A Dynamic Prediction and Injection-Production Optimization Method for CO2 Sequestration in Closed Mines Considering Adsorption-Seepage-Chemical Reaction Coupling
By constructing a dynamic prediction model that couples adsorption, permeation, and chemical reaction, the problem of inaccurate prediction of CO2 storage capacity in existing technologies has been solved. This has enabled accurate prediction of CO2 storage capacity and CH4 production capacity, as well as optimization of injection and production schemes, ensuring the safety and efficiency of the storage project.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 重庆朝阳气体有限公司
- Filing Date
- 2026-04-15
- Publication Date
- 2026-05-29
AI Technical Summary
Existing assessment methods fail to effectively consider the impact of chemical reactions such as mineral dissolution/precipitation on reservoir property evolution, resulting in inaccurate predictions of CO2 sequestration, a lack of scientific basis for injection and production schemes, and difficulty in ensuring the safety and efficiency of sequestration projects.
A dynamic prediction model coupling adsorption-permeation-chemical reaction was constructed. Parameters were obtained through indoor experiments, a multiphysics coupling model was established, numerical simulation and multi-objective optimization were performed, the injection and production scheme was optimized, and the dynamic feedback of chemical reaction on the porosity and permeability structure was considered.
It improves the accuracy of CO2 storage capacity and CH4 production capacity prediction, provides a scientific basis for injection and production decisions, ensures the safety and efficiency of storage projects, and reduces the risk of CO2 breakthrough.
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Figure CN122113754A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of carbon dioxide geological storage technology, and relates to a dynamic prediction and injection-production optimization method for CO2 storage in closed mines, especially a simulation and optimization method that considers the multi-field coupling of adsorption-seepage-chemical reaction. Background Technology
[0002] Carbon dioxide geological sequestration technology has attracted widespread attention as an important approach to achieving emission reduction. Among them, utilizing closed or abandoned coal mines for CO2 sequestration can not only effectively dispose of greenhouse gases, but also potentially achieve the synergistic exploitation of residual coalbed methane resources through CO2 displacement technology, resulting in significant economic and environmental benefits.
[0003] Accurately assessing the CO2 sequestration potential of closed mines and optimizing injection-production schemes are crucial for ensuring the safe and efficient operation of the project. Currently, relevant assessment methods mainly fall into several categories: One category is static estimation based on volumetric methods. For example, Chinese patent CN120670695A discloses a method for calculating CO2 sequestration potential based on coal seam geological characteristics. This method obtains the changes in parameters such as adsorption capacity and porosity over time through experiments, and then calculates the theoretical sequestration capacity. This type of method can provide preliminary sequestration capacity data, but it ignores the dynamic impact of the complex physicochemical reactions between fluids and coal / rock on reservoir properties after CO2 injection. Another category of methods, such as those disclosed in Chinese patent CN116378759B, studies sequestration efficiency and influencing factors by constructing a coupled model of coal deformation and gas migration. However, its core focus is on the changes in coal / rock mechanical properties caused by CO2 pressure and the gas seepage law, without deeply considering the long-term evolution of porosity and permeability caused by chemical processes such as mineral dissolution / precipitation, as well as the feedback impact on sequestration efficiency and gas breakthrough risk.
[0004] However, the actual geological sequestration process is a long-term and complex process involving the coupling of multiple physical fields, including adsorption and / or desorption, seepage migration, mineral dissolution and / or precipitation. In particular, after supercritical CO2 injection, it reacts chemically with water and minerals in coal, such as calcite and dolomite, leading to mineral dissolution or the precipitation of new minerals, thereby altering the pore structure and permeability characteristics of the coal and rock. This evolution of reservoir properties caused by chemical reactions, in turn, affects the seepage path and adsorption behavior of CO2, ultimately determining the long-term sequestration status, sequestration efficiency, and the production capacity of co-extracting CH4. Existing assessment methods generally lack effective characterization of this coupled feedback mechanism of chemical reaction-property evolution-seepage adsorption, resulting in significant uncertainty in the prediction of long-term sequestration dynamics, safety boundaries, and co-extracting effects, making it difficult to directly guide the layout of injection-production well networks and the optimization of injection pressure regimes.
[0005] Therefore, there is an urgent need for an evaluation method that can comprehensively consider the coupling effects of adsorption, seepage, and chemical reaction, and can dynamically predict the amount of sequestration, assess the potential for synergistic mining, and optimize injection-production engineering schemes. Summary of the Invention
[0006] In view of this, the present invention aims to solve the technical problem that traditional assessment methods cannot realistically simulate the dynamic evolution of reservoir properties caused by mineral dissolution or precipitation during long-term storage, resulting in inaccurate prediction of storage volume and lack of scientific basis for injection and production schemes. The present invention provides a method for dynamic prediction and injection and production optimization of CO2 storage volume in closed mines that considers the coupling of adsorption-seepage-chemical reaction.
[0007] To solve the above-mentioned technical problems, the present invention provides A method for dynamic prediction and injection-production optimization of CO2 sequestration in closed mines considering the coupling of adsorption-seepage-chemical reaction includes the following steps: S1. Construct a basic database for the coupled model: Obtain the adsorption parameters, seepage parameters, mineral composition and chemical reaction kinetic parameters of the target closed mine coal and rock through indoor experiments.
[0008] S2. Establish an adsorption-percolation coupled dynamic prediction model considering chemical reaction feedback: Construct a coupled model including an adsorption submodule, a percolation submodule, and a chemical reaction submodule, wherein the chemical reaction submodule calculates the change in coal and rock porosity caused by the chemical reaction based on the mineral dissolution-precipitation kinetic equation. ;Based on the preset porosity evolution relationship, and the updated porosity Real-time calculation of penetration rate at the current moment and the permeability Feedback is sent to the seepage submodule for seepage calculation in the next time step, realizing a closed-loop dynamic simulation of chemical reaction → pore seepage evolution → seepage adsorption.
[0009] S3. Establish the geometric model and initial conditions of the target closed mine: Based on the mine geology and mining data, construct the geometric model for numerical simulation and set the initial reservoir state.
[0010] S4. Numerical simulation of multiple injection and production schemes: Deploy an injection and production well network in the coupled model, perform numerical simulations on different injection and production schemes, and predict the dynamic changes of CO2 storage, CH4 production and CO2 breakthrough time under each scheme; the injection and production schemes include at least one or more of the following: different CO2 injection pressures, different production well operating regimes or different injection and production well network forms.
[0011] S5. Selection and determination of injection and production schemes based on multi-objective optimization: Based on the simulation results of step S4, and combined with the preset optimization objectives and constraints, a comprehensive evaluation of each injection and production scheme is conducted, and the best injection and production scheme is selected. The optimization objectives include at least two of the following: maximizing the cumulative CO2 storage, maximizing the cumulative CH4 production, and minimizing the CO2 breakthrough risk. The constraints include at least the reservoir pressure not exceeding the preset upper limit value, and / or the CO2 concentration in the produced gas of the production well not exceeding the preset threshold value.
[0012] Furthermore, the adsorption submodule describes the competitive adsorption behavior of multi-component gases based on the extended Langmuir equation; the percolation submodule describes gas transport based on Darcy's law and the mass conservation equation; and the mineral dissolution-precipitation kinetic equation used in the chemical reaction submodule is... ,in, Let m be the reaction rate of mineral m. The specific surface area of the reaction. The reaction rate constant is... It is the ion activity product. The reaction equilibrium constant is used; the porosity-permeability evolution relationship can be represented by an exponential model. ,in, denoted as the initial permeability, and n as an exponent determined by lithology or the Kozeny-Carman equation.
[0013] Furthermore, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.
[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.
[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By introducing a dynamic feedback mechanism of chemical reaction on the pore structure, the model can more realistically reflect the evolution of reservoir properties during long-term storage, thereby significantly improving the accuracy of prediction of CO2 storage and CH4 production capacity.
[0016] (2) This invention not only provides prediction, but also optimizes injection and production based on the prediction results. It can quantitatively evaluate the impact of different engineering parameters, such as gas injection pressure and well pattern, on storage efficiency and risk, and provides a scientific and quantifiable basis for the formulation of well pattern layout and pressure regime in actual engineering.
[0017] (3) By dynamically predicting risk indicators such as CO2 breakthrough time, this invention provides a clear early warning threshold for the safety monitoring of the sealing project, which helps to avoid leakage risks in advance and ensure the long-term safety of the sealing.
[0018] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description
[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 The overall flowchart of the method for dynamic prediction and injection-production optimization of CO2 sequestration in closed mines, which considers the coupling of adsorption-seepage-chemical reaction, is provided in the embodiments of the present invention. Detailed Implementation
[0020] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.
[0021] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.
[0022] Example 1 uses the closed Yutianbao mine in the Qijiang block of Chongqing as an example to conduct dynamic prediction of CO2 sequestration and injection-production optimization. Figure 1 As shown, the method of the present invention mainly includes the following steps: S1. Construct the basic database for the coupling model. First, large coal samples were collected from the target area. To ensure sample representativeness, large coal samples with dimensions >30cm in length, width, and height were selected from the coal wall of newly exposed underground working faces along the same horizontal bedding plane. These samples were wrapped in shock-absorbing membranes and transported to the laboratory in foam isolation boxes.
[0023] Coal samples were processed into standard specimens, and a series of indoor experiments were conducted to obtain the key parameters required for modeling. The excess adsorption capacity of coal samples for CO2 and CH4 was tested using a high-temperature, high-pressure isothermal adsorption apparatus at 45℃, 60℃, and 85℃. The Langmuir adsorption constant was obtained by fitting the MDR+k model, with R² > 0.9579.
[0024] In this embodiment, the Langmuir volume constant of CO2 at 45℃ was measured by isothermal adsorption experiment. =0.092 m 3 / kg, Langmuir pressure constant =1.57 MPa; CH4 =0.022m 3 / kg, =2.8 MPa.
[0025] Initial porosity of coal samples was determined using a pressure-controlled porosimeter. The initial permeability was k0 = 2.05% and the gas-water relative permeability curve was obtained.
[0026] The mineral composition of the coal sample was analyzed by X-ray diffraction (XRD). The results showed that the main minerals were quartz (38%), calcite (3%), dolomite (2%), and illite (5%). A supercritical CO2 environment was simulated through a high-temperature and high-pressure immersion experiment (40℃, 8.1 MPa). Combined with XRD and ion concentration analysis, the dissolution rate constants and activation energies of calcite and dolomite were determined. In this embodiment, the dissolution rate constant of calcite was taken as 10 kcal. 5.5 mol / m 2 / s.
[0027] The density of coal, ρ, was determined to be 1.34 g / cm³ through uniaxial / triaxial compression tests. 3 The elastic modulus E = 3.5 GPa and the Poisson's ratio ν = 0.35.
[0028] S2. Establish an adsorption-percolation coupling dynamic prediction model that considers chemical reaction feedback. Based on the parameters obtained in step S1, a multiphysics coupling model is constructed on the COMSOL Multiphysics numerical simulation platform. This model comprises three core sub-modules that form a closed-loop feedback loop: (1) Adsorption Submodule: The Langmuir extended equation is used to describe the competitive adsorption or desorption behavior of multi-component gases (CO2 and CH4) on the coal matrix surface. The equation is as follows:
[0029] in, Represents CO2 or CH4; m represents the adsorption amount of component i. 3 / kg; Let m be the Langmuir volume constant. 3 / kg; , is the reciprocal of the Langmuir pressure constant, in MPa 1 ; Let be the partial pressure of component i, in MPa.
[0030] (2) Seepage Submodule: Based on Darcy's law and the mass conservation equation, it describes the gas migration in coal pores and fractures. The governing equation is:
[0031] in, Current porosity; This refers to the gas saturation level. The density of the gas is kg / m³. The density of the gas under standard conditions; The bulk density of coal and rock is expressed in kg / m³. The Darcy velocity vector is given in m / s; k is the current permeability in m². Here is the gas dynamic viscosity, Pa·s; For source and sink terms, it represents the flow rate of injection and production wells.
[0032] (3) Chemical Reaction Submodule: Introducing the mineral dissolution-precipitation reaction kinetic equation to calculate the changes in mineral content and porosity caused by the CO2-water-coal-rock reaction. Taking calcite as an example, its reaction kinetic equation can be expressed as:
[0033] in, The dissolution or precipitation rate of calcite is represented by positive values for dissolution and negative values for precipitation, in mol / s. is the reaction surface area of calcite, in m²; Here is the reaction rate constant, in mol / m² / s; It is the ion activity product; This is the equilibrium constant for the calcite dissolution reaction. The rate of change in porosity caused by the reaction can be obtained by dividing the sum of the reaction rates of each mineral by the molar volume of the mineral.
[0034] in, Let m be the molar density of mineral m, in mol / m³.
[0035] Porosity change calculated by the chemical reaction submodule It is used to update the porosity value in the seepage submodule in real time, i.e. Then, using a preset porosity-permeability evolution relationship, the updated porosity is converted into the current permeability. This embodiment uses a classic exponential model to describe the change in permeability with porosity:
[0036] Where n is a lithology-related index, and for coal and rock, n=3 in this embodiment. The updated permeability k(t) is fed back to the seepage submodule in real time for the next seepage calculation, thus forming a closed-loop dynamic simulation from chemical reaction to pore-seepage evolution and then to seepage adsorption.
[0037] S3. Establish the geometric model and initial conditions of the target closed mine. Based on the geological data and mining records of the Yutianbao Coal Mine, a simplified two-dimensional geometric model reflecting the main characteristics of the mine was constructed. The model extends 1000m along the strike of the coal seam and vertically includes the coal seam and key roof and floor strata, with a total thickness of 50m. The model considers different physical property zones such as goaf, coal pillars, and natural fracture zones.
[0038] The initial conditions are set as follows: based on a coal seam depth of approximately 950m, the initial pressure P0 is set to 8.1 MPa; the constant temperature T is set to 40℃; and the initial water saturation S is set to... w =0.8, gas saturation S g =0.2.
[0039] Based on the XRD test results, different initial mineral mass fractions were assigned to each zone.
[0040] S4, Numerical Simulation of Multi-Scenario Injection and Aid Scheme A CO2 injection well was deployed at the center of the model, and a production well was deployed on each side, 300m from the center, to simulate a typical unit of a five-point well network. To investigate the effect of different injection pressures, three different injection-production schemes were designed: Option A: CO2 injection pressure is 10 MPa.
[0041] Option B: CO2 injection pressure is 12 MPa.
[0042] Option C: CO2 injection pressure is 14 MPa.
[0043] In all scenarios, production wells were operated at a constant bottomhole flowing pressure of 3 MPa for a simulation period of 2200 days. Coupled models were run to record the dynamic changes of key indicators for each scenario, including: cumulative CO2 sequestration, cumulative CH4 production, average reservoir pressure, and CO2 concentration in the produced gas from the production wells.
[0044] Besides the scenario described above where injection pressure is the variable, other implementations can also change the well pattern or production regime to explore the impact of different engineering parameters on the sealing effect. For example, in some implementations, a "one injection, one production" well pattern can be adopted, with a central injection well and a single production well at the periphery; in other implementations, for closed mines with a large number of legacy roadways, the roadways can be included in the model as dominant pathways, and the relative positions of the injection wells and roadways can be designed.
[0045] In addition to using the exponential model, the Kozeny-Carman equation can also be used to determine the porosity-permeability evolution relationship:
[0046] This equation can more accurately reflect changes in permeability when porosity varies significantly. In some implementations, a suitable model can be selected based on the rock type and experimental data.
[0047] In the chemical reaction submodule, in addition to calcite and dolomite, the dissolution / precipitation of other minerals can also be considered, such as ferrocalcite and boehmite.
[0048] S5. Comparison and Determination of Injection-Production Schemes Based on Multi-Objective Optimization Simulation results show that higher injection pressure leads to higher CO2 sequestration and CH4 production, but also earlier CO2 breakthrough in the production well and a faster increase in CO2 concentration in the produced gas. Under scheme C (14 MPa), the CO2 breakthrough time is approximately 700 days; under scheme B (12 MPa), the breakthrough time is approximately 1100 days; and under scheme A (10 MPa), the breakthrough time is approximately 1600 days.
[0049] This embodiment aims to maximize the cumulative CO2 storage (2200 days) and the cumulative CH4 production (2200 days) as dual optimization objectives, while setting constraints: during the simulation period, the peak CO2 concentration in the gas produced by the production well must not exceed 2% to ensure that the gas separation cost in the later stage is controllable.
[0050] A weighted scoring method was used for comprehensive evaluation. Based on the project objectives, the weights were set as follows: weight of cumulative CO2 sequestration. CH4 cumulative production weight CO2 breakthrough time weight The later the breakthrough, the higher the score. After normalizing the indicators for each plan, the comprehensive score is calculated as follows: Option A: 0.5 × 0.65 + 0.3 × 0.40 + 0.2 × 1.00 = 0.615 Option B: 0.5 × 0.85 + 0.3 × 0.70 + 0.2 × 0.65 = 0.765 Option C: 0.5×1.00 + 0.3×1.00 + 0.2×0.35 = 0.870 It should be noted that the above weight values , , The values assigned are merely illustrative and can be adjusted according to specific engineering objectives in practical applications. The sum of all weights is 1. The optimization method is not limited to the weighted scoring method. In other implementations, a multi-objective evolutionary algorithm can be used to generate a Pareto front, allowing decision-makers to select an option based on their preferences. The optimization objectives can also be expanded, for example, by adding indicators such as "lowest unit CO2 storage cost" and "minimum wellhead pressure fluctuation of the injection well."
[0051] The results showed that Scheme C scored the highest, but it also had the highest risk of CO2 breakthrough. If the safety weight needs to be increased, Scheme B may be a better choice. Therefore, Scheme B is ultimately recommended as the initial injection pressure that balances storage efficiency and safety, and the composition of the produced gas should be monitored in real time during the project implementation to dynamically adjust the injection strategy.
[0052] To further verify the model's universality, a gas coal sample (Ro≈0.8%) was collected from a coal mine in the Ordos Basin. The experimental and modeling steps in Example 1 were repeated to obtain the adsorption parameters, porosity-permeability parameters, and mineral composition of the coal sample. The same model framework was used for simulation. The results show that, due to the lower adsorption capacity of gas coal compared to anthracite, its theoretical CO2 sequestration is relatively low, but its mineral content is higher. Therefore, the contribution of chemical reactions to porosity evolution is more significant. This model successfully captured this difference, validating its application potential in coal seams of different ranks.
[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A method for dynamic prediction and injection-production optimization of CO2 sequestration in closed mines considering adsorption-seepage-chemical reaction coupling, characterized in that, Includes the following steps: S1. Construct a basic database for the coupled model and obtain the adsorption parameters, seepage parameters, mineral composition and chemical reaction kinetic parameters of the target closed coal and rock through indoor experiments; S2. Establish an adsorption-percolation coupling dynamic prediction model that takes into account chemical reaction feedback; S3. Establish the geometric model and initial conditions of the target closed mine; S4. Deploy the injection-production well network and conduct numerical simulations of different injection-production schemes; S5. Based on the simulation results, compare and determine the injection and extraction schemes; S2 further includes: A coupled model is constructed, comprising an adsorption submodule, a percolation submodule, and a chemical reaction submodule. The chemical reaction submodule calculates the change in coal and rock porosity caused by the chemical reaction based on the mineral dissolution-precipitation kinetics equation. ; Based on the preset porosity evolution relationship, and according to the updated porosity Real-time calculation of penetration rate at the current moment ,in, Initial porosity; The permeability Feedback is sent to the seepage submodule for seepage calculation in the next time step; In step S5, the selection and determination of the injection and extraction schemes are based on multi-objective optimization. The optimization objectives include at least two of the following: maximizing the cumulative CO2 storage, maximizing the cumulative CH4 production, and minimizing the CO2 breakthrough risk. The optimization process is subject to preset constraints.
2. The method according to claim 1, characterized in that, The adsorption submodule describes the competitive adsorption behavior of multi-component gases based on the extended Langmuir equation; The permeation submodule describes gas transport based on Darcy's law and the mass conservation equation.
3. The method according to claim 1, characterized in that, The mineral dissolution-precipitation kinetic equation used in the chemical reaction submodule is as follows: in, Let m be the reaction rate of mineral m. The specific surface area of the reaction. The reaction rate constant is... It is the ion activity product. This is the reaction equilibrium constant.
4. The method according to claim 1, characterized in that, The porosity-permeability evolution relationship follows an exponential model: in, denoted as the initial permeability, and n is an exponent determined by lithology.
5. The method according to claim 1, characterized in that, In S4, the injection and production scheme includes at least one or more of the following: different CO2 injection pressures, different production well operating regimes, or different injection and production well network forms.
6. The method according to claim 1, characterized in that, In S5, the constraints include at least the reservoir pressure not exceeding a preset upper limit value, and / or the CO2 concentration in the produced gas from the production well not exceeding a preset threshold value.
7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6.
8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.