Stratigraphic pore structure analysis method and device based on topological analysis

Abstract

The application provides a stratum pore structure analysis method and device based on topology analysis, equipment and a storage medium. The method comprises the following steps: obtaining a fault scanning image of a rock sample of a site to be sealed; performing image segmentation processing on the fault scanning image to obtain geometric parameters of pores; constructing a pore network skeleton based on the geometric parameters; measuring and calculating topological invariants according to the pore network skeleton; classifying the connectivity of the pores according to the pore network skeleton and the topological invariants; classifying the pores according to the topological invariants and the connectivity classification result; and determining carbon dioxide injection parameters of the site to be sealed according to the pore classification result and the connectivity classification result. The application can capture the topological correlation characteristics of pore space and provide accurate data support for safe and effective judgment of reservoir sealing.

Description

Technical Field

[0001] This application relates to the field of carbon dioxide geological storage technology, and in particular to a method, apparatus, equipment and storage medium for analyzing the pore structure of strata based on topological analysis. Background Technology

[0002] Carbon dioxide geological storage is a core technology for achieving carbon neutrality and controlling greenhouse gas emissions. Its storage efficiency, long-term safety, and injection capacity are highly dependent on the pore structure of the reservoir rock. As the core space for carbon dioxide transport and storage, the morphology, distribution, and connectivity of pores directly determine the carbon dioxide injection rate, storage capacity, and leakage risk, making them core evaluation indicators for site selection and engineering design. Existing pore characterization techniques mostly focus on macroscopic porosity and permeability calculations, or single-scale pore size distribution detection. This has the following limitations:

[0003] The inability to capture the topological correlation characteristics of pore space and the subjective judgment of pore connectivity paths, degree of closure, and spatial structure lead to low reservoir storage efficiency and the safety hazard of carbon dioxide escape. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] To achieve the above objectives, in a first aspect, embodiments of this application provide a method for geological carbon dioxide sequestration. The method includes the following steps: acquiring fault scan images of rock samples from the site to be sequestrated; performing image segmentation processing on the fault scan images to obtain geometric parameters of the pores; constructing a pore network skeleton based on the geometric parameters; calculating topological invariants based on the pore network skeleton; classifying the pores according to their connectivity based on the pore network skeleton and topological invariants; classifying the pores according to the topological invariants and connectivity classification results; and determining the carbon dioxide injection parameters for the site to be sequestrated based on the pore classification results and connectivity classification results.

[0006] In one implementation, the pore network skeleton uses pores as nodes and connecting channels as edges to characterize the topological connection relationships between pores.

[0007] In one implementation, the topological invariants include the Euler characteristic number, the number of connected components, the hole genus, the connected dimension, and the curvature invariant.

[0008] In one implementation, when classifying the connectivity of pores, the proportion of connected pores, the tortuosity of the transport path, and the channel conductivity index are quantified.

[0009] In one implementation, the formula for calculating the proportion of connected pores is:

[0010] In the formula, The proportion of connected pores, The total number of connected pores. This represents the total number of nodes in the porous network skeleton. The formula for calculating the tortuosity of the transport path is:

[0011] In the formula, The tortuosity of the transport path, This represents the actual transport path length of carbon dioxide within the porous network framework. The straight-line distance between the starting and ending points of the migration; The formula for calculating the channel throughput is:

[0012] In the formula, Channel throughput, The number of connection channels that enable carbon dioxide transfer. This represents the total number of edges in the porous network skeleton.

[0013] In one implementation, the logic for determining the connectivity classification of pores based on the pore network skeleton and topological invariants includes: The connectivity is considered complete when the number of connected branches is 1, the Euler characteristic number is ≤0, the proportion of connected pores is ≥90%, the channel conductivity is ≥80%, and the tortuosity of the transport path is ≤1.5. The connectivity is determined to be moderate if 1 < number of connected branches ≤ 3, 0 < Euler characteristic number ≤ 2, 60% ≤ proportion of connected pores < 90%, 50% ≤ channel conductivity < 80%, and 1.5 < tortuosity of transport path ≤ 2.0. The connectivity is determined to be weak if 3 < number of connected branches ≤ 10, 2 < Euler characteristic number ≤ 5, 30% ≤ proportion of connected pores < 60%, 20% ≤ channel conductivity < 50%, and the tortuosity of the transport path > 2.0. If the number of connected branches is greater than 10, the Euler characteristic number is greater than 5, the proportion of connected pores is less than or equal to 30%, and the channel conductivity is 0, the connectivity is determined to be completely isolated.

[0014] In one implementation, the logic for classifying porosity based on topological invariants and connectivity grading results includes: When the proportion of connected pores is ≥80%, the tortuosity of the transport path is ≤1.5, the channel conductivity is ≥80%, the curvature invariant is ≤0.8, and the connectivity dimension is ≥2.5, it is determined to be a dominant transport channel pore; When the percentage of connected pores is less than 60%, the tortuosity of the transport path is greater than 2.0, the channel conductivity is less than 50%, and the genus of the pores is greater than or equal to 1, it is determined to be a closed storage space pore. When the mean square value of the transport path is less than 2.0, and the channel conductivity is less than 80% and the curvature invariant is greater than 0.8, it is identified as a channel pore with leakage risk. When the proportion of connected pores is ≤10% and the channel conductivity is 0, it is determined to be a transitional pore.

[0015] In one implementation, the carbon dioxide injection parameters include injection pressure, injection rate, and injection volume, and the carbon dioxide injection parameters satisfy the following conditions: Formation pore fluid pressure ≤ injection pressure ≤ formation fracturing pressure; When the connectivity is fully connected, the injection rate is 0.8-1.8 m³ / (m²·d); when the connectivity is moderately connected, the injection rate is 0.3-0.8 m³ / (m²·d); when the connectivity is weakly connected, the injection rate is 0.1-0.3 m³ / (m²·d); when the connectivity is completely isolated, no carbon dioxide injection is performed. If the injection volume is ≤ 80% of the total volume of the effective connected pores and the proportion of leakage risk channels in the pores is ≥ 20%, the injection volume should be reduced by 20%.

[0016] Secondly, embodiments of this application provide a formation pore structure analysis device based on topology analysis, comprising: an acquisition module for acquiring fault scan images of rock samples from a site to be sealed; a first processing module for performing image segmentation processing on the fault scan images to obtain geometric parameters of the pores; a second processing module for constructing a pore network skeleton based on the geometric parameters; a third processing module for calculating topological invariants based on the pore network skeleton; a fourth processing module for performing connectivity classification based on the pore network skeleton and the topological invariants; a fifth processing module for classifying pores based on the topological invariants and the connectivity classification results; and a sixth processing module for determining carbon dioxide injection parameters for the site to be sealed based on the pore classification results and the connectivity classification results.

[0017] Thirdly, embodiments of this application provide a computer-readable storage medium storing a carbon dioxide geological sequestration program, which, when executed, implements the method provided in the first aspect.

[0018] Compared with existing technologies, the advantages of this application are as follows: It captures the topological correlation characteristics of pore space through topological invariants, quantifying and characterizing pore connectivity paths, closure levels, and spatial structures. This allows for the capture of topological correlation characteristics of pore space, providing accurate data support for the effective judgment of reservoir storage safety. Based on topological invariants and the pore network skeleton, pore connectivity is divided into four levels: fully connected, moderately connected, weakly connected, and completely isolated. This accurately quantifies pore connectivity paths and closure levels, effectively solving the problem of subjective judgment bias and making the judgment of reservoir storage safety more scientific and accurate. Based on the connectivity classification results and quantitative indicators, it accurately identifies dominant carbon dioxide migration channels, closed storage spaces, and leakage risk channels, clarifying the pore closure level and potential leakage risks. This provides a precise basis for judging reservoir storage safety and precise data support for avoiding carbon dioxide escape.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic flowchart of a formation pore structure analysis method based on topology analysis provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a formation pore structure analysis method and apparatus based on topology analysis provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0022] The following describes, with reference to the accompanying drawings, a method and apparatus for analyzing formation pore structure based on topology analysis, according to embodiments of this application.

[0023] Figure 1 This is a schematic flowchart illustrating a formation pore structure analysis method based on topology analysis provided in an embodiment of this application. Figure 1 As shown, the method may include, but is not limited to, the following steps: Reference Figure 1This application provides a method for geological storage of carbon dioxide, the method comprising the following steps: S1. Obtain fault scan images of rock samples from the site to be sealed.

[0024] For example, the rock sample is a pristine rock that has not been damaged by external forces, and the rock sample was collected from a deep saline aquifer, coal seam, or sandstone stratum at the site to be sealed. The rock sample was scanned using an X-ray micro-CT device to obtain a tomographic image of the rock sample at the site to be sealed. S2. Perform image segmentation processing on the tomographic scan image to obtain the geometric parameters of the pores.

[0025] Among them, the geometric parameters include at least one of the following: pore size distribution, porosity, throat radius, tortuosity, specific surface area, pore length-to-diameter ratio, volume distribution, etc. S3. Construct a pore network skeleton based on geometric parameters. This pore network skeleton uses pores as nodes and connecting channels as edges to represent the topological connection relationship between pores.

[0026] S4. Calculate the topological invariants based on the porosity network skeleton.

[0027] Among them, topological invariants include at least one of the following: Euler characteristic number, number of connected components, hole genus, connected dimension, and curvature invariant.

[0028] S5. Based on the pore network skeleton and topological invariants, classify the connectivity of pores.

[0029] S6. Classify pores based on topological invariants and connectivity classification results, and obtain pore classification results.

[0030] The pore classification results include at least one of the following: dominant transport channel pores, closed storage space pores, leakage risk channel pores, and transitional pores.

[0031] S7. Determine the carbon dioxide injection parameters for the site to be sealed based on the porosity classification results and connectivity grading results.

[0032] In one implementation, the image segmentation process for the tomographic scan image is performed using a deep learning-based semantic segmentation model, which takes the tomographic scan image as input and a binarized image as output.

[0033] The semantic segmentation model uses the U-Net architecture as the basic semantic segmentation model. The input layer receives the preprocessed rock CT image. The intermediate layer extracts image features through the encoder (containing 4-6 convolutional layers and pooling layers). The decoder (containing 4-6 deconvolutional layers and stitching layers) realizes feature fusion and classification. The output layer uses the sigmoid activation function to output a binary segmentation result with the same size as the input image.

[0034] The training process of the aforementioned semantic segmentation model may include: performing grayscale normalization, Gaussian filtering, image enhancement (contrast adjustment, sharpening), and size unification (adjusting all CT images to 512×512 pixels) on CT images of rocks of different lithologies (including carbonate rocks, sandstone, shale, and coal seams); performing binarization annotation on the corresponding pore annotation data (porosity regions are labeled as 1, and non-porosity regions are labeled as 0) to obtain the dataset; dividing the dataset into training, validation, and test sets in a 7:2:1 ratio; inputting the training set into the semantic segmentation model for iterative training, and testing the performance of the semantic segmentation model using the validation set after each epoch, recording the segmentation accuracy and Dice coefficient of the validation set; employing an early stopping strategy during training, pausing training if the Dice coefficient of the validation set does not improve for several consecutive epochs (e.g., 5 epochs) to avoid model overfitting; the core training objective is to optimize the binarization segmentation effect to ensure accurate differentiation between porosity and non-porosity regions.

[0035] During training, the semantic segmentation model is considered to have completed training when the following conditions are met simultaneously: the loss function value of the training set tends to stabilize, that is, the loss value fluctuates by no more than 5% over 5 consecutive epochs and there is no obvious upward trend; the pore segmentation accuracy of the validation set is ≥95%, and the Dice coefficient is ≥0.92 (the Dice coefficient is used to measure the overlap between the segmentation result and the labeled data, and the value ranges from 0 to 1, with the closer to 1, the better the segmentation effect); the pore segmentation accuracy of the test set is ≥93%, the Dice coefficient is ≥0.90, and the difference in segmentation performance between different lithological samples is no more than 3%, ensuring that different lithological samples can be accurately extracted for pore geometric parameters through binarization segmentation.

[0036] The formula for calculating the Euler characteristic is as follows:

[0037] In the formula, Let be an Euler characteristic. This represents the total number of nodes (total number of pores) in the pore network skeleton. This represents the total number of edges (connection channels) in the porous network skeleton. This represents the number of faces (the number of closed regions enclosed by nodes and edges) in the porous network skeleton. This represents the number of connected branches in the porous network skeleton.

[0038] The formula for calculating the genus of a hole is:

[0039] In the formula, It is a hole inequality.

[0040] In one implementation, the connectivity dimension is calculated using box counting to quantify the spatial coverage and connectivity efficiency of the porous network; the curvature invariant is obtained by calculating the Gaussian curvature and average curvature of each node and edge in the porous network skeleton and then integrating and summing them to characterize the bending and twisting features of the porous network.

[0041] By capturing the topological correlation characteristics of pore space through topological invariants, and quantitatively characterizing pore connectivity paths, closure degree, and spatial structure, it is possible to capture the topological correlation characteristics of pore space and provide accurate data support for judging the safety and effectiveness of reservoir sealing.

[0042] In one implementation, when classifying the connectivity of pores, the proportion of connected pores, the tortuosity of the migration path, and the channel conductivity are used as quantitative indicators to evaluate the reservoir injection capacity and sealing performance.

[0043] The formula for calculating the proportion of connected pores is:

[0044] In the formula, The proportion of connected pores, This represents the total number of connected pores. The formula for calculating the tortuosity of the transport path is:

[0045] In the formula, The tortuosity of the transport path, This represents the actual transport path length of carbon dioxide within the porous network framework. The straight-line distance between the starting and ending points of the migration; The formula for calculating channel throughput is:

[0046] In the formula, Channel throughput, The number of connection channels that enable carbon dioxide transfer.

[0047] Based on this, the steps for determining the connectivity classification of pores according to the pore network skeleton and topological invariants include: when the number of connected branches = 1, the Eulerian characteristic number ≤ 0, the proportion of connected pores ≥ 90%, the channel conductivity ≥ 80%, and the tortuosity of the transport path ≤ 1.5, the connectivity is determined to be complete, at which point the pore network is completely connected with no independent isolated pores; when 1 < the number of connected branches ≤ 3, 0 < the Eulerian characteristic number ≤ 2, 60% ≤ the proportion of connected pores < 90%, 50% ≤ the channel conductivity < 80%, and 1.5 < the tortuosity of the transport path ≤ 2.0, the connectivity is determined to be... The connectivity is defined as moderate connectivity, where there are a few independent pores and the overall connectivity is good. When 3 < number of connected branches ≤ 10, 2 < Euler characteristic number ≤ 5, 30% ≤ percentage of connected pores < 60%, 20% ≤ channel conductivity < 50%, and the tortuosity of the transport path > 2.0, the connectivity is defined as weak connectivity, where there are many independent pores and the connectivity is poor. When the number of connected branches > 10, Euler characteristic number > 5, percentage of connected pores ≤ 30%, and channel conductivity = 0, the connectivity is defined as completely isolated, where all pores are independent and isolated, and there are no connected channels.

[0048] Based on topological invariants and the pore network framework, pore connectivity is divided into four levels: fully connected, moderately connected, weakly connected, and completely isolated. This accurately quantifies the pore connectivity paths and the degree of closure, effectively solving the problem of subjective judgment bias and making the judgment of reservoir storage safety more scientific and accurate.

[0049] Furthermore, the logic for classifying pores based on topological invariants and connectivity grading results includes: When the proportion of connected pores is ≥80%, the tortuosity of the transport path is ≤1.5, the channel conductivity is ≥80%, the curvature invariant is ≤0.8, and the connectivity dimension is ≥2.5, the pores are classified as dominant transport channels. These pores correspond to fully connected or moderately connected levels and possess the structural conditions for efficient carbon dioxide transport. When the proportion of connected pores is 30% ≤ connected pores <60%, the tortuosity of the transport path is >2.0, and the channel conductivity is ≤50%, the pores are classified as dominant transport channels. When the percentage of connected pores is % and the pore genus is ≥1, it is determined to be a closed storage space pore. This type of pore corresponds to the weak connectivity level, and its structural characteristics can achieve stable carbon dioxide storage. When 1.5 < migration path tortuosity < 2.0, and 50% ≤ channel conductivity < 80%, and curvature invariance > 0.8, it is determined to be a leakage risk channel pore. This type of pore corresponds to the moderate connectivity and weak connectivity level. When the percentage of connected pores is ≤10% and the channel conductivity = 0, it is determined to be a transitional pore. This type of pore corresponds to the completely isolated level and has no connected channels.

[0050] The assessment logic for reservoir injection capacity includes: fully connected or moderately connected reservoirs with a dominant migration channel porosity of ≥80% are considered to have strong injection capacity; weakly connected reservoirs with a closed reservoir porosity of ≥30% are considered to have medium injection capacity; and completely isolated reservoirs are considered to have weak injection capacity.

[0051] The evaluation logic for reservoir sealing performance includes: weak connectivity level, with a closed reservoir space porosity of ≥30% and a pore genus g≥1, is judged as having good sealing performance; moderate connectivity or weak connectivity level, with a leakage risk channel porosity of ≥20%, is judged as having sealing performance risk; and complete isolation level, is judged as having no effective reservoir space, and the sealing performance is not meaningful for evaluation.

[0052] In one implementation, the carbon dioxide injection parameters for the aforementioned site to be sealed must meet the following conditions: formation pore fluid pressure ≤ injection pressure ≤ formation fracturing pressure; when connectivity is fully connected, the injection rate is 0.8-1.8 m³ / (m²·d); when connectivity is moderately connected, the injection rate is 0.3-0.8 m³ / (m²·d); when connectivity is weakly connected, the injection rate is 0.1-0.3 m³ / (m²·d); when connectivity is completely isolated, no carbon dioxide injection is performed; when the injection volume is ≤ 80% of the total volume of the effective connected pores, and the proportion of leakage risk channel pores is ≥ 20%, the injection volume is further reduced by 20%.

[0053] By combining pore classification, connectivity grading, and reservoir evaluation results, carbon dioxide injection parameters can be determined. This avoids the problem of low storage efficiency caused by unreasonable injection parameters, and prevents formation pressure overload by controlling the injection volume, thus achieving safe and efficient injection for reservoir storage.

[0054] Please see Figure 2 , Figure 2 This is a schematic diagram of a formation porosity structure analysis method and apparatus based on topology analysis provided in an embodiment of this application. Figure 2 As shown, the device 200 includes: an acquisition module 201 for acquiring tomographic scan images of rock samples from the site to be sealed; a first processing module 202 for performing image segmentation processing on the tomographic scan images to acquire the geometric parameters of the pores; a second processing module 203 for constructing a pore network skeleton based on the geometric parameters; a third processing module 204 for calculating topological invariants based on the pore network skeleton; a fourth processing module 205 for performing connectivity classification based on the pore network skeleton and the topological invariants; a fifth processing module 206 for classifying pores based on the topological invariants and the connectivity classification results; and a sixth processing module 207 for determining the carbon dioxide injection parameters of the site to be sealed based on the pore classification results and the connectivity classification results.

[0055] It should be noted that the foregoing explanation of the embodiment of the formation pore structure analysis method based on topology analysis also applies to the apparatus of the formation pore structure analysis method based on topology analysis in this embodiment, and will not be repeated here.

[0056] To implement the above embodiments, this application also proposes an electronic device. Please see [link to relevant documentation]. Figure 3 , Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. For example... Figure 3 As shown, the electronic device 300 includes: a processor 301, and a memory 302 communicatively connected to the processor 301; the memory 302 stores computer execution instructions; the processor 301 executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0057] To implement the above embodiments, this application also proposes a storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the methods provided in the foregoing embodiments.

[0058] To implement the above embodiments, this application also proposes a program product, including at least one of a program and instructions, wherein when the program and instructions are executed by an electronic device, they implement the steps of the method provided in the foregoing embodiments.

[0059] It should be noted that the acquisition, transmission, storage, use, and processing of data in this application comply with the relevant provisions of national laws and regulations and do not violate public order and good morals.

[0060] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0061] It is worth noting that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0062] In the description of this application, unless otherwise stated, " / " means "or", for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone.

[0063] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0064] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0065] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0066] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0067] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0068] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0069] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0070] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for analyzing formation pore structure based on topological analysis, characterized in that, include: Obtain tomographic images of rock samples from the site to be sealed; The tomographic scan image is segmented to obtain the geometric parameters of the pores; A pore network skeleton is constructed based on the aforementioned geometric parameters; Calculate the topological invariants based on the porosity network skeleton; Connectivity is classified according to the porosity network skeleton and the topological invariants; Pore ​​classification is performed based on the topological invariants and the connectivity classification results; Based on the porosity classification results and connectivity grading results, the carbon dioxide injection parameters for the site to be sealed are determined.

2. The method for geological storage of carbon dioxide according to claim 1, characterized in that, The pore network skeleton uses pores as nodes and connecting channels as edges to represent the topological connection relationships between pores.

3. The method for geological storage of carbon dioxide according to claim 1, characterized in that, The topological invariants include Euler characteristic number, number of connected components, hole genus, connected dimension, and curvature invariant.

4. The method for geological sequestration of carbon dioxide according to claim 3, characterized in that, When classifying the connectivity of pores, the proportion of connected pores, the tortuosity of transport paths, and the conductivity of channels are quantified.

5. A method for geological carbon dioxide sequestration according to claim 4, characterized in that, The formula for calculating the proportion of connected pores is: In the formula, The proportion of connected pores, The total number of connected pores. This represents the total number of nodes in the porous network skeleton. The formula for calculating the tortuosity of the transport path is: In the formula, The tortuosity of the transport path, This represents the actual transport path length of carbon dioxide within the porous network framework. The straight-line distance between the starting and ending points of the migration; The formula for calculating the channel throughput is: In the formula, Channel throughput, The number of connection channels that enable carbon dioxide transfer. This represents the total number of edges in the porous network skeleton.

6. A method for geological carbon dioxide sequestration according to claim 5, characterized in that, The logic for classifying the connectivity of pores based on the pore network skeleton and topological invariants includes: The connectivity is considered complete when the number of connected branches is 1, the Euler characteristic number is ≤0, the proportion of connected pores is ≥90%, the channel conductivity is ≥80%, and the tortuosity of the transport path is ≤1.

5. The connectivity is determined to be moderate if 1 < number of connected branches ≤ 3, 0 < Euler characteristic number ≤ 2, 60% ≤ proportion of connected pores < 90%, 50% ≤ channel conductivity < 80%, and 1.5 < tortuosity of transport path ≤ 2.

0. The connectivity is determined to be weak if 3 < number of connected branches ≤ 10, 2 < Euler characteristic number ≤ 5, 30% ≤ proportion of connected pores < 60%, 20% ≤ channel conductivity < 50%, and the tortuosity of the transport path > 2.

0. If the number of connected branches is greater than 10, the Euler characteristic number is greater than 5, the proportion of connected pores is less than or equal to 30%, and the channel conductivity is 0, the connectivity is determined to be completely isolated.

7. A formation pore structure analysis device based on topology analysis, characterized in that, include: The acquisition module is used to acquire fault scan images of rock samples from the site to be sealed. The first processing module is used to perform image segmentation processing on the tomographic scan image to obtain the geometric parameters of the pores; The second processing module is used to construct a pore network skeleton based on the geometric parameters; The third processing module is used to calculate topological invariants based on the porosity network skeleton; The fourth processing module is used to perform connectivity classification based on the porosity network skeleton and the topological invariants; The fifth processing module is used to classify porosity based on the topological invariants and the connectivity classification results; The sixth processing module is used to determine the carbon dioxide injection parameters of the site to be sealed based on the porosity classification results and connectivity grading results.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 6.

9. A storage medium storing instructions, characterized in that, When the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the method of any one of claims 1 to 6.

10. A program product comprising at least one of a program and instructions, characterized in that, When at least one of the program or instructions is executed by an electronic device, it implements the steps of the method according to any one of claims 1 to 6.

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