A method of hydrate saturation evaluation and related devices

Abstract

The application discloses a hydrate saturation evaluation method and related equipment, and the method comprises the following steps: obtaining the motion trajectory data of atoms in a system of a target type hydrate through a molecular dynamics model, so as to quantitatively obtain a static dielectric constant and a characteristic relaxation time of a dielectric polarization process, and then converting the first complex relative dielectric constant spectrum of the hydrate through a mathematical model to establish a finite element model of a hydrate-containing porous medium; simulating and quantifying the second complex relative dielectric constant spectrum of the hydrate-containing porous medium by using the finite element model; and based on the two complex relative dielectric constant spectra, a saturation evaluation model is constructed by combining a dielectric mixing theory model to output a hydrate saturation evaluation result. The application can effectively overcome the technical bottleneck that a conventional logging method cannot identify the type of hydrate, provides a core theoretical tool for hydrate saturation evaluation, and can be widely applied to the fields of hydrate resource exploration and development and hydrate method marine carbon sequestration technology.

Description

Technical Field

[0001] This invention relates to the fields of hydrate resource exploration and development and hydrate-based marine carbon sequestration, and particularly to a method and related equipment for evaluating hydrate saturation. Background Technology

[0002] With the increasingly severe global climate change situation, the development of safe and efficient carbon dioxide sequestration technologies has become an urgent need to achieve the "dual carbon" strategic goals. Among them, CO2 subsea sequestration technology based on hydrates has shown great application potential due to its extremely high storage capacity, excellent long-term stability, and good adaptability to geological environments. This technology is mainly implemented through two paths: one is to directly inject CO2 into high-pressure, low-temperature environments such as deep sea or permafrost layers, so that it forms solid CO2 hydrates and is then sequestered; the other is to use CO2 to replace CH4 during the extraction of natural gas hydrates (mainly CH4), thereby sequestering CO2 in the reservoir as hydrates while extracting energy, achieving synergistic effects between energy extraction and carbon sequestration.

[0003] During the aforementioned CO2–CH4 replacement process, the saturation of CH4 hydrates and CO2 hydrates within the reservoir is dynamically changing. To achieve accurate monitoring and efficiency assessment of the replacement process, there is an urgent need to develop a method capable of real-time, synchronous evaluation of the saturation of multiple hydrate types. Currently, reservoir saturation evaluation mainly relies on geophysical logging techniques, such as resistivity logging, dielectric logging, and sonic logging. However, traditional methods have significant limitations in distinguishing between CH4 hydrates and CO2 hydrates: because both exhibit high resistivity, resistivity logging is difficult to effectively differentiate them; under the same saturation and occurrence pattern, the sonic velocities of sediments containing both types of hydrates are similar, and sonic logging also lacks the ability to distinguish between them.

[0004] Theoretically, hydrates exhibit unique dielectric dispersion characteristics in the kHz to MHz electromagnetic frequency band. Due to their cage-like structure and the differences in the physicochemical properties of their internal guest molecules (CH4 or CO2), different types of hydrates should have different dispersion characteristics. This provides a physical basis for using dielectric spectra to distinguish hydrate types and invert their saturation. However, existing research mostly focuses on optical or higher frequency bands, neglecting the crucial mid-to-low frequency range (10 MHz) for monitoring CO2–CH4 substitution processes. -2 ~10 8 Within the Hz range, the specific dielectric dispersion characteristics and differences between CH4 hydrate and CO2 hydrate remain unclear. The lack of fundamental dielectric data severely restricts the accurate understanding of the electrical response of hydrate-containing porous media and is a key bottleneck hindering the accurate identification of hydrate types and evaluation of saturation based on dielectric spectroscopy. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, electronic device, storage medium, and program product for evaluating hydrate saturation, aiming to solve at least one problem in the prior art.

[0006] To achieve the above objectives, one aspect of this invention proposes a method for evaluating hydrate saturation, the method comprising:

[0007] Molecular dynamics models were used to obtain atomic trajectory data of the target type of hydrate within the system. Based on the quantization of motion trajectory data, the static dielectric constant and the characteristic relaxation time of the dielectric polarization process are obtained, and then the first complex relative dielectric constant spectrum of the hydrate is obtained by mathematical model transformation. A finite element model of hydrate-containing porous media was established based on the first complex relative permittivity spectrum. The second complex relative permittivity spectrum of the hydrate-containing porous medium was obtained by simulation and quantification using the finite element model. Based on the first and second complex relative permittivity spectra, a saturation evaluation model is constructed using a dielectric mixing theory model to output hydrate saturation evaluation results.

[0008] In some embodiments, the method further includes the following steps: The first geometric model of the target type of hydrate is obtained by constructing a hydrate unit cell of a preset type. Based on the first geometric model, a molecular dynamics model is established by combining preset conditions. The preset conditions include the force fields corresponding to different components in the system, as well as preset temperature and preset pressure.

[0009] In some embodiments, the static dielectric constant and the characteristic relaxation time of the dielectric polarization process are obtained by quantization based on motion trajectory data, including the following steps: The Cartesian component of the dipole moment is determined based on the motion trajectory data, and then the fluctuation data of the total dipole moment vector is obtained by quantization. The static dielectric constant is calculated based on fluctuation data and the first parameter. The expression for the static dielectric constant is: ; In the formula, Indicates the static dielectric constant; Indicates fluctuation data; , , and As the first parameter, Represents the vacuum permittivity. Represents the volume of the system. Represents the Boltzmann constant. This indicates the preset temperature of the molecular dynamics model; The characteristic relaxation time of the dielectric polarization process is determined by fitting the autocorrelation function of the total dipole moment vector to the form of a stretching exponential function.

[0010] In some embodiments, the first complex relative permittivity spectrum of the hydrate is obtained through mathematical model transformation, including the following steps: The static permittivity and characteristic relaxation time are substituted into the preset mathematical model as the first input parameters to calculate the complex relative permittivity corresponding to different angular frequencies in the preset frequency range, and then the first complex relative permittivity spectrum is obtained by summarizing them. The mathematical model is expressed as follows: ; In the formula, Represents the complex relative permittivity; Indicates the relative permittivity at high frequencies; Determined based on static dielectric constant, Indicates the relative permittivity at low frequencies; Represents the imaginary unit; Indicates angular frequency; Indicates the characteristic relaxation time; Indicates DC conductivity; It represents the vacuum permittivity.

[0011] In some embodiments, the hydrate-containing porous medium includes framework particles, hydrates, and pore water. A finite element model of the hydrate-containing porous medium is established based on a first complex relative permittivity spectrum, comprising the following steps: A second geometric model is obtained by modeling the two-dimensional geometry of porous media containing one or more combinations of target type hydrates. The conductivity of pore water and the first complex conductivity of different types of framework particles are obtained by quantification based on a preset calculation formula. Based on the conductivity and the first complex conductivity, the conductivity of each component in the second geometric model is assigned a value, and the relative permittivity is assigned a value through the first complex relative permittivity spectrum to obtain the initial finite element model. The conductivity value of the framework particles is determined based on the real part of the first complex conductivity. An alternating electric field is applied to the initial finite element model, and the geometric region is discretized using a free triangular mesh. Then, the electric field control equations are solved using the parallel sparse direct method to establish the target finite element model.

[0012] In some embodiments, the second complex relative permittivity spectrum of the hydrate-containing porous medium is obtained by simulation and quantization using a finite element model, including the following steps: Simulation data on the change of current over time in hydrate-containing porous media were obtained based on the finite element model. Fourier fitting is performed on the simulated data to obtain the current fitting data; The phase angle is obtained by using correlation analysis based on current fitting data. The second complex conductivity of the hydrate-containing porous medium is obtained by quantifying the maximum current value in the current fitting data. Based on the imaginary part of the second complex conductivity, the real part of the complex relative permittivity of the hydrate-containing porous medium is obtained by combining the vacuum permittivity and the angular frequency quantization within a preset frequency range. Based on the real part of the second complex conductivity, the imaginary part of the complex relative permittivity of the hydrate-containing porous medium is obtained by combining the vacuum permittivity and the angular frequency quantization within a preset frequency range. The second complex relative permittivity spectrum is obtained by summing the real and imaginary parts of the complex relative permittivity corresponding to each angular frequency.

[0013] In some embodiments, the components of the hydrate-containing porous medium include framework particles, hydrates, and pore water. The target type of hydrate includes CO2 hydrate and CH4 hydrate. Based on the first and second complex relative permittivity spectra, a saturation evaluation model is constructed in conjunction with a dielectric mixing theory model to output the hydrate saturation evaluation results, including the following steps: Based on the first complex relative permittivity spectrum, combined with the preset complex relative permittivity spectra of the framework particles, the complex relative permittivity spectrum of pore water, and the volume fraction of the framework particles, the third complex relative permittivity spectrum is obtained by solving the dielectric mixing model for comparison of numerical solutions. Based on the third complex relative permittivity spectrum obtained from the second complex relative permittivity spectrum and the dielectric mixing model, the total hydrate saturation is obtained by iterative optimization using a nonlinear least squares algorithm with the goal of minimizing the sum of squared errors of the complex relative permittivity spectrum of porous media. Based on the total hydrate saturation, a hydrate saturation evaluation model for the CO2-CH4 replacement process is established, and then the target hydrate saturation of each target type of hydrate is obtained through iterative optimization and inversion. The CO2-CH4 substitution process characterizes the process of replacing CH4 in CH4 hydrate with CO2 to obtain CO2 hydrate.

[0014] To achieve the above objectives, another aspect of the present invention provides a hydrate saturation evaluation device, the device comprising: The first module is used to obtain the trajectory data of atoms in the system of the target type of hydrate through molecular dynamics models; The second module is used to quantize the static dielectric constant and the characteristic relaxation time of the dielectric polarization process based on the motion trajectory data, and then transform it through a mathematical model to obtain the first complex relative dielectric constant spectrum of the hydrate. The third module is used to establish a finite element model of hydrate-containing porous media based on the first complex relative permittivity spectrum; The fourth module is used to simulate and quantify the second complex relative permittivity spectrum of hydrate-containing porous media using a finite element model; The fifth module is used to construct a saturation evaluation model based on the first and second complex relative permittivity spectra and in combination with the dielectric mixing theory model, so as to output the hydrate saturation evaluation results.

[0015] In some embodiments, the apparatus further includes a sixth module for performing the following operations: The first geometric model of the target type of hydrate is obtained by constructing a hydrate unit cell of a preset type. Based on the first geometric model, a molecular dynamics model is established by combining preset conditions. The preset conditions include the force fields corresponding to different components in the system, as well as preset temperature and preset pressure.

[0016] To achieve the above objectives, another aspect of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method.

[0017] To achieve the above objectives, another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method.

[0018] To achieve the above objectives, another aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0019] The embodiments of the present invention include at least the following beneficial effects: The present invention provides a method, apparatus, electronic device, storage medium, and program product for evaluating hydrate saturation. This scheme obtains the atomic motion trajectory data of the target type of hydrate within the system through a molecular dynamics model; quantizes the static dielectric constant and the characteristic relaxation time of the dielectric polarization process based on the motion trajectory data, and then transforms it through a mathematical model to obtain the first complex relative permittivity spectrum of the hydrate; establishes a finite element model of the porous medium containing hydrate based on the first complex relative permittivity spectrum; uses the finite element model to simulate and quantize the second complex relative permittivity spectrum of the porous medium containing hydrate; and constructs a saturation evaluation model based on the first and second complex relative permittivity spectra, combined with a dielectric mixing theory model, to output the hydrate saturation evaluation results. This invention first obtains the complex relative permittivity spectrum of the target hydrate through molecular dynamics simulation, filling the gap in key dielectric data; then, it constructs a multi-scale coupled model of "molecular dynamics-finite element-dielectric hybrid theory", which can realize the synchronous and quantitative differentiation and evaluation of the saturation of various types of hydrates, effectively overcoming the technical bottleneck of traditional logging methods being unable to identify hydrate types, and providing a core theoretical tool for hydrate saturation evaluation. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of an implementation environment for the method of evaluating hydrate saturation provided in this embodiment of the invention; Figure 2 This is a schematic flowchart of a hydrate saturation evaluation method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the overall process of the hydrate saturation evaluation method provided in the embodiments of the present invention; Figure 4 This is a schematic diagram of an example of a SI-type hydrate structure provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the curve showing the change of the static dielectric constant of the hydrate as a function of simulation time, provided in an embodiment of the present invention. Figure 6 This is a schematic diagram of the characteristic relaxation time fitting of the hydrate polarization process provided in the embodiments of the present invention; Figure 7 This is a schematic diagram of the complex relative permittivity spectrum of the hydrate provided in the embodiments of the present invention; Figure 8 This is a schematic diagram of the geometric structure of the hydrate-containing porous medium provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of the complex relative permittivity spectrum of a porous medium containing CH4 hydrate provided in an embodiment of the present invention; Figure 10This is a schematic diagram of the complex relative permittivity spectrum of a porous medium containing CH4–CO2 hydrate provided in an embodiment of the present invention; Figure 11 This is a schematic diagram comparing the saturation spectrum of the porous media containing CH4 hydrates and the numerical model provided in the embodiments of the present invention; Figure 12 This is a schematic diagram comparing the saturation spectrum of the porous medium containing CH4-CO2 hydrate and the numerical model provided in the embodiments of the present invention; Figure 13 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0022] It is understood that the terms “first,” “second,” etc., used in this invention may be used herein to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are used only to distinguish one concept from another. For example, first information may also be referred to as second information without departing from the scope of embodiments of the invention, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to determination” as used herein may be interpreted as “when…” or “when…” or “in response to determination.”

[0023] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0025] In related technologies, existing research mostly focuses on optical or higher frequency bands, while the mid-to-low frequency range (10) is crucial for monitoring CO2–CH4 replacement processes. -2~10 8 Within the Hz range, the specific dielectric dispersion characteristics and differences between CH4 hydrate and CO2 hydrate remain unclear. The lack of fundamental dielectric data severely restricts the accurate understanding of the electrical response of hydrate-containing porous media and is a key bottleneck hindering the accurate identification of hydrate types and evaluation of saturation based on dielectric spectroscopy.

[0026] In view of this, this invention provides a method and related equipment for evaluating hydrate saturation. This method obtains atomic trajectory data of the target type of hydrate within the system through a molecular dynamics model; quantifies the static dielectric constant and characteristic relaxation time of the dielectric polarization process based on the trajectory data, and then transforms this data through a mathematical model to obtain the first complex relative permittivity spectrum of the hydrate; establishes a finite element model of the porous medium containing hydrate based on the first complex relative permittivity spectrum; uses the finite element model to simulate and quantify the second complex relative permittivity spectrum of the porous medium containing hydrate; and constructs a saturation evaluation model based on the first and second complex relative permittivity spectra, combined with a dielectric mixing theory model, to output the hydrate saturation evaluation results. This invention first obtains the complex relative permittivity spectrum of the target hydrate through molecular dynamics simulation, filling a key gap in dielectric data; then constructs a multi-scale coupled model of "molecular dynamics-finite element-dielectric mixing theory," which enables simultaneous, quantitative differentiation and evaluation of the saturation of multiple types of hydrates, effectively overcoming the technical bottleneck of traditional logging methods being unable to identify hydrate types, and providing a core theoretical tool for hydrate saturation evaluation.

[0027] It is understood that the hydrate saturation evaluation method provided by this invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of terminals or servers. When the computer device in the embodiments is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet, laptop, or desktop computer, but it is not limited to these.

[0028] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the present invention. (Refer to...) Figure 1The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0029] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0030] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0031] Terminal 102 can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0032] For example, based on Figure 1 The implementation environment shown in this embodiment of the invention provides a hydrate saturation evaluation method. The following description uses the application of this hydrate saturation evaluation method in server 101 as an example. It can be understood that this hydrate saturation evaluation method can also be applied to terminal 102.

[0033] Reference Figure 2 , Figure 2 This is an optional flowchart of the hydrate saturation evaluation method provided in the embodiments of the present invention. The subject executing the hydrate saturation evaluation method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S100 to S500.

[0034] Step S100: Obtain the trajectory data of atoms in the system of the target type of hydrate through a molecular dynamics model; It should be noted that in some embodiments, the method may further include the following steps: constructing a first geometric model of the target type hydrate based on a preset type of hydrate unit cell; establishing a molecular dynamics model based on the first geometric model and in combination with preset conditions; wherein, the preset conditions include the force fields corresponding to different components in the system, as well as preset temperature and preset pressure.

[0035] For example, in some specific embodiments, taking CH4 hydrate and CO2 hydrate as examples, a molecular dynamics model is established, and the specific steps can be implemented as follows: (1) Construct geometric models of CH4 hydrate and CO2 hydrate based on SI-type hydrate unit cells; (2) Apply corresponding force fields to each component in the system and set appropriate temperature and pressure conditions to complete the establishment of the molecular dynamics model.

[0036] In some specific implementations, numerical simulations based on the above molecular dynamics model can be used to obtain the trajectory data of atoms within the system.

[0037] Step S200: Based on the motion trajectory data, the static dielectric constant and the characteristic relaxation time of the dielectric polarization process are quantized and then transformed through a mathematical model to obtain the first complex relative dielectric constant spectrum of the hydrate. It should be noted that, in some embodiments, the process of quantizing the static dielectric constant and the characteristic relaxation time of the dielectric polarization process based on motion trajectory data may include the following steps: determining the Cartesian component of the dipole moment based on the motion trajectory data, and then quantizing the fluctuation data of the total dipole moment vector; calculating the static dielectric constant based on the fluctuation data and the first parameter; and determining the characteristic relaxation time of the dielectric polarization process by fitting the autocorrelation function of the total dipole moment vector into the form of a stretching exponential function.

[0038] For example, in some specific implementations, the static dielectric constant is calculated based on the fluctuations of the total dipole moment of the system by analyzing cloud trajectory data, and the characteristic relaxation time of the dielectric polarization process of the hydrate is determined by fitting the autocorrelation function of the total dipole moment. The specific steps can be implemented as follows: (1) Obtaining the static dielectric constant of hydrates: The static dielectric constant of the hydrate is calculated based on the Neumann formula and using the fluctuation data of the total dipole moment of the system. The calculation method of dipole moment fluctuation is shown in Equation (1), and the calculation method of static dielectric constant is shown in Equation (2).

[0039] (1) (2) In the formula: It is the total dipole moment vector of the system, D; Characterizing fluctuations, The variance characterizing the total dipole moment vector, i.e., fluctuation data; , and Let D be the Cartesian component of the dipole moment; It is the static dielectric constant of the system, which is dimensionless; It is the vacuum permittivity. = 8.854×10 -12 F·m -1 ; The volume of the system is expressed in m. 3 ; Boltzmann's constant, =1.38 × 10 -23 J.K. -1 ; Let K be the temperature.

[0040] (2) Obtaining the characteristic relaxation time of the dielectric polarization process of hydrates: The autocorrelation function of the total dipole moment (Equation (3)) is fitted to a stretching exponential function (Equation (4)) to determine the characteristic relaxation time of the dielectric polarization process of hydrates.

[0041] (3) (4) In the formula: For time, s; The characteristic relaxation time of the dielectric polarization process of hydrates is given in seconds. The stretch index is used.

[0042] It should be noted that, in some embodiments, the first complex relative permittivity spectrum of hydrates can be obtained by transforming a mathematical model, which may include the following steps: substituting the static permittivity and characteristic relaxation time as first input parameters into a preset mathematical model, calculating the complex relative permittivity corresponding to different angular frequencies in a preset frequency range, and then summarizing to obtain the first complex relative permittivity spectrum.

[0043] For example, in some specific embodiments, the frequency-dependent broadband complex relative permittivity spectrum can be calculated using the Cole-Cole model. Specifically, the acquisition of the complex relative permittivity spectrum of hydrates can be achieved as follows: Using the static permittivity and characteristic relaxation time determined above as input parameters, and substituting them into the Cole-Cole model (Equation (5)), the frequency-dependent complex relative permittivity spectrum can be calculated, with a frequency range of 10. -2 ~10 8 Hz.

[0044] (5) In the formula: It is the complex relative permittivity of the hydrate; It is the high-frequency relative permittivity; Determined based on static dielectric constant, It is the low-frequency relative permittivity; It is the imaginary unit; It is angular frequency, rad·s -1 ; It is the characteristic relaxation time; It is the distribution factor. = 0.01; DC conductivity, S·m -1 In these calculations, the high-frequency relative permittivity of the hydrate was set to 4.

[0045] Step S300: Establish a finite element model of the hydrate-containing porous medium based on the first complex relative permittivity spectrum; It should be noted that the hydrate-containing porous medium includes framework particles, hydrates, and pore water. In some embodiments, step S300 may include the following steps: modeling the two-dimensional geometric structure of the porous medium containing one or more combinations of target type hydrates to obtain a second geometric model; quantizing the conductivity of pore water and the first complex conductivity of different types of framework particles based on a preset calculation formula; assigning conductivity values ​​to each component in the second geometric model based on the conductivity and the first complex conductivity, and assigning relative permittivity values ​​through the first complex relative permittivity spectrum to obtain an initial finite element model; wherein, the conductivity value of the framework particles is determined based on the real part of the first complex conductivity; applying an AC electric field to the initial finite element model, discretizing the geometric region using a free triangular mesh, and then solving the electric field control equation using a parallel sparse direct method to establish the target finite element model.

[0046] For example, in some specific embodiments, the real part of the complex relative permittivity spectrum of the hydrate obtained in the aforementioned steps can be used as an input parameter in the form of a frequency-complex relative constant real part lookup table to establish a finite element model of the porous medium containing hydrates. The specific steps can be implemented as follows: (1) Establish two-dimensional geometric structures of porous media containing CH4 hydrate, porous media containing CO2 hydrate, and porous media containing CO2–CH4 hydrate. The porous media containing hydrate includes framework particles (quartz sand / montmorillonite), hydrate, and pore water. (2) Calculate the complex conductivity of the skeleton particles and the conductivity of the pore water. When the skeleton particles of the hydrate-containing porous medium are quartz sand, the calculation of the complex conductivity of the skeleton particle surface is shown in Equation (6), and the calculation of the conductivity of the pore water is shown in Equation (7); when the skeleton particles of the hydrate-containing porous medium are montmorillonite, the calculation of the complex conductivity of the skeleton particle surface is shown in Equation (8), and the calculation of the conductivity of the pore water is shown in Equation (9). (6) (7) (8) (9) In the formula: It is the complex electrical conductivity of the surface of quartz sand particles, in S·m. -1 ; It is the low-frequency surface conductivity of the skeletal particles, in S·m -1 ; It is the high-frequency surface conductivity of the skeletal particles, in S·m -1 ; The characteristic relaxation time, in seconds, is the polarization time of the double electric layer of the framework particles. It is the electrical conductivity of pore water (with quartz sand as the framework particles), in S·m. -1 ; The molar concentration of pore water is expressed in mol / L. It is the complex electrical conductivity of the montmorillonite particle surface, in S·m. -1 ; It is the electrical conductivity of pore water (with montmorillonite as the framework particles), S·m -1 ; Na is the diffusion layer + mobility, m 2 ·s -1 ·V -1 ; The distribution coefficient is dimensionless. Density of solid skeleton particles, kg·m -3 ; Porosity is a dimensionless quantity. The cation exchange capacity is expressed in C·kg⁻¹. -1 ; The saturation index is dimensionless. The value represents water saturation, which is dimensionless.

[0047] The low-frequency and high-frequency surface conductivity of the framework particles are given by equations (10) and (11), respectively. Equation (12) describes the relationship between the characteristic relaxation time of the double-layer polarization process of the framework particles and the peak frequency of the imaginary part of the surface complex conductivity.

[0048] (10) (11) (12) In the formula: It is the characteristic pore throat size of the porous medium, in meters; is the peak frequency of the imaginary part of the complex conductivity of the skeletal particle surface, in Hz; , The specific surface area conductances of the diffusion layer and the Stern layer are respectively, which can be calculated by equations (13) and (14).

[0049] (13) (14) In the formula: The elementary charge, =1.602×10 -19 C; For Stern layer Na + mobility, m 2 s -1 V -1 ; Na is the diffusion layer + Surface position density, m -2 ; For Stern layer Na + Surface position density, m -2 .

[0050] The characteristic relaxation time of the double-layer polarization process of framework particles in hydrate-containing porous media can be calculated by equation (15): (15) In the formula: for Na + The diffusion coefficient in the Stern layer, m 2 ·s -1 .

[0051] The relationship between the characteristic pore throat size of an ideal particulate material and parameters such as formation factor and particle radius (as shown in Equation (16)). In an ideal particulate material, the particles are regular spheres with the same diameter.

[0052] (16) In the formula: Formation factors; Let be the particle radius, in meters. This represents the cementation index. and It can be calculated using Archie's formula: (17) (18) In the formula: σ 0 represents the conductivity of the water-saturated porous medium, in S·m. -1 ; The conductivity of the porous medium containing hydrates, in S·m -1 ; and Lithology coefficient, Generally, 1 is taken. In most cases, it is close to 1.

[0053] (3) Assign corresponding electrical conductivity and relative permittivity to each component in the geometric model. When the framework particles are quartz sand, the electrical conductivity and relative permittivity of the framework particles are calculated by the real part of equation (6) and equation (19), respectively, and the electrical conductivity and relative permittivity of pore water are calculated by equation (7) and equation (21), respectively; when the framework particles are montmorillonite, the electrical conductivity and relative permittivity of the framework particles are calculated by the real part of equation (8) and equation (20), and the electrical conductivity and relative permittivity of pore water are calculated by equation (9) and equation (21), respectively; the electrical conductivity of the hydrate is set to 1×10 -5 S·m -1 The relative permittivity is calculated using the real part of equation (5).

[0054] (19) (20) (twenty one) In the formula: is the relative permittivity of the quartz sand particles; is the relative permittivity of montmorillonite particles; S·m represents the imaginary part of the complex conductivity of the quartz sand particle surface. -1 ; S·m represents the imaginary part of the complex conductivity of the montmorillonite particle surface. -1 ; It is the relative permittivity of pore water.

[0055] (4) Apply an alternating electric field U =sin( ωt The geometric region was discretized using a free triangular mesh, and the governing equations of the electric field were solved using the Multi-frontal Massively Parallel Sparse Direct Solver (MUMPS), thus completing the establishment of the finite element model.

[0056] Step S400: The second complex relative permittivity spectrum of the hydrate-containing porous medium is obtained by simulation and quantification using a finite element model; It should be noted that, in some embodiments, step S400 may include the following steps: obtaining simulated data of the current change over time in the hydrate-containing porous medium based on a finite element model; performing Fourier fitting on the simulated data to obtain current fitting data; solving for the phase angle using correlation analysis based on the current fitting data; quantizing the second complex conductivity of the hydrate-containing porous medium based on the maximum current value in the current fitting data; quantizing the real part of the complex relative permittivity of the hydrate-containing porous medium based on the imaginary part of the second complex conductivity, combined with the vacuum permittivity and angular frequencies within a preset frequency range; quantizing the imaginary part of the complex relative permittivity of the hydrate-containing porous medium based on the real part of the second complex conductivity, combined with the vacuum permittivity and angular frequencies within a preset frequency range; and summing the real part and imaginary part of the complex relative permittivity corresponding to each angular frequency to obtain the second complex relative permittivity spectrum.

[0057] For example, in some specific embodiments, data on the change of current in hydrate-containing porous media over time can be obtained through simulation calculations based on a finite element model, thus obtaining broadband (10) data of hydrate-containing porous media. -2 ~10 8 The electrical response data (Hz) is obtained through the following steps: (1) Extract the current and time data and perform Fourier fitting. The fitting formula is as follows: (twenty two) In the formula: I It is electric current, A; k 1. k 2. k 3 is the fitting constant.

[0058] (2) The phase angle is determined using correlation analysis, and the specific formula is as follows: (twenty three) In the formula: It is voltage, V; It's the phase angle, in rad.

[0059] (3) Calculate the complex conductivity of the porous medium containing hydrates according to formula (22): (twenty four) In the formula: It is the complex conductivity of hydrate-containing porous media, in S·m. -1 ; It is the maximum current value, in A; It is the maximum voltage value, in V; It is the length of the hydrate-containing porous medium along the direction of the applied electric field, in meters; It is the cross-sectional area of ​​the porous medium, m 2 .

[0060] (4) Complex relative permittivity of porous media The real and imaginary parts are shown in equations (25) and (26) respectively: (25) (26) In the formula: It is the real part of the complex relative permittivity of hydrate-containing porous media; It is the imaginary part of the complex relative permittivity of hydrate-containing porous media; It is the real part of the complex conductivity of hydrate-containing porous media, S·m -1 ; It is the imaginary part of the complex conductivity of hydrate-containing porous media, S·m -1 .

[0061] Step S500: Based on the first and second complex relative permittivity spectra, and combined with the dielectric mixing theory model, a saturation evaluation model is constructed to output the hydrate saturation evaluation results. It should be noted that the components of the hydrate-containing porous medium include framework particles, hydrates, and pore water. The target hydrate types include CO2 hydrates and CH4 hydrates. In some embodiments, step S500 may include the following steps: based on the first complex relative permittivity spectrum, combined with the preset complex relative permittivity spectra of framework particles, pore water, and the volume fraction of framework particles, a third complex relative permittivity spectrum for comparison of numerical solutions is obtained by solving the dielectric mixing model; based on the second complex relative permittivity spectrum and the third complex relative permittivity spectrum obtained by solving the dielectric mixing model, with the goal of minimizing the sum of squared errors of the complex relative permittivity spectrum of the porous medium, the total hydrate saturation is obtained by iterative optimization using a nonlinear least squares algorithm; based on the total hydrate saturation, a hydrate saturation evaluation model for the CO2-CH4 replacement process is established, and then the target hydrate saturation of each target hydrate type is obtained by iterative optimization; wherein, the CO2-CH4 replacement process characterizes the process of replacing CH4 in CH4 hydrate with CO2 to obtain CO2 hydrate.

[0062] Specifically, based on the second complex relative permittivity spectrum (obtained through numerical simulation, and in some optional embodiments, also through field measurement), combined with the first complex relative permittivity spectrum (i.e., the complex relative permittivity spectrum of hydrates), the known complex relative permittivity spectrum of pore water, the known complex relative permittivity spectrum of framework particles (the complex relative permittivity spectra of pore water and framework particles are assumed to be known parameters in this embodiment of the invention, and have been obtained in advance through calculation, acquisition, or simulation), and the known volume fraction of framework particles (equal to 1 minus porosity, which is obtained by testing using methods known in the field), with the goal of minimizing the sum of squares of the error (the deviation between the calculated value and the value obtained through numerical simulation or actual measurement) of the complex relative permittivity spectrum of the hydrate-containing porous medium calculated by the dielectric mixing model, the total hydrate saturation is obtained by iterative optimization using a nonlinear least squares algorithm.

[0063] For example, in some specific embodiments, based on the Maxwell-Garnett (MG) dielectric mixing theory, the complex relative permittivity spectrum (10) covering the hydrate polarization frequency band is used. 5 ~10 7 Using Hz as input, a hydrate saturation evaluation model applicable to the CO2–CH4 replacement process is established. The MG theory, based on the physical assumption that the dispersed phase is spherical particles, is a simple, purely theoretical model suitable for multiphase (skeleton particles, pore water, hydrate) mixed systems. The specific steps for establishing the saturation evaluation model are as follows: (1) Establish a hydrate saturation evaluation model to calculate the total hydrate saturation in the porous medium (Equation (27)). Before CO2–CH4 replacement, the total hydrate saturation is the CH4 hydrate saturation. Substitute the volume fraction of each phase and the complex relative permittivity spectrum into Equation (27) to obtain the complex relative permittivity spectrum of the porous medium. Compare it with the numerical solution (i.e., the second complex relative permittivity spectrum). Substitute the complex relative permittivity spectrum of each phase into Equation (27). With the goal of minimizing the sum of squared errors of the complex relative permittivity spectrum of the porous medium, iteratively optimize it using a nonlinear least squares algorithm to invert and obtain the total hydrate saturation.

[0064] (27) In the formula: It is the complex relative permittivity of pore water; It is the complex relative permittivity of the skeletal particles; It is the complex relative permittivity of the hydrate; It is the volume fraction of skeletal particles in the porous medium; It is the volume fraction of hydrates in porous media; It is the saturation of total hydrates.

[0065] (2) The total hydrate saturation is obtained through the above steps. Based on this, a hydrate saturation evaluation model applicable to the CO2–CH4 replacement process is established to calculate the saturation of CH4 hydrate and CO2 hydrate. The volume fraction and complex relative permittivity spectrum of each phase are substituted into equation (28) to obtain the complex relative permittivity spectrum of the porous medium, which is compared with the numerical solution. The complex relative permittivity spectrum of each phase is substituted into equation (28). With the goal of minimizing the sum of squared errors of the complex relative permittivity spectrum of the porous medium, the saturation of CH4 hydrate and CO2 hydrate is obtained by iterative optimization through a nonlinear least squares algorithm.

[0066] (28) (29) In the formula: It is the complex relative permittivity of CH4 hydrate; It is the volume fraction of CH4 hydrate in porous media; It is the saturation of CH4 hydrate; It is the complex relative permittivity of CO2 hydrate; It is the volume fraction of CO2 hydrate in porous media; It refers to the CO2 hydrate saturation.

[0067] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0068] To address the shortcomings of existing technologies and the unclear dielectric dispersion characteristics of CH4 hydrate and CO2 hydrate, this invention proposes a novel numerical modeling method to obtain the broadband complex relative permittivity spectrum of these two hydrates. The complex relative permittivity spectrum of hydrates is a key input parameter for obtaining the dielectric response of hydrate-containing porous media and constructing a saturation evaluation model suitable for the CO2–CH4 replacement process. Based on this, a method for simultaneously evaluating the saturation of multiple types of hydrates (such as CO2 hydrate and CH4 hydrate) based on broadband electrical spectra is proposed. The implementation of this invention can provide a theoretical basis for the development of real-time monitoring technology for the saturation of CH4 hydrate and CO2 hydrate during the CO2–CH4 replacement process.

[0069] To address the aforementioned problems, this invention provides a numerical simulation method for obtaining broadband complex relative permittivity spectra of CH4 hydrates and CO2 hydrates, and a method for evaluating the saturation of multiple types of hydrates applicable to CO2–CH4 substitution processes. The technical roadmap is as follows: Figure 3 As shown. The method includes the following steps: 1. Establish molecular dynamics models for CH4 hydrate and CO2 hydrate. The specific steps are as follows: (1) Construct geometric models of CH4 hydrate and CO2 hydrate based on SI-type hydrate unit cells; (2) Apply corresponding force fields to each component in the system and set appropriate temperature and pressure conditions to complete the establishment of the molecular dynamics model; 2. Numerical simulations were performed based on the above molecular dynamics model to obtain the trajectory data of atoms within the system; 3. Analyze these trajectory data, calculate the static dielectric constant based on the fluctuations of the total dipole moment of the system, determine the characteristic relaxation time of the dielectric polarization process of the hydrate by fitting the autocorrelation function of the total dipole moment, and calculate the frequency-dependent broadband complex relative dielectric constant spectrum using the Cole-Cole model. The specific steps are as follows: (1) Obtaining the static dielectric constant of hydrates: The static dielectric constant of the hydrate is calculated based on the Neumann formula and using the fluctuation data of the total dipole moment of the system. The calculation method of dipole moment fluctuation is shown in Equation (1), and the calculation method of static dielectric constant is shown in Equation (2).

[0070] (1) (2) In the formula: It is the total dipole moment vector of the system, D; Characterizing fluctuations, The variance characterizing the total dipole moment vector, i.e., fluctuation data; , and Let D be the Cartesian component of the dipole moment; It is the static dielectric constant of the system, which is dimensionless; It is the vacuum permittivity. = 8.854×10 -12 F·m -1 ; The volume of the system is expressed in m. 3 ; Boltzmann's constant, =1.38 × 10 -23 J.K. -1 ; Let K be the temperature.

[0071] (2) Obtaining the characteristic relaxation time of the dielectric polarization process of hydrates: The autocorrelation function of the total dipole moment (Equation (3)) is fitted to a stretching exponential function (Equation (4)) to determine the characteristic relaxation time of the dielectric polarization process of hydrates.

[0072] (3) (4) In the formula: For time, s; The characteristic relaxation time of the dielectric polarization process of hydrates is given in seconds. The stretch index is used.

[0073] (3) Obtaining the complex relative permittivity spectrum of hydrates: Using the static permittivity and characteristic relaxation time determined above as input parameters, and substituting them into the Cole-Cole model (Equation (5)), the frequency-dependent complex relative permittivity spectrum can be calculated, with a frequency range of 10. -2 ~10 8 Hz.

[0074] (5) In the formula: It is the complex relative permittivity of the hydrate; It is the high-frequency relative permittivity; Determined based on static dielectric constant, It is the low-frequency relative permittivity; It is the imaginary unit; It is angular frequency, rad·s -1 ; It is the characteristic relaxation time; It is the distribution factor. = 0.01; DC conductivity, S·m -1 In these calculations, the high-frequency relative permittivity of the hydrate was set to 4.

[0075] 4. The real part of the complex relative permittivity spectrum of the hydrate obtained in the above steps is used as an input parameter in the form of a frequency-complex relative constant real part lookup table to establish a finite element model of the porous medium containing hydrates. The specific steps are as follows: (1) Establish two-dimensional geometric structures of porous media containing CH4 hydrate, porous media containing CO2 hydrate, and porous media containing CO2–CH4 hydrate. The porous media containing hydrate includes framework particles (quartz sand / montmorillonite), hydrate, and pore water. (2) Calculate the complex conductivity of the skeleton particles and the conductivity of the pore water. When the skeleton particles of the hydrate-containing porous medium are quartz sand, the calculation of the complex conductivity of the skeleton particle surface is shown in Equation (6), and the calculation of the conductivity of the pore water is shown in Equation (7); when the skeleton particles of the hydrate-containing porous medium are montmorillonite, the calculation of the complex conductivity of the skeleton particle surface is shown in Equation (8), and the calculation of the conductivity of the pore water is shown in Equation (9). (6) (7) (8) (9) In the formula: It is the complex electrical conductivity of the surface of quartz sand particles, in S·m. -1 ; It is the low-frequency surface conductivity of the skeletal particles, in S·m -1 ; It is the high-frequency surface conductivity of the skeletal particles, in S·m -1 ; The characteristic relaxation time, in seconds, is the polarization time of the double electric layer of the framework particles. It is the electrical conductivity of pore water (with quartz sand as the framework particles), in S·m. -1 ; The molar concentration of pore water is expressed in mol / L. It is the complex electrical conductivity of the montmorillonite particle surface, in S·m. -1 ; It is the electrical conductivity of pore water (with montmorillonite as the framework particles), S·m -1 ; Na is the diffusion layer + mobility, m 2 ·s -1 ·V -1 ; The distribution coefficient is dimensionless. Density of solid skeleton particles, kg·m -3 ; Porosity is a dimensionless quantity. The cation exchange capacity is expressed in C·kg⁻¹. -1 ; The saturation index is dimensionless. The value represents water saturation, which is dimensionless.

[0076] The low-frequency and high-frequency surface conductivity of the framework particles are given by equations (10) and (11), respectively. Equation (12) describes the relationship between the characteristic relaxation time of the double-layer polarization process of the framework particles and the peak frequency of the imaginary part of the surface complex conductivity.

[0077] (10) (11) (12) In the formula: It is the characteristic pore throat size of the porous medium, in meters; is the peak frequency of the imaginary part of the complex conductivity of the skeletal particle surface, in Hz; , The specific surface area conductances of the diffusion layer and the Stern layer are respectively, which can be calculated by equations (13) and (14).

[0078] (13) (14) In the formula: The elementary charge, =1.602×10 -19 C; For Stern layer Na + mobility, m 2 s -1 V -1 ; Na is the diffusion layer + Surface position density, m -2 ; For Stern layer Na + Surface position density, m -2 .

[0079] The characteristic relaxation time of the double-layer polarization process of framework particles in hydrate-containing porous media can be calculated by equation (15): (15) In the formula: for Na + The diffusion coefficient in the Stern layer, m 2 ·s -1 .

[0080] The relationship between the characteristic pore throat size of an ideal particulate material and parameters such as formation factor and particle radius (as shown in Equation (16)). In an ideal particulate material, the particles are regular spheres with the same diameter.

[0081] (16) In the formula: Formation factors; Let be the particle radius, in meters. This represents the cementation index. and It can be calculated using Archie's formula: (17) (18) In the formula: σ 0 represents the conductivity of the water-saturated porous medium, in S·m. -1 ; The conductivity of the porous medium containing hydrates, in S·m -1 ; and Lithology coefficient, Generally, 1 is taken. In most cases, it is close to 1.

[0082] (3) Assign corresponding electrical conductivity and relative permittivity to each component in the geometric model. When the framework particles are quartz sand, the electrical conductivity and relative permittivity of the framework particles are calculated by the real part of equation (6) and equation (19), respectively, and the electrical conductivity and relative permittivity of pore water are calculated by equation (7) and equation (21), respectively; when the framework particles are montmorillonite, the electrical conductivity and relative permittivity of the framework particles are calculated by the real part of equation (8) and equation (20), and the electrical conductivity and relative permittivity of pore water are calculated by equation (9) and equation (21), respectively; the electrical conductivity of the hydrate is set to 1×10 -5 S·m -1 The relative permittivity is calculated using the real part of equation (5).

[0083] (19) (20) (twenty one) In the formula: is the relative permittivity of the quartz sand particles; is the relative permittivity of montmorillonite particles; S·m represents the imaginary part of the complex conductivity of the quartz sand particle surface. -1 ; S·m represents the imaginary part of the complex conductivity of the montmorillonite particle surface. -1 ; It is the relative permittivity of pore water.

[0084] (4) Apply an alternating electric field U =sin( ωt The geometric region was discretized using a free triangular mesh, and the governing equations of the electric field were solved using the Multi-frontal Massively Parallel Sparse Direct Solver (MUMPS), thus completing the establishment of the finite element model.

[0085] 5. Based on the above finite element model, simulation calculations were performed to obtain data on the time-varying current of the hydrate-containing porous medium, thus obtaining broadband (10) data for the hydrate-containing porous medium. -2 ~10 8 The electrical response data (Hz) is obtained through the following steps: (1) Extract the current and time data and perform Fourier fitting. The fitting formula is as follows: (twenty two) In the formula: I It is electric current, A; k 1. k 2. k 3 is the fitting constant.

[0086] (2) The phase angle is determined using correlation analysis, and the specific formula is as follows: (twenty three) In the formula: It is voltage, V; It's the phase angle, in rad.

[0087] (3) Calculate the complex conductivity of the porous medium containing hydrates according to formula (22): (twenty four) In the formula: It is the complex conductivity of hydrate-containing porous media, in S·m. -1 ; It is the maximum current value, in A; It is the maximum voltage value, in V; It is the length of the hydrate-containing porous medium along the direction of the applied electric field, in meters; It is the cross-sectional area of ​​the porous medium, m 2 .

[0088] (4) Complex relative permittivity of porous media The real and imaginary parts are shown in equations (25) and (26) respectively: (25) (26) In the formula: It is the real part of the complex relative permittivity of hydrate-containing porous media; It is the imaginary part of the complex relative permittivity of hydrate-containing porous media; It is the real part of the complex conductivity of hydrate-containing porous media, S·m -1 ; It is the imaginary part of the complex conductivity of hydrate-containing porous media, S·m -1 .

[0089] 6. Based on the Maxwell-Garnett (MG) dielectric mixing theory, the complex relative permittivity spectrum (10) covering the polarization frequency band of hydrates was analyzed. 5 ~10 7 Using Hz as input, a hydrate saturation evaluation model applicable to the CO2–CH4 replacement process is established. The MG theory, based on the physical assumption that the dispersed phase is spherical particles, is a simple, purely theoretical model suitable for multiphase (skeleton particles, pore water, hydrate) mixed systems. The specific steps for establishing the saturation evaluation model are as follows: (1) Establish a hydrate saturation evaluation model to calculate the total hydrate saturation in porous media (Equation (27)). Before CO2–CH4 replacement, the total hydrate saturation is the CH4 hydrate saturation. Substitute the volume fraction of each phase and the complex relative permittivity spectrum into Equation (27) to obtain the complex relative permittivity spectrum of the porous media, and compare it with the numerical solution. Substitute the complex relative permittivity spectrum of each phase into Equation (27), with the goal of minimizing the sum of squared errors of the complex relative permittivity spectrum of the porous media, and iteratively optimize it using a nonlinear least squares algorithm to invert and obtain the total hydrate saturation.

[0090] (27) In the formula: It is the complex relative permittivity of pore water; It is the complex relative permittivity of the skeletal particles; It is the complex relative permittivity of CH4 hydrate; It is the volume fraction of skeletal particles in the porous medium; It is the volume fraction of hydrates in porous media; It is the saturation of total hydrates.

[0091] (2) The total hydrate saturation is obtained through the above steps. Based on this, a hydrate saturation evaluation model applicable to the CO2–CH4 replacement process is established to calculate the saturation of CH4 hydrate and CO2 hydrate. The volume fraction and complex relative permittivity spectrum of each phase are substituted into equation (28) to obtain the complex relative permittivity spectrum of the porous medium, which is compared with the numerical solution. The complex relative permittivity spectrum of each phase is substituted into equation (28). With the goal of minimizing the sum of squared errors of the complex relative permittivity spectrum of the porous medium, the saturation of CH4 hydrate and CO2 hydrate is obtained by iterative optimization through a nonlinear least squares algorithm.

[0092] (28) (29) In the formula: It is the complex relative permittivity of CH4 hydrate; It is the volume fraction of CH4 hydrate in porous media; It is the saturation of CH4 hydrate; It is the complex relative permittivity of CO2 hydrate; It is the volume fraction of CO2 hydrate in porous media; It refers to the CO2 hydrate saturation.

[0093] In summary, this invention first establishes a broadband (10⁻¹⁰) method for calculating CH₄ and CO₂ hydrates based on molecular dynamics theory. -2 -10 8 A novel method for analyzing the complex relative permittivity spectrum (10 Hz) was developed. Secondly, the obtained complex relative permittivity spectrum of hydrates was used as an input parameter to a finite element model of porous media containing hydrates, and the broadband dielectric response characteristics of porous media under different hydrate saturation and framework particle conditions were obtained and analyzed. Finally, based on the Maxwell-Garnett (MG) dielectric mixing theory, a new method for analyzing the complex relative permittivity spectrum (10 Hz) covering the hydrate polarization frequency band was developed. 5 ~10 7 Using Hz as input, a new model for evaluating the saturation of CH4 and CO2 hydrates applicable to the CO2–CH4 replacement process was constructed. This provides a theoretical basis for the development of real-time monitoring technology for the saturation of CH4 and CO2 hydrates in porous media (hydrate reservoirs under field conditions) during the CO2–CH4 replacement process.

[0094] To achieve real-time monitoring of hydrate saturation during the CO2–CH4 replacement process, this invention proposes a method for calculating the broadband (10) hydrate content of CH4 and CO2. -2 ~10 8 A novel method for analyzing the complex relative permittivity spectrum (Hz) is used to qualitatively distinguish hydrate types and quantitatively invert saturation. First, the static permittivity and characteristic relaxation times of the dielectric polarization process of CH4 and CO2 hydrates are obtained using a molecular dynamics numerical model. These are then substituted into the Cole-Cole model to obtain the complex relative permittivity (dispersion) spectrum of the hydrates. Second, the complex relative permittivity spectrum (10 Hz) covering the polarization frequency band of the hydrates is analyzed. 5 ~10 7 Using (Hz) as input to the finite element model of porous media, the broadband complex relative permittivity spectrum of porous media under different hydrate saturation levels is obtained. Finally, based on the Maxwell-Garnett (MG) dielectric mixing theory, a saturation evaluation model suitable for the replacement process is constructed. The implementation of this invention will provide theoretical support for saturation monitoring of the CO2–CH4 replacement process and the coordinated development of natural gas hydrate resources and carbon sequestration.

[0095] The method logic of the present invention will be further explained below with reference to some specific application scenarios: Specific Implementation Example 1: Obtaining the dielectric parameters of hydrates; Step 1: Construct a 3×3×3 supercell based on the SI-type hydrate unit cell, containing 216 cage-like structures, including 162 large cages (5 12 6 2 ), 54 small steamers (5 12 In this model, guest molecules (CH4 and CO2) occupy the large and small cages respectively, to construct the geometric models of CH4 hydrate and CO2 hydrate (e.g., Figure 4 ); Step 2: Apply corresponding force fields to each component in the mixture. H2O is modeled using the TIP4P / Ice model, while CH4 and CO2 are described using the OPLS-UA force field and the EPM2 force field, respectively. Step 3: Minimize the energy of the system using the steepest descent algorithm, with a step size of 0.01 nm and a convergence criterion of the maximum interatomic force being less than 200 kJ·mol⁻¹. -1 ·nm -1 A 500 ps pre-equilibrium simulation was performed in a canonical ensemble (NVT). During this stage, the oxygen atoms of the water cage framework were fixed to relax the configuration of the guest molecule and the water cage. A Nose-Hoover thermotherapy bath was used to relax the system to the target temperature of 277 K to prevent the hydrate from falling into a metastable state. After removing all positional constraints, a 5 ns equilibrium simulation was performed in an isothermal-isobaric ensemble (NPT). During this stage, a Nose-Hoover thermotherapy bath and a Parrinello-Rahman pressure bath were used to maintain the system temperature and pressure at 277 K and 10 MPa, respectively, and the system density was adjusted to the target value. The resulting final configuration was used for subsequent production simulations. A 3000 ns production simulation was then performed in the NVT ensemble. Step 3: Collect the total dipole moment fluctuation data of the system to calculate the static dielectric constant of CH4 hydrate and CO2 hydrate, such as... Figure 5 As shown; Step 4: Calculate the total dipole moment autocorrelation function C ( t The fit is a stretching exponential function, such as... Figure 6 As shown, the characteristic relaxation times of the dielectric polarization process of CH4 hydrate and CO2 hydrate were determined. Step 5: Using the determined static permittivity and characteristic relaxation time, calculate the complex relative permittivity spectrum of CH4 hydrate and CO2 hydrate using equation (5), as follows: Figure 7 As shown.

[0096] Specific Implementation Example 2: Obtaining the electrical spectrum of a porous dielectric containing hydrates; Step 1: Construct a 3×3×3 supercell based on the SI-type hydrate unit cell, containing 216 cage-like structures, including 162 large cages (5 12 6 2 ), 54 small steamers (5 12 In this model, guest molecules (CH4 and CO2) occupy the large and small cages respectively to construct the geometric models of CH4 hydrate and CO2 hydrate. Step 2: Apply corresponding force fields to each component in the mixture. H2O is modeled using the TIP4P / Ice model, while CH4 and CO2 are described using the OPLS-UA force field and EPM2 force field, respectively. The steepest descent algorithm is used to minimize the energy of the system, with a step size of 0.01 nm and a convergence criterion of the maximum interatomic force being less than 200 kJ·mol⁻¹. -1 ·nm -1 A 500 ps pre-equilibrium simulation was performed in a canonical ensemble (NVT). During this stage, the oxygen atoms of the water cage framework were fixed to relax the configuration of the guest molecule and the water cage. A Nose-Hoover thermotherapy bath was used to relax the system to the target temperature of 277 K to prevent the hydrate from falling into a metastable state. After removing all positional constraints, a 5 ns equilibrium simulation was performed in an isothermal-isobaric ensemble (NPT). During this stage, a Nose-Hoover thermotherapy bath and a Parrinello-Rahman pressure bath were used to maintain the system temperature and pressure at 277 K and 10 MPa, respectively, and the system density was adjusted to the target value. The resulting final configuration was used for subsequent production simulations. A 3000 ns production simulation was then performed in the NVT ensemble. Step 3: Collect the total dipole moment fluctuation data of the system to calculate the static dielectric constant of CH4 hydrate and CO2 hydrate; calculate the total dipole moment autocorrelation function. C ( t The static dielectric constant and characteristic relaxation time are fitted to the stretching exponential function to determine the characteristic relaxation time of the dielectric polarization process of CH4 and CO2 hydrate; using the determined static dielectric constant and characteristic relaxation time, the complex relative dielectric constant spectrum of CH4 hydrate and CO2 hydrate is calculated by Equation (5); Step 4: Establish the two-dimensional geometry of the hydrate-containing porous medium (e.g., Figure 8 As shown), the size is 970.8 μm × 582.5 μm. The circles represent quartz sand particles and hydrates (CH4 hydrate, CO2 hydrate), and the remainder is pore water. The quartz sand and particles are arranged alternately. The porosity of the porous medium is 0.4 under hydrate-free conditions. Step 5: The electrical conductivity and relative permittivity of the quartz sand particles can be calculated using the real part of formula (6) and equation (19) and assigned to the quartz sand particles in the geometry; the electrical conductivity of the hydrate is set to 1×10⁻⁶. -5 S / m, the relative permittivity is calculated using the real part of formula (5); the conductivity and relative permittivity of pore water are calculated using formulas (7) and (21), respectively; an AC electric field needs to be applied to the model. U = sin( ωt ), with a frequency range of 10 -2 Hz ~10 8 Hz; Step 6: Numerical simulations were performed using a free triangular mesh to discretize the geometric region, obtaining the complex relative permittivity spectra of the porous media containing CH4 hydrate and the porous media containing CH4-CO2 hydrate, as shown below. Figure 9 (The skeleton particles are quartz sand) Figure 10 (The skeleton particles are quartz sand). Analysis shows that at 10... -2 Within the ~10 Hz frequency band, the real part of the complex relative permittivity of porous media tends to stabilize; in the 10-10 Hz band... 3 Within the Hz frequency band, the real part of the complex relative permittivity of porous media decreases significantly with increasing frequency, because the double-layer polarization gradually weakens; at 10 3 -10 6 Within the Hz frequency band, the real part of the complex relative permittivity of porous media tends to stabilize again; at 10 6 -10 7 Within the Hz frequency band, the real part of the complex relative permittivity of porous media decreases sharply with increasing frequency. This is because the real part of the complex relative permittivity of hydrates decreases significantly with increasing frequency in this band; at 10 7 -10 9 Within the Hz frequency band, the real part of the complex relative permittivity of porous media tends to stabilize as the frequency increases. This is because the real part of the complex relative permittivity of hydrates tends to stabilize within this frequency band.

[0097] Specific Example 3: Obtaining the hydrate saturation during the CO2-CH4 replacement process; Step 1: Construct a 3×3×3 supercell based on the SI-type hydrate unit cell, containing 216 cage-like structures, including 162 large cages (5 12 6 2 ), 54 small steamers (5 12 In this model, guest molecules (CH4 and CO2) occupy the large and small cages respectively to construct the geometric models of CH4 hydrate and CO2 hydrate. Step 2: Apply corresponding force fields to each component in the mixture. H2O is modeled using the TIP4P / Ice model, while CH4 and CO2 are described using the OPLS-UA force field and EPM2 force field, respectively. The steepest descent algorithm is used to minimize the energy of the system, with a step size of 0.01 nm and a convergence criterion of the maximum interatomic force being less than 200 kJ·mol⁻¹. -1 ·nm -1 A 500 ps pre-equilibrium simulation was performed in a canonical ensemble (NVT). During this stage, the oxygen atoms of the water cage framework were fixed to relax the configuration of the guest molecule and the water cage. A Nose-Hoover thermotherapy bath was used to relax the system to the target temperature of 277 K to prevent the hydrate from falling into a metastable state. After removing all positional constraints, a 5 ns equilibrium simulation was performed in an isothermal-isobaric ensemble (NPT). During this stage, a Nose-Hoover thermotherapy bath and a Parrinello-Rahman pressure bath were used to maintain the system temperature and pressure at 277 K and 10 MPa, respectively, and the system density was adjusted to the target value. The resulting final configuration was used for subsequent production simulations. A 3000 ns production simulation was then performed in the NVT ensemble. Step 3: Collect the total dipole moment fluctuation data of the system to calculate the static dielectric constant of CH4 hydrate and CO2 hydrate; calculate the total dipole moment autocorrelation function. C ( t The static dielectric constant and characteristic relaxation time are fitted to the stretching exponential function to determine the characteristic relaxation time of the dielectric polarization process of CH4 hydrate and CO2 hydrate; using the determined static dielectric constant and characteristic relaxation time, the complex relative dielectric constant spectrum of CH4 hydrate and CO2 hydrate is calculated by Equation (5); Step 4: Establish the two-dimensional geometry of the hydrate-containing porous medium (e.g., Figure 8 As shown), the size is 970.8 μm × 582.5 μm. The circles represent quartz sand particles and hydrates (CH4 hydrate, CO2 hydrate), and the remainder is pore water. The quartz sand and particles are arranged alternately. The porosity of the porous medium is 0.4 under hydrate-free conditions. Step 5: The electrical conductivity and relative permittivity of the quartz sand particles can be calculated using the real part of formula (6) and equation (19) and assigned to the quartz sand particles in the geometry; the electrical conductivity of the hydrate is set to 1×10⁻⁶. -5 S / m, the relative permittivity is calculated using the real part of formula (5); the conductivity and relative permittivity of pore water are calculated using formulas (7) and (21), respectively; an AC electric field needs to be applied to the model. U = sin( ωt ), with a frequency range of 10-2 -10 8 Hz; Step 6: Discretize the geometric region using a free triangular mesh and perform numerical simulation to obtain the electrical spectrum of the porous medium under different CH4 hydrate and CO2 hydrate saturation conditions; Step 7: Substitute the volume fraction of each phase and the complex relative permittivity spectrum into equation (27) to obtain the complex relative permittivity spectrum of the porous medium, and compare it with the numerical solution, such as... Figure 11 As shown; Substitute the relative permittivity spectra of each phase into equation (27), with the goal of minimizing the sum of squared errors of the relative permittivity spectra of porous media, the total hydrate saturation is obtained by iterative optimization through nonlinear least squares algorithm, and the results are shown in Table 1 (Comparison of reference values ​​and calculated values ​​of CH4 hydrate saturation (including porous media containing CH4 hydrate)). Table 1

[0098] Step 8: Taking a total hydrate saturation of 0.4 as an example, substitute the volume fraction of each phase and the complex relative permittivity spectrum into equation (28) to obtain the complex relative permittivity spectrum of the porous medium, and compare it with the numerical solution, such as... Figure 12 As shown; substituting the relative permittivity spectra of each phase into equation (28), with the goal of minimizing the sum of squared errors of the relative permittivity spectra of porous media, the saturation of CH4 hydrate and CO2 hydrate during the CO2–CH4 replacement process is obtained by iterative optimization using a nonlinear least squares algorithm, and the results are shown in Tables 2 and 3.

[0099] Table 2

[0100] Table 3

[0101] This invention also provides a hydrate saturation evaluation device that can implement the above-described method. The device may include: The first module is used to obtain the trajectory data of atoms in the system of the target type of hydrate through molecular dynamics models; The second module is used to quantize the static dielectric constant and the characteristic relaxation time of the dielectric polarization process based on the motion trajectory data, and then transform it through a mathematical model to obtain the first complex relative dielectric constant spectrum of the hydrate. The third module is used to establish a finite element model of hydrate-containing porous media based on the first complex relative permittivity spectrum; The fourth module is used to simulate and quantify the second complex relative permittivity spectrum of hydrate-containing porous media using a finite element model; The fifth module is used to construct a saturation evaluation model based on the first and second complex relative permittivity spectra and in combination with the dielectric mixing theory model, so as to output the hydrate saturation evaluation results.

[0102] In some embodiments, the apparatus further includes a sixth module for performing the following operations: The first geometric model of the target type of hydrate is obtained by constructing a hydrate unit cell of a preset type. Based on the first geometric model, a molecular dynamics model is established by combining preset conditions. The preset conditions include the force fields corresponding to different components in the system, as well as preset temperature and preset pressure.

[0103] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0104] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0105] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0106] like Figure 13 As shown, Figure 13 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (aSIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention. The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RaM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0107] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0109] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0110] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0111] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0112] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0113] The hydrate saturation evaluation method, device, electronic device, storage medium, and program product provided in this invention obtain atomic trajectory data of the target type of hydrate within the system through a molecular dynamics model; quantize the static dielectric constant and characteristic relaxation time of the dielectric polarization process based on the trajectory data, and then transform them through a mathematical model to obtain the first complex relative permittivity spectrum of the hydrate; establish a finite element model of the porous medium containing hydrate based on the first complex relative permittivity spectrum; use the finite element model to simulate and quantize the second complex relative permittivity spectrum of the porous medium containing hydrate; and construct a saturation evaluation model based on the first and second complex relative permittivity spectra, combined with a dielectric mixing theory model, to output the hydrate saturation evaluation results. This invention first obtains the complex relative permittivity spectrum of the target hydrate through molecular dynamics simulation, filling a key gap in dielectric data; then constructs a multi-scale coupled model of "molecular dynamics-finite element-dielectric mixing theory," which enables simultaneous, quantitative differentiation and evaluation of the saturation of multiple types of hydrates, effectively overcoming the technical bottleneck of traditional well logging methods being unable to identify hydrate types, and providing a core theoretical tool for hydrate saturation evaluation.

[0114] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0115] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present invention, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0116] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0117] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0118] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present invention should be within the scope of the claims of the present invention.

Claims

1. A method for evaluating hydrate saturation, characterized in that, The method includes the following steps: Molecular dynamics models were used to obtain atomic trajectory data of the target type of hydrate within the system. Based on the motion trajectory data, the static dielectric constant and the characteristic relaxation time of the dielectric polarization process are obtained by quantization, and then the first complex relative dielectric constant spectrum of the hydrate is obtained by mathematical model transformation. A finite element model of a porous medium containing hydrates is established based on the first complex relative permittivity spectrum; The second complex relative permittivity spectrum of the hydrate-containing porous medium was obtained by simulation and quantization using the finite element model. Based on the first and second complex relative permittivity spectra, a saturation evaluation model is constructed using a dielectric mixing theory model to output hydrate saturation evaluation results.

2. The method according to claim 1, characterized in that, The method further includes the following steps: A first geometric model of the target type of hydrate is constructed based on a preset type of hydrate unit cell. Based on the first geometric model, the molecular dynamics model is established by combining preset conditions. The preset conditions include the force fields corresponding to different components in the system, as well as preset temperature and preset pressure.

3. The method according to claim 1, characterized in that, The process of quantizing the static dielectric constant and the characteristic relaxation time of the dielectric polarization process based on the motion trajectory data includes the following steps: Based on the motion trajectory data, the Cartesian component of the dipole moment is determined, and then the fluctuation data of the total dipole moment vector is obtained by quantization. The static dielectric constant is calculated based on the fluctuation data and the first parameter. The expression for the static dielectric constant is as follows: ； In the formula, Indicates the static dielectric constant; Indicates fluctuation data; , , and As the first parameter, Represents the vacuum permittivity. Represents the volume of the system. Represents the Boltzmann constant. This indicates the preset temperature of the molecular dynamics model; The characteristic relaxation time of the dielectric polarization process is determined by fitting the autocorrelation function of the total dipole moment vector to a stretching exponential function.

4. The method according to claim 1, characterized in that, The process of obtaining the first complex relative permittivity spectrum of the hydrate through mathematical model transformation includes the following steps: The static permittivity and the characteristic relaxation time are substituted into a preset mathematical model as the first input parameters to calculate the complex relative permittivity corresponding to different angular frequencies in a preset frequency range, and then the first complex relative permittivity spectrum is obtained by summarizing the results. The mathematical model is expressed as follows: ； In the formula, Represents the complex relative permittivity; Indicates the relative permittivity at high frequencies; Determined based on static dielectric constant, Indicates the relative permittivity at low frequencies; Represents the imaginary unit; Indicates angular frequency; Indicates the characteristic relaxation time; Indicates DC conductivity; It represents the vacuum permittivity.

5. The method according to claim 1, characterized in that, The hydrate-containing porous medium comprises framework particles, hydrates, and pore water. The step of establishing a finite element model of the hydrate-containing porous medium based on the first complex relative permittivity spectrum includes the following steps: A second geometric model is obtained by modeling the two-dimensional geometry of porous media containing one or more combinations of the target type of hydrate; The conductivity of the pore water and the first complex conductivity of different types of skeleton particles are obtained by quantification based on a preset calculation formula. Based on the conductivity and the first complex conductivity, the conductivity values ​​of each component in the second geometric model are assigned, and the relative permittivity is assigned through the first complex relative permittivity spectrum to obtain the initial finite element model. The conductivity value of the skeleton particles is determined based on the real part of the first complex conductivity. An alternating electric field is applied to the initial finite element model, the geometric region is discretized using a free triangular mesh, and then the electric field control equations are solved using the parallel sparse direct method to establish the target finite element model.

6. The method according to claim 1, characterized in that, The process of simulating and quantizing the second complex relative permittivity spectrum of the hydrate-containing porous medium using the finite element model includes the following steps: Simulation data on the change of current over time in the hydrate-containing porous medium were obtained based on the finite element model. Fourier fitting is performed on the simulated data to obtain current fitting data; Based on the current fitting data, the phase angle is obtained by using correlation analysis. The second complex conductivity of the hydrate-containing porous medium is obtained by quantifying the maximum current value in the current fitting data. Based on the imaginary part of the second complex conductivity, the real part of the complex relative permittivity of the hydrate-containing porous medium is obtained by combining the vacuum permittivity and the angular frequency quantization within a preset frequency range. Based on the real part of the second complex conductivity, the imaginary part of the complex relative permittivity of the hydrate-containing porous medium is obtained by combining the vacuum permittivity and the angular frequency quantization within the preset frequency range. The second complex relative permittivity spectrum is obtained by summing the real part and the imaginary part of the complex relative permittivity corresponding to each of the angular frequencies.

7. The method according to claim 1, characterized in that, The components of the hydrate-containing porous medium include framework particles, hydrates, and pore water. The target type of hydrate includes CO2 hydrate and CH4 hydrate. The saturation evaluation model is constructed based on the first and second complex relative permittivity spectra and combined with the dielectric mixing theory model to output the hydrate saturation evaluation results. The steps include: Based on the first complex relative permittivity spectrum, combined with the preset complex relative permittivity spectrum of the skeleton particles, the complex relative permittivity spectrum of the pore water, and the volume fraction of the skeleton particles, a third complex relative permittivity spectrum for comparing numerical solutions is obtained by solving the dielectric mixing model. Based on the second complex relative permittivity spectrum and the third complex relative permittivity spectrum obtained by solving the dielectric mixing model, the total hydrate saturation is obtained by iterative optimization through a nonlinear least squares algorithm with the goal of minimizing the sum of squared errors of the complex relative permittivity spectrum of porous media. Based on the total hydrate saturation, a hydrate saturation evaluation model for the CO2-CH4 replacement process is established, and then the target hydrate saturation of each target type of hydrate is obtained through iterative optimization and inversion. The CO2-CH4 replacement process characterizes the process of replacing CH4 in the CH4 hydrate with CO2 to obtain the CO2 hydrate.

8. A device for evaluating hydrate saturation, characterized in that, The device includes: The first module is used to obtain the trajectory data of atoms in the system of the target type of hydrate through molecular dynamics models; The second module is used to quantize the static dielectric constant and the characteristic relaxation time of the dielectric polarization process based on the motion trajectory data, and then transform it through a mathematical model to obtain the first complex relative dielectric constant spectrum of the hydrate. The third module is used to establish a finite element model of the hydrate-containing porous medium based on the first complex relative permittivity spectrum; The fourth module is used to simulate and quantize the second complex relative permittivity spectrum of the hydrate-containing porous medium using the finite element model. The fifth module is used to construct a saturation evaluation model based on the first and second complex relative permittivity spectra and in combination with the dielectric mixing theory model, so as to output the hydrate saturation evaluation results.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

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