A method, device and equipment for predicting mechanical properties of carbon storage in saline shale

Abstract

The present invention provides a method, device, and equipment for predicting the mechanical properties of carbon sequestration in saline shale, belonging to the field of shale oil and gas reservoir development. The method comprises: obtaining a saline shale sample, simulating the dissolution reaction of the shale sample under preset environmental conditions to obtain the porosity and multi-scale mechanical properties of the shale sample; fitting a first variation pattern of porosity over time; defining a dissolution damage variable and determining a second variation pattern of the damage variable over time; fitting a third variation pattern of the multi-scale mechanical properties with the porosity and damage variable; combining the second and third variation patterns to obtain a multi-scale mechanical property evolution prediction model for shale; obtaining target porosity data for the target shale, and obtaining prediction results for the mechanical property evolution of the saline shale at different time scales based on the mechanical property evolution prediction model. In this way, the mechanical properties of shale at different time scales can be predicted with high accuracy and reliability.

Description

Technical Field

[0001] The present invention belongs to the field of shale oil and gas reservoir development, and specifically relates to a method, device and equipment for predicting the mechanical properties of carbon sequestration in saline shale. Background Art

[0002] As climate change becomes increasingly serious, reducing carbon dioxide (CO2) emissions has become a core global concern. To achieve this goal, carbon capture, utilization, and storage (CCUS) technology is considered a key strategic tool. Deep saline aquifer geological storage, due to its large storage capacity and widespread distribution, is considered one of the most promising long-term storage methods.

[0003] Shale caprocks serve as a natural barrier to CO2 storage, and the long-term changes in their mechanical properties have a decisive impact on the safety of CO2 storage. However, existing technologies still lack in-depth research on the quantitative characterization and cross-scale prediction of shale performance evolution under the coupled chemical, physical, and mechanical multi-fields.

[0004] Currently widely used simulation software such as TOUGH focuses more on the simulation of chemical reactions and fluid migration, lacks support for the degradation of caprock mechanical properties, and mechanical modeling is mostly concentrated on short time scales or single stress fields, failing to incorporate the impact of long-term chemical changes on caprock performance. It is difficult to establish a complete cross-scale mechanical model, resulting in the inability to accurately predict the changes in the mechanical properties of shale over long time scales. Summary of the Invention

[0005] In order to solve the problem in the prior art that it is impossible to accurately predict the changes in the mechanical properties of shale over long time scales, the present invention provides a method, device and equipment for predicting the mechanical properties of carbon sequestration in saline shale.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] First, a method for predicting the mechanical properties of carbon sequestration in saline shale is provided, comprising the following steps:

[0008] S1. Shale acquisition and dissolution simulation.

[0009] Obtain shale samples from saline layers and simulate the dissolution reaction of shale samples under preset environmental conditions to provide basic data for subsequent determination of porosity and multi-scale mechanical property changes of shale samples;

[0010] S2. Modeling of porosity evolution.

[0011] The Boltzmann growth curve is used to fit the first variation law of porosity over time; the dissolution damage variable is defined according to the change of porosity, and the second variation law of the dissolution damage variable over time is determined based on the first variation law;

[0012] S3. Modeling the relationship between shale mechanical properties and porosity.

[0013] An exponential decay function is used to fit the third variation law of multi-scale mechanical properties with porosity and dissolution damage variables; by combining the second variation law with the third variation law, the multi-scale mechanical property evolution law of shale is obtained, and the multi-scale mechanical property evolution law is used as a prediction model;

[0014] S4. Application of prediction model.

[0015] Target porosity data of the target shale is obtained, and the target porosity data is input into the prediction model to obtain prediction results of the mechanical property evolution of the saline shale at different time scales.

[0016] Optionally, the porosity includes pore volume and pore size distribution, and the multi-scale mechanical properties include microhardness and microelastic modulus as well as macro compressive strength and macroelastic modulus; the porosity and multi-scale mechanical properties of the shale sample obtained by simulating the dissolution reaction of the shale sample under preset environmental conditions include:

[0017] X-ray diffraction (XRD) is used to record and analyze the dissolution and precipitation processes of mineral components in shale at preset intervals. Nuclear magnetic resonance (NMR) technology is used to determine the pore volume and pore size distribution of shale at different dissolution times. A nanoindenter is used to test the microhardness and microelastic modulus of shale, and a triaxial compression system is used to measure the macro compressive strength and macro elastic modulus of shale.

[0018] Optionally, the formula of the first change rule is:

[0019] ;

[0020] in, e is a natural constant, is the reaction rate constant; T and T0 are the observation time and initial time respectively; the fitting constants A1 and A2 are related to the porosity in the initial state and the state of complete dissolution of the mineral after the dissolution reaction respectively; the initial porosity is introduced and final porosity Perform equivalent substitution to obtain the approximate expression of the first change law:

[0021] .

[0022] Alternatively, the dissolution damage variable is defined based on the change in porosity as:

[0023] ;

[0024] in, is the porosity expression with respect to time T; is the initial porosity, It is the theoretical maximum porosity when all soluble minerals are completely dissolved;

[0025] The formula of the second change law is:

[0026] .

[0027] Optionally, the formula of the third change rule is:

[0028] ;

[0029] Among them, P0 is the initial performance value, 、 、 is a material constant that describes the effect of dissolution damage on the mechanical properties of shale, where .

[0030] Optionally, the formula for the multi-scale mechanical property evolution law is:

[0031] .

[0032] Secondly, a device for predicting the mechanical properties of carbon storage in saline shale is provided, including:

[0033] The acquisition module is used to obtain shale samples from saline layers, simulate the dissolution reaction of shale samples under preset environmental conditions, and obtain the porosity and multi-scale mechanical properties of the shale samples;

[0034] A fitting module is configured to fit a first variation pattern of porosity over time using a Boltzmann growth curve; define a dissolution damage variable based on the change in porosity, and determine a second variation pattern of the damage variable over time based on the first variation pattern; fit a third variation pattern of multi-scale mechanical properties with porosity and damage variables using an exponential decay function; and obtain a multi-scale mechanical property evolution pattern of shale by combining the second and third variation patterns, and use the multi-scale mechanical property evolution pattern as a prediction model.

[0035] The determination module is used to obtain target porosity data of the target shale, input the target porosity data into the prediction model, and obtain the prediction results of the mechanical property evolution of the saline shale at different time scales.

[0036] In addition, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for predicting the mechanical properties of carbon sequestration in saline shale is implemented.

[0037] Finally, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned method for predicting the mechanical properties of carbon sequestration in saline shale is implemented.

[0038] The method for predicting the mechanical properties of carbon sequestration in saline shale provided by the present invention has the following beneficial effects:

[0039] By simulating the dissolution reaction of shale samples under preset environmental conditions, the state changes of shale samples at different time points can be accurately captured. The Boltzmann growth curve is used to fit the first variation law of porosity over time, accurately describing how porosity increases or changes over time. By defining a dissolution damage variable and determining its second variation law based on porosity changes, the extent of dissolution damage to shale samples can be quantified, clarifying the process by which shale mechanical properties are affected by dissolution. Using an exponential decay function to fit the third variation law of multi-scale mechanical properties with porosity and damage variables, a mathematical relationship between mechanical properties, porosity, and damage variables can be established, thereby constructing the temporal variation law of shale's multi-scale mechanical properties. This method, through a comprehensive analysis of multi-scale mechanical properties, determines the evolution of shale mechanical properties with dissolution, accurately simulates the shale degradation process over long timescales, and can predict shale mechanical properties at different timescales with high accuracy and reliability. This method not only facilitates the design of more efficient CO2 storage projects, but also reduces potential leakage risks, providing a scientific basis for risk control and monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] To more clearly illustrate the embodiments of the present invention and its design, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0041] Figure 1 A schematic flow chart of a method for predicting mechanical properties of carbon sequestration in saline shale provided by the present invention according to an exemplary embodiment.

[0042] Figure 2 The present invention provides an XRD spectrum according to an exemplary embodiment.

[0043] Figure 3 This is a diagram showing changes in the content of major minerals in shale tested by XRD according to an exemplary embodiment of the present invention.

[0044] Figure 4This is a schematic diagram of a curve showing changes in shale pore volume and NMR transverse relaxation time T2 in a SC-CO2+saltwater environment at different times according to an exemplary embodiment of the present invention.

[0045] Figure 5 This is a schematic diagram of the distribution of shale porosity and pore size under different dissolution times according to an exemplary embodiment of the present invention.

[0046] Figure 6 A schematic diagram of the variation trend of microhardness and microelastic modulus with reaction time provided by the present invention according to an exemplary embodiment.

[0047] Figure 7 This is a schematic diagram of the variation of macro compressive strength and macro elastic modulus with reaction time according to an exemplary embodiment of the present invention.

[0048] Figure 8 This is a schematic diagram of the correlation between the porosity, micro-elastic modulus and macro-elastic modulus of dissolved shale under different reaction time conditions provided by an exemplary embodiment of the present invention; wherein, a is a schematic diagram of the correlation between the porosity of dissolved shale and the micro-elastic modulus and macro-elastic modulus, and b is a schematic diagram of the correlation between the micro-elastic modulus and macro-elastic modulus of dissolved shale.

[0049] Figure 9 This is a schematic diagram of the correlation between the porosity, microhardness and macro-compressive strength of dissolved shale under different reaction time conditions provided by an exemplary embodiment of the present invention; wherein, a is a schematic diagram of the correlation between the porosity, microhardness and macro-compressive strength of dissolved shale, and b is a schematic diagram of the correlation between the microhardness and macro-compressive strength of dissolved shale.

[0050] Figure 10 This is a block diagram of a device for predicting the mechanical properties of carbon sequestration in saline shale according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the technical solution of the present invention and to be able to implement it, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are not intended to limit the scope of protection of the present invention.

[0052] The technical solutions provided by various embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0053] The purpose of this paper is to develop a quantitative model and method for predicting changes in the mechanical properties of shale caprocks during CO2 storage in deep saline aquifers. This model experimentally quantifies the effect of mineral dissolution on porosity and establishes a bridge between porosity and the physical and mechanical properties of rock (such as hardness, compressive strength, and elastic modulus), thereby enabling quantitative prediction of shale mechanical properties.

[0054] This study experimentally validates and establishes a quantitative model for the temporal evolution of dissolution. This model fully accounts for the dynamics of the dissolution process and effectively describes the impact of mineral dissolution on porosity changes through a time-dependent damage variable (Ds(t)). This model can predict the temporal evolution of shale mechanical properties (such as hardness, compressive strength, and elastic modulus) during CO2 storage in deep saline formations, providing data support for long-term storage safety assessments.

[0055] Through experimental research on the mineral dissolution process, this paper establishes a quantitative relationship between mineral dissolution and porosity changes. This relationship reflects the impact of different mineral dissolution rates on porosity evolution and provides a reliable basis for subsequent mechanical property predictions. This model can more accurately describe the impact of mineral dissolution on shale pore structure, and further infer the impact of porosity on mechanical properties.

[0056] This paper utilizes a multiscale coupled analysis approach, building on the traditional single-scale model, to account for the interactions among multiple dimensions, including mineral dissolution, porosity changes, and mechanical properties. This integrated model enables a more comprehensive prediction of shale caprock degradation and its impact on mechanical properties during long-term storage, thereby improving the accuracy and reliability of predictions.

[0057] This method derives model parameters by fitting experimental data, avoiding the complex simulation and calculation processes of traditional methods. Furthermore, the model's application no longer relies on high-precision experimental conditions. By embedding the model into commercial software, it enables rapid and efficient prediction and analysis of CO2 storage environments, demonstrating high engineering application value.

[0058] First, the present invention provides a method for predicting the mechanical properties of carbon storage in saline shale. Figure 1 As shown, the following steps are included:

[0059] S101. Obtain a shale sample from a saline layer, simulate the dissolution reaction of the shale sample under preset environmental conditions, and obtain the porosity and multi-scale mechanical properties of the shale sample.

[0060] Specifically, a Chang 8 shale sample can be collected from an actual reservoir environment, such as the saline aquifer 1,610 meters deep in the Ordos Basin, and cut into cylindrical specimens with a diameter of 25 mm and a height of 50 mm. The sample is then treated with a vacuum-saturated salt solution to simulate the deep saline aquifer environment.

[0061] The system simulates the dissolution reaction of shale samples by supercritical carbon dioxide (SC-CO2) and saline water at a preset temperature and pressure. The state of the shale samples is recorded and analyzed at preset intervals to obtain the porosity and multi-scale mechanical properties of the shale samples. The preset temperature and pressure are the temperature and pressure conditions of the shale samples in the target formation.

[0062] In this step, it is necessary to simulate the long-term impact of the combined action of SC-CO2 and salt water on shale under high temperature and high pressure conditions.

[0063] For example, a high-temperature and high-pressure reactor with a design pressure of 15.7 MPa and a temperature of 328.55 K is used to inject Na + , Ca 2 + Mg 2+ The plasma-induced simulated saline solution and SC-CO2 gas were mixed, with the ratio of SC-CO2 gas to simulated saline solution set at 1:9 in the reactor. Chemical, physical, and mechanical tests were performed on samples at multiple dissolution reaction time points (0, 7, 14, 21, 28, and 35 days) to observe the effects of different dissolution stages on the shale.

[0064] Then the shale samples were subjected to multi-dimensional performance tests, such as Figure 2 and Figure 3 As shown, it includes the analysis of mineral composition: X-ray diffraction (XRD) is used to record and analyze the dissolution and precipitation processes of various mineral components in shale, such as the dissolution of carbonate minerals leading to the precipitation of Ca 2+ , Mg 2+ release.

[0065] S102. Use the Boltzmann growth curve to fit the first variation law of porosity over time.

[0066] In this step, fitting is performed based on the above experimental data to explore the correlation between the variables. Specifically, based on the experimental data and fitting analysis results, a multi-scale coupling model of porosity-damage variables-mechanical properties is established. This model can uniformly describe the effects of SC-CO2 and saltwater dissolution on the microscopic to macroscopic properties of shale. Then, through the time dependence of the damage variables, the evolution trend of the pore structure and mechanical properties of shale at different time points can be quantitatively predicted. Among them, nuclear magnetic resonance (NMR) technology can be used to measure the pore volume and pore size distribution of shale at different dissolution times, and quantitatively analyze the evolution trend of micropores, mesopores, and macropores, such as Figure 4 and Figure 5 shown.

[0067] First, we explore the relationship between porosity (φ) and time (T): Based on the experimental data, we use the Boltzmann growth curve to fit and reveal the first change law of porosity over time:

[0068] ;

[0069] Among them, e is a natural constant, is the reaction rate constant; the fitting constants A1 and A2 are closely related to the porosity in the initial state and the completely dissolved state, respectively, and can be determined experimentally. In order to describe these two states, the initial porosity ( ) and final porosity ( ) is replaced by an equivalent method to obtain the approximate expression of the first change law:

[0070] .

[0071] S103 . Define a dissolution damage variable according to the change in porosity, and determine a second time-dependent change law of the dissolution damage variable based on the first change law.

[0072] In this step, we first define the dissolution damage variable according to the change of porosity. :

[0073] ;

[0074] is the porosity expression with respect to time T; is the initial porosity, is the theoretical maximum porosity when all soluble minerals are completely dissolved. Then, combined with the first variation law, the variation law of porosity with time is substituted into the dissolution damage variable In the definition formula, the damage variable with respect to time is determined. That is, based on the dissolution damage variable, the porosity and time models are further combined to obtain the second variation law:

[0075] .

[0076] S104. Use the exponential decay function to fit the third variation law of multi-scale mechanical properties and porosity and dissolution damage variables.

[0077] In this step, it is necessary to explore the relationship between mechanical properties, such as compressive strength and elastic modulus, and porosity. First, micromechanical properties tests are carried out. Nanoindenter is used to test the microhardness and microelastic modulus of shale, and the decreasing trend of hardness at different dissolution time points is observed. Figure 6Then, the macroscopic mechanical properties test was carried out, and the macroscopic compressive strength and macroscopic elastic modulus of shale were measured with the use of triaxial compression system as the changes of dissolution time were shown as follows: Figure 7 shown.

[0078] By fitting the exponential decay function, a quantitative relationship between the effects of porosity on microhardness, microelastic modulus and macroscopic properties is established, such as Figure 8 The correlation between porosity and micro- and macro-elastic moduli is shown in Figure 9 The correlation between porosity, microhardness and macro compressive strength is shown.

[0079] For these multi-scale mechanical property parameters, such as microhardness, microelastic modulus, macroelastic modulus, and macrocompressive strength, they are all expressed by a unified exponential fitting function with respect to porosity, that is, the formula of the third variation law is:

[0080] ;

[0081] Among them, P0 is the initial performance value, 、 、 is a material constant that describes the effect of dissolution damage on the mechanical properties of shale, where .

[0082] S105. By combining the second change law and the third change law, a multi-scale mechanical property evolution law of shale is obtained, and the multi-scale mechanical property evolution law is used as a prediction model.

[0083] Combining the second and third change laws mentioned above, we can obtain the long-term evolution model of the mechanical properties of shale under the action of CO2, that is, the multi-scale mechanical property evolution law of shale. This multi-scale mechanical property evolution law is used as a prediction model. The specific formula is:

[0084] .

[0085] S106 , obtaining target porosity data of the target shale, inputting the target porosity data into the prediction model, and obtaining prediction results of the mechanical property evolution of the saline shale at different time scales.

[0086] The model of the present invention can be directly applied to the design and risk assessment of CO2 storage facilities, providing a scientific basis for ensuring the long-term stability of geological storage facilities.

[0087] By simulating the dissolution reaction of SC-CO2 and saline water at a preset temperature and pressure, this method accurately captures the state changes of shale samples at different time points. The Boltzmann growth curve is used to fit the first variation of porosity over time, accurately describing how porosity increases or changes over time. By defining a dissolution damage variable and determining its second variation based on porosity changes, the extent of dissolution damage to shale samples can be quantified, clarifying how the mechanical properties of shale are affected by dissolution. Using an exponential decay function to fit the third variation of multi-scale mechanical properties with porosity and damage variables, this method establishes a mathematical relationship between mechanical properties, porosity, and damage variables, and thus constructs the temporal variation patterns of the multi-scale mechanical properties of shale. This method, through a comprehensive analysis of multi-scale mechanical properties, determines the evolution of shale mechanical properties with dissolution, accurately simulates the shale degradation process over long timescales, and can predict shale mechanical properties at different timescales with high accuracy and reliability. This method not only facilitates the design of more efficient CO2 storage projects, but also reduces potential leakage risks, providing a scientific basis for risk control and monitoring.

[0088] Secondly, the present invention also provides a device for predicting the mechanical properties of carbon storage in saline shale. Figure 10 Shown, including:

[0089] The acquisition module 1001 is used to obtain a shale sample from a saline layer, simulate the dissolution reaction of the shale sample under preset environmental conditions, and obtain the porosity and multi-scale mechanical properties of the shale sample.

[0090] Fitting module 1002 is used to fit a first variation law of porosity over time using a Boltzmann growth curve; define a dissolution damage variable based on the change in porosity, and determine a second variation law of the damage variable over time based on the first variation law; use an exponential decay function to fit a third variation law of multi-scale mechanical properties with porosity and damage variables; and obtain a multi-scale mechanical property evolution law of shale by combining the second variation law and the third variation law, and use the multi-scale mechanical property evolution law as a prediction model.

[0091] The determination module 1003 is used to obtain target porosity data of the target shale, input the target porosity data into the prediction model, and obtain prediction results of the mechanical property evolution of the saline shale at different time scales.

[0092] Using this device, by simulating the dissolution reaction of SC-CO2 and saline water at a preset temperature and pressure, the state changes of shale samples at different time points can be accurately captured. The Boltzmann growth curve is used to fit the first variation of porosity over time, accurately describing how porosity increases or changes over time. By defining a dissolution damage variable and determining its second variation based on porosity changes, the extent of dissolution damage to shale samples can be quantified, clarifying the process by which shale mechanical properties are affected by dissolution. Using an exponential decay function to fit the third variation of multi-scale mechanical properties with porosity and damage variables, this method establishes a mathematical relationship between mechanical properties, porosity, and damage variables, and thus constructs the temporal variation of the multi-scale mechanical properties of shale. This method, through a comprehensive analysis of multi-scale mechanical properties, determines the evolution of shale mechanical properties with dissolution, accurately simulates the shale degradation process over long timescales, and can predict shale mechanical properties at different timescales with high accuracy and reliability. This method not only facilitates the design of more efficient CO2 storage projects, but also reduces potential leakage risks, providing a scientific basis for risk control and monitoring.

[0093] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 The steps of a method for predicting the mechanical properties of carbon sequestration in saline shale are provided.

[0094] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The steps of a method for predicting the mechanical properties of carbon sequestration in saline shale are provided.

[0095] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0099] It should be noted that the above specific embodiments can enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although this specification has described the present invention in detail, those skilled in the art should understand that the present invention can still be modified or replaced with equivalents; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are included in the scope of protection of the patent for the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A method for predicting the mechanical properties of carbon sequestration in saline shale, characterized in that: The following steps are involved: Obtain shale samples from saline layers, simulate the dissolution reaction of shale samples under preset environmental conditions, and obtain the porosity and multi-scale mechanical properties of the shale samples; The Boltzmann growth curve is used to fit the first variation pattern of porosity over time. A dissolution damage variable is defined based on the change in porosity, and a second variation pattern of the dissolution damage variable over time is determined based on the first variation pattern. An exponential decay function is used to fit the third variation pattern of multi-scale mechanical properties with porosity and dissolution damage variables. By combining the second and third variation patterns, the multi-scale mechanical property evolution pattern of shale is obtained, and the multi-scale mechanical property evolution pattern is used as a prediction model. Obtaining target porosity data of target shale, inputting the target porosity data into the prediction model, and obtaining prediction results of the mechanical property evolution of saline shale at different time scales; The formula of the first change rule is: Where e is the natural constant, k is the reaction rate constant; T and T0 are the observation time and initial time, respectively; the fitting constants A1 and A2 are related to the porosity in the initial state and the state of complete dissolution of the mineral after the dissolution reaction, respectively; the initial porosity φ0 and the final porosity φ m Perform equivalent substitution to obtain the approximate expression of the first change law: The dissolution damage variable is defined according to the change of porosity as: Where φ(T) is the porosity expression with respect to time T; φ0 is the initial porosity, φ m It is the theoretical maximum porosity when all soluble minerals are completely dissolved; The formula of the second change law is: The formula of the third change rule is: P=P0+λexp(-ψD s ); Where P0 is the initial performance value, λ, β, and ψ are material constants that describe the influence of dissolution damage on the mechanical properties of shale, where ψ = 1 / β(φ m -φ0); The formula for the evolution law of multi-scale mechanical properties is:

2. The method for predicting mechanical properties of carbon sequestration in saline shale according to claim 1, characterized in that: The porosity includes pore volume and pore size distribution, and the multi-scale mechanical properties include microhardness and microelastic modulus as well as macro compressive strength and macroelastic modulus. The porosity and multi-scale mechanical properties of the shale sample obtained by simulating the dissolution reaction of the shale sample under preset environmental conditions include: X-ray diffraction (XRD) is used to record and analyze the dissolution and precipitation processes of mineral components in shale at preset intervals. Nuclear magnetic resonance (NMR) technology is used to determine the pore volume and pore size distribution of shale at different dissolution times. A nanoindenter is used to test the microhardness and microelastic modulus of shale, and a triaxial compression system is used to measure the macro compressive strength and macro elastic modulus of shale.

3. A device for predicting the mechanical properties of carbon storage in saline shale, characterized in that: include: The acquisition module is used to obtain shale samples from saline layers, simulate the dissolution reaction of shale samples under preset environmental conditions, and obtain the porosity and multi-scale mechanical properties of the shale samples; A fitting module is configured to fit a first variation pattern of porosity over time using a Boltzmann growth curve; define a dissolution damage variable based on the change in porosity, and determine a second variation pattern of the damage variable over time based on the first variation pattern; fit a third variation pattern of multi-scale mechanical properties with porosity and damage variables using an exponential decay function; and obtain a multi-scale mechanical property evolution pattern of shale by combining the second and third variation patterns, and use the multi-scale mechanical property evolution pattern as a prediction model. The formula of the first change rule is: Where e is the natural constant, k is the reaction rate constant; T and T0 are the observation time and initial time, respectively; the fitting constants A1 and A2 are related to the porosity in the initial state and the state of complete dissolution of the mineral after the dissolution reaction, respectively; the initial porosity φ0 and the final porosity φ m Perform equivalent substitution to obtain the approximate expression of the first change law: The dissolution damage variable is defined according to the change of porosity as: Where φ(T) is the porosity expression with respect to time T; φ0 is the initial porosity, φ m It is the theoretical maximum porosity when all soluble minerals are completely dissolved; The formula of the second change law is: The formula of the third change rule is: P=P0+λexp(-ψD s ); Where P0 is the initial performance value, λ, β, and ψ are material constants that describe the influence of dissolution damage on the mechanical properties of shale, where ψ = 1 / β(φ m -φ0); The formula for the evolution law of multi-scale mechanical properties is: The determination module is used to obtain target porosity data of the target shale, input the target porosity data into the prediction model, and obtain the prediction results of the mechanical property evolution of the saline shale at different time scales.

4. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

5. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 2 when executing the program.

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