A method for predicting the effect of carbon dioxide-hydrogen cycle biological methanation in a depleted gas reservoir, an electronic device and a readable storage medium

Abstract

The present application belongs to the field of energy development and utilization, and particularly relates to a method for predicting the effect of carbon dioxide-hydrogen cycle biological methanation in a depleted gas reservoir, an electronic device and a readable storage medium. The present application discloses a method for predicting the effect of carbon dioxide-hydrogen cycle biological methanation in a depleted gas reservoir, comprising the following steps: obtaining target depleted gas reservoir data and methanogen data; establishing a carbon dioxide-hydrogen methanation reaction kinetics model considering environmental impact and spatial constraints; constructing a PHREEQC-MATLAB coupled cycle biological methanation numerical simulation platform; calculating the methane release rate at each time step in different cycles, and predicting the effect of carbon dioxide-hydrogen cycle biological methanation in a depleted gas reservoir. The present application can realize accurate prediction and quantitative evaluation of the effect of cycle biological methanation in a depleted gas reservoir, and can provide more reliable theoretical basis and technical support for early gas reservoir site selection and process optimization design.

Description

Technical Field

[0001] This invention belongs to the field of energy development and utilization, and specifically relates to a method for predicting the effect of biomethanation in a carbon dioxide-hydrogen cycle in a depleted gas reservoir, an electronic device, and a readable storage medium. Background Technology

[0002] The carbon dioxide-hydrogen cycle biomethanation technology in depleted gas reservoirs mainly includes the following key processes: injecting carbon dioxide and hydrogen into the depleted gas reservoir; shutting in the well and using methanogenic bacteria to biochemically convert the mixed gas into methane; extracting renewable natural gas with methane as the main component during peak energy demand periods; and using the carbon dioxide produced from the renewable natural gas for the next stage of biochemical conversion. This technology has multiple synergistic benefits, not only enabling the biosynthesis of renewable natural gas, but also achieving geological sequestration and recycling of carbon dioxide, large-scale underground energy storage, and enhanced natural gas extraction, demonstrating significant technological advantages and application prospects.

[0003] Given the complexity of the technology and the large investment required, accurate prediction of the biomethanation effect of the carbon dioxide-hydrogen cycle is of significant practical importance. This not only helps improve resource utilization efficiency and economic benefits but also effectively promotes the development of a carbon circular economy. However, existing prediction methods have obvious limitations: most methods can only assess the biomethanation effect within a single cycle. Even some methods that can predict multi-cycle processes fail to fully consider the impact of dynamic changes in environmental parameters such as pH, salinity, and temperature caused by complex biogeochemical reactions under reservoir conditions on conversion efficiency. Therefore, developing a new method that can accurately predict the biomethanation effect of the carbon dioxide-hydrogen cycle in depleted gas reservoirs is particularly urgent. This will provide a more reliable theoretical basis and technical support for early gas reservoir site selection and process optimization design. Summary of the Invention

[0004] To address the problems and shortcomings of existing technologies, the present invention aims to provide a method, electronic device, and readable storage medium for predicting the biomethanation effect of carbon dioxide-hydrogen cycle in depleted gas reservoirs, thereby solving the problem of inaccurate prediction results and providing a scientific basis for site selection and optimization design.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] The first aspect of this invention provides a method for predicting the biomethanation effect of a carbon dioxide-hydrogen cycle in a depleted gas reservoir, comprising the following steps:

[0007] S1. Obtain data on the target depleted gas reservoir and methanogenic bacteria;

[0008] S2. Based on the classic double Monod model, considering the influence of environmental factors and the limitation of underground space, a kinetic model of carbon dioxide-hydrogen methanation reaction is constructed.

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

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

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

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

[0013] The substrate consumption model is as follows:

[0014]

[0015] In the formula: r s Y is the substrate consumption rate, N is the yield coefficient, and μ is the number of methanogens. gr The growth rate of methanogens is given by the following method based on the classic double Monod model:

[0016]

[0017] In the formula: C A and C D The concentrations of carbon dioxide and hydrogen, K, respectively. A and K D The half-saturation constants for carbon dioxide and hydrogen are μ, respectively. max The maximum methanogenic growth rate; μ max Calculations are performed using a product form:

[0018] μ max =μ opt ψ T ψ pH ψ s

[0019] Where: μ opt ψ represents the specific growth rate of methanogens under optimal conditions. Tψ pH ψ s These are dimensionless influencing factors related to temperature, pH, and salinity, and their calculation methods are as follows:

[0020]

[0021] Where: T, pH, C s These represent the current temperature, pH value, and salinity, respectively. min T max and T opt These represent the minimum, maximum, and optimum temperatures for the survival of methanogens, and pH values, respectively. min pH max and pH opt These represent the minimum, maximum, and optimal pH values ​​for the survival of methanogens, respectively. s,opt and C s,max These represent the upper limit of the optimal salinity range for the survival of methanogens and the maximum salinity, respectively.

[0022] The biomass evolution model for methanogenic bacteria is as follows:

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

[0024] In the formula: r bio The rate of biomass change by methanogens is given by denoted as d, and the decay coefficient is given by F. X The biomass capacity factor is calculated as follows:

[0025]

[0026] Where: N max This represents the maximum number of methanogenic bacteria that an underground space can support.

[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, including loading the PHREEQC thermodynamic database, reading and parsing the local input parameter file, and defining the simulation time parameters;

[0029] ② Enter the single-step simulation calculation stage, and use MATLAB to call PHREEQC to execute the bio-methanation reaction simulation at the current time step;

[0030] ③ MATLAB obtains the PHREEQC simulation results to calculate the changes in environmental parameters, passes 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 loop 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 per cycle, and the length of a single time step.

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

[0034]

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

[0036] A second aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step in the method for predicting the effect of biomethanation of carbon dioxide-hydrogen cycle in depleted gas reservoirs as described in the first aspect.

[0037] A third aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a computer processor, performs any step in the method for predicting the effect of biomethanation of carbon dioxide-hydrogen cycle in depleted gas reservoirs as described in the first aspect.

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

[0039] This invention comprehensively considers the influence of environmental factors such as temperature, salinity, and pH, as well as underground space limitations, on carbon dioxide-hydrogen biomethanation, thus better reflecting actual underground methanation conditions. Simultaneously, it embeds a methanation reaction kinetic model into PHREEQC and couples it with MATLAB to achieve real-time acquisition of simulation data at each time step and dynamic updates of input parameters, thereby simulating the dynamic changes and impacts of environmental factors during the cyclic biomethanation process. Furthermore, by introducing dynamic methane release rate and average methane release rate, the effectiveness of the carbon dioxide-hydrogen cyclic biomethanation can be evaluated more accurately and quantitatively. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the simulation method of the cyclic biological methanation numerical simulation platform established based on PHREEQC and MATLAB in Embodiment 1 of the present invention;

[0041] Figure 2 This is a graph showing the dynamic evolution of pH in each cycle in Example 1 of the present invention;

[0042] Figure 3 This is a diagram showing the dynamic evolution of salinity in each cycle in Example 1 of the present invention;

[0043] Figure 4 This is a diagram showing the dynamic temperature evolution results without heat loss in each cycle of Embodiment 1 of the present invention;

[0044] Figure 5 This is a graph showing the evolution of methane release rate in each cycle in Example 1 of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0046] Example 1

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

[0048] S1. Acquire data on the target depleted gas reservoir (formation water composition, rock mineral composition, temperature, pressure) and methanogenic bacteria data (environmental adaptation parameters, reproductive kinetic parameters).

[0049] S2. Based on the classic dual Monod model, considering the influence of environmental factors (temperature, salinity, pH) and underground space constraints, a kinetic model of the carbon dioxide-hydrogen methanation reaction is constructed, including a substrate consumption model and a methanogenic biomass evolution model:

[0050] ①The substrate consumption model is as follows:

[0051]

[0052] In the formula: r s Y is the substrate consumption rate, N is the yield coefficient, and μ is the number of methanogens. gr The growth rate of methanogens is given by the following method based on the classic double Monod model:

[0053]

[0054] In the formula: C A and C D The concentrations of carbon dioxide and hydrogen, K, respectively. A and K D The half-saturation constants for carbon dioxide and hydrogen are μ, respectively. max The maximum methanogenic growth rate; μ max Calculations are performed using a product form:

[0055] μ max =μ opt ψ T ψ pH ψ s

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

[0057]

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

[0059] ②The biomass evolution model of methanogens is as follows:

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

[0061] In the formula: r bio The rate of biomass change by methanogens is given by denoted as d, and the decay coefficient is given by F. X The biomass capacity factor is calculated as follows:

[0062]

[0063] Where: N max This represents the maximum number of methanogenic bacteria that an underground space can support.

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

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

[0066] ② Enter the single-step simulation calculation stage, and use MATLAB to call PHREEQC to execute the bio-methanation reaction simulation at the current time step;

[0067] ③ MATLAB obtains the PHREEQC simulation results to calculate the changes in environmental parameters, passes 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 loop 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 numerical simulation platform for cyclic biomethanation, calculate the methane release rate at each time step in different cycles, and predict the effect of carbon dioxide-hydrogen cyclic biomethanation in depleted gas reservoirs.

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

[0071]

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

[0073] In one specific embodiment, the target depleted gas reservoir is a carbonate rock reservoir at a depth of approximately 2500 m, with an original formation pressure of 28 MPa and an average formation temperature of 68.3 °C. The reservoir rock mineral composition is mainly calcite (mass fraction > 40%) and dolomite (mass fraction > 50%), while also containing small amounts of quartz, feldspar, and clay minerals. Formation water composition is detailed in Table 1, and methanogenic bacteria data are shown in Table 2.

[0074] Table 1 Formation water composition

[0075]

[0076] Table 2. Methanogen data

[0077]

[0078] A kinetic model of carbon dioxide-hydrogen methanation reaction based on a dual Monod model was constructed, and the cyclic methanation process was numerically simulated using the PHREEQC-MAT LAB coupled simulation method. During the simulation, the reservoir mineral composition was simplified to 45% calcite and 55% dolomite, and the phreeqc.dat thermodynamic database was used for calculations. The simulation was set to a total of 5 cycles, with each cycle containing 720 time steps, each time step being 1 day. Through numerical simulation, key parameters such as pH, salinity, heat-loss-free temperature, and methane release rate were obtained for each cycle, and their variation patterns are shown below. Figure 2-5 As shown.

[0079] Depend on Figure 2 It can be seen that within each cycle, the pH value undergoes a dynamic transition from acidic to neutral and then to alkaline. This change leads to a trend of initial weakening followed by strengthening of the inhibitory effect on methanogen activity. Figure 3 It is known that because methanogens produce a large amount of water during their metabolism, the system salinity continuously decreases with increasing cycle number. Therefore, if the initial salinity is high, this change will be highly beneficial for enhancing the activity of methanogens. Figure 4 It is known that, under conditions of no heat loss, the exothermic metabolic effect of methanogens causes the reservoir temperature to rise continuously with the number of cycles, thereby enhancing the inhibitory effect on methanogens. This process is directly reflected in the dynamic changes of the methane release rate, as shown in the following figure. Figure 5 As shown, both the maximum dynamic methane release rate and the average methane release rate decrease with increasing cycle number. The maximum dynamic methane release rate consistently exceeds the maximum average methane release rate, primarily because the average methane release rate takes into account the slow reaction phase at the beginning of the reaction. These simulation results clearly demonstrate that the carbon dioxide-hydrogen cycle biomethanation simulation method proposed in this invention can effectively predict and assess the dynamic evolution characteristics of the biomethanation process in depleted gas reservoirs.

[0080] Therefore, the present invention can accurately predict and quantitatively assess the conversion effect and dynamic evolution characteristics of the carbon dioxide-hydrogen cycle 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, wherein the memory stores a computer program, and the processor executes the computer program to perform any step in a method for predicting the effect of biomethanation of carbon dioxide-hydrogen cycle in a depleted gas reservoir as described in Example 1.

[0083] The hardware of the electronic device in this embodiment also includes a GPU, a display buffer memory, a RAMD / A converter, and a heat sink that work in conjunction with the processor; the GPU is responsible for processing the graphics display of the electronic device, providing image rendering and acceleration functions, and using its parallel computing advantages to accelerate the processing of large-scale data-intensive tasks.

[0084] Furthermore, the method for predicting the biomethanation effect of a carbon dioxide-hydrogen cycle in a depleted gas reservoir described in Example 1 can be implemented as a computer software program. For example, this embodiment includes a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the method. In such an embodiment, the computer program can be downloaded and installed from a network, and / or installed from a removable medium. When the computer program is executed by a processor, it performs the functions defined in the method of this application.

[0085] Example 3

[0086] A computer-readable storage medium storing a computer program that, when executed by a processor, implements any step in a method for predicting the effect of biomethanation of a carbon dioxide-hydrogen cycle in a depleted gas reservoir as described in Example 1.

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

[0088] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Python and C++, as well as 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, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0089] In this embodiment, the computer-readable storage medium can be accelerated using hardware such as a GPU. The parallel computing advantage of the GPU is used to accelerate any step in the method for predicting the effect of biomethanation of carbon dioxide-hydrogen cycle 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, and are not intended to limit the scope of protection of the present invention. Those skilled in the art can modify or make equivalent substitutions to the technical solutions of the present invention based on the concept 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 cycle biomethanation in a depleted gas reservoir, characterized by, The method comprises the following steps: S1, obtaining target depleted gas reservoir data and methanogen data; S2, based on the classic double Monod model, considering the influence of environmental factors and the limitation of underground space, a carbon dioxide-hydrogen methanation reaction kinetics model is constructed; S3, the methanation reaction kinetics model is coupled to the geochemical simulation software PHREEQC, and a carbon dioxide-hydrogen cycle biological methanation numerical simulation platform in the depleted gas reservoir is established by combining MATLAB software; S4, based on the data obtained in step S1 and the cycle biological methanation numerical simulation platform, the methane release rate at each time step in different cycles is calculated, and the effect of carbon dioxide-hydrogen cycle biological methanation in the depleted gas reservoir is predicted; The carbon dioxide-hydrogen methanation reaction kinetics model in step S2 comprises 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, μ gr is the specific growth rate of methanogens; the specific growth rate of methanogens μ gr is calculated based on the classical double Monod model. 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 for carbon dioxide and hydrogen, respectively, μ max is the maximum specific growth rate of methanogens; and μ max is calculated using the product form: mu max = mu opt psi T psi pH psi s Where: μ opt ψ represents the specific growth rate of methanogens under optimal conditions. T ψ pH ψ s These are dimensionless influencing factors related to temperature, pH, and salinity, and their calculation methods are as follows: wherein: T, pH, C s are the current temperature, pH and salinity, respectively, T min , T max and T opt are the minimum, maximum and optimum temperature for methanogen survival, respectively, pH min , pH max and pH opt are the minimum, maximum and optimum pH for methanogen survival, respectively, C s,opt and C s,max are the upper limit of the optimum salinity range and the maximum salinity for methanogen survival, respectively. The methanogen biomass evolution model is: r bio = -r s F X Y-dN where: r bio is the rate of change of methanogen biomass, d is the decay coefficient, F X is the biomass capacity factor, which is calculated as: where: N max Maximum number of methanogens that the subsurface space is able to support.

2. The method according to claim 1, wherein, The target depleted gas reservoir data in step S1 is formation water composition, rock mineral composition, temperature and pressure; the methanogen data is environmental adaptation parameter and reproduction kinetics parameter; and the environmental factor is temperature, salinity and pH.

3. The method according to claim 1, wherein, In step S3, the simulation method of the cycle biological methanation numerical simulation platform established based on PHREEQC and MATLAB is as follows: ①First, system initialization processing is performed, including loading the PHREEQC thermodynamic database, reading and analyzing the local input parameter file, and defining the simulation time parameter; ②Enter the single-step simulation calculation stage, and perform the biological methanation reaction simulation of the current time step by calling PHREEQC through MATLAB; ③MATLAB obtains the simulation results of PHREEQC, calculates the change of environmental parameters, transmits 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; ④Enter 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 parameter is the total number of cycles, the number of time steps in a single cycle, and the length of a single time step.

4. The method according to claim 1, wherein, The methane release rate in step S4 comprises a dynamic methane release rate v d and an average methane release rate v a : where: v d and v a are the dynamic and average methane release rates, respectively, and are the methane concentrations at n and n-1 time steps, respectively, is the methane concentration at the beginning of each cycle, Δt is the single time step, and t n is the cumulative time from the beginning of each cycle to the n time step.

5. An electronic device comprising a memory and a processor, said memory having stored thereon a computer program, characterized in that, The processor executes the computer program to realize any step in the depleted gas reservoir carbon dioxide-hydrogen cycle biological methanation effect prediction method according to any one of claims 1-4.

6. A computer-readable storage medium, characterized in that, The computer program is stored on the computer readable storage medium, and the computer program is executed by the computer processor to realize any step in the depleted gas reservoir carbon dioxide-hydrogen cycle biological methanation effect prediction method according to any one of claims 1-4.

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