Method for quantitatively evaluating the amount of methane stored in a hydrate shell in a cold spring environment

Abstract

The present application relates to the field of marine carbon cycle evaluation and hydrate environmental effect evaluation, and provides a method for quantitatively evaluating the amount of methane stored in the shell of hydrate in cold spring environment, comprising: collecting time series Raman spectrum data in situ, and obtaining time series normalized multi-phase methane peak area data after preprocessing; in the hydrate nucleation and growth stage and the hydrate thin shell formation and growth stage, respectively using Avrami-Erofeyev model and Ginstling-Brounshtein model to construct thin shell growth kinetics model, calculating the conversion rate of hydrate, shell thickness and diffusion coefficient of hydrate shell, according to the parameters of cold spring environment, adding the gaseous methane in hydrate state and the residual gaseous methane in the shell to obtain the single bubble storage capacity, and using Fick diffusion law and mass conservation law to construct hydrate stability zone thin shell bubble diffusion loss model; combining the bubble release frequency and the annual operating time to calculate the total amount of methane stored annually, realizing the cross-scale quantitative evaluation from micro interface to macro scale, and improving the accuracy and reliability of the evaluation of the cold spring methane hydrate storage capacity.

Description

Technical Field

[0001] This invention relates to the field of marine carbon cycle assessment and hydrate environmental effect evaluation, and particularly to a method for quantitatively assessing methane sequestration in hydrate shells in cold seep environments. Background Technology

[0002] Cold seeps are crucial conduits for the release of methane from the seabed into the ocean and atmosphere. Globally, cold seep systems release approximately 50–300 Tg of methane into the ocean annually, with a global warming potential (GWP) 28–36 times that of CO2 on a 100-year timescale. Therefore, accurately assessing the methane transport flux from the seabed to the air-sea interface is essential for understanding the global carbon cycle and climate change. In cold seep environments, as methane bubbles flow through the hydrate stability zone (HSZ), a solid hydrate shell forms on the bubble surface. This shell is believed to act as a barrier, hindering the diffusion and dissolution of methane into the surrounding seawater. However, quantitative assessment of this sequestration process still faces a series of technical challenges.

[0003] Physical detection methods (such as seabed seismic exploration and shallow seismic profiling) can only achieve macroscopic detection of the extent and thickness of hydrates, and are difficult to quantitatively calculate the amount of methane stored. Chemical sampling methods rely on deep-sea drilling and gravity sampling to obtain hydrate samples, and then detect the methane content through laboratory pyrolysis and gas chromatography. However, the sampling process is prone to damaging the hydrate shell structure and causing methane to escape. Furthermore, it cannot achieve in-situ and continuous monitoring, and the detection process is cumbersome and has poor timeliness. Summary of the Invention

[0004] To address the lack of quantitative calculation methods for assessing methane sequestration in cold seep hydrate shells in existing technologies, a method for quantitatively assessing methane sequestration in cold seep environmental hydrate shells is provided, comprising the following steps: Time-series Raman spectral data of the hydrate shell growth process were collected in situ and preprocessed to obtain a time-normalized multiphase methane peak area dataset. Based on the multiphase methane peak area dataset, hydrate conversion rate, hydrate formation rate and phase migration flux were extracted. In the hydrate nucleation and growth stage and the hydrate thin-shell formation and growth stage, the Avrami-Erofeyev model and the Ginstling-Brounshtein model were used to construct hydrate thin-shell growth kinetic models, respectively, and the hydrate conversion rate, shell thickness and hydrate shell diffusion coefficient were calculated. Based on cold seep environmental parameters, the methane sequestration capacity of a single bubble hydrate shell was calculated by adding hydrated methane and gaseous methane stored within the hydrate shell. A diffusion loss model for thin-shell bubbles in the hydrate stability zone was constructed using Fick's diffusion law and the law of conservation of mass to calculate the sequestration efficiency of the thin-shell hydrate shell. Combined with the frequency of cold spring bubble release Cold Spring Annual Operating Time Calculate the total annual sequestration volume of methane from the hydrate shell. : , In the formula, This represents the molar mass of methane.

[0005] Specifically, the preprocessing includes the following steps: The neighboring pixel comparison thresholding method is used to remove randomly occurring pulsed cosmic ray clutter peaks in deep-sea in-situ detection; An adaptive iterative weighted least squares method is used to fit the dynamic baseline, eliminating the baseline drift accumulated over long-term monitoring. A 5th-order Savitzky-Golay filter was used for smoothing. Based on the Raman spectra of standard substances obtained in situ, multi-peak Gaussian fitting was performed on the characteristic region of 2890–2930 cm⁻¹ to generate the characteristic peak areas of the three-phase state at each time point. Using the integral area of ​​the CH symmetric stretching vibration band as the internal standard, the characteristic peak areas of multiphase methane were normalized time-by-time.

[0006] Specifically, the transition time between the hydrate nucleation and growth stage and the hydrate thin-shell formation and growth stage is determined by the peak point of the rate curve of the hydrate formation rate and the phase migration flux.

[0007] Specifically, the conversion rate function of the Avrami-Erofeyev model for: , In the formula This is the characteristic rate constant for nucleation and growth. Avrami growth index, To determine the nucleation and growth time, the failure point of the Avrami-Erofeyev model was identified through fitting plots. The conversion rate function of the Ginnstling-Brounshtein model for: , In the formula, a, b, and c are fitting parameters, where parameter a is the background diffusion rate, parameter b is the conversion-driven diffusion rate parameter, and parameter c is the time-conversion synergistic acceleration coefficient. The cumulative integral amount for conversion rate. To control the growth time for diffusion, The hydrate conversion rate.

[0008] Specifically, the integral quantity I is calculated using the trapezoidal rule, and the formula is as follows: , In the formula, Let be the conversion rate at time i. Let i be the time at the i-th moment. Let be the time at the (i+1)th moment.

[0009] Specifically, the amount of methane sequestered in the single-bubble hydrate shell The calculation method is as follows: diffusion-conversion coupling term The dominant shell mass transfer process is derived with respect to time, and then substituted into the diffusion relationship of the spherical surface layer. The instantaneous effective diffusion coefficient is obtained. : , In the formula, The initial radius of the bubble; Based on the thickness of the hydrate stability zone Determine and calculate the formation time of the hydrate shell. , , In the formula, This represents the average rising speed of the bubbles; Take the moment when the bubble ends its passage through the hydrate stability zone. The hydrate shell diffusion coefficient is defined as the characteristic moment when the hydrate shell forms and enters the stable diffusion loss stage. The equivalent diffusion coefficient at that moment is: , In the formula, c is the fitting parameter; The hydrate conversion rate at the end of the residence time; As of Conversion rate cumulative integral; Define a characteristic diffusion rate constant that is independent of bubble size. : ; Based on hydrate conversion rate Geometric relationship with shell thickness δ: , The shell thickness δ is calculated using the following formula: , In the formula, The hydrate conversion rate represents the residence time period.

[0010] Specifically, the amount of methane sequestered in the hydrate shell is determined by the amount of methane in hydrate form. and residual gaseous methane in the shell The methane content is composed of two parts, and the methane sequestration capacity of the single bubble hydrate shell is obtained. : , , , In the formula, This represents the methane concentration in type I hydrates when the cage structure is completely occupied by gas molecules. , The molar density of methane under in-situ pressure and temperature conditions is calculated using the equation of state based on the thermobaric conditions. Let V be the gas phase volume of the bubble nucleus.

[0011] Specifically, the methane sequestration efficiency R of the hydrate shell is: , In the formula, The change in the total number of moles of methane in the bubbles over time. This represents the total number of moles of methane in the bubble at the initial moment. For shell thickness, Where is the bubble radius. The characteristic time for diffusion in the thin-shell hydrate shell is calculated based on Fick's diffusion law by combining the molar diffusion flux of the shell with the mass-conserved rate of decrease in the core.

[0012] Specifically, a three-dimensional benchmark model of Raman characteristic peak area ratio, cage occupancy, and methane molar content was constructed based on standard interference-free experimental conditions. The characteristic peak area ratio was used as the benchmark. occupancy rate of hydrate cages The independent variable is the molar concentration of methane. Using multiple nonlinear regression as the dependent variable, a baseline theoretical model is constructed: , In the formula, These are the regression coefficients of the benchmark theoretical model; Using a deep-sea high-pressure cryogenic reactor to simulate different temperature and pressure combinations, the Raman spectral response of hydrate samples was tested. Temperature and pressure correction functions were fitted to obtain the results, and the in-situ temperature-pressure coupling correction coefficient was derived through coupling. ; Hydrate-sediment mixture simulation samples with different impurity content gradients were prepared. Raman spectra were acquired under standard operating conditions, and the attenuation law of characteristic peaks by impurity content γ was quantitatively analyzed. The quantitative response relationship of impurity interference was obtained by fitting, and the sediment impurity correction coefficient was established. ; The calibrated temperature, pressure, and impurity correction coefficients for the two types of in-situ environments are coupled and embedded into a three-dimensional benchmark model to construct a quantitative correction model adapted to the actual cold seep in-situ environment, which is used to adjust the methane molar concentration. Perform correction: .

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention adopts an in-situ non-contact Raman spectroscopy monitoring method, which does not require sampling and does not damage the hydrate shell structure. It completely avoids the defects of methane escape and structural damage in traditional sampling and detection methods. It can be well adapted to the complex cold seep detection environment of deep sea low temperature, high pressure, high impurities and dynamic changes. It has strong environmental adaptability and in-situ detection capability. At the same time, the detection process is simple, highly automated, and the detection time of a single point is short. It can realize grid-based continuous monitoring and rapid reserve calculation. 2. This invention utilizes the specific recognition capability of Raman spectroscopy for CH bonds. Based on the resolvable frequency shifts and half-width differences of gaseous, dissolved, and hydrated methane on the CH symmetric stretching vibration band, and through multi-peak Gaussian fitting peak division and peak area normalization, it can accurately distinguish the three types of methane occurrence forms: gaseous, dissolved, and hydrated. It can also fully analyze the methane sequestration mechanism of the entire process of hydrate nucleation, growth, and diffusion rate control. 3. This invention employs segmented modeling. During the hydrate nucleation and growth stage and the hydrate thin-shell formation and growth stage, the Avrami-Erofeyev model and the Ginsting-Brounshtein model are used to construct kinetic models for the growth of the hydrate thin-shell, respectively. The methane sequestration capacity of a single-bubble hydrate shell is calculated. Furthermore, using Fick's diffusion law and the law of conservation of mass, the first-order diffusion process of methane bubbles in the outer hydrate shell is kinetically modeled to calculate the sequestration efficiency of the thin-shell hydrate shell. Combining the cold seep bubble release frequency and annual operating time, the total annual methane sequestration capacity of the hydrate shell is calculated. The theoretical geometric sequestration capacity is corrected to the effective sequestration capacity considering mass transfer losses, making the calculated annual methane sequestration capacity more consistent with physical reality. 4. This invention constructs a three-dimensional benchmark model with characteristic peak area ratio, cage occupancy rate, and methane molar content as the core, and couples in-situ temperature and pressure correction coefficient with sediment impurity correction coefficient, thereby achieving coordinated and accurate correction of temperature and pressure fluctuations and impurity interference, and eliminating system deviations caused by environmental disturbances. Attached Figure Description

[0014] Figure 1 This is a flowchart of a method for quantitatively assessing the methane sequestration in the hydrate shell of a cold seep environment based on Raman spectroscopy, as proposed in this invention. Figure 2 These are time-series Raman spectra collected during the formation of the hydrate shell. Figure 3 Fitting graph of the Avrami-Erofeyev model constructed in this invention; Figure 4 The image shows the fitting plot of the Ginsling-Brounshtein model constructed in this invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0016] A method for quantitatively assessing the methane sequestration in the hydrate shell of a cold seep environment includes the following specific steps.

[0017] S1: In-situ acquisition of the original dataset.

[0018] To achieve dynamic quantitative characterization of the entire growth process of hydrate shells, confocal Raman spectroscopy was used to conduct non-destructive and continuous monitoring of the hydrate shells at the gas-liquid interface in a closed high-pressure system that maintains the in-situ temperature and pressure conditions of the cold seep.

[0019] The formation of hydrate shells in cold seep environments follows a continuous evolutionary pattern of "induction phase – rapid nucleation phase – diffusion-controlled phase." Single-point or discrete sampling can only capture the growth process at a single point and cannot capture the dynamic trajectory of the conversion rate over time. By continuously acquiring spectra at fixed time intervals, the necessary time-series data base is provided for subsequent kinetic model fitting.

[0020] Raman spectroscopy, based on inelastic scattering of molecular vibrations, has a specific ability to recognize CH bonds. Due to differences in intermolecular forces, gaseous, dissolved, and hydrated methane exhibit distinct CH stretching vibration bands (2890–2930 cm⁻¹). -1 There are discernible frequency shifts and differences in full width at half maximum (FWHM). Gaseous methane molecules have no hydrogen bonds, have the highest vibrational frequency, and have a FWHM < 6 cm. -1 Dissolved methane experiences a weak interaction with water molecules, causing its frequency to redshift to (~2905 cm⁻¹). -1 It also broadens significantly, with a half-peak width of 12-20cm. -1Hydrated methane is trapped in a cage-like structure, which breaks down into large cages (~2915 cm⁻¹). -1 ) and xiaolongbao (~2903cm) -1 The bimodal structure provides a physical basis for the subsequent differentiation of the three phase states.

[0021] Because the hydrate shell exists in a metastable equilibrium under the high pressure and low temperature environment of the deep sea, it will immediately decompose or undergo structural distortion once the temperature is increased or the sample is depressurized. This leads to the escape of gaseous methane and changes in cage occupancy, causing subsequent quantitative analysis to lose its physical authenticity. Therefore, in-situ monitoring is employed. By integrating a Raman probe into a high-pressure reactor or deep-sea lander, the gas-liquid interface spectrum is directly acquired under undisturbed temperature and pressure conditions, ensuring that the spectral signal truly reflects the phase distribution in the in-situ environment of the cold seep. The hydrate shell only forms at the interface where the bubble surface contacts the seawater. By focusing the laser at the gas-liquid interface, the spectral signal is ensured to originate from the shell itself rather than the surrounding dissolved phase or the main gas phase.

[0022] By comparing different parameter combinations through preliminary experiments, the optimal power, integration time, and number of integration parameters were selected to ensure the signal-to-noise ratio and stability of the spectral data. Time-series Raman spectroscopy was used to monitor the formation process of the hydrate shell at the gas-liquid interface in situ, and raw datasets were collected during the changes of the research object.

[0023] S2: Raman spectroscopy data preprocessing.

[0024] The original dataset contained instrument noise, environmental interference, and overlapping signals from three phases, making it unsuitable for direct quantitative analysis. A series of preprocessing steps, including cosmic ray removal, baseline correction, smoothing and denoising, and fitting, were performed to distinguish the Raman spectra of methane molecules in different phases, and peak areas were normalized.

[0025] Cosmic ray removal: The neighborhood pixel comparison threshold method is used to remove randomly occurring pulsed cosmic ray clutter peaks in deep-sea in-situ detection, so as to avoid misidentification as phase characteristic peaks or distorted baseline fitting results.

[0026] Baseline correction: An adaptive iterative weighted least squares method is used to fit the dynamic baseline, eliminating the baseline drift accumulated over long-term monitoring and avoiding the baseline residue that leads to an overestimation of the peak area of ​​the three-phase system.

[0027] Smoothing and denoising: A 5th-order Savitzky-Golay filter is used for smoothing, which preserves the details of the characteristic peaks while filtering out high-frequency noise.

[0028] Gaussian fitting peak division: Based on the Raman spectra of standard substances (pure gaseous state, dissolved state, and hydrated state) obtained in situ, peak division was performed in the range of 2890–2930 cm⁻¹. -1Multi-peak Gaussian fitting is performed on the characteristic interval, and Raman frequency shift and half-peak width are used as the criteria to generate the characteristic peak area of ​​the three phases at each time.

[0029] Peak area normalization: based on the CH symmetrical stretching vibration band (2890~2930cm) -1 Using the integral area as an internal standard, the characteristic peak areas of multiphase methane are normalized time-series to obtain a time-normalized multiphase methane peak area dataset, providing a standardized data base for subsequent kinetic quantitative analysis.

[0030] S3: Quantitative analysis of the growth kinetics of hydrate shells and extraction of kinetic parameters.

[0031] Based on the normalized peak area of ​​the time-normalized multiphase methane peak area dataset, the proportion of each phase of methane is calculated in real time, and the transformation law of multiphase methane over time during hydrate formation is quantified.

[0032] Based on the quantitative relationship of the dynamic conversion process of multiphase methane, kinetic characteristic parameters such as hydrate conversion rate, hydrate formation rate and phase migration flux are calculated and extracted, providing important experimental data support for the construction of kinetic models.

[0033] hydrate conversion rate Defined as the amount of hydrated methane at time t With the system's maximum convertible total methane The ratio of to represents the degree of saturation in the growth of the hydrate shell. ; hydrate formation rate The first-order differential of the conversion rate with respect to time is used to characterize the growth capacity of the hydrate shell at each time point. ; The dynamic fluctuations of gaseous, dissolved, and hydrated methane fluxes at a time-series scale were quantitatively statistically analyzed to characterize the dynamic accumulation process of methane trapped in hydrate shells.

[0034] S4: Construction of the kinetic model for the growth of hydrate thin-shells.

[0035] The growth rate of the hydrate shell increases rapidly during the growth phase, but slows down in the later stage. As the shell thickens from a micron-sized film, the rate-controlling step shifts from interfacial reaction to pore diffusion.

[0036] When cold seep bubbles traverse the hydrate stability zone, the methane supersaturation at the gas-liquid interface is extremely high, triggering random nucleation of hydrate crystals. Initially, the nuclei are sparse, the interfacial reaction area is sufficient, and shell growth is controlled by the nucleation frequency and crystal growth rate. As the shell thickens, methane molecules must diffuse through the pores of the hydrate framework to the growth front. The diffusion path lengthens, resistance increases, and growth gradually becomes dominated by diffusion flux, forming a two-stage composite growth mechanism.

[0037] To investigate the growth pattern of hydrate thin shells, a piecewise independent fitting strategy was adopted. The Avrami-Erofeyev model and the Ginsting-Brounshtein model were used to model the dynamics of the hydrate thin shell growth process, thus elucidating the dynamic laws governing the formation of hydrate thin shells.

[0038] The Avrami-Erofeyev model, based on assumptions of stochastic nucleation, isotropic growth, and constant growth rate, is adapted to the interfacial reaction control stage of hydrate nucleation. The Avrami-Erofeyev conversion function... for: , In the formula This is the characteristic rate constant for nucleation and growth. Avrami growth index, To determine the nucleation and growth time, the failure point of the Avrami-Erofeyev model was identified through fitting plots. After the hydrate shell forms, the diffusion of methane molecules through the shell pores is restricted, entering a slow growth stage. The nucleation and growth stages of the hydrate and the thin-shell formation growth stage are distinguished by the peak points of the rate curves and the phase migration flux, thus determining the transition time. The Ginstling-Brounshtein model, based on the diffusion of reactants through the spherical product layer in solid-state reactions and the Fick's law governing the diffusion of methane molecules through the shell pores, is suitable for the diffusion-controlled stage. Furthermore, considering the dynamic evolution of the hydrate shell pore structure, a cumulative integral is introduced. And its interaction term with time. Ginstling-Brounshtein conversion function for: , In the formula, a, b, and c are fitting parameters, where parameter a is the background diffusion rate parameter, corresponding to the inherent diffusion capability of the initial product layer, parameter b is the conversion-driven diffusion rate parameter, and parameter c is the time-conversion synergistic acceleration coefficient, describing the nonlinear diffusion enhancement under the combined effect of reaction time and cumulative conversion. The cumulative integral amount for conversion rate. To control the growth time for diffusion, The hydrate conversion rate.

[0039] The feature parameters of the Avrami-Erofeyev model and the Ginstling-Brounshtein model were obtained sequentially through numerical fitting.

[0040] Taking the logarithm twice on both sides of the Avrami equation for the feature parameters of the Avrami-Erofeyev model yields a linear form: , The hydrate conversion rate during the hydrate nucleation and growth stage was obtained by fitting the hydrate conversion rate. This characterizes the rapid nucleation and growth capacity of hydrates in their early stages.

[0041] The parameters of the Ginstling-Brounshtein model are determined through linear regression using its formula, which is: , The integral I is calculated using the trapezoidal rule, and the formula is as follows: , In the formula, Let be the conversion rate at time i. Let i be the time at the i-th moment. Let be the time at the (i+1)th moment.

[0042] By fitting the hydrate conversion rate during the hydrate thin-shell formation growth stage, values ​​a, b, and c were obtained to characterize the mass transfer confinement effect after hydrate shell densification. The growth time was controlled by diffusion. Let x be the first independent variable and I be the cumulative integral of the conversion rate as the second independent variable. The left-hand side of the Ginstling-Brounshtein model is... Defined as dependent variable y, a ternary linear regression model is constructed, and the Levenberg-Marquardt least squares method is used for fitting. The lower bound of the fitting is uniformly constrained to be greater than 0. The goal is to minimize the sum of squared residuals. The model is solved iteratively through the normal equation. The model converges when the rate of change of residuals is less than a preset threshold. After convergence, the fitted values ​​of a, b, and c and the standard error are output.

[0043] Based on the division of the hydrate nucleation and growth stage and the shell mass transfer control stage, the Avrami-Erofeyev model and the Ginsting-Brounshtein model are matched piecewise to construct a piecewise dynamic characterization system, providing a complete parameter set for quantitatively solving interface mass transfer parameters such as effective diffusion coefficient and dynamic shell thickness.

[0044] S5: Calculation of total annual methane storage.

[0045] Environmental parameters of the target cold seep were collected and processed through in-situ deep-sea measurements to serve as the basis for calculating the methane sequestration capacity of the hydrate shell, ensuring the accuracy and applicability of the calculated data. Environmental parameters include the thickness of the hydrate stability zone. cold spring bubble flow bubble radius Average rising speed of bubbles Bubble release frequency N.

[0046] Based on the growth kinetics model of hydrate thin shells, and combined with bubble geometry and marine environmental parameters, the methane sequestration capacity of a single bubble hydrate shell is calculated.

[0047] The hydrate shell is not a static and dense medium during its growth. Its internal pore structure evolves dynamically as water molecules permeate outward, volume expansion and stress release occur, and guest molecules redistribute, forming an ever-evolving network of pore channels or microcracks. The mass transfer resistance of the shell is influenced by the combined effects of geometric thickness and pore connectivity.

[0048] In the three-parameter Ginstling-Brounshtein model, a and b typically approach 0, and the diffusion-transformation coupling term... The dominant mass transfer process in the shell is determined by differentiating it with respect to time and substituting it into the diffusion relationship of the spherical surface layer. The instantaneous effective diffusion coefficient can be obtained. : , In the formula, The initial radius of the bubble; Based on the thickness of the hydrate stability zone Determine and calculate the formation time of the hydrate shell. , , In the formula, This represents the average rising speed of the bubbles; Take the moment when the bubble ends its passage through the hydrate stability zone. The hydrate shell diffusion coefficient is defined as the characteristic moment when the hydrate shell forms and enters the stable diffusion loss stage. The equivalent diffusion coefficient at this moment characterizes the comprehensive equivalent mass transfer resistance of bubbles traversing the hydrate stability zone at its end and after the shell structure has fully developed. , In the formula, c is the fitting parameter; The hydrate conversion rate at the end of the residence time; As of Conversion rate cumulative integral; To facilitate comparison of shell mass transfer rates under different bubble sizes, a characteristic diffusion rate constant independent of bubble size is defined. : ; Based on hydrate conversion rate Geometric relationship with shell thickness δ: , The shell thickness δ is calculated using the following formula: , In the formula, The hydrate conversion rate during the residence time period; The amount of methane sequestered in the hydrate shell is determined by the amount of methane in hydrate form. and residual gaseous methane in the shell The methane content of the single bubble hydrate shell is obtained by calculating and summing the methane content of the two parts separately. : , , , In the formula, This represents the methane concentration in type I hydrates when the cage structure is completely occupied by gas molecules. , The molar density of methane under in-situ pressure and temperature conditions is calculated using the equation of state based on the thermobaric conditions. Let V be the gas phase volume of the bubble nucleus.

[0049] By combining the methane sequestration capacity of single bubble hydrate shells with basic parameters of marine cold seep environment, the macro-scale quantification of methane sequestration capacity of cold seep hydrates is completed. By introducing a thin-shell bubble diffusion loss model, the geometric sequestration capacity is transformed into the effective sequestration capacity and extrapolated to the annual scale.

[0050] The hydrate shell is not absolutely dense, and methane molecules will slowly diffuse into the surrounding seawater through the pores of the shell. Using Fick's diffusion law and the law of conservation of mass, the first-order diffusion process of methane bubbles in the outer hydrate shell is kineticly modeled to calculate the storage efficiency of the thin-shell hydrate shell.

[0051] radius is Bubbles, shell thickness In a hydrate shell, the methane concentration inside the shell is in equilibrium with the gas phase of the core, while the concentration on the outside is approximately zero. Under the thin-shell approximation, Fick's diffusion law is used to calculate the characteristic time of diffusion in the thin-shell hydrate shell by simultaneously applying the molar diffusion flux of the shell and the mass-conserved reduction rate of the core. Characteristic timescales representing the diffusion loss of methane from the bubble core through the shell: ,

[0052] Calculate the methane sequestration efficiency R in the hydrate shell to characterize the residence time. Remaining methane percentage: , In the formula, The change in the total number of moles of methane in the bubbles over time. This represents the total number of moles of methane in the bubble at the initial moment. Based on the frequency of cold seep bubble release and annual operating time, calculate the total annual methane sequestration in the hydrate shell. : , In the formula, N is the frequency of cold spring bubble release. This refers to the annual operating time of the cold spring. The values ​​represent the molar mass of methane, all obtained from in-situ observations.

[0053] S6: Construction of a quantitative correction model for cold seep environments.

[0054] In real cold seep environments, fluctuations in temperature and pressure, as well as sediment impurities, cause interference. Temperature alters the Raman scattering cross-section and peak width, pressure shifts vibrational frequencies, and impurity particles attenuate light scattering or enhance fluorescence background. This results in different Raman signal responses for the same methane concentration under different environments, causing Raman spectral responses to deviate from baseline conditions. A quantitative correction model for cold seep environments is constructed to correct annual sequestration results, outputting results adapted to real in-situ cold seep conditions, thus completing a quantitative assessment.

[0055] To achieve accurate correction for environmental interference, a three-dimensional benchmark model based on standard interference-free experimental conditions was first established, consisting of Raman characteristic peak area ratio, cage occupancy, and methane molar content. occupancy rate of hydrate cages The independent variable is the molar concentration of methane. Using multiple nonlinear regression as the dependent variable, a baseline theoretical model is constructed: , In the formula, These are the regression coefficients of the benchmark theoretical model.

[0056] Using a deep-sea high-pressure cryogenic reactor, in-situ conditions with a full gradient from 0 to 4000 m (temperature 0–10℃, pressure 0–40 MPa) were simulated. Raman spectral responses of hydrate samples were tested under different temperature and pressure combinations. By comparing these responses with the baseline methane concentration under standard conditions (5℃, 10 MPa), temperature and pressure correction functions were fitted and coupled to obtain the in-situ temperature-pressure coupling correction coefficient. .

[0057] Typical impurity types of cold seeps were selected, and hydrate-sediment mixture simulation samples with different impurity content gradients were prepared. Raman spectra of each sample were collected under standard operating conditions to simulate the in-situ impurity environment of the cold seep. The attenuation law of the characteristic peak integral area and peak area ratio K caused by impurity content γ was quantitatively analyzed. The quantitative response relationship of impurity interference was obtained by fitting, and the sediment impurity correction coefficient was established. .

[0058] The calibrated in-situ environmental correction coefficients for temperature, pressure, and impurities are coupled and embedded into a three-dimensional benchmark model to construct a quantitative correction model adapted to the actual in-situ environment of cold seeps. This achieves synergistic and accurate correction of multiple environmental factors, and the corrected methane molar concentration is then calculated. for: , In the formula, The molar concentration of methane, This is the in-situ temperature-pressure coupling correction factor. This is the correction factor for sediment impurities.

[0059] Substituting the in-situ environmental parameters of the target cold seep into a multi-factor coupled calibration model, the annual total methane sequestration in the hydrate shell was calculated. The correction was performed to obtain the total annual methane sequestration volume in the corrected hydrate shell.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for quantitatively assessing the methane sequestration in the hydrate shell of a cold seep environment, characterized in that, Includes the following steps: Time-series Raman spectral data of the hydrate shell growth process were collected in situ and preprocessed to obtain a time-normalized multiphase methane peak area dataset. Based on the multiphase methane peak area dataset, hydrate conversion rate, hydrate formation rate and phase migration flux were extracted. In the hydrate nucleation and growth stage and the hydrate thin-shell formation and growth stage, the Avrami-Erofeyev model and the Ginstling-Brounshtein model were used to construct hydrate thin-shell growth kinetic models, respectively, and the hydrate conversion rate, shell thickness and hydrate shell diffusion coefficient were calculated. Based on cold seep environmental parameters, the methane sequestration capacity of a single bubble hydrate shell was calculated by adding hydrated methane and gaseous methane stored within the hydrate shell. A diffusion loss model for thin-shell bubbles in the hydrate stability zone was constructed using Fick's diffusion law and the law of conservation of mass to calculate the sequestration efficiency of the thin-shell hydrate shell. Combined with the frequency of cold spring bubble release Cold Spring Annual Operating Time Calculate the total annual sequestration volume of methane from the hydrate shell. : ， In the formula, This represents the molar mass of methane.

2. The method for quantitatively assessing the methane sequestration in the hydrate shell of a cold seep environment according to claim 1, characterized in that, The in-situ acquisition includes: in a closed high-pressure system that maintains the in-situ temperature and pressure conditions of the cold seep, using confocal Raman spectroscopy to lock the laser focus at the gas-liquid interface, and continuously acquiring time-series Raman spectral data of the hydrate shell formation process at fixed time intervals.

3. The method for quantitatively assessing the methane sequestration in the hydrate shell of a cold seep environment according to claim 1, characterized in that, The preprocessing includes, in sequence: The neighboring pixel comparison thresholding method is used to remove randomly occurring pulsed cosmic ray clutter peaks in deep-sea in-situ detection; An adaptive iterative weighted least squares method is used to fit the dynamic baseline, eliminating the baseline drift accumulated over long-term monitoring. A 5th-order Savitzky-Golay filter was used for smoothing. Based on the in-situ obtained standard material Raman spectra, the 2890–2930 cm⁻¹ range was analyzed. -1 Multi-peak Gaussian fitting is performed on the characteristic interval to generate the characteristic peak area of ​​the three-phase state at each time step; Using the integral area of ​​the CH symmetric stretching vibration band as the internal standard, the characteristic peak areas of multiphase methane were normalized time-by-time.

4. The method for quantitatively assessing the methane sequestration in the hydrate shell of a cold seep environment according to claim 1, characterized in that, The transition time between the hydrate nucleation and growth stage and the hydrate thin-shell formation and growth stage is determined by the peak point of the rate curve of the hydrate formation rate and the phase migration flux.

5. The method for quantitatively assessing the methane sequestration in the hydrate shell of a cold seep environment according to claim 1, characterized in that, The conversion rate function of the Avrami-Erofeyev model for: ， In the formula This is the characteristic rate constant for nucleation and growth. Avrami growth index, To determine the nucleation and growth time, the failure point of the Avrami-Erofeyev model was identified through fitting plots. The conversion rate function of the Ginnstling-Brounshtein model for: ， In the formula, a, b, and c are fitting parameters, where parameter a is the background diffusion rate, parameter b is the conversion-driven diffusion rate parameter, and parameter c is the time-conversion synergistic acceleration coefficient. The cumulative integral amount for conversion rate. To control the growth time for diffusion, The hydrate conversion rate.

6. The method for quantitatively assessing the methane sequestration in the hydrate shell of a cold seep environment according to claim 5, characterized in that, The integral I is calculated using the trapezoidal rule, and the formula is as follows: ， In the formula, Let be the conversion rate at time i. Let i be the time at the i-th moment. Let be the time at the (i+1)th moment.

7. The method for quantitatively assessing the methane sequestration in the hydrate shell of a cold seep environment according to claim 5, characterized in that, The methane sequestration capacity of the single bubble hydrate shell The calculation method is as follows: diffusion-conversion coupling term The dominant shell mass transfer process is derived with respect to time, and then substituted into the diffusion relationship of the spherical surface layer. The instantaneous effective diffusion coefficient is obtained. : ， In the formula, The initial radius of the bubble; Based on the thickness of the hydrate stability zone Determine and calculate the formation time of the hydrate shell. , ， In the formula, This represents the average rising speed of the bubbles; Take the moment when the bubble ends its passage through the hydrate stability zone. The hydrate shell diffusion coefficient is defined as the characteristic moment when the hydrate shell forms and enters the stable diffusion loss stage. The equivalent diffusion coefficient at that moment is: ， In the formula, c is the fitting parameter; The hydrate conversion rate at the end of the residence time; As of Conversion rate cumulative integral; Define a characteristic diffusion rate constant that is independent of bubble size. : ； Based on hydrate conversion rate Geometric relationship with shell thickness δ: ， The shell thickness δ is calculated using the following formula: ， In the formula, The hydrate conversion rate represents the residence time period.

8. The method for quantitatively assessing the methane sequestration in the hydrate shell of a cold seep environment according to claim 7, characterized in that, The amount of methane sequestered in the hydrate shell is determined by the amount of methane in the hydrate state. and residual gaseous methane in the shell The methane content is composed of two parts, and the methane sequestration capacity of the single bubble hydrate shell is obtained. : ， ， ， In the formula, This represents the methane concentration in type I hydrates when the cage structure is completely occupied by gas molecules. , The molar density of methane under in-situ pressure and temperature conditions is calculated using the equation of state based on the thermobaric conditions. Let V be the gas phase volume of the bubble nucleus.

9. The method for quantitatively assessing the methane sequestration in the hydrate shell of a cold seep environment according to claim 1, characterized in that, The methane sequestration efficiency R of the hydrate shell is: ， In the formula, The change in the total number of moles of methane in the bubbles over time. This represents the total number of moles of methane in the bubble at the initial moment. For shell thickness, Where is the bubble radius, The characteristic time for diffusion in the thin-shell hydrate shell is calculated based on Fick's diffusion law by combining the molar diffusion flux of the shell with the mass-conserved rate of decrease in the core.

10. The method for quantitatively assessing the methane sequestration in the hydrate shell of a cold seep environment according to claim 1, characterized in that, A three-dimensional benchmark model based on the Raman characteristic peak area ratio, cage occupancy, and methane molar content was constructed under standard, interference-free experimental conditions, using the characteristic peak area ratio as the starting point. occupancy rate of hydrate cages The independent variable is the molar concentration of methane. Using multiple nonlinear regression as the dependent variable, a baseline theoretical model is constructed: ， In the formula, These are the regression coefficients of the benchmark theoretical model; Using a deep-sea high-pressure cryogenic reactor to simulate different temperature and pressure combinations, the Raman spectral response of hydrate samples was tested. Temperature and pressure correction functions were fitted to obtain the results, and the in-situ temperature-pressure coupling correction coefficient was derived through coupling. ; Hydrate-sediment mixture simulation samples with different impurity content gradients were prepared. Raman spectra were acquired under standard operating conditions, and the attenuation law of characteristic peaks by impurity content γ was quantitatively analyzed. The quantitative response relationship of impurity interference was obtained by fitting, and the sediment impurity correction coefficient was established. ; The calibrated temperature, pressure, and impurity correction coefficients for the two types of in-situ environments are coupled and embedded into a three-dimensional benchmark model to construct a quantitative correction model adapted to the actual cold seep in-situ environment, which is used to adjust the methane molar concentration. Perform correction: 。

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