A method and system for multi-scale fractured-cave geological modeling of a carbonate reservoir

Abstract

The application discloses a kind of carbonate reservoir multi-scale broken fracture karst cave geological modeling method and system, comprising: to poststack depth domain seismic data body is handled, the boundary of fault body is determined, constructs three-dimensional fault body model;The horizon tracking of the top and bottom surface of three-dimensional fault body model is carried out to the seismic event, and top and bottom surface layer model is constructed;Top and bottom surface area in top and bottom surface layer model is handled, and reservoir matrix geological model is obtained;Based on the response characteristics of poststack depth domain seismic data body, determine the shape of cave;Based on reservoir matrix geological model and cave shape, obtain cave model;To poststack depth domain seismic data body is filtered, edge detection processing and tracking, and identify fracture development zone of cave, and construct discrete fracture network model;Based on the obtained cave model and discrete fracture network model, obtain geological model.The application solves the problem that fracture zone is not clear and fracture cave spatial distribution is not clear under complex conditions.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of carbonate reservoir characterization and modeling, and relates to a carbonate reservoir multi-scale fault-fracture-cave geological modeling method and system. BACKGROUND

[0002] The ultra-deep carbonate reservoir has a complex tectonic karst geological background, a large-scale strike-slip fault zone caused by multi-period intense tectonic activities, a large number of induced tectonic fractures, and a large number of developed dissolution pores caused by deep hydrothermal, atmospheric leaching, and dissolution of light and dark rivers, which are the main oil and gas reservoir spaces of the reservoir. At present, the strike-slip fault zone in this area is not clear, the spatial distribution of fractures and caves is not clear, and the multi-scale and multi-type geological model has not been truly established, which cannot quickly and effectively explore the reservoir. SUMMARY

[0003] The purpose of the present application is to solve the problems in the prior art and provide a carbonate reservoir multi-scale fault-fracture-cave geological modeling method and system, which can solve the problems of unclear fault zone and unclear spatial distribution of fractures and caves in complex situations.

[0004] To achieve the above-mentioned purpose, the following technical solutions are adopted:

[0005] A carbonate reservoir multi-scale fault-fracture-cave geological modeling method comprises:

[0006] Processing the post-stack depth domain seismic data volume to determine the boundary of the fault volume and construct a three-dimensional fault volume model;

[0007] Based on the three-dimensional fault volume model, performing horizon tracking on the seismic events of the top and bottom surfaces of the three-dimensional fault volume model to construct a top and bottom surface layer model;

[0008] Processing the top and bottom surface regions in the top and bottom surface layer model to obtain a reservoir matrix geological model;

[0009] Based on the response characteristics of the post-stack depth domain seismic data volume, determining the shape of the cave;

[0010] Based on the reservoir matrix geological model and the shape of the cave, assigning values to the geology in the shape of the cave to obtain a cave model;

[0011] Filtering, edge detection processing and tracking the post-stack depth domain seismic data volume to identify the cave fracture development zone, and based on the random simulation method, constructing a discrete fracture network model;

[0012] Based on the obtained cave model and discrete fracture network model, obtaining a geological model.

[0013] A further improvement of the present invention is that:

[0014] The post-stack depth domain seismic data volume is processed to determine the boundaries of the fault body and construct a three-dimensional fault body model, specifically as follows:

[0015] Based on the post-stack depth domain seismic data volume, the main faults are identified by extracting variance volume attributes, and the perturbation range of the faults on the ant volume is identified. At the same time, a threshold is set to characterize the boundary of the fault body on the post-stack depth domain seismic data volume. The characterized boundary of the fault body is converted into a Fault Polygon to clarify the area where the fault body intersects with the top and bottom surfaces. The fault body is further characterized by generating Fault Pillars. Based on the Fault Pillars, a three-dimensional fault body model is constructed by generating Fault Surfaces.

[0016] Based on the three-dimensional fault body model, the phase axes of earthquakes at the top and bottom surfaces of the three-dimensional fault body model are traced to construct the top and bottom surface models, specifically:

[0017] By tracing the reflected seismic phase axes, layer tracing is carried out on the top and bottom surfaces of the model to construct a layer model of the top and bottom surfaces.

[0018] The top and bottom surface regions in the top and bottom surface model are processed to obtain the reservoir matrix geological model, specifically as follows:

[0019] The top and bottom areas were gridded using Pillar Gridding; based on the actual geological stratification, a reservoir matrix geological model was constructed using the method of first making zones and then making layers.

[0020] Based on the response characteristics of post-stack depth domain seismic data volumes, the morphology of karst caves is determined, specifically as follows:

[0021] Based on the different response characteristics of karst caves in the post-stack depth domain seismic data volume, the root mean square amplitude attribute is selected as the basis for identifying the karst cave boundary. Based on the response characteristics of karst caves on the root mean square amplitude, threshold limiting and filtering are performed on the entire post-stack depth domain seismic data volume so that the root mean square amplitude volume exists only in the region of the responding karst cave. The geological body carving technique is used to carve the karst cave in the root mean square amplitude response region, and the karst cave morphology is delineated deterministically.

[0022] Based on the reservoir matrix geological model and cave morphology, the geology in the cave morphology is assigned values ​​to obtain the cave model. Specifically, within the range of the reservoir matrix geological model, the sculpted cave geological bodies are assigned to the geological grid using the Geobody modeling method to construct the cave model.

[0023] The post-stack depth domain seismic data volume is filtered, edge-detected, and tracked to identify karst fissure development zones. Based on stochastic simulation, a discrete fissure network model is constructed. Specifically, the signal-to-noise ratio of the post-stack depth domain seismic data volume is enhanced by filtering, and edge detection is performed on the filtered data volume by extracting chaotic attribute volumes. The detected edges are enhanced using the 3DEdge Enhancement method. Ant tracking is performed on the post-stack depth domain seismic data volume by setting a dominant tracking azimuth to identify karst fissure development zones. These karst fissure development zones are used as spatial constraints in stochastic simulation, and a discrete fissure network model is constructed by stochastically simulating fissure surfaces.

[0024] A multi-scale fault-cavity geological modeling system for carbonate reservoirs includes:

[0025] The first processing module is used to process the post-stack depth domain seismic data volume, determine the boundary of the fault body, and construct a three-dimensional fault body model.

[0026] The layer tracing module, based on the three-dimensional fault body model, performs layer tracing on the phase axis of the earthquake where the top and bottom surfaces of the three-dimensional fault body model are located, and constructs the top and bottom surface layer model;

[0027] The second processing module is used to process the top and bottom surface regions in the top and bottom surface model to obtain the reservoir matrix geological model.

[0028] The response feature determination module determines the karst cave morphology based on the response features of the post-stack depth domain seismic data volume.

[0029] The first acquisition module, based on the reservoir matrix geological model and the cave morphology, assigns values ​​to the geology in the cave morphology to acquire the cave model;

[0030] The third processing module is used to filter, perform edge detection processing and tracking on the post-stack depth domain seismic data volume, identify the development zone of karst caves and cracks, and construct a discrete crack network model based on the stochastic simulation method.

[0031] The second acquisition module acquires a geological model based on the acquired cave model and discrete fracture network model.

[0032] A terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0033] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] This invention uses methods such as root mean square amplitude, ant tracking, variance, and coherence to characterize and construct fractures and karst caves, ultimately building a geological model that includes fault fracture zones, discrete fracture networks, karst caves, and reservoir matrix. This effectively solves the problems of unclear understanding of fault zones and unclear spatial distribution of fractures and karst caves under complex conditions. At the same time, processing based on post-stack depth domain seismic data improves the simulation level of the model, making it closer to the actual underground reservoir model and contributing to the scientific exploration of reservoirs. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of a multi-scale fracture-cavity geological modeling method for carbonate reservoirs according to an embodiment of the present invention;

[0038] Figure 2 This is a structural diagram of the multi-scale fracture-cavity geological modeling system for carbonate reservoirs according to an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0040] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0041] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0042] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0043] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0044] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0045] The present invention will now be described in further detail with reference to the accompanying drawings:

[0046] See Figure 1 This invention provides a multi-scale fault-cavity geological modeling method for carbonate reservoirs, including:

[0047] Step 1: On the first and second-order fault-sensitive variance volume seismic attribute volumes, with the goal of identifying and characterizing fault bodies, this step breaks away from the conventional approach of simply interpreting fault planes and identifies the actual underground fault development pattern: the fault body. Subsurface fault systems, especially strike-slip faults, are fault bodies with a certain bandwidth, rather than two-dimensional "surfaces"; therefore, defining the boundaries of the fault body is crucial in this step. In this example, based on the post-stack depth domain seismic data volume, the main faults are identified by extracting variance volume attributes, and the perturbation range of the faults on the ant-shaped volume is identified. Simultaneously, a threshold is set to characterize the boundaries of the fault body on the seismic data volume. The actual characterized fault body boundaries are converted into Fault Polygons, clarifying the areas where the faults intersect with the top and bottom surfaces. The fault body is then meticulously characterized using the generation of Fault Pillars. Based on the Fault Pillars, a three-dimensional fault body model is constructed by generating Fault Surfaces.

[0048] Step 2: Construct a reservoir matrix geological model. Step 2 requires two main datasets: a three-dimensional fault body model and a bedding plane model.

[0049] First, a three-dimensional fault body model is constructed. This model was already constructed in step 1 and can be directly used in step 2. Second, a layer model is constructed. Based on the three-dimensional fault body model identified and established in step 1, layer tracing is performed on the top and bottom surfaces of the model by tracking the reflected seismic phase axes. Based on the tracing results, a deterministic modeling method is used to construct the layer models for the top and bottom surfaces. Corner gridding is applied to the top and bottom surface regions using Pillar Gridding. According to the actual geological stratification, a reservoir matrix geological model is constructed following the method of "Make Zones" followed by "Make Layers".

[0050] Step 3: Identifying and Constructing the Cave Model. The data used in Step 3 is the root mean square amplitude (RMS) attribute volume, and the identification method used is geological body sculpting. Based on the different response characteristics of caves in the post-stack depth domain seismic data volume, the RMS amplitude attribute is selected as the basis for identifying cave boundaries. Based on the response characteristics of caves on the RMS amplitude, the entire seismic data volume is thresholded and filtered, ensuring that only areas responding to caves remain in the RMS amplitude volume. Through human-computer interaction, geological body sculpting techniques are used to sculpt caves in the RMS amplitude response areas, definitively outlining the cave morphology. Within the reservoir matrix geological model, the sculpted cave geological bodies are assigned to the geological grid using Geobody modeling, constructing the cave model.

[0051] Step 4: Construct a discrete fracture network model. The distribution of fractures in the real subsurface is complex, non-uniform, and highly random. Considering the significant control of fracture development by faults, a stochastic modeling method based on discrete fracture network modeling is used to construct a realistic subsurface fracture network model of the reservoir.

[0052] The methods used include ant tracking and random simulation. Ant tracking is used to identify fracture development zones, providing spatial constraints for subsequent random fracture simulation. First, the post-stack depth domain seismic data volume is filtered, with median filtering used in step 4 to enhance the signal-to-noise ratio. Second, edge detection is performed on the filtered post-stack depth domain seismic data volume, using chaotic attribute extraction in step 4, which yields good edge detection results. Based on the chaotic data, the detected edges are enhanced using 3D Edge Enhancement in step 4. Then, ant tracking is performed on the processed post-stack depth domain seismic data volume, starting by setting a preferred tracking azimuth. Finally, the ant tracking results are obtained, identifying the fracture development zones. The ant tracking results are then applied to fracture modeling. Here, the identified fracture development zones are used as spatial constraints in random simulation, and the model is constructed by randomly simulating fracture patches.

[0053] The obtained cave model and discrete fracture network model are both geological models.

[0054] Step 5: Model Validation. The fractures identified by the discrete fracture network model and the karst caves identified by the cave model are compared with those identified by geophysical imaging logging. A horizontal comparison is conducted using fracture density and cave density as evaluation indicators. The specific comparison process is as follows: A well not involved in the modeling process is selected, which must contain electrical logging curves or imaging logging information that can identify fractures and caves. Through well logging imaging, the locations of fracture and cave development in the well are obtained, and the fracture density and cave density are calculated. Simultaneously, the cave density of the well in the cave model and the fracture density in the discrete fracture network model are extracted to obtain the parameters of the cave model and the discrete fracture network model for that well. The error values ​​are obtained by comparing the fracture density identified by the discrete fracture network model with that identified by geophysical imaging logging, and the cave density identified by the cave model with that identified by geophysical imaging logging. Compared with the error values ​​obtained by traditional geophysical imaging logging modeling methods, the results show that the error between the discrete fracture network model and the actual value is around 10%, and the accuracy of the cave model is about 5-10% higher than that of the traditional method.

[0055] See Figure 2 This invention discloses a multi-scale fault-cavity geological modeling system for carbonate reservoirs, comprising:

[0056] The first processing module is used to process the post-stack depth domain seismic data volume, determine the boundary of the fault body, and construct a three-dimensional fault body model.

[0057] The layer tracing module, based on the three-dimensional fault body model, performs layer tracing on the phase axis of the earthquake where the top and bottom surfaces of the three-dimensional fault body model are located, and constructs the top and bottom surface layer model;

[0058] The second processing module is used to process the top and bottom surface regions in the top and bottom surface model to obtain the reservoir matrix geological model.

[0059] The response feature determination module determines the karst cave morphology based on the response features of the post-stack depth domain seismic data volume.

[0060] The first acquisition module, based on the reservoir matrix geological model and the cave morphology, assigns values ​​to the geology in the cave morphology to acquire the cave model;

[0061] The third processing module is used to filter, perform edge detection processing and tracking on the post-stack depth domain seismic data volume, identify the development zone of karst caves and cracks, and construct a discrete crack network model based on the stochastic simulation method.

[0062] The second acquisition module acquires a geological model based on the acquired cave model and discrete fracture network model.

[0063] An embodiment of the present invention provides a terminal device. This terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0064] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0065] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0066] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0067] The memory can be used to store the computer program and / or module. The processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0068] If the modules / units integrated in the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0069] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-scale fault-vuggy geological modeling method for carbonate reservoirs, characterized in that, include: Process the post-stack depth domain seismic data volume to determine the boundaries of the fault body and construct a three-dimensional fault body model; Based on the three-dimensional fault body model, the in-phase axis of the earthquake at the top and bottom surfaces of the three-dimensional fault body model is traced to construct the top and bottom surface model. Process the top and bottom surface regions in the top and bottom surface model to obtain the reservoir matrix geological model; Based on the response characteristics of post-stack depth domain seismic data volumes, the morphology of karst caves is determined; Specifically, based on the different response characteristics of karst caves in the post-stack depth domain seismic data volume, the root mean square amplitude attribute is selected as the basis for identifying the karst cave boundary. Based on the response characteristics of karst caves in the root mean square amplitude, the entire post-stack depth domain seismic data volume is thresholded and filtered so that the root mean square amplitude volume only exists in the region of the responding karst cave. The geological body carving technique is used to carve the karst cave in the root mean square amplitude response region, and the karst cave morphology is delineated in a deterministic manner. Based on the reservoir matrix geological model and cave morphology, the geology of the cave morphology is assigned values ​​to obtain the cave model; The post-stack depth domain seismic data volume is filtered, edge detected and tracked to identify karst fissure development zones. Based on the stochastic simulation method, a discrete fissure network model is constructed. The process involves filtering, edge detection, and tracking of the post-stack depth domain seismic data volume to identify karst fissure development zones. Based on a stochastic simulation method, a discrete fissure network model is constructed. Specifically, this involves: filtering the post-stack depth domain seismic data volume to enhance its signal-to-noise ratio; extracting chaotic attribute volumes to perform edge detection on the filtered data volume; using 3D Edge Enhancement to enhance the detected edges; and employing ant-like tracking of the data volume by setting a dominant tracking azimuth to identify karst fissure development zones. These zones are then used as spatial constraints in the stochastic simulation, and the discrete fissure network model is constructed by randomly simulating fissure surfaces. Based on the obtained cave model and discrete fracture network model, a geological model is obtained.

2. The multi-scale fault-vuggy geological modeling method for carbonate reservoirs according to claim 1, characterized in that, The process of processing the post-stack depth domain seismic data volume to determine the boundaries of the fault body and construct a three-dimensional fault body model is as follows: Based on the post-stack depth domain seismic data volume, the main faults are identified by extracting variance volume attributes, and the perturbation range of the faults on the ant volume is identified. At the same time, a threshold is set to characterize the boundary of the fault body on the post-stack depth domain seismic data volume. The characterized boundary of the fault body is converted into a Fault Polygon to clarify the area where the fault body intersects with the top and bottom surfaces. The fault body is further characterized by generating Fault Pillars. Based on the Fault Pillars, a three-dimensional fault body model is constructed by generating Fault Surfaces.

3. The multi-scale fracture-cavity geological modeling method for carbonate reservoirs according to claim 1, characterized in that, The method involves using a three-dimensional fault model to trace the phase axes of earthquakes at the top and bottom surfaces of the three-dimensional fault model, and then constructing a top and bottom surface model. Specifically: By tracing the reflected seismic phase axes, layer tracing is carried out on the top and bottom surfaces of the model to construct a layer model of the top and bottom surfaces.

4. The multi-scale fracture-cavity geological modeling method for carbonate reservoirs according to claim 1, characterized in that, The process of processing the top and bottom surface regions in the top and bottom surface model to obtain the reservoir matrix geological model specifically involves: The top and bottom areas were gridded using Pillar Gridding; based on the actual geological stratification, a reservoir matrix geological model was constructed using the method of first making zones and then making layers.

5. The multi-scale fracture-cavity geological modeling method for carbonate reservoirs according to claim 1, characterized in that, The method of assigning geological values ​​to the cave morphology based on the reservoir matrix geological model and the cave morphology to obtain the cave model is as follows: within the range of the reservoir matrix geological model, the sculpted cave geological body is assigned to the geological grid using the Geobody modeling method to construct the cave model.

6. A multi-scale fault-cavity geological modeling system for carbonate reservoirs, characterized in that, include: The first processing module is used to process the post-stack depth domain seismic data volume, determine the boundary of the fault body, and construct a three-dimensional fault body model. The layer tracing module, based on the three-dimensional fault body model, performs layer tracing on the phase axis of the earthquake where the top and bottom surfaces of the three-dimensional fault body model are located, and constructs the top and bottom surface layer model; The second processing module is used to process the top and bottom surface regions in the top and bottom surface model to obtain the reservoir matrix geological model. The response feature determination module determines the karst cave morphology based on the response features of the post-stack depth domain seismic data volume. Specifically, based on the different response characteristics of karst caves in the post-stack depth domain seismic data volume, the root mean square amplitude attribute is selected as the basis for identifying the karst cave boundary. Based on the response characteristics of karst caves in the root mean square amplitude, the entire post-stack depth domain seismic data volume is thresholded and filtered so that the root mean square amplitude volume only exists in the region of the responding karst cave. The geological body carving technique is used to carve the karst cave in the root mean square amplitude response region, and the karst cave morphology is delineated in a deterministic manner. The first acquisition module, based on the reservoir matrix geological model and the cave morphology, assigns values ​​to the geology in the cave morphology to acquire the cave model; The third processing module is used to filter, perform edge detection processing and tracking on the post-stack depth domain seismic data volume, identify the development zone of karst caves and cracks, and construct a discrete crack network model based on the stochastic simulation method. The process involves filtering, edge detection, and tracking of the post-stack depth domain seismic data volume to identify karst fissure development zones. Based on a stochastic simulation method, a discrete fissure network model is constructed. Specifically, this involves: filtering the post-stack depth domain seismic data volume to enhance its signal-to-noise ratio; extracting chaotic attribute volumes to perform edge detection on the filtered data volume; using 3D Edge Enhancement to enhance the detected edges; and employing ant-like tracking of the data volume by setting a dominant tracking azimuth to identify karst fissure development zones. These zones are then used as spatial constraints in the stochastic simulation, and the discrete fissure network model is constructed by randomly simulating fissure surfaces. The second acquisition module acquires a geological model based on the acquired cave model and discrete fracture network model.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-5.

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