Method for predicting carbon dioxide-hydrogen circulating biological methanation effect in exhausted gas reservoir, electronic equipment and readable storage medium

Abstract

The invention belongs to the field of energy development and utilization, and particularly relates to a carbon dioxide-hydrogen cycle biological methanation effect prediction method in an exhausted gas reservoir, electronic equipment and a readable storage medium. The invention discloses a method for predicting a carbon dioxide-hydrogen circulating biological methanation effect in an exhausted gas reservoir. The method comprises the following steps: acquiring target exhausted gas reservoir data and methanogen data; establishing a carbon dioxide-hydrogen methanation reaction kinetic model considering environmental influence and space limitation; the method comprises the following steps: constructing a PHREEQC-MATLAB coupled circulating biological methanation numerical simulation platform; and calculating the methane release rate under each time step in different cycles, and predicting the biological methanation effect of the carbon dioxide-hydrogen cycle in the exhausted gas reservoir. According to the method, accurate prediction and quantitative evaluation of the circulating biological methanation effect of the exhausted gas reservoir can be realized, and more reliable theoretical basis and technical support can be provided for early-stage gas reservoir site selection and process optimization design.

Description

Technical Field

[0001] The present invention belongs to the field of energy development and utilization, and particularly relates to a method for predicting the effect of carbon dioxide-hydrogen cyclic biomethanation in depleted gas reservoirs, an electronic device, and a readable storage medium. Background Art

[0002] The carbon dioxide-hydrogen cyclic biomethanation technology in depleted gas reservoirs mainly includes the following key process flows: injecting carbon dioxide and hydrogen into depleted gas reservoirs; shutting in the well and using methanogens to biochemically convert the mixed gas into methane; extracting the regenerated natural gas mainly composed of methane during the peak period of energy demand; and capturing the carbon dioxide generated by the utilization of the regenerated natural gas for the next stage of biochemical conversion. This technology has multiple synergistic benefits. It can not only achieve the biosynthesis of renewable natural gas, but also realize functions such as geological sequestration and recycling of carbon dioxide, large-scale underground storage of energy, and enhanced exploitation of natural gas, showing significant technical advantages and application prospects.

[0003] In view of the complex process flow and large investment scale of this technology, accurately predicting the effect of carbon dioxide-hydrogen cyclic biomethanation has important practical significance. This not only helps to improve resource utilization efficiency and economic benefits, but also effectively promotes the development of the carbon circular economy. However, the existing prediction methods have obvious limitations: most methods can only evaluate the biomethanation effect within a single cycle. Even if some methods can predict the multi-cycle process, they do not fully consider the influence of the dynamic changes of environmental parameters such as pH value, salinity, and temperature caused by complex biogeochemical reactions under reservoir conditions on the conversion efficiency. Therefore, it is particularly urgent to develop a new method that can accurately predict the effect of carbon dioxide-hydrogen cyclic biomethanation in depleted gas reservoirs, which will provide a more reliable theoretical basis and technical support for the early gas reservoir site selection and process optimization design. Summary of the Invention

[0004] Aiming at the problems and deficiencies existing in the prior art, the purpose of the present invention is to provide a method for predicting the effect of carbon dioxide-hydrogen cyclic biomethanation in depleted gas reservoirs, an electronic device, and a readable storage medium, which are used to solve the problem of inaccurate prediction results, so as to provide a scientific basis for site selection and optimization design.

[0005] Based on the above purpose, the present invention adopts the following technical solutions:

[0006] The first aspect of the present invention provides a method for predicting the effect of carbon dioxide-hydrogen cyclic biomethanation in depleted gas reservoirs, including the following steps:

[0007] S1. Obtain the data of the target depleted gas reservoir and the data of methanogens;

[0008] S2. Based on the classical double Monod model, considering the influence of environmental factors and the limitations of underground space, construct a kinetic model for the methanation reaction of carbon dioxide - hydrogen;

[0009] S3. Couple the methanation reaction kinetic model to the geochemical simulation software PHREEQC, and establish a numerical simulation platform for the cyclic bio - methanation of carbon dioxide - hydrogen in depleted gas reservoirs in combination with MATLAB software;

[0010] S4. Based on the data obtained in step S1 and the numerical simulation platform for cyclic bio - methanation, calculate the methane release rate at each time step in different cycles, and predict the effect of cyclic bio - methanation of carbon dioxide - hydrogen in depleted gas reservoirs.

[0011] Furthermore, the target depleted gas reservoir data in step S1 are formation water components, rock mineral compositions, temperature, and pressure; the methanogenic bacteria data are environmental adaptation parameters and reproduction kinetic parameters; the environmental factors are temperature, salinity, and pH.

[0012] Furthermore, the kinetic model for the methanation reaction of carbon dioxide - hydrogen in step S2 includes a substrate consumption model and a methanogenic bacteria biomass evolution model;

[0013] The substrate consumption model is:

[0014]

[0015] where: r s is the substrate consumption rate, Y is the yield coefficient, N is the number of methanogenic bacteria, and μ gr is the specific growth rate of methanogenic bacteria; the method for calculating the specific growth rate of methanogenic bacteria based on the classical double Monod model is:

[0016]

[0017] where: C A and C D are the concentrations of carbon dioxide and hydrogen respectively, K A and K D are the half - saturation constants of carbon dioxide and hydrogen respectively, μ max is the maximum specific growth rate of methanogenic bacteria; μ max is calculated in a product form:

[0018] μ max = μ opt ψ T ψ pH ψ s

[0019] where: μ opt is the specific growth rate of methanogenic bacteria under optimal conditions, and ψ T, ψ pH , ψ s are dimensionless influence factors related to temperature, pH, and salinity respectively, and their calculation methods are as follows:

[0020]

[0021] In the formula: T, pH, C s are the current temperature, pH value, and salinity respectively, T min , T max and T opt are the minimum, maximum, and optimal temperatures for the survival of methanogens respectively, pH min , pH max and pH opt are the minimum, maximum, and optimal pH values for the survival of methanogens respectively, C s,opt and C s,max are the upper limit of the optimal salinity range and the maximum salinity for the survival of methanogens respectively;

[0022] The biomass evolution model of methanogens is as follows:

[0023] r bio =-r s F X Y - dN

[0024] In the formula: r bio is the change rate of methanogen biomass, d is the attenuation coefficient, F X is the biomass capacity factor, and its calculation method is as follows:

[0025]

[0026] In the formula: N max is the maximum number of methanogens that the underground space can carry.

[0027] Furthermore, in step S3, the simulation method of the cyclic biomethanation numerical simulation platform established based on PHREEQC and MATLAB is as follows:

[0028] ① First, perform system initialization processing, including loading the PHREEQC thermodynamic database, reading and parsing the local input parameter file, and defining the simulation time parameter;

[0029] ② Enter the single-step simulation calculation stage, and call PHREEQC through MATLAB to execute the biomethanation reaction simulation of the current time step;

[0030] ③ MATLAB obtains the PHREEQC simulation results to calculate the change of environmental parameters, transfers the updated environmental parameters to PHREEQC, advances to the next time step, and repeats the above calculation steps until all time steps of the current cycle are completed;

[0031] ④ Enter a new cycle, reset the gas pressure and the number of methanogens, and repeat the simulation calculation process until the preset number of cycles is completed;

[0032] The simulation time parameters are the total number of cycles, the number of time steps in a single cycle, and the single time step size.

[0033] Further, the methane release rate in step S4 includes the dynamic methane release rate ν d and the average methane release rate ν a :

[0034]

[0035] In the formula: ν d and ν a are the dynamic methane release rate and the average methane release rate respectively, and are the methane concentrations at the nth and (n - 1)th time steps respectively, is the methane concentration at the start of each cycle, Δt is the single time step size, and t n is the cumulative time from the start of each cycle to the nth time step.

[0036] The second aspect of the present invention provides an electronic device, including a memory and a processor. A computer program is stored on the memory, and when the processor executes the computer program, any step in the method for predicting the carbon dioxide - hydrogen cyclic biomethanation effect in an exhausted gas reservoir as described in the first aspect is implemented.

[0037] The third aspect of the present invention provides a computer - readable storage medium. A computer program is stored on the computer - readable storage medium, and when the computer program is executed by a computer processor, any step in the method for predicting the carbon dioxide - hydrogen cyclic biomethanation effect in an exhausted gas reservoir as described in the first aspect is implemented.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] The present invention comprehensively considers the influence of environmental factors such as temperature, salinity, pH, and underground space limitations on carbon dioxide - hydrogen biomethanation, which is more in line with the actual conditions of underground methanation. At the same time, the methanation reaction kinetic model is embedded in PHREEQC, and through coupling with MATLAB, real - time acquisition of simulation data at each time step and dynamic update of input parameters are realized, so as to simulate the dynamic changes of environmental factors and their influence during the cyclic biomethanation process. In addition, by introducing the dynamic methane release rate and the average methane release rate, the carbon dioxide - hydrogen cyclic biomethanation effect can be more accurately quantitatively evaluated. Description of the Drawings

[0040] Figure 1 Schematic diagram of the simulation method of the cyclic biomethanation numerical simulation platform established based on PHREEQC and MATLAB in Embodiment 1 of the present invention;

[0041] Figure 2 Graph of the dynamic evolution of pH in each cycle in Embodiment 1 of the present invention;

[0042] Figure 3 Graph of the dynamic evolution of salinity in each cycle in Embodiment 1 of the present invention;

[0043] Figure 4 Graph of the dynamic evolution of the temperature without heat loss in each cycle in Embodiment 1 of the present invention;

[0044] Figure 5 Graph of the evolution result of the methane release rate in each cycle in Embodiment 1 of the present invention. Detailed implementation manners

[0045] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below through embodiments in combination with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] Embodiment 1

[0047] This embodiment provides a method for predicting the effect of carbon dioxide - hydrogen cyclic biomethanation in depleted gas reservoirs, including the following steps:

[0048] S1. Obtain the data of the target depleted gas reservoir (formation water components, rock mineral composition, temperature, pressure) and the data of methanogens (environmental adaptation parameters, reproduction kinetic parameters).

[0049] S2. Based on the classical double Monod model, considering the influence of environmental factors (temperature, salinity, pH) and the limitation of underground space, construct a carbon dioxide - hydrogen methanation reaction kinetic model, including a substrate consumption model and a methanogen biomass evolution model:

[0050] ① The substrate consumption model is:

[0051]

[0052] Where: r s is the substrate consumption rate, Y is the yield coefficient, N is the number of methanogens, and μ gr is the specific growth rate of methanogens; the method for calculating the specific growth rate of methanogens based on the classical double Monod model is:

[0053]

[0054] Where: C A and C D are the concentrations of carbon dioxide and hydrogen respectively, K A and K D are the half-saturation constants of carbon dioxide and hydrogen respectively, μ max is the maximum specific growth rate of methanogens; μ max is calculated in a product form:

[0055] μ max = μ opt ψ T ψ pH ψ s

[0056] Where: μ opt is the specific growth rate of methanogens under optimal conditions, ψ T , ψ pH , ψ s are dimensionless influence factors related to temperature, pH, and salinity respectively, and their calculation methods are as follows:

[0057]

[0058] Where: T, pH, C s are the current temperature, pH value, and salinity respectively, T min , T max and T opt are the minimum, maximum, and optimal temperatures for methanogen survival respectively, pH min , pH max and pH opt are the minimum, maximum, and optimal pH values for methanogen survival respectively, C s,opt and C s,max are the upper limit of the optimal salinity range and the maximum salinity for methanogen survival respectively.

[0059] ② The methanogen biomass evolution model is:

[0060] r bio = -r s F X Y - dN

[0061] Where: r bio is the rate of change of methanogen biomass, d is the decay coefficient, F X is the biomass capacity factor, and its calculation method is:

[0062]

[0063] Where: N max is the maximum number of methanogens that the underground space can carry.

[0064] S3. Couple the methanation reaction kinetic model to the geochemical simulation software PHREEQC, and establish a numerical simulation platform for the carbon dioxide - hydrogen cycle biomethanation in depleted gas reservoirs in combination with MATLAB software. The platform simulation method is as follows: Figure 1 shown as:

[0065] ① First, perform system initialization processing, including loading the PHREEQC thermodynamic database, reading and parsing the local input parameter file, and defining the simulation time parameters (total number of cycles, number of time steps per cycle, and single time step size);

[0066] ② Enter the single - step simulation calculation stage. Call PHREEQC through MATLAB to perform the biomethanation reaction simulation for the current time step;

[0067] ③ MATLAB obtains the PHREEQC simulation results to calculate the change in environmental parameters, transfers the updated environmental parameters to PHREEQC, advances to the next time step, and repeats the above calculation steps until all time steps in the current cycle are completed;

[0068] ④ Enter a new cycle, reset the gas pressure and the number of methanogens, and repeat the simulation calculation process until the preset number of cycles is completed.

[0069] S4. Based on the data obtained in step S1 and the cyclic biomethanation numerical simulation platform, calculate the methane release rate at each time step in different cycles, and predict the carbon dioxide - hydrogen cycle biomethanation effect in depleted gas reservoirs.

[0070] The methane release rate includes the dynamic methane release rate ν d and the average methane release rate ν a :

[0071]

[0072] In the formula: ν d and ν a are the dynamic methane release rate and the average methane release rate respectively, and are the methane concentrations at the nth and (n - 1)th time steps respectively, is the methane concentration at the beginning of each cycle, Δt is the single time step size, and t n is the cumulative time from the beginning of each cycle to the nth time step.

[0073] In a specific embodiment, the target depleted gas reservoir is a carbonate reservoir with a burial depth of about 2,500 m, an original formation pressure of 28 MPa, and an average formation temperature of 68.3 °C. The rock mineral composition of the reservoir is mainly calcite (mass fraction > 40%) and dolomite (mass fraction > 50%), and also contains a small amount of formation minerals such as quartz, feldspar, and clay minerals. The formation water components are shown in Table 1, and the methanogenic bacteria data are shown in Table 2.

[0074] Table 1 Formation water components

[0075]

[0076] Table 2 Methanogenic bacteria data

[0077]

[0078] Based on the kinetic model of carbon dioxide-hydrogen methanation reaction constructed by the dual Monod model, combined with the PHREEQC-MATLAB coupling simulation method, the numerical simulation of the cyclic methanation process is carried out. During the simulation process, the reservoir mineral components are simplified to 45% calcite and 55% dolomite, and the phreeqc.dat thermodynamic database is selected for calculation. The total number of simulation cycles is set to 5, each cycle contains 720 time steps, and the time step is 1 day. Through numerical simulation, key parameters such as pH value, salinity, temperature without heat loss, and methane release rate in each cycle are obtained, and their variation laws are as follows Figures 2 - 5 shown.

[0079] It can be seen from Figure 2 that within each cycle, the pH value undergoes a dynamic transformation from acidic to neutral and then to alkaline, and this change leads to a trend of first weakening and then strengthening of the inhibitory effect on the activity of methanogenic bacteria. It can be seen from Figure 3 that due to the large amount of water produced during the metabolism of methanogenic bacteria, the salinity of the system continuously decreases with the increase of the number of cycles. Therefore, if the initial salinity is high, this change will be extremely beneficial to enhancing the activity of methanogenic bacteria. It can be seen from Figure 4 that in the case of no heat loss, the exothermic effect of methanogenic bacteria metabolism causes the reservoir temperature to continuously increase with the increase of the number of cycles, thereby enhancing the inhibitory effect on methanogenic bacteria. This process is directly reflected in the dynamic change of the methane release rate, and its change is as follows Figure 5 shown: with the increase of the number of cycles, both the maximum dynamic methane release rate and the average methane release rate show a downward trend, and the maximum dynamic methane release rate is always higher than the maximum average methane release rate, which is mainly attributed to the fact that the average methane release rate takes into account the influence of the slow reaction stage at the initial stage of the reaction. The above simulation results fully show that the carbon dioxide-hydrogen cyclic bio-methanation simulation method proposed by the present invention can effectively predict and evaluate the dynamic evolution characteristics of the bio-methanation process in depleted gas reservoirs.

[0080] Therefore, adopting the present invention can accurately predict and quantitatively evaluate the conversion effect and dynamic evolution characteristics of the carbon dioxide-hydrogen cyclic biomethanation process in depleted gas reservoirs, providing a reliable theoretical basis and technical support for the feasibility evaluation of this technology.

[0081] Example 2

[0082] An electronic device includes a memory and a processor. A computer program is stored on the memory. When the processor executes the computer program, any step in the method for predicting the carbon dioxide-hydrogen cyclic biomethanation effect in a depleted gas reservoir as described in Example 1 is implemented.

[0083] The hardware of the electronic device in this embodiment further includes a GPU, a display buffer memory, a RAMD / A converter, and a radiator that cooperate with the processor; the GPU is responsible for processing the graphic display of the electronic device, providing image rendering and acceleration functions, and accelerating the processing of large-scale data-intensive tasks using its parallel computing advantage.

[0084] Furthermore, the process of the method for predicting the carbon dioxide-hydrogen cyclic biomethanation effect in a depleted gas reservoir as described in Example 1 can be implemented as a computer software program. For example, this embodiment includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the method. In such an embodiment, the computer program can be downloaded and installed from the network, and / or installed from a removable medium. When the computer program is executed by the processor, the above functions defined in the method of the present application are executed.

[0085] Example 3

[0086] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, any step in the method for realizing the carbon dioxide-hydrogen cyclic biomethanation effect prediction in a depleted gas reservoir as described in Example 1 is implemented.

[0087] The computer-readable medium described in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. And in this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0088] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Python and C++, and also include conventional procedural programming languages or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0089] The computer-readable storage medium of this embodiment can be accelerated by using hardware such as a GPU, and the parallel computing advantage of the GPU is used to accelerate any step in the method for predicting the effect of carbon dioxide-hydrogen cyclic biomethanation in a depleted gas reservoir as described in Embodiment 1.

[0090] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting the protection scope of the present invention. Those skilled in the art can modify or equivalently replace the technical solutions of the present invention according to the idea of the present invention, without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A method for predicting the effect of carbon dioxide - hydrogen cyclic biomethanation in depleted gas reservoirs, characterized in that It includes the following steps: S1. Obtain target depleted gas reservoir data and methanogen data; S2. Based on the classical double Monod model, considering the influence of environmental factors and underground space limitations, construct a carbon dioxide-hydrogen methanation reaction kinetic model; S3. Couple the methanation reaction kinetic model to the geochemical simulation software PHREEQC, and establish a numerical simulation platform for carbon dioxide-hydrogen cyclic bio-methanation in depleted gas reservoirs in combination with MATLAB software; S4. Based on the data obtained in step S1 and the numerical simulation platform for cyclic bio-methanation, calculate the methane release rate at each time step in different cycles, and predict the carbon dioxide-hydrogen cyclic bio-methanation effect in depleted gas reservoirs.

2. A method for predicting the effect of carbon dioxide-hydrogen cyclic biomethanation in a depleted gas reservoir according to claim 1, characterized in that The target depleted gas reservoir data described in step S1 are formation water components, rock mineral compositions, temperature, and pressure; the methanogen data are environmental adaptation parameters and reproduction kinetic parameters; the environmental factors are temperature, salinity, and pH.

3. A method for predicting the effect of carbon dioxide-hydrogen cyclic biomethanation in a depleted gas reservoir according to claim 1, characterized in that, The carbon dioxide-hydrogen methanation reaction kinetic model described in step S2 includes a substrate consumption model and a methanogen biomass evolution model; The substrate consumption model is: where: r s is the substrate consumption rate, Y is the yield coefficient, N is the number of methanogens, and μ gr is the specific growth rate of methanogens; the method for calculating the specific growth rate μ gr of methanogens based on the classical dual Monod model is as follows: Where: C A and C D are the concentrations of carbon dioxide and hydrogen respectively, K A and K D are the half-saturation constants of carbon dioxide and hydrogen respectively, μ max is the maximum specific growth rate of methanogens; μ max is calculated in a product form: μ max = μ opt ψ T ψ pH ψ s where: μ opt is the specific growth rate of methanogens under optimal conditions, ψ T , ψ pH , ψ s are dimensionless influence factors related to temperature, pH, and salinity, respectively, and their calculation methods are as follows: where: T, pH, C s are the current temperature, pH value, and salinity respectively, T min , T max and T opt are the minimum, maximum, and optimal temperatures for the survival of methanogens respectively, pH min , pH max and pH opt are the minimum, maximum, and optimal pH values for the survival of methanogens respectively, C s,opt and C s,max are the upper limit of the optimal salinity range and the maximum salinity for the survival of methanogens respectively; The methanogen biomass evolution model is: r bio = -r s F X Y - dN where: r bio is the change rate of methanogen biomass, d is the decay coefficient, F X is the biomass capacity factor, and its calculation method is as follows: Where: N max is the maximum number of methanogens that the underground space can carry.

4. A method for predicting the effect of carbon dioxide-hydrogen cyclic biomethanation in a depleted gas reservoir according to claim 1, characterized in that In step S3, the simulation method of the numerical simulation platform for cyclic bio-methanation established based on PHREEQC and MATLAB is as follows: ① First, perform system initialization processing, including loading the PHREEQC thermodynamic database, reading and parsing the local input parameter file, and defining the simulation time parameters; ② Enter the single-step simulation calculation stage, and call PHREEQC through MATLAB to execute the bio-methanation reaction simulation at the current time step; ③ MATLAB obtains the PHREEQC simulation results to calculate the change in environmental parameters, transfers the updated environmental parameters to PHREEQC, advances to the next time step, and repeats the above calculation steps until all time steps in the current cycle are calculated; ④ Enter a new cycle, reset the gas pressure and the number of methanogens, and repeat the simulation calculation process until the preset number of cycles is completed; The simulation time parameters are the total number of cycles, the number of time steps in a single cycle, and the length of a single time step.

5. A method for predicting the effect of carbon dioxide-hydrogen cyclic biomethanation in a depleted gas reservoir according to claim 1, characterized in that The methane release rate described in step S4 includes the dynamic methane release rate ν d and the average methane release rate ν a : Where: ν d and ν a are the dynamic methane release rate and the average methane release rate respectively, and are the methane concentrations at the nth and (n - 1)th time steps respectively, is the methane concentration at the start of each cycle, Δt is the single time step, and t n is the cumulative time from the start of each cycle to the nth time step.

6. An electronic device, comprising a memory and a processor, wherein a computer program is stored on the memory, characterized in that, When the processor executes the computer program, it implements any step in the method for predicting the carbon dioxide-hydrogen cyclic bio-methanation effect in depleted gas reservoirs as described in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a computer processor, it implements any step in the method for predicting the carbon dioxide-hydrogen cyclic bio-methanation effect in depleted gas reservoirs as described in any one of claims 1-5.

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