) is a schematic diagram of the simulation model with K = 37 of the deep-sea lobe provided by an embodiment of the present application;

[0053] Figure 8The comparison diagram between the simulation result of the single configuration of the deep-sea lobe body provided by the embodiment of the present application and the actual well

[0054] Figure 9 The structural block diagram of a turbidite lobe body configuration characterization device provided by the embodiment of the present application

[0055] Figure 10 The structural schematic diagram of an electronic device provided by the embodiment of the present application Specific embodiments

[0056] In order to better understand the technical solution of the present application, the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0057] It should be clear that the described embodiments are only a part of the embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work belong to 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 of "a", "the" and "said" used in the embodiments of the present application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0059] It should be understood that the term " / and / " used herein is only a description of the associated relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the front and back associated objects.

[0060] At present, as one of the important sedimentary units in the deep-sea sedimentary system, the deep-sea lobe body has been studied more for the configuration mode. Due to the influence of the development position and origin, the development scale of the deep-sea turbidite lobe body varies greatly, but there is a lack of a quantitative characterization method for the configuration mode at the single lobe level in the prior art.

[0061] In view of the above problems, the present application provides a turbidite lobe body configuration characterization method, which quantitatively characterizes the deep-sea turbidite lobe body based on multiple-point statistical simulation. By quantitatively obtaining the sediment configuration parameters of a single lobe body in a specific area and obtaining a three-dimensional training image based on high-resolution shallow seismic, the multiple-point statistical simulation of the single lobe level of the deep-sea turbidite lobe body is realized, which has an important supporting role for the accurate evaluation of oil and gas reservoir reserves, the guidance of development plan design, the evaluation of development risks and efficient management.

[0062] To make the technical content and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with the accompanying drawings and embodiments with respect to a deep-sea turbidite lobe in a certain block of the Lower Congo Basin in West Africa. It should be noted that: Unless otherwise defined, the relevant terms used in the present invention should be technical terms that can be understood by those with ordinary skills in the technical field to which the present invention belongs. The following will be described in detail with reference to the accompanying drawings.

[0063] See Figure 1 , which is a schematic flow chart of a method for characterizing turbidite lobe configuration provided by an embodiment of the present application. Figure 2 This is a specific quantitative characterization flow chart of a method for characterizing turbidite lobe configuration provided by an embodiment of the present application. As Figure 1 shown, it mainly includes the following steps:

[0064] Step S101: Identify the boundary of a single lobe through the planar and sectional distribution characteristics of the deep-sea turbidite lobe, obtain the sedimentary morphological parameters of the single lobe, fit the quantitative relationships of "length - thickness" and "width - thickness", obtain the empirical formulas of the length / thickness and width / thickness of the single lobe, and use the empirical formulas as the constraint parameters for lobe configuration simulation.

[0065] There are already relatively mature configuration schemes for deep-water turbidite lobes. In this embodiment, a typical block of deep-water turbidite lobe deposition is selected for the configuration. Preferably, three-dimensional seismic attributes that can clearly reflect the sedimentary characteristics of the lobe are selected, such as the root mean square amplitude attribute (RMS). Based on the Petrel software platform, isochronous stratigraphic slices are extracted to provide slices for identifying the planar distribution of turbidite lobes, as shown in Figure 3(a). Figure 3(b) is a schematic diagram of the stacking pattern of deep-sea turbidite lobes provided by an embodiment of the present application. A composite lobe is often formed by the stacking and extension of multiple single lobes. The lobes are parallel reflections in the section, and the medium to strong amplitude reflects the layered stacking characteristics of a single lobe. The boundary of a single lobe can be interpreted according to the continuity of the event axis and the reflection intensity. As Figure 4(a) 、 4(b) shown, samples of a single lobe can be clearly picked up according to the seismic reflection profile, thereby realizing the quantitative research on the sedimentary parameters of a single lobe.

[0066] The deposition parameters of lobes are the most basic simulation parameters in the development of geological research and 3D geological modeling. The reasonable application of sediment quantitative parameters can improve the reliability of predicting the configuration distribution of underground lobe reservoirs. The boundary reflection characteristics of lobes are relatively clear, and the reasonable application of quantitative scale relationships can predict the configuration distribution of underground lobe reservoirs. The commonly used geometric parameters mainly include lobe width, lobe length, lobe thickness, width-depth ratio, etc. Select the slice with the largest distribution range of lobes in multiple stratigraphic slices, and intercept the seismic profile along the longest axis of lobe extension and the widest axis perpendicular to the lobe direction to measure the width, length, and thickness of a single lobe. By analyzing the distribution ranges of lobe length, width, and thickness and fitting the correlation relationships, empirical formulas for single lobe length / thickness and width / thickness are obtained.

[0067] In a specific embodiment, it is effectively applied to a deep-sea turbidite lobe reservoir in a certain block of the Lower Congo Basin in West Africa. For the typical deep-sea lobes developed in the shallow layer of the Lower Congo Basin in West Africa, the seismic data has a relatively high resolution (main frequency 50 - 70 HZ), and the morphological characteristics of deep-sea lobes can be clearly identified.

[0068] Based on the Petrel software platform, along the layer profile of the RMS attribute body, select the seismic profile perpendicular to the widest point in the extension direction of the deep-sea turbidite lobe and the longest point along the extension direction. As can be seen from Figures 4(a) and 4(b), there are intensity changes within the parallel reflections, marking the boundaries of single lobes. A total of 16 single lobe sample points are selected, and the sample length, width, and thickness are obtained for comprehensive analysis, as shown in Table 1.

[0069] Table 1 Statistical table of quantitative parameters of single Miocene lobes

[0070]

[0071]

[0072] After analysis, the extension length of a single lobe ranges from 800 m to 1800 m, with an average of 1250 m. Among them, 75% of the lobe lengths are greater than 1000 m, and 50% of the lobe lengths are between 1000 m and 1500 m. The width of a single lobe generally ranges from 300 m to 800 m, with an average of 520 m. Among them, 50% of the lobe widths are between 400 m and 600 m; the thickness of a single lobe ranges from 35 m to 50 m, with an average of 30 m; the length-width ratio of the lobe is between 1.8 and 3.2, and 70% of the lobe length-width ratios are between 2.2 and 2.8; considering that the extension range of the lobe is directly related to the hydrodynamic intensity, such as Figure 5(a) 、 5(b), 5(c), combined with 5(d), the thickness of the lobe has a good linear positive correlation with the extended length and width, respectively. Based on this, the empirical formula of the lobe thickness and length and width is obtained as follows: Length = 3.39*Th ickness-259.15, Width = 15.875*Th ickness-109, with correlation coefficients of 0.84 and 0.82, respectively. The correlation is relatively high, indicating that the empirical formula can be used as a constraint parameter for lobe configuration simulation.

[0073] Step S102: extracting a preferred attribute volume based on high-resolution shallow seismic data, and realizing a single leaf body three-dimensional training image by using seismic volume carving technology;

[0074] In this embodiment, the composite lobe three-dimensional target body is extracted by geophysical carving technology. Geophysical carving technology is a commonly used existing technology in this technical field. Based on the seismic data, the sensitive attributes are selected to extract the attribute body. The attribute parameter distribution range and upper and lower thresholds are determined for each configuration unit, and the lobe edge and lobe center are preliminarily truncated and divided by the attribute threshold. Based on the requirements of the training image for stability, the turbidite lobe quantitative knowledge base further refines the internal superposition characteristics of the target geological body and establishes a quantitative three-dimensional training image of the turbidite sandstone sediment that can reflect the actual situation, as shown in Figure 6 (a), Figure 6 (b), and Figure 6 (c), where the acquired training image must meet the requirements of reflecting the geological characteristics of the sedimentary body and having second-order stability.

[0075] In a specific embodiment, a composite lobe with a clear reflection structure in the study area is preferred, and the RMS attribute is extracted, and the attribute thresholds of the lobe edge and center are respectively truncated and assigned. Training images are established for the two sedimentary configurations of the center of a single lobe and the edge of the lobe. A fine characterization of the internal superposition features of the lobe is established and realized, as shown in Figures 6(a), 6(b), and 6(c). The obtained training images have high stability while realizing the simulation of the lobe compensatory sedimentation superposition.

[0076] Step S103: A single blade-order configuration model is implemented through multi-point statistical simulation, and verified by comparison with actual drilling.

[0077] On the basis of obtaining the deposition morphology parameters and training images of a single lobe body, based on well logging data, with the seismic lobe body distribution as a collaborative constraint, the lobe body distribution in the study area is simulated by virtue of the generated training images, and a sedimentary facies model of the Miocene reservoir in the study area is established. The simulation process using the sedimentary facies model mainly includes data preparation, training image scanning and stable search tree construction, matching parameter setting, obtaining the cumulative conditional probability distribution function of each simulated point data event in a preset order, and sampling to obtain a simulation realization. The simulation results are tested to verify that the simulation results effectively reflect the deposition pattern of a single lobe body and the composite degree of the model and the wells.

[0078] With the help of the SNESIM multiple-point statistical algorithm of the Petrel software platform, the multiple-point statistical simulation of a single lobe body is carried out. The lobe body thickness of the target layer is calculated according to the well logging curve, and the ranges of the lobe body length (700m - 1084m), width (290m - 450m), and thickness (25m - 35m) are calculated by using empirical formulas as constraint conditions for the simulation of sedimentary configuration units. The lobe body scale of the training image is consistent with the actual lobe body scale parameters of the established model, that is, the scale coefficient is 1.

[0079] The simulation results show that most lobe bodies can exhibit compensatory deposition, such as Figure 7(a) , 7(b) , 7(c), and there are certain degrees of fractures or instabilities locally but they can be repaired. The model prediction compliance rate reaches 87% as shown in Figure 8 , which further illustrates the accuracy and reliability of the quantitative simulation method for deep-sea turbidite lobe body configuration provided by the present invention.

[0080] Corresponding to the above embodiments, the present application also provides a device for characterizing turbidite lobe body configuration.

[0081] Referring to Figure 9 , it is a structural block diagram of a device for characterizing turbidite lobe body configuration provided by the present application. As shown in Figure 9 , it mainly includes the following modules.

[0082] A deposition morphology parameter acquisition device, configured to identify the boundary of a single lobe body through the planar and sectional distribution characteristics of a deep-sea turbidite lobe body, acquire the deposition morphology parameters of the single lobe body, fit the quantitative relationships of "length - thickness" and "width - thickness", obtain empirical formulas for the length / thickness and width / thickness of the single lobe body, and use the empirical formulas as constraint parameters for lobe body configuration simulation;

[0083] A three-dimensional training image acquisition device, configured to extract an optimized attribute body based on high-resolution shallow seismic data and implement a three-dimensional training image of a single lobe body through seismic body carving technology;

[0084] A contrast verification device is used to implement configuration modeling at the single leaf level through multi-point statistical simulation and verify it by comparing with the actual drilled well.

[0085] It should be noted that for the specific content involved in the embodiments of this application, reference can be made to the descriptions of the above method embodiments. For the sake of brevity of expression, it will not be elaborated here.

[0086] Corresponding to the above embodiments, the embodiments of this application also provide an electronic device.

[0087] See Figure 10 , which is a schematic structural diagram of an electronic device provided by the embodiments of this application. As Figure 10 shown, the electronic device 1000 may include: a processor 1001, a memory 1002, and a communication unit 1003. These components communicate through one or more buses. Those skilled in the art can understand that the structural diagram of the electronic device shown in the figure does not constitute a limitation on the embodiments of this application. It can be a bus structure, a star structure, and may also include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0088] Among them, the communication unit 1003 is used to establish a communication channel so that the electronic device can communicate with other devices.

[0089] The processor 1001 is the control center of the electronic device, connecting various parts of the entire electronic device through various interfaces and lines. By running or executing software programs and / or modules stored in the memory 1002, and calling the data stored in the memory, it executes various functions of the electronic device and / or processes data. The processor may be composed of an integrated circuit (IC). For example, it may be composed of a single packaged IC, or may be composed of connecting multiple packaged ICs with the same or different functions. For example, the processor 1001 may only include a central processing unit (CPU). In the embodiments of this application, the CPU may be a single operation core or may include multiple operation cores.

[0090] 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, a magnetic disk, or an optical disc.

[0091] When the execution instructions in the memory 1002 are executed by the processor 1001, the electronic device 1000 is enabled to execute some or all of the steps in the above method embodiments.

[0092] Corresponding to the above embodiments, an embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium may store a program. When the program runs, it can control the device where the computer-readable storage medium is located to execute some or all of the steps in the above method embodiments. In a specific implementation, the computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM for short), a random access memory (RAM for short), or the like.

[0093] Corresponding to the above embodiments, an embodiment of the present application further provides a computer program product. The computer program product includes executable instructions. When the executable instructions are executed on a computer, the computer is enabled to execute some or all of the steps in the above method embodiments.

[0094] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent the situation where A exists alone, A and B exist simultaneously, or B exists alone. Where A and B may be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c may represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c may be single or multiple.

[0095] Those of ordinary skill in the art can realize that the units and algorithm steps described in the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0096] 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 foregoing method embodiments and will not be elaborated herein.

[0097] In several embodiments provided by the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0098] As described above, the above is only the specific implementation manner of the present application. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. The protection scope of the present application shall be subject to the protection scope of the claimed rights.

Claims

1. A method for characterizing the configuration of turbidite lobes, characterized in that Including: Identifying the boundary of a single lobe through the planar and sectional distribution characteristics of deep-sea turbidite lobes, obtaining the sedimentary morphological parameters of a single lobe, fitting the quantitative relationships of "length-thickness" and "width-thickness", obtaining the empirical formulas of length / thickness and width / thickness of a single lobe, and using the empirical formulas as the constraint parameters for lobe configuration simulation; Extracting preferred attribute volumes based on high-resolution shallow seismic data and realizing three-dimensional training images of single lobes through seismic volume carving technology; Realizing the configuration modeling at the single lobe level through multiple-point statistics simulation and verifying it by comparing with actual drilled wells.

2. The turbidity current lobe body configuration characterization method according to claim 1, wherein The method of identifying the boundary of a single lobe through the planar and sectional distribution characteristics of deep-sea turbidite lobes, obtaining the sedimentary morphological parameters of a single lobe, and fitting the quantitative relationships of "length-thickness" and "width-thickness", obtaining the length / thickness and width / thickness of a single lobe, includes: Selecting typical blocks of deep-water turbidite lobe deposits and three-dimensional seismic attributes that can reflect the sedimentary characteristics of lobes; Extracting through isochronous stratigraphic slices to provide slices for identifying the planar distribution of turbidite lobes; Interpretating the boundary of a single lobe according to the continuity of the event axis and the reflection intensity, picking up samples of a single lobe according to the seismic reflection profile, and quantifying the sedimentary parameters of a single lobe; Selecting the slice with the largest distribution range of lobes in multiple stratigraphic slices, intercepting the seismic profile along the longest axis of lobe extension and the widest axis perpendicular to the lobe direction, and measuring the width, length and thickness of a single lobe; By analyzing the distribution range of the length, width and thickness of the lobe and fitting the correlation relationship, obtaining the length / thickness and width / thickness of a single lobe.

3. The turbidity fan lobe configuration characterization method according to claim 1, characterized in that The method of extracting preferred attribute volumes based on high-resolution shallow seismic data and realizing three-dimensional training images of single lobes through seismic volume carving technology, includes: Extracting three-dimensional target volumes of compound lobes through seismic volume carving technology; Extracting attribute volumes by preferentially selecting sensitive attributes based on seismic data; Determining the distribution range and upper and lower threshold values of attribute parameters for each configuration unit respectively, and preliminarily truncating and dividing the lobe edge and lobe center with the attribute threshold; Based on the quantitative knowledge base of turbidite lobes, further finely depicting the internal stacking characteristics of the target geological body, and establishing a quantitative three-dimensional training image of turbidite sandstone deposits with the help of human-computer interaction.

4. The turbidity current lobe configuration characterization method according to claim 1, wherein The method of realizing the configuration modeling at the single lobe level through multiple-point statistics simulation and verifying it by comparing with actual drilled wells, includes: On the basis of having obtained the sedimentary morphological parameters and training images of a single lobe, based on the hard data of well logging interpretation, with the distribution of Miocene lobes in seismic interpretation as the collaborative constraint, using the generated training images to simulate the distribution of lobes in the study area and establishing a sedimentary facies model of the Miocene reservoir in the study area; Using the established sedimentary facies model for simulation, and the simulation process mainly includes data preparation, training image scanning and stable search tree construction, matching parameter setting, obtaining the cumulative conditional probability distribution function of each simulated point data event in the preset order and sampling to obtain simulation realizations; Examining the simulation results to determine whether the simulation results reflect the sedimentary pattern of a single lobe and the composite degree of the sedimentary facies model and the well.

5. A turbidity current lobe configuration characterization device, characterized in that Including: A sedimentary morphology parameter acquisition device is used to identify the boundary of a single lobe through the planar profile distribution characteristics of a deep-sea turbidite lobe, acquire the sedimentary morphology parameters of a single lobe, fit the quantitative relationships of "length - thickness" and "width - thickness", obtain the empirical formulas of the length / thickness and width / thickness of a single lobe, and use the empirical formulas as constraint parameters for lobe configuration simulation; A three-dimensional training image acquisition device is used to extract an optimized attribute volume based on high-resolution shallow seismic data and realize a three-dimensional training image of a single lobe through seismic volume carving technology; A comparison and verification device is used to perform configuration modeling at the single lobe level through multiple-point statistical simulation and verify it by comparing with actual drilled wells.

6. An electronic device, characterized in that, It includes: A processor; A memory; And a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions that, when executed by the processor, cause the electronic device to execute the method described in any one of claims 1 - 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program runs, the device where the computer-readable storage medium is located executes the method described in any one of claims 1 - 4.

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