Low-frequency model construction method and device, equipment and storage medium

Abstract

The invention discloses a low-frequency model construction method, device and equipment and a storage medium, and the method comprises the steps: determining a straight-plate low-frequency model and a seismic attribute body based on original geological data corresponding to a carbonate reservoir; performing post-stack inversion on the straight plate low-frequency model based on the original geological data, and determining a cave low-frequency model; based on the seismic attribute body, determining a fracture low-frequency model and a reservoir boundary low-frequency model; performing fusion processing on the straight plate low-frequency model, the cave low-frequency model, the crack low-frequency model and the reservoir boundary low-frequency model to obtain an original low-frequency model; and performing post-stack inversion on the original low-frequency model based on the original geological data, and determining a target low-frequency model corresponding to the carbonate reservoir. Through the technical scheme of the embodiment of the invention, automatic construction of the carbonate rock earthquake low-frequency model is realized, the carbonate rock earthquake low-frequency model can adapt to complex topographic changes of a carbonate rock reservoir, complex geologic features of the carbonate rock reservoir can be reflected, and comprehensiveness and accuracy of the low-frequency model are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic exploration, and in particular to a low-frequency model construction method, device, equipment and storage medium. BACKGROUND

[0002] Seismic inversion is a main method and means for reservoir lateral prediction in oil exploration and development. The construction of a low-frequency model is a key link of seismic inversion, and the precision of the low-frequency model can obviously affect the seismic inversion result.

[0003] At present, the traditional low-frequency model construction method is mainly based on a low-frequency modeling method of a geological model. However, the traditional seismic low-frequency model construction method cannot adapt to the complex topographic changes of a carbonate reservoir, cannot reflect the complex geological characteristics of the carbonate reservoir, and thus results in low comprehensiveness and accuracy of the low-frequency model. SUMMARY

[0004] The present application provides a low-frequency model construction method, device, equipment and storage medium to realize automatic construction of a carbonate seismic low-frequency model, adapt to the complex topographic changes of a carbonate reservoir, reflect the complex geological characteristics of the carbonate reservoir, and improve the comprehensiveness and accuracy of the low-frequency model.

[0005] In a first aspect, an embodiment of the present application provides a low-frequency model construction method, comprising:

[0006] determining a straight plate low-frequency model and a seismic attribute volume based on original geological data corresponding to a carbonate reservoir;

[0007] performing post-stack inversion on the straight plate low-frequency model based on the original geological data to determine a cave low-frequency model;

[0008] determining a fracture low-frequency model and a reservoir boundary low-frequency model based on the seismic attribute volume;

[0009] performing fusion processing on the straight plate low-frequency model, the cave low-frequency model, the fracture low-frequency model and the reservoir boundary low-frequency model to obtain an original low-frequency model;

[0010] performing post-stack inversion on the original low-frequency model based on the original geological data to determine a target low-frequency model corresponding to the carbonate reservoir.

[0011] In a second aspect, an embodiment of the present application further provides a low-frequency model construction device, comprising:

[0012] a first model determination module configured to determine a straight plate low-frequency model and a seismic attribute volume based on original geological data;

[0013] a second model determining module configured to determine a cave low-frequency model by post-stack inversion on the original geological data and the straight plate low-frequency model;

[0014] a third model determining module configured to determine a fracture low-frequency model and a reservoir boundary low-frequency model based on the seismic attribute volume;

[0015] a model fusion module configured to fuse the straight plate low-frequency model, the cave low-frequency model, the fracture low-frequency model and the reservoir boundary low-frequency model to obtain an original low-frequency model;

[0016] a target model determining module configured to determine a target low-frequency model corresponding to the carbonate reservoir by post-stack inversion on the original low-frequency model and the original geological data.

[0017] In a third aspect, an electronic device is provided, and the electronic device includes at least one processor, and

[0018] a memory in communication with the at least one processor; and

[0019] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the low-frequency model construction method provided in any of the embodiments of the present application.

[0020] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the low-frequency model construction method provided in any of the embodiments of the present application when the processor executes the computer instructions.

[0021] The technical scheme of the embodiment of the present application can ensure the reliability and accuracy of the model by determining the straight plate low-frequency model and the seismic attribute volume based on the original geological data corresponding to the carbonate reservoir. The distribution and morphological characteristics of the cave in the carbonate reservoir can be more accurately reflected by determining the cave low-frequency model based on the post-stack inversion of the straight plate low-frequency model based on the original geological data. The boundary position and morphology of the reservoir can be more clearly reflected by determining the fracture low-frequency model and the reservoir boundary low-frequency model based on the seismic attribute volume. The original low-frequency model is obtained by fusing the straight plate low-frequency model, the cave low-frequency model, the fracture low-frequency model and the reservoir boundary low-frequency model, and the original low-frequency model after the fusion processing contains various low-frequency information such as straight plate, cave, fracture and reservoir boundary, and can more comprehensively reflect the complex geological characteristics of the carbonate reservoir. The accuracy and reliability of the target low-frequency model can be further improved by determining the target low-frequency model corresponding to the carbonate reservoir based on the post-stack inversion of the original low-frequency model based on the original geological data. The automatic construction of the carbonate rock seismic low-frequency model is realized through the straight plate low-frequency model, the cave low-frequency model, the fracture low-frequency model and the reservoir boundary low-frequency model, which can adapt to the complex topographic changes of the carbonate reservoir, can comprehensively reflect the complex geological characteristics of the carbonate reservoir, and can improve the comprehensiveness and accuracy of the low-frequency model.

[0022] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0024] Figure 1 is a flow chart of a low-frequency model construction method according to the first embodiment of the present application;

[0025] Figure 2 is a flow chart of a carbonate rock seismic low-frequency model construction process according to the first embodiment of the present application;

[0026] Figure 3 is a flow chart of a low-frequency model construction method according to the second embodiment of the present application;

[0027] Figure 4 is a structural schematic diagram of a low-frequency model construction device according to the third embodiment of the present application;

[0028] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the low-frequency model construction method of this invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "target," "current," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Example 1

[0032] Figure 1 This is a flowchart illustrating a low-frequency model construction method provided in Embodiment 1 of the present invention. This embodiment is applicable to the construction of low-frequency seismic models for carbonate rocks. Figure 1 As shown, this method can be executed by a low-frequency model building device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method specifically includes the following steps:

[0033] S110. Based on the original geological data corresponding to the carbonate reservoir, determine the straight plate low-frequency model and seismic attribute volume.

[0034] Carbonate reservoirs refer to oil and gas reservoirs primarily composed of carbonate rocks. Carbonate rocks are a class of sedimentary rocks mainly composed of carbonate minerals such as calcite (calcium carbonate) and dolomite, including limestone, dolomite, and their transitional types. Raw geological data refers to fundamental data used to describe and interpret subsurface geological features, rock physical properties, reservoir characteristics, and oil and gas distribution patterns. For example, raw geological data can be raw seismic and well logging data. A straight-plate low-frequency model refers to an initial geological model constructed based on the raw geological data corresponding to carbonate reservoirs. This model mainly reflects the general morphology and geological framework of the reservoir and serves as the basis for subsequent model construction. Seismic attribute volumes refer to a set of various attributes related to subsurface geological structures extracted from seismic data. Seismic attribute volumes can include: ant volumes, discontinuities, structural tensor volumes, and frequency-division seismic volumes. Ant volumes refer to seismic attribute volumes obtained based on ant colony tracking algorithms. Discontinuities refer to attribute volumes within seismic attribute volumes that reflect the discontinuity of geological structures. A structural tensor volume can refer to a seismic attribute volume calculated based on structural tensor theory. A frequency-division seismic volume can refer to a seismic attribute volume obtained based on frequency-division interpretation techniques.

[0035] Specifically, such as Figure 2 As shown, raw geological data corresponding to carbonate reservoirs were collected, including but not limited to well logging data, core analysis data, and seismic interpretation data. Based on these data, a straight-plate low-frequency model was constructed using geostatistical methods or mathematical interpolation techniques. The straight-plate low-frequency model mainly reflects the general morphology and geological framework of the reservoir. Attribute analysis was performed on the raw geological data to extract seismic attributes related to fractures, reservoir boundaries, etc., forming seismic attribute volumes such as ant bodies, discontinuities, structural tensor volumes, and frequency-division seismic volumes. These provide basic data and framework for subsequent model construction, reflecting subtle changes in subsurface geological structures and providing important basis for the subsequent low-frequency model construction.

[0036] For example, "determine the straight plate low-frequency model based on the original geological data corresponding to the carbonate reservoir" in S110 includes: determining seismic horizon interpretation information based on the original seismic data corresponding to the carbonate reservoir; and constructing the straight plate low-frequency model based on the seismic horizon interpretation information.

[0037] Seismic stratigraphic interpretation information refers to a series of information obtained during the process of identifying and tracing underground geological strata through seismic data. This includes seismic reflection characteristics, the correspondence between seismic profiles and geological profiles, the determination of reflection standard layers, principles and methods for reflection wave comparison, and the identification and processing of complex geological phenomena. For example, seismic stratigraphic interpretation information can include seismic stratigraphic interpretation and well logging stratigraphic calibration.

[0038] Specifically, based on the original seismic data corresponding to the carbonate reservoir, the stratigraphic interfaces (layers) are identified through manual or automatic tracking methods, and their positions and morphologies in three-dimensional space are determined. The stratigraphic interfaces in the well logging data are then calibrated with the seismic layer interpretation results to ensure their vertical consistency, thereby obtaining seismic layer interpretation information. Based on the seismic layer interpretation information, a preliminary straight-plate model framework is constructed. Low-frequency information extracted from the well logging and seismic data is used to fill the straight-plate model framework, thus constructing a straight-plate low-frequency model.

[0039] S120. Based on the original geological data, the low-frequency model of the straight plate is inverted after stacking to determine the low-frequency model of the cave.

[0040] Among them, the cave low-frequency model can refer to the low-frequency geological model constructed after geological modeling and seismic interpretation techniques have focused on and identified the characteristics of cave-type reservoirs.

[0041] Specifically, post-stack inversion technology is used to combine raw geological data (such as well logging data) with seismic data to correct and optimize the straight-plate low-frequency model. During the inversion process, special attention is paid to cave response characteristics in the seismic data, such as amplitude anomalies and frequency variations, to identify cave distribution areas. Based on the identified cave characteristics, the straight-plate low-frequency model is locally adjusted to construct a cave-type low-frequency model, thereby improving the low-frequency model's ability to characterize cave-type reservoirs and more accurately reflecting the heterogeneity within the reservoir.

[0042] For example, S120 may include: inverting the straight plate low-frequency model based on the post-stack seismic data in the original geological data to obtain the residual impedance volume; extracting features from the original geological data to obtain the target cave features; and constructing a cave low-frequency model based on the residual impedance volume and the target cave features.

[0043] Residual impedance volumes refer to impedance anomalies extracted from seismic data after a series of seismic interpretations and reservoir predictions, which differ significantly from known geological models or background impedance. These anomalies may represent information such as changes in the physical properties of subsurface rock strata, fluid distribution, or reservoir characteristics. Target cavern characteristics refer to the spatial distribution and morphological features of cavernous reservoirs.

[0044] Specifically, such as Figure 2As shown, necessary preprocessing is performed on the post-stack seismic data in the original geological data, including denoising, improving the signal-to-noise ratio, and spectral analysis, to ensure that the data quality meets the inversion requirements. The preprocessed post-stack seismic data is then used to invert the straight-plate low-frequency model. During the inversion process, model parameters (such as wave velocity and density) are continuously adjusted to match the simulated seismic response with the actual seismic data. After multiple iterative calculations, a stratigraphic model that best matches the seismic data is obtained, and the residual impedance volume is extracted. The residual impedance volume reflects the difference between the actual geological structure and the initial model, i.e., the part of the seismic data not fully explained by the initial model. Features related to caves, such as beaded reflections and random reflections, are identified in the seismic data. These features are usually closely related to the existence of cave-type reservoirs. Based on the residual impedance volume and the cave features in the seismic data, a low-frequency cave model is constructed to characterize the spatial distribution and morphological features of cave-type reservoirs, accurately reflecting the heterogeneity within the reservoir. It should be noted that during the construction process, geostatistical methods (such as stochastic simulation) can be used to create a three-dimensional model of the cave to more intuitively demonstrate the complexity and heterogeneity of cave-type reservoirs.

[0045] S130. Based on the seismic attribute volume, determine the low-frequency model of fractures and the low-frequency model of reservoir boundaries.

[0046] Among them, the low-frequency fracture model refers to a model specifically designed to characterize the low-frequency characteristics of subsurface fracture systems. Fractures are important fluid migration channels and storage spaces in oil and gas reservoirs; therefore, the low-frequency fracture model is of great significance for the exploration and development of fractured oil and gas reservoirs. The low-frequency reservoir boundary model refers to a model describing the low-frequency characteristics of the boundary between the reservoir and the surrounding rock. It is mainly used to characterize the morphology, thickness, and boundary location of the reservoir in three-dimensional space.

[0047] Specifically, such as Figure 2 As shown, based on the seismic attribute volume, fractures are predicted using attributes such as coherence and curvature, combined with advanced technologies such as deep learning. Based on the fracture prediction results, a low-frequency fracture model is constructed to reflect the distribution and density of fractures in the reservoir. Similarly, reservoir boundaries are identified using attributes such as amplitude and frequency from the seismic attribute volume. Based on the reservoir boundary identification results, a low-frequency reservoir boundary model is constructed to clarify the spatial extent and morphology of the reservoir, improving the accuracy of the low-frequency model in representing fractures and reservoir boundaries.

[0048] S140. The low-frequency models of straight plates, caves, fractures, and reservoir boundaries are fused to obtain the original low-frequency model.

[0049] Among them, the original low-frequency model can refer to a geological model that reflects the low-frequency characteristics of underground geological bodies, based on seismic data and other geological data (such as well logging data, drilling data, etc.).

[0050] Specifically, by employing appropriate fusion algorithms (such as weighted average, multi-attribute fusion, etc.), the low-frequency models of straight plates, caves, fractures, and reservoir boundaries are fused to improve the overall model's representational ability and enable the low-frequency models to more comprehensively reflect the complex geological characteristics of carbonate reservoirs.

[0051] For example, S140 may include: embedding the cave low-frequency model, fracture low-frequency model and reservoir boundary low-frequency model into the straight plate low-frequency model based on a preset fusion method to obtain the original low-frequency model.

[0052] Specifically, based on the low-frequency models of caves, fractures, and reservoir boundaries, the corresponding low-frequency features (such as caves, fractures, and reservoir boundaries) are obtained. These low-frequency features are then used as constraints or additional information and added to the straight plate low-frequency model to obtain the original low-frequency model. This allows the low-frequency model to more comprehensively reflect the complex geological characteristics of carbonate reservoirs.

[0053] S150. Based on the original geological data, perform post-stack inversion on the original low-frequency model to determine the target low-frequency model corresponding to the carbonate reservoir.

[0054] The target low-frequency model can refer to a geological model that accurately reflects the low-frequency characteristics of underground geological bodies, which is ultimately constructed through a series of data processing, fusion and interpretation processes.

[0055] Specifically, by using post-stack inversion technology and combining it with the original geological data, the fused original low-frequency model is further corrected and optimized to obtain the target low-frequency model corresponding to the carbonate reservoir, thereby obtaining a more accurate and reliable target low-frequency model corresponding to the carbonate reservoir.

[0056] For example, S150 may include: inverting the original low-frequency model based on the post-stack seismic data in the original geological data, and iteratively updating the original low-frequency model according to the inversion results to obtain the target low-frequency model corresponding to the carbonate reservoir.

[0057] Specifically, based on the post-stack seismic data in the original geological data, a suitable post-stack inversion method is selected. Commonly used post-stack inversion methods include recursive inversion, sparse pulse inversion, and geostatistical inversion. The initial low-frequency model is inverted using the post-stack seismic data. During the inversion process, model parameters are iteratively adjusted to achieve the best match between the model's predicted seismic response and the actual seismic data. Well logging data can be used to calibrate and quality control the inversion results to ensure their accuracy. The inversion results are analyzed in detail, paying particular attention to whether the low-frequency characteristics of the carbonate reservoir are accurately reflected. Based on the inversion and analysis results, the original low-frequency model is iteratively updated. Model parameters, such as velocity and density, are adjusted to better reflect the true condition of the subsurface strata. The inversion and iterative update process is repeated until satisfactory inversion results and a satisfactory low-frequency model are obtained, improving the accuracy of the low-frequency model.

[0058] The technical solution of this invention, by determining the low-frequency model of the straight plate and the seismic attribute volume based on the original geological data corresponding to the carbonate reservoir, can ensure the reliability and accuracy of the model. Post-stack inversion of the low-frequency model of the straight plate based on the original geological data determines the low-frequency model of the cave, which can more accurately reflect the distribution and morphological characteristics of caves in the carbonate reservoir. Determining the low-frequency models of fractures and reservoir boundaries based on the seismic attribute volume can more clearly reflect the boundary location and morphology of the reservoir. Fusing the low-frequency models of the straight plate, cave, fracture, and reservoir boundaries yields the original low-frequency model. This fused original low-frequency model contains multiple low-frequency information such as straight plates, caves, fractures, and reservoir boundaries, and can more comprehensively reflect the complex geological characteristics of the carbonate reservoir. Post-stack inversion of the original low-frequency model based on the original geological data determines the target low-frequency model corresponding to the carbonate reservoir, which can further improve the accuracy and reliability of the target low-frequency model. By using low-frequency models of straight sections, caves, fractures, and reservoir boundaries, the automatic construction of low-frequency seismic models for carbonate rocks is achieved. This model can adapt to the complex topographical changes in carbonate reservoirs, comprehensively reflect the complex geological characteristics of carbonate reservoirs, and improve the comprehensiveness and accuracy of low-frequency models.

[0059] Example 2

[0060] Figure 3 This is a flowchart of a low-frequency model construction method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment optimizes the step "determining the low-frequency fracture model and the low-frequency reservoir boundary model based on the seismic attribute volume". Explanations of terms that are the same as or corresponding to those in the above embodiments are not repeated here.

[0061] See Figure 3The alternative low-frequency model construction method provided in this embodiment specifically includes the following steps:

[0062] S210. Based on the original geological data corresponding to the carbonate reservoir, determine the straight plate low-frequency model and seismic attribute volume.

[0063] S220. Based on the original geological data, the low-frequency model of the straight plate is inverted after stacking to determine the low-frequency model of the cave.

[0064] S230. Perform attribute analysis based on the seismic attribute volume to obtain the well-side attribute curve and well curve, and construct a deep learning network based on the well-side attribute curve and well curve.

[0065] Among these, well-side trace attribute curves refer to curves obtained from seismic data volumes after performing attribute analysis on traces near the well location (i.e., well-side traces). These curves reflect specific attribute changes of seismic waves on the well-side traces, such as amplitude, frequency, and phase, which are closely related to the physical characteristics of the underground geological body. Well curves can refer to curves showing changes in depth or time in well logging data. Deep learning networks can refer to a machine learning model that simulates the learning process of the human brain by constructing multi-layered neural network structures.

[0066] Specifically, on the seismic attribute volume, attribute analysis is performed on the traces near the well location (well-side traces) to obtain well-side trace attribute curves. Simultaneously, well curves, i.e., the variation curves of well logging data in depth or time, are obtained by combining the well-side trace attribute curves and the well curves. Preprocessing, such as denoising and normalization, is performed on the well-side trace attribute curves and the well curves to improve data quality. The preprocessed data is then used to train a deep learning network. Through continuous iterative optimization of network parameters, the network can accurately predict fractures and reservoir boundaries without manual intervention, thus improving the automation of the analysis.

[0067] S240. Based on deep learning networks, perform fracture prediction and reservoir boundary prediction to obtain predicted fracture attributes and predicted reservoir boundary attributes.

[0068] Predicted fracture properties refer to a series of seismic attributes related to fracture development, which can directly or indirectly indicate the presence, distribution, direction, density, and other characteristics of fractures. Predicted reservoir boundary properties refer to seismic attributes that can delineate the location and morphology of reservoir boundaries. These properties help determine the spatial distribution, thickness variations, and contact relationships of reservoirs with other strata.

[0069] Specifically, the seismic attribute volume corresponding to the carbonate reservoir is input into a trained deep learning network. The network outputs prediction results based on the input data, including predicted fracture attributes and predicted reservoir boundary attributes. These attributes are usually represented in the form of images, curves or numerical values, thereby achieving rapid prediction and improving work efficiency.

[0070] S250. Based on the predicted fracture attributes and the predicted reservoir boundary attributes, construct the low-frequency fracture model corresponding to the predicted fracture attributes and the low-frequency reservoir boundary model corresponding to the predicted reservoir boundary attributes.

[0071] Specifically, low-frequency information can be extracted from predicted fracture attributes and predicted reservoir boundary attributes using methods such as filtering and downsampling. Based on the extracted low-frequency information, low-frequency fracture models and low-frequency reservoir boundary models are constructed. These models reflect the low-frequency characteristics of fractures and reservoir boundaries, such as morphology and distribution range, and can reflect the macroscopic characteristics of subsurface geological bodies, which is of great significance for understanding geological structures and predicting oil and gas resource distribution.

[0072] S260. The low-frequency models of straight plates, caves, fractures, and reservoir boundaries are fused to obtain the original low-frequency model.

[0073] S270. Based on the original geological data, perform post-stack inversion on the original low-frequency model to determine the target low-frequency model corresponding to the carbonate reservoir.

[0074] The technical solution of this invention, through attribute analysis based on seismic attribute volumes, obtains wellbore attribute curves and well curves. Based on these curves, a deep learning network is constructed, fully utilizing the information in the seismic attributes. The deep learning network is used for fracture and reservoir boundary prediction, obtaining predicted fracture and reservoir boundary attributes, thus improving the accuracy of fracture and reservoir boundary predictions. Based on these predicted fracture and reservoir boundary attributes, low-frequency fracture models and low-frequency reservoir boundary models are constructed, which more comprehensively reflect the characteristics of the subsurface geological structure and enhance the model's applicability under complex geological conditions. Through the construction and training of the deep learning network, the information in the seismic attributes is fully utilized, improving the accuracy of fracture and reservoir boundary predictions. The constructed low-frequency fracture and reservoir boundary models more comprehensively reflect the characteristics of the subsurface geological structure, enhancing the model's applicability under complex geological conditions.

[0075] Example 3

[0076] Figure 4 This is a schematic diagram of a low-frequency model building device provided in Embodiment 3 of the present invention. Figure 4As shown, the device includes: a first model determination module 310, a second model determination module 320, a third model determination module 330, a model fusion module 340, and a target model determination module 350.

[0077] The first model determination module 310 is used to determine the straight-plate low-frequency model and seismic attribute volume based on the original geological data.

[0078] The second model determination module 320 is used to perform post-stack inversion on the straight plate low-frequency model based on the original geological data to determine the cave low-frequency model.

[0079] The third model determination module 330 is used to determine the low-frequency fracture model and the low-frequency reservoir boundary model based on the seismic attribute volume.

[0080] The model fusion module 340 is used to fuse the straight plate low-frequency model, the cave low-frequency model, the fracture low-frequency model and the reservoir boundary low-frequency model to obtain the original low-frequency model;

[0081] The target model determination module 350 is used to perform post-stack inversion on the original low-frequency model based on the original geological data to determine the target low-frequency model corresponding to the carbonate reservoir.

[0082] The technical solution of this embodiment, by determining the straight plate low-frequency model and seismic attribute volume based on the original geological data corresponding to the carbonate reservoir, can ensure the reliability and accuracy of the model. Post-stack inversion of the straight plate low-frequency model based on the original geological data determines the cave low-frequency model, which can more accurately reflect the distribution and morphological characteristics of caves in the carbonate reservoir. Based on the seismic attribute volume, determining the fracture low-frequency model and reservoir boundary low-frequency model can more clearly reflect the boundary location and morphology of the reservoir. The straight plate low-frequency model, the cave low-frequency model, the fracture low-frequency model, and the reservoir boundary low-frequency model are fused to obtain the original low-frequency model. The fused original low-frequency model contains multiple low-frequency information such as straight plates, caves, fractures, and reservoir boundaries, which can more comprehensively reflect the complex geological characteristics of the carbonate reservoir. Post-stack inversion of the original low-frequency model based on the original geological data determines the target low-frequency model corresponding to the carbonate reservoir, which can further improve the accuracy and reliability of the target low-frequency model. By using low-frequency models of straight sections, caves, fractures, and reservoir boundaries, the automatic construction of low-frequency seismic models for carbonate rocks is achieved. This model can adapt to the complex topographical changes in carbonate reservoirs, comprehensively reflect the complex geological characteristics of carbonate reservoirs, and improve the comprehensiveness and accuracy of low-frequency models.

[0083] Optionally, the first model determination module 310 is specifically used for: determining seismic horizon interpretation information based on the original seismic data corresponding to the carbonate reservoir; and constructing a straight-plate low-frequency model based on the seismic horizon interpretation information.

[0084] Optionally, the seismic attribute volume includes: ant volume, discontinuity volume, structural tensor volume, and frequency-division seismic volume.

[0085] Optionally, the second model determination module 320 is specifically used for: inverting the straight plate low-frequency model based on the post-stack seismic data in the original geological data to obtain the residual impedance volume; extracting features from the original geological data to obtain the target cave features; and constructing a cave low-frequency model based on the residual impedance volume and the target cave features.

[0086] Optionally, the third model determination module 330 is specifically used for: performing attribute analysis based on the seismic attribute volume to obtain well-side attribute curves and well curves, and constructing a deep learning network based on the well-side attribute curves and well curves; performing fracture prediction and reservoir boundary prediction based on the deep learning network to obtain predicted fracture attributes and predicted reservoir boundary attributes; and constructing a low-frequency fracture model corresponding to the predicted fracture attributes and a low-frequency reservoir boundary model corresponding to the predicted reservoir boundary attributes based on the predicted fracture attributes and the predicted reservoir boundary attributes.

[0087] Optionally, the model fusion module 340 is used to embed the cave low-frequency model, the fracture low-frequency model and the reservoir boundary low-frequency model into the straight plate low-frequency model based on a preset fusion method to obtain the original low-frequency model.

[0088] Optionally, the target model determination module 350 is specifically used to: invert the original low-frequency model based on the post-stack seismic data in the original geological data, and iteratively update the original low-frequency model according to the inversion results to obtain the target low-frequency model corresponding to the carbonate reservoir.

[0089] The low-frequency model construction apparatus provided in this embodiment of the invention can execute the low-frequency model construction method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0090] Figure 5A schematic diagram of an electronic device 12 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as desktop computers, workbenches, servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0091] like Figure 5 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0092] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0093] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0094] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 5 Not shown; usually referred to as a "hard drive"). Although Figure 5Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0095] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0096] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with electronic device 12, and / or with any device that enables electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0097] Processing unit 16 executes various functional applications and data processing by running programs stored in system memory 28, such as implementing the steps of a low-frequency model construction method provided in this embodiment, the method including:

[0098] Based on the original geological data corresponding to the carbonate reservoir, the straight plate low-frequency model and seismic attribute volume were determined;

[0099] Based on the original geological data, the low-frequency model of the straight plate was inverted after stacking to determine the low-frequency model of the cave.

[0100] Based on the aforementioned seismic attribute volume, determine the low-frequency model of fractures and the low-frequency model of reservoir boundaries;

[0101] The low-frequency model of the straight plate, the low-frequency model of the cave, the low-frequency model of the fracture, and the low-frequency model of the reservoir boundary are fused to obtain the original low-frequency model;

[0102] Based on the original geological data, the original low-frequency model is inverted after stacking to determine the target low-frequency model corresponding to the carbonate reservoir.

[0103] Of course, those skilled in the art will understand that the processor can also implement the technical solution of the low-frequency model construction method provided in any embodiment of the present invention.

[0104] This embodiment provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the program implements the steps of the low-frequency model construction method provided in any embodiment of the present invention, the method comprising:

[0105] Based on the original geological data corresponding to the carbonate reservoir, the straight plate low-frequency model and seismic attribute volume were determined;

[0106] Based on the original geological data, the low-frequency model of the straight plate was inverted after stacking to determine the low-frequency model of the cave.

[0107] Based on the aforementioned seismic attribute volume, determine the low-frequency model of fractures and the low-frequency model of reservoir boundaries;

[0108] The low-frequency model of the straight plate, the low-frequency model of the cave, the low-frequency model of the fracture, and the low-frequency model of the reservoir boundary are fused to obtain the original low-frequency model;

[0109] Based on the original geological data, the original low-frequency model is inverted after stacking to determine the target low-frequency model corresponding to the carbonate reservoir.

[0110] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0111] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0112] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0113] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0114] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0115] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A low-frequency model construction method, characterized in that, The method comprises: determining a straight plate low-frequency model and a seismic attribute volume based on original geological data corresponding to the carbonate reservoir; determining a cave low-frequency model by post-stack inversion on the straight plate low-frequency model based on the original geological data; determining a fracture low-frequency model and a reservoir boundary low-frequency model based on the seismic attribute volume; fusing the straight plate low-frequency model, the cave low-frequency model, the fracture low-frequency model and the reservoir boundary low-frequency model to obtain an original low-frequency model; determining a target low-frequency model corresponding to the carbonate reservoir by post-stack inversion on the original low-frequency model based on the original geological data.

2. The method of claim 1, wherein, The method comprises: determining a straight plate low-frequency model based on original geological data corresponding to the carbonate reservoir, comprising: determining seismic horizon interpretation information based on original seismic data corresponding to the carbonate reservoir; 3. The method of claim 1, wherein, constructing a straight plate low-frequency model based on the seismic horizon interpretation information.

4. The method of claim 1, wherein, The seismic attribute volume comprises: an ant body, a discontinuous body, a structural tensor body and a frequency division seismic body. The method comprises: determining a cave low-frequency model by post-stack inversion on the straight plate low-frequency model based on the original geological data, comprising: obtaining a residual wave impedance volume by inverting the straight plate low-frequency model based on post-stack seismic data in the original geological data; 5. The method of claim 1, wherein, extracting features from the original geological data to obtain target cave features; constructing a cave low-frequency model based on the residual wave impedance volume and the target cave features. The method comprises: determining a fracture low-frequency model and a reservoir boundary low-frequency model based on the seismic attribute volume, comprising:

6. The method of claim 1, wherein, performing attribute analysis based on the seismic attribute volume to obtain well trace attribute curves and well curves, and constructing a deep learning network based on the well trace attribute curves and the well curves; performing fracture prediction and reservoir boundary prediction based on the deep learning network to obtain predicted fracture attributes and predicted reservoir boundary attributes; 7. The method of claim 1, wherein, constructing a fracture low-frequency model corresponding to the predicted fracture attributes and a reservoir boundary low-frequency model corresponding to the predicted reservoir boundary attributes based on the predicted fracture attributes and the predicted reservoir boundary attributes. The method comprises:

8. A low-frequency model construction apparatus characterized by comprising: fusing the cave low-frequency model, the fracture low-frequency model and the reservoir boundary low-frequency model into the straight plate low-frequency model based on a preset fusion manner to obtain an original low-frequency model. The method comprises: determining a target low-frequency model corresponding to the carbonate reservoir by inverting the original low-frequency model based on post-stack seismic data in the original geological data and iteratively updating the original low-frequency model according to the inversion result. The method comprises: a first model determination module configured to determine a straight plate low-frequency model and a seismic attribute volume based on original geological data; a second model determination module configured to determine a cave low-frequency model by post-stack inversion on the straight plate low-frequency model based on the original geological data; a third model determining module configured to determine a fracture low-frequency model and a reservoir boundary low-frequency model based on the seismic attribute volume; a model fusion module configured to fuse the straight plate low-frequency model, the cave low-frequency model, the fracture low-frequency model and the reservoir boundary low-frequency model to obtain an original low-frequency model; a target model determining module configured to perform post-stack inversion on the original low-frequency model based on the original geological data to determine a target low-frequency model corresponding to the carbonate reservoir.

9. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the low-frequency model construction method in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the low-frequency model construction method in any one of claims 1-7 when executed.

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