Method for effectively monitoring carbon dioxide sequestration leakage

Abstract

The invention relates to the field of oil and gas geophysical prospecting engineering, and discloses a method for effectively monitoring carbon dioxide sequestration leakage, which comprises the following steps: S1, establishing a geologic model for carbon dioxide sequestration, carrying out oil reservoir simulation, and constructing a time shift synthesis model of different carbon dioxide saturations; s2, analyzing reservoir rock physical response characteristics under different carbon dioxide saturations by using a rock physics theory, and deducing an elastic parameter model of the reservoir rock; s3, seismic forward modeling is carried out by using the viscoacoustic wave equation of the standard linear solid attenuation model, time-lapse seismic data are generated, and reverse time migration imaging is carried out; and S4, on the basis of the reverse time migration profile obtained in the step S3, the leakage conditions of the injected carbon dioxide in the front-middle-later period are monitored in real time by using a spectrum ratio method of an imaging domain. According to the working process provided by the invention, through integration of reservoir simulation, rock physical analysis and time-lapse seismic monitoring technologies, comprehensive monitoring and prediction of carbon dioxide plume migration are realized.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas geophysical exploration engineering, and in particular to a method for effectively monitoring leakage of carbon dioxide storage. Background Art

[0002] As global climate change becomes increasingly severe, carbon capture and storage (CCS) technology has attracted widespread attention as an effective method to reduce carbon dioxide emissions. CCS technology in saline aquifers has become a research hotspot due to its huge storage potential, but the risk of leakage during carbon dioxide injection cannot be ignored and requires comprehensive monitoring. Time-lapse seismic monitoring is a proven and reliable large-scale carbon dioxide monitoring method that has been successfully applied in multiple projects such as Sleipner, Otway, and Ketzin. However, although traditional time-lapse seismic monitoring methods can provide elastic properties such as P-wave velocity (Vp) and attenuation (Qp), they cannot directly quantify the saturation of the carbon dioxide plume. Although reservoir simulation can predict the distribution of carbon dioxide, it lacks a direct connection with the elastic properties in seismic data.

[0003] To address this problem, several studies have developed integrated workflows that combine reservoir, rock physics, and seismic data. In 2010, a workflow was developed for coalbed CO2 storage, while increasing coalbed methane production. In 2020, a workflow for enhanced oil recovery (EOR) was established, combining an analog-to-seismic approach with seismic reservoir characterization. In 2017, a workflow was proposed for detecting and quantifying P-wave velocity time-shift anomalies using elastic full waveform inversion (FWI). However, these workflows are mainly applied to coalbeds and EOR, and there are few studies specifically targeting saline formations, especially studies focusing on the attenuation effects of CO2 injection.

[0004] To further quantify the attenuation effect caused by CO2 injection, a new integrated workflow is proposed. This workflow combines attenuation and spectral ratio methods and is specifically applied to CO2 storage monitoring in saline aquifers, focusing on typical saline aquifers in the Pearl River Mouth Basin (PRMB) in the northern South China Sea. We synthesized reservoir and seismic related attributes based on field information in the region, established the relationship between CO2 saturation and Vp and Qp using rock physics theory, and generated time-lapse seismic data. By analyzing the attenuation effect of CO2 injection in the reservoir in the 4D imaging domain through reverse time migration (RTM) and spectral ratio methods, the present invention can more accurately predict the migration path of the CO2 plume and its leakage in the early, middle and late stages, providing technical support for the effective monitoring of offshore CO2 storage projects. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for effectively monitoring carbon dioxide storage leakage, aiming to use the SLS attenuation model and the spectral ratio method in the imaging domain to monitor the plume leakage of early, middle and late carbon dioxide storage, so as to more comprehensively understand and characterize the migration and distribution of carbon dioxide in deep saline layers.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for effectively monitoring carbon dioxide storage leakage comprises the following steps:

[0007] S1. Establish a geological model for CO2 storage and perform reservoir simulation to construct a time-lapse synthetic model with different CO2 saturations;

[0008] S2. Using rock physics theory, analyze the physical response characteristics of reservoir rocks under different carbon dioxide saturations and deduce their elastic parameter models;

[0009] S3. Use the viscoacoustic wave equation of the standard linear solid attenuation model to carry out seismic forward modeling, generate time-lapse seismic data, and perform reverse time migration imaging;

[0010] S4. Based on the reverse time migration profile obtained in S3, the leakage of the injected carbon dioxide in the early, middle and late stages is monitored in real time using the spectrum ratio method in the imaging domain.

[0011] Preferably, a two-dimensional geological model for carbon dioxide storage is established based on geological attributes, including a permeability model and a porosity model, and a two-dimensional velocity model and a density model required for seismic processing in S2-S4 are established based on the provided logging data.

[0012] Preferably, in S2, the maximum inverse quality factor of the P wave is used to calculate Qp, and the low-frequency compression modulus M0 and the volume density ρ are used to calculate the P wave velocity Vp of the rock:

[0013]

[0014] Among them, M0 is the compression modulus at very low frequency, and M ∞ Corresponds to the compression modulus at very high frequencies.

[0015] Preferably, in step S2, in order to calculate the low-frequency modulus, a fluid approximation substitution method of longitudinal wave velocity (Vp) is used:

[0016]

[0017] M s is the compression modulus of the mineral matrix, M Dryis the compression modulus of the rock skeleton, φ is the total porosity, K f is the effective bulk modulus of the fluid in the rock, calculated as the harmonic mean of water and carbon dioxide.

[0018] Preferably, in S2,

[0019]

[0020] The high frequency compression modulus is the compression modulus M of wet rock. W and the rock M containing only carbon dioxide G The harmonic mean of W and K g are the bulk moduli of water and carbon dioxide, respectively, S W is the water saturation.

[0021] Preferably, in order to calculate the longitudinal wave velocity Vp of the rock, the low-frequency compression modulus M0 and the bulk density ρ are used, as shown below:

[0022]

[0023] Preferably, in step S3, the wave will be attenuated when propagating in the attenuation medium caused by carbon dioxide. The viscoacoustic wave equation of a single SLS is used for forward simulation, and its expression is:

[0024]

[0025] Among them, τ ε and τ σ are the strain and stress relaxation times, r is the memory variable, ν p It's speed.

[0026] Preferably, in the equation τε and τ σ The calculation expression is:

[0027]

[0028] Among them, Q p is the quality factor, which is used to characterize the strength of seismic wave attenuation, and ω0 is the reference frequency.

[0029] Preferably, in step S4, the spectrum ratio method is used to calculate the ratio between the monitoring spectrum R2(k) and the baseline spectrum R1(k). It can be expressed as:

[0030] In[R2(k) / R1(k)]=ρ[k]+G0

[0031] where k is the local wave number of the migration event and ρ is calculated as:

[0032]

[0033] The integration is performed along the wave propagation path dl, which quantifies the accumulated attenuation on the time-lapse seismic image.

[0034] The present invention provides a method for effectively monitoring carbon dioxide storage leakage, which has the following beneficial effects:

[0035] The workflow proposed in the present invention realizes comprehensive monitoring and prediction of carbon dioxide plume migration by integrating reservoir simulation, rock physics analysis and time-lapse seismic monitoring technology. Compared with the existing technology, the present invention introduces the spectral ratio method in the attenuation and imaging domains, which significantly improves the quantification accuracy of the attenuation effect after carbon dioxide injection and enhances the safety and reliability of monitoring. At the same time, the workflow is optimized for the specific geological characteristics of the Pearl River Mouth Basin, has stronger adaptability and timeliness, and can timely identify potential early-mid-late leakage risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic flow chart of a workflow of a method for effectively monitoring leakage before, during and after carbon dioxide storage provided by an embodiment of the present invention;

[0037] Figure 2 A two-dimensional porosity and permeability model provided for one embodiment of the present invention;

[0038] Figure 3 A two-dimensional initial velocity and density model provided for one embodiment of the present invention;

[0039] Figure 4 A change in the velocity of carbon dioxide and a change in the attenuation of injected carbon dioxide provided by one embodiment of the present invention;

[0040] Figure 5 A two-dimensional reverse time migration result provided by an embodiment of the present invention;

[0041] Figure 6 A spectrum diagram provided for an embodiment of the present invention;

[0042] Figure 7 An imaging result 100 years after the injection of carbon dioxide is provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Embodiments of the present invention: A method for effectively monitoring leakage of carbon dioxide storage, such as Figure 1 As shown, the following steps are included:

[0045] S1. Establish a geological model for CO2 storage and perform reservoir simulation to construct a time-lapse synthetic model of different CO2 saturations. The establishment of a CO2 storage model is the basis for quantifying the changes in reservoir properties at different CO2 saturations.

[0046] S2, using rock physics theory, analyze the reservoir rock physical response characteristics under different carbon dioxide saturation and deduce its elastic parameter model. The correlation coefficient calculation method here is:

[0047] As seismic waves propagate through the various layers of the earth, their energy gradually decays. In this paper, we use the maximum inverse quality factor of the P wave to calculate Qp, and use the low-frequency compressibility modulus M0 and the bulk density ρ to calculate the P wave velocity Vp of the rock:

[0048]

[0049] Among them, M0 is the compression modulus at very low frequency, and M ∞ Corresponds to the compression modulus at very high frequencies.

[0050]

[0051] To calculate the low frequency moduli, we use a fluid approximation using only the longitudinal wave velocity (Vp). s is the compression modulus of the mineral matrix, M Dry is the compression modulus of the rock skeleton, φ is the total porosity, K f is the effective bulk modulus of the fluid in the rock, calculated by the harmonic mean of water and carbon dioxide. The high-frequency compression modulus is the compression modulus of wet rock M W and the rock M containing only carbon dioxide G The harmonic mean of .

[0052] Among them, K W and K g are the bulk moduli of water and carbon dioxide, respectively, S W is the water saturation.

[0053]

[0054] To calculate the longitudinal wave velocity Vp of the rock, we use the low-frequency compressibility modulus M0 and the bulk density ρ as follows:

[0055]

[0056] For this storage model, we use the extracted baseline velocity values. As for the quality factor Qp, the baseline value is set to 100,000, indicating that there is no attenuation in the seismic frequency range before CO2 injection, and the rock physics analysis of the reservoir after CO2 injection is performed at normal temperature and pressure conditions. Figure 4 (a) is the velocity change in the 25th year after CO2 injection and (b) is the attenuation change in the 25th year after CO2 injection; (c) is the velocity change in the 100th year after CO2 injection; (d) is the attenuation change in the 100th year after CO2 injection. Figure 4 (ad) show the velocity change in the 25th year of CO2 injection and (b) the attenuation change in the 25th year after CO2 injection; the velocity change in the 100th year after CO2 injection; and the attenuation change in the 100th year after CO2 injection, respectively. After CO2 injection, the velocity at 25 years is significantly lower than that at 100 years. At the same time, the continuous CO2 injection from before injection to 25 years and then to 100 years has led to a gradual increase in the range of the CO2 plume in the reservoir. After the injection was stopped, the injected CO2 further displaced the water in the reservoir, expanding the range of the CO2 plume ( Figure 4 d)

[0057] S3, using the viscoacoustic wave equation of the standard linear solid (SLS) attenuation model to carry out seismic forward simulation, generate time-lapse seismic data, and perform reverse time migration imaging. When the wave propagates in the attenuation medium caused by carbon dioxide, it will be attenuated. In order to accurately describe the wave propagation characteristics in the attenuation medium, based on the standard linear solid (SLS) theory, we use the viscoacoustic wave equation of a single SLS for forward simulation, and its expression is:

[0058]

[0059] Among them, τ ε and τ σ are the strain and stress relaxation times, r is the memory variable, ν p is the speed. τε and τ σ The calculation expression is:

[0060]

[0061] Among them, Q pis the quality factor, which is used to characterize the strength of seismic wave attenuation, and ω0 is the reference frequency.

[0062] Figure 5 The reverse time migration two-dimensional profile results of the carbon dioxide storage model are shown. (a) is the reverse time migration profile before carbon dioxide injection; (b) is the reverse time migration profile 25 years after carbon dioxide injection; (c) is the reverse time migration profile 100 years after carbon dioxide injection. Figure 5 The solid orange arrows indicate the leftward migration of the CO2 plume at the fault, with the horizontal extension after 100 years exceeding the migration after 25 years. Figure 5 (b) and Figure 5 (c), we can see that the result after 100 years ( Figure 5 c) exhibits a more pronounced and widespread attenuation due to the migration of the CO2 plume. In addition, Figure 5 It is also shown that CO2 injection leads to a decrease in seismic amplitude and resolution. This phenomenon is caused by the increase in CO2 saturation in the reservoir, which leads to significant attenuation.

[0063] S4, based on the reverse time migration profile obtained in S3, and using the spectrum ratio method in the imaging domain, real-time monitoring of the leakage of the injected carbon dioxide in the early, middle and late stages is performed.

[0064] In order to more accurately study the impact of CO2 plume distribution on time-lapse seismic response, we noticed that CO2 injection increased the attenuation within the reservoir, resulting in a significant reduction in amplitude and resolution, and phase distortion, which led to misinterpretation of time-lapse seismic data. A common method to quantify the attenuation effect is to measure the spectral changes in seismic data caused by attenuation. In order to measure the attenuation effect from time-lapse seismic imaging, we need to convert the equation from the frequency domain to the imaging domain. In this study, the spectral ratio method is used to calculate the ratio between the monitoring spectrum R2(k) and the baseline spectrum R1(k). It can be expressed as:

[0065] In[R2(k) / R1(k)]=ρ[k]+G0 (10)

[0066] where k is the local wave number of the migration event and ρ is calculated as:

[0067]

[0068] In equation (11), the integration is performed along the wave propagation path dl, which quantifies the accumulated attenuation on the time-lapse seismic image. Therefore, it can be used well to quantify the attenuation caused by injected CO2 over a given monitoring time frame.

[0069] In addition, using the baseline data as a reference, Figure 6(a) shows the difference between the migration profile results of the 25th year of CO2 injection and the injection; (b) shows the difference between the migration profile results of the 100th year of CO2 injection and the injection; (c) shows the difference between the migration profile results of the 100th year of CO2 injection and the injection; Figure 6 (a, b) Spectrum diagram in attenuation region I; (d) only provides Figure 6 (a, b) Spectral ratio in attenuation region I; (e) provides only Figure 6 (a, b) Spectrum diagram in attenuation region II; (f) only provides Figure 6 (a, b) Spectral ratio in attenuation region II, Figure 6 The baseline migration images and Figure 5 is the difference between (b, c), thus estimating the distribution of carbon dioxide. Figure 6 As shown in (ab), the image differences clearly represent the CO2 response in seismic imaging, proving that seismic imaging technology can effectively monitor the migration of CO2.

[0070] To better evaluate the attenuation effect, we calculated Figure 6 The wavenumber spectra corresponding to the two black dashed boxes are as follows: Figure 6 (c) and (e) show that a significant attenuation effect appears in the spectrum 100 years after CO2 injection. It is worth noting that we observe a significant decrease in amplitude, especially in the high-frequency part. However, the CO2 plume spectrum in region II, located near the CO2 injection well, is relatively stable over the monitoring period of 25 to 100 years, as shown in Figure 2. Figure 6 (c) and (e). This stability is mainly attributed to the cessation of CO2 injection after 25 years.

[0071] To quantify the attenuation caused by CO2 injection, we calculated the attenuation caused by CO2 injection 25 and 100 years after injection using the spectral ratio method using equation (9). We then generated a logarithmic plot of the slope, revealing a linear trend at different frequencies, such as Figure 6 (d) and (f). These curves are roughly linear, and each curve shows a different negative slope, indicating that CO2 injection has a significant attenuation effect on the formation within 25 years. In addition, by calculating the quality factor (Qp), we can effectively quantify the spectral attenuation effect between 25 years and 100 years of CO2 injection. Figure 6 In (d), the calculated Qp values ​​are 23.98 (red curve) and 19.32 (black curve); Figure 6 In (f), they are 15.52 (red curve) and 12.79 (black curve), respectively. Based on the calculated Qp values, we observe that the attenuation effect becomes more significant as the CO2 plume migrates.

[0072] like Figure 4-6 As shown, the proposed method effectively describes the CO2 injected, enabling quantitative characterization of the CO2 plume. Therefore, our method provides an opportunity to predict CO2 leakage using known reservoir properties. In addition, we can detect the pre-, mid-, and late-stage CO2 leakage from seismic images using the image domain spectral ratio method, enabling us to monitor safety hazards based on time-lapse seismic datasets.

[0073] To monitor CO2 leakage, we performed a reservoir simulation of leakage and observed the migration of the CO2 plume. Our workflow allows us to understand and predict how the characteristics of saline aquifers affect CO2 leakage. When the permeability and caprock porosity are too high or faults are reactivated, leakage pathways may form, which may increase the risk of seismic activity or affect the safety of reservoir storage, making it susceptible to CO2 leakage. We increased the permeability of the caprock to simulate leakage and applied this new workflow to monitor its leakage.

[0074] The same rock physics theory is used to obtain time-lapse seismic data and perform reverse time migration imaging. The imaging results 100 years after the injection of carbon dioxide are as follows: Figure 7 (a) shows the 2D reverse time migration (RTM) imaging result of the 100th year after the leakage occurred; (b) shows the 2D reverse time migration (RTM) imaging result of the 100th year after the leakage occurred. Figure 5 (a) The difference between the reverse time profiles of the baseline seismic data before CO2 injection; (c) The difference between the attenuation area I and the attenuation area II where leakage occurred only in the 100th year after CO2 injection. Figure 6 (b) Spectrum of region II in the 100th year where no leakage occurred; (d) Spectrum of attenuation region I in the 100th year where only carbon dioxide injection occurred and leakage occurred Figure 6 (b) Spectral ratio of region II in the 100th year without leakage; (e) Spectral ratio of attenuation region II in the 100th year with leakage of only injected carbon dioxide Figure 6 (b) Spectrum of region II in the 100th year without leakage; (f) Spectrum of attenuation region II in the 100th year with leakage of only injected carbon dioxide. Figure 6 (b) Spectral ratio of the 100th year in region II where no leakage occurs. By comparison Figure 6 (b) and Figure 7 (b), we observed that the RTM imaging results of CO2 leakage showed a larger attenuation area than the RTM imaging results without CO2 leakage. This result was further verified in the imaging difference 7(b), Figure 7(b) shows the attenuation effect of the CO2 plume out of the caprock, emphasizing the preliminary demonstration of the leakage effect of the seismic imaging method. To better illustrate the application of the proposed method in monitoring CO2 leakage, we used the image domain spectral ratio method to perform spectral analysis on the imaging results. Figure 6 Select two regions I and II at the same location, such as Figure 7 (b) as shown. Figure 7 (cd) and Figure 7 (ef) show the spectra of region I and region II, respectively.

[0075] First, Region I Figure 7 (c) shows the spectrum comparison between with leakage (black line) and without leakage (red line). Figure 7 (d) is the corresponding spectrum ratio. Figure 7 In (cd), we find that the one without leakage shows similar results to the baseline imaging result (blue line), while the one with leakage shows a significantly attenuated spectrum. Figure 7 The spectrum ratio (black line) in (d) shows a linear downward trend after the leak occurs. This result shows that after the CO2 leak occurs, we can observe a significant attenuation in the spectrum of the imaging result.

[0076] Then, in Figure 7 In (ef), the spectra of the imaging results with and without leakage both show attenuation compared with the baseline imaging results (blue line). However, the spectrum attenuation of the imaging results with leakage is stronger, and its spectrum ratio shows a larger downward trend compared with the imaging results without leakage. Therefore, the above findings show that the proposed method can effectively characterize the state of the CO2 plume and provide potential possibilities for monitoring CO2 leakage.

[0077] In a specific embodiment:

[0078] 1. Establishing geological model:

[0079] A two-dimensional porosity and permeability model was constructed based on the geological characteristics of a typical saline layer in the northern South China Sea in the Pearl River Mouth Basin (PRMB). The model size is 18km(x)×4km(y)×3km(z). The reservoir is characterized by anticline structure, which is conducive to the upward migration and storage of carbon dioxide over time. The model contains two main fault areas, each with a dip angle of 55°. The distances between the left and right faults are 40m and 30m, respectively. The injection well is located on the right side of the 30m fault. During the simulation, the model boundary is set to an impermeable boundary to reflect the actual reservoir conditions. Considering that the geochemical reaction between carbon dioxide and minerals takes a long time, the simulation only observes the migration and distribution of carbon dioxide saturation within 100 years.

[0080] 2. Reservoir simulation:

[0081] The migration of the CO2 plume was dynamically predicted using the reservoir simulation software Ecolipse 300 (E300) developed by Schlumberger. Single-well injection was carried out starting in 2024, with continuous CO2 injection for 25 years, followed by 75 years of plume monitoring. It was assumed that CO2 was injected into the saline layer in a supercritical state, the depth of the target injection layer was 1100m, the gas diffusion coefficient of CO2 was set to 0.001, and the maximum bottom hole pressure was set to 600bar.

[0082] 3. Rock physical response analysis:

[0083] Based on the velocity and density values ​​extracted from the logging data, the velocity model and density model are constructed, such as Figure 3 As shown, the physical response characteristics of reservoir rocks under different carbon dioxide saturations are analyzed, and their elastic parameter models are deduced. The maximum inverse quality factor of P waves is used to calculate Qp, and the P-wave velocity of rocks is calculated using the low-frequency compression modulus and bulk density.

[0084] 4. Spectrum Analysis:

[0085] Based on the reverse time migration profile obtained in S3, the spectral ratio method in the imaging domain is used to monitor the leakage of CO2 before, during and after injection in real time. The ratio between the monitored spectrum and the baseline spectrum is calculated to quantify the attenuation effect, and a logarithmic plot of the slope is generated to reveal the linear trend at different frequencies to evaluate the spectral attenuation effect between 25 and 100 years of CO2 injection.

[0086] 5. Leakage monitoring and verification:

[0087] By simulating different permeability conditions, the permeability of the cap layer was increased to simulate leakage, and the RTM imaging results with and without leakage were compared. It was observed that the imaging results with leakage showed a larger attenuation area. The imaging results were further spectrally analyzed using the image domain spectral ratio method, verifying the spectral attenuation caused by carbon dioxide leakage.

[0088] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for effectively monitoring carbon dioxide storage leakage, characterized in that: The following steps are involved: S1. Establish a geological model for CO2 storage and perform reservoir simulation to construct a time-lapse synthetic model with different CO2 saturations; S2. Using rock physics theory, analyze the physical response characteristics of reservoir rocks under different carbon dioxide saturations and deduce their elastic parameter models; S3. Use the viscoacoustic wave equation of the standard linear solid attenuation model to carry out seismic forward modeling, generate time-lapse seismic data, and perform reverse time migration imaging; S4. Based on the reverse time migration profile obtained in S3, the leakage of the injected carbon dioxide in the early, middle and late stages is monitored in real time using the spectrum ratio method in the imaging domain.

2. A method for effectively monitoring carbon dioxide storage leakage according to claim 1, characterized in that: According to the geological attributes, a two-dimensional geological model for carbon dioxide storage is established, including a permeability model and a porosity model. The two-dimensional velocity model and density model required for seismic processing in S2-S4 are established based on the provided logging data.

3. A method for effectively monitoring carbon dioxide storage leakage according to claim 1, characterized in that: In S2, the maximum inverse quality factor of the P wave is used to calculate Qp, and the low-frequency compression modulus M0 and the volume density ρ are used to calculate the P wave velocity Vp of the rock: Among them, M0 is the compression modulus at very low frequency, and M ∞ Corresponds to the compression modulus at very high frequencies.

4. A method for effectively monitoring carbon dioxide storage leakage according to claim 2, characterized in that: In step S2, in order to calculate the low-frequency modulus, the fluid approximation substitution method of Vp is used: M s is the compression modulus of the mineral matrix, M Dry is the compression modulus of the rock skeleton, φ is the total porosity, K f is the effective bulk modulus of the fluid in the rock, calculated as the harmonic mean of water and carbon dioxide.

5. A method for effectively monitoring carbon dioxide storage leakage according to claim 1, characterized in that: In S2, The high-frequency compression modulus is the compression modulus M of wet rock. W and the rock M containing only carbon dioxide G The harmonic mean of W and K g are the bulk moduli of water and carbon dioxide, respectively, S W is the water saturation.

6. A method for effectively monitoring carbon dioxide storage leakage according to claim 4, characterized in that: To calculate the longitudinal wave velocity Vp of the rock, the low-frequency compressibility modulus M0 and the bulk density ρ are used as follows:

7. A method for effectively monitoring carbon dioxide storage leakage according to claim 1, characterized in that: In step S3, a single standard linear solid viscoacoustic wave equation is used for forward simulation, and its expression is: Among them, τ ε and τ σ are the strain and stress relaxation times, r is the memory variable, ν p It's speed.

8. A method for effectively monitoring carbon dioxide storage leakage according to claim 7, characterized in that: In the equation τε and τ σ The calculation expression is: Among them, Q p is the quality factor, which is used to characterize the strength of seismic wave attenuation, and ω0 is the reference frequency.

9. A method for effectively monitoring carbon dioxide storage leakage according to claim 1, characterized in that: In step S4, the spectrum ratio method is used to calculate the ratio between the monitoring spectrum R2(k) and the baseline spectrum R1(k), which is expressed as: In[R2(k) / R1(k)]=ρ[k]+G0 where k is the local wave number of the migration event and ρ is calculated as:

Images