Dynamic intelligent regulation and seepage enhancement optimization method and system for carbon dioxide mineralization sealing

Abstract

The application discloses a kind of carbon dioxide mineralization sealing dynamic intelligent regulation and control and permeation optimization method and system, the optimization method includes the following steps: real-time acquisition mineralization sealing process in situ monitoring injection parameter and the relevant data of reaction product;According to the relevant data of injection parameter and reaction product monitored in situ, form two optimization objective functions of mineralization rate and free CO2 volume;Build mineralization sealing multi-objective optimization model, select the optimal injection parameter setting value from Pareto solution set, obtain the optimal condition parameter;The optimal injection parameter in the Pareto optimal solution set is input to the constructed in situ enhanced regulation and control module, according to the real-time change of reservoir response, mineralization reaction process and injection condition, real-time adjustment control variable;Fracturing is carried out to target storage rock mass, quantitatively evaluates its seepage enhancement effect, based on the reservoir parameters after modification, dynamically update injection scheme, realize enhanced mineralization regulation and control system.

Description

Technical Field

[0001] This invention relates to the field of fluid seepage, and in particular to a method and system for dynamic intelligent control and permeation optimization of carbon dioxide mineralization and storage. Background Technology

[0002] CO2 mineralization and storage, as an effective CO2 capture and storage technology, involves injecting CO2 into underground rock formations, causing it to react chemically with the rocks to form stable carbonate minerals, thus achieving long-term carbon sequestration. This method not only reduces atmospheric CO2 emissions but also enhances formation permeability and improves oil and gas recovery. Fluid flow characteristics have a significant impact on the reaction rate and storage efficiency of CO2 mineralization and storage. Identifying dominant flow channels and evaluating their permeability enhancement effects are crucial for optimizing storage strategies and improving storage efficiency.

[0003] Currently, research on fluid seepage characteristics during CO2 mineralization and storage primarily relies on experimental and numerical simulation methods. Experimental simulations are realistic and intuitive, but costly, time-consuming, and difficult to simulate complex underground conditions. While numerical simulations can simulate complex multiphase flows and reaction processes, they lack effective quantitative evaluation methods for identifying dominant seepage channels and assessing permeability enhancement effects. Furthermore, existing research often focuses on the impact of single parameters on fluid seepage characteristics, neglecting systematic analysis under the coupling of multiple parameters. During CO2 mineralization and storage, changes in injection conditions such as injection pressure, temperature, and CO2 concentration, as well as geological parameters, all affect fluid seepage characteristics. However, the interaction effects of these parameters and their specific mechanisms of influence on dominant seepage channels and permeability enhancement effects have not been fully investigated.

[0004] Therefore, this invention aims to provide a method for determining the dominant seepage channels and evaluating the permeability enhancement effect of CO2 mineralization and storage. By combining experimental data and numerical simulation, a quantitative model for evaluating the permeability enhancement effect of mineralization and storage is established that comprehensively considers the influence of multiple parameters, so as to achieve accurate prediction of the storage effect and quantitative evaluation of the seepage enhancement effect. Summary of the Invention

[0005] This solution addresses the problems and needs raised above by proposing a dynamic intelligent control and permeation optimization method and system for carbon dioxide mineralization and storage. The above technical objectives can be achieved by adopting the following technical features, and it also brings about several other technical effects.

[0006] One objective of this invention is to propose a dynamic intelligent control and permeation optimization method for carbon dioxide mineralization and storage, comprising the following steps:

[0007] S10: Determine the target area for mineralization and storage, collect relevant data on injection parameters and reaction products during the mineralization and storage process in real time, take samples regularly to complete data collection and analysis, and deploy a microseismic monitoring system to monitor microseismic events during the fracturing stage.

[0008] S20: Based on the injection parameters and relevant data of reaction products monitored on site, the amount of CO2 mineralization per unit time is calculated using a geochemical reaction kinetic model, and the remaining volume of free CO2 is estimated by combining pore volume and reservoir pressure, forming two optimization objective functions: mineralization rate and remaining volume of free CO2.

[0009] S30: Based on two optimization objective functions, the Pareto optimal solution set is calculated using the NSGA-II multi-objective evolutionary algorithm. Combined with the actual reservoir conditions and safety constraints, a multi-objective optimization model for mineralization and storage is constructed. The optimal injection parameter setting value is selected from the Pareto optimal solution set to obtain the optimal field implementation condition parameters.

[0010] S40: An enhanced control module is constructed based on a combination of fuzzy PID control and model predictive control. The optimal injection parameters are input into the field enhanced control module. Based on the real-time changes in reservoir response, mineralization reaction process and injection conditions, the control variables of CO2 injection are adjusted in real time to achieve continuous optimization and adaptive adjustment of the injection scheme.

[0011] S50: Combines the optimized injection parameter settings output by the enhanced control module with the mineralization fracturing and permeability enhancement method to perform fracturing and transformation on the target storage rock mass, quantitatively evaluates its seepage enhancement effect, and dynamically updates the injection scheme based on the transformed reservoir parameters, thereby realizing an enhanced mineralization control system that combines dynamic control of injection parameters with artificial permeability enhancement.

[0012] Furthermore, the dynamic intelligent control and permeation optimization method for carbon dioxide mineralization and storage according to the present invention may also have the following technical features:

[0013] In one example of the present invention, step S20 specifically includes the following steps:

[0014] S21: The cumulative mineralization amount is calculated by inverting and fitting the relevant data of the injection parameters and reaction products using a geochemical reaction kinetic model. The mineralization rate is used as the first objective function F1, and its expression is as follows:

[0015]

[0016] In the formula, Reaction time The quality of internally cured CO2;

[0017] S22: Calculate the pore volume occupied by unmineralized free CO2, which is minimized by the second objective function F2, where F2 is the gas volume stored in the form of free CO2. :

[0018]

[0019] In the formula, The total mass of CO2 injected. To accumulate the mass of CO2 used in the mineralization reaction, Let be the density of CO2 under the temperature and pressure conditions P and T.

[0020] In one example of the present invention, step S30, calculating the Pareto optimal solution set using the NSGA-II multi-objective evolutionary algorithm, includes the following steps:

[0021] S31: Population evolution operation, generating an initial population representing different combinations of injected parameters. Through a tournament selection mechanism, the individual with the best fitness is chosen from the parent population as the parent. Using a simulated binary crossover operation, the selected parents are crossovered to generate new offspring individuals. The formula for calculating offspring individuals is:

[0022]

[0023]

[0024] In the formula, For random variables in binary crossover operations, These are a pair of parent individuals, representing combinations of parameters such as pressure, temperature, and flow rate. It consists of two offspring individuals;

[0025] The offspring individuals are subjected to polynomial mutation, using the following formula:

[0026]

[0027] In the formula, These are the values ​​of the parameter before and after the mutation. These are the upper and lower bounds of the parameters. This represents the perturbation quantity in the polynomial mutation operation;

[0028] S32: Non-dominated ordination and density assessment. The parent and offspring populations are merged, and non-dominated ordination ranks are determined based on dominance relationships. Within the same non-dominated rank, crowding is calculated to assess distribution density. The crowding degree of an individual is... The expression is:

[0029]

[0030] In the formula, For individual i in the objective function Adjacent function values ​​on, These are the maximum and minimum values ​​of the objective function in the current non-dominated layer;

[0031] S33: Elite retention screening, selecting new populations from merged populations based on higher priority non-dominance level and lower priority crowding density;

[0032] S34: Iterative convergence. Repeat steps S31 to S33 until the iteration termination condition is met. Finally, output the first non-dominated level as the Pareto optimal solution set of the bi-objective optimization problem.

[0033] In one example of the present invention, in step S30, a multi-objective optimization model for mineralization and storage is constructed by combining actual reservoir conditions and safety constraints. The optimal injection parameter settings are then selected from the Pareto solution set to obtain the optimal field implementation conditions parameters for injection pressure, flow rate, and concentration. This includes the following steps:

[0034] S301: The Pareto optimal solution set obtained by the NSGA-II algorithm Its expression is:

[0035]

[0036] In the formula, This represents the optimal combination of injection pressure, temperature, and flow rate corresponding to the i-th Pareto optimal solution. For the corresponding objective function value, Let m be the objective function for mineralization rate and free CO2 volume, and m be the number of nondominated solutions.

[0037] S302: To achieve dynamic control, set the optimal setpoint. The mapping is expressed as:

[0038]

[0039] In the formula, For mapping functions, This is a vector representing the system state.

[0040] S303: Execute the decision-making process in each control cycle to select the optimal setpoint. The data is sent to the enhanced control module in real time; the execution output and new monitoring data are used as feedback inputs to the enhanced control module and the NSGA-II optimization steps for short-term adjustment and medium-to-long-term updates, forming a closed-loop adaptive system.

[0041] In one example of the present invention, the fuzzy PID control in step S40 includes the following steps:

[0042] S41: Input ambiguity, reception error and error change rate The key input variable has the following expression:

[0043]

[0044]

[0045] In the formula, Let k be the target value and the actual value at sampling time k. The sampling period;

[0046] S42: Construct a fuzzy rule base. Based on the IF-THEN rule, output the adjustment amount of the PID parameters according to the fuzzy state of the current error and the rate of change of error. When the error is large and the rate of change of error is large, the fuzzy rule increases the proportional gain K. p Adjust the integral gain K appropriately. i Simultaneously reduce the differential gain K d ;

[0047] S43: Convert the fuzzy output into a precise value, and use the centroid method to calculate the centroid position of the fuzzy output set to obtain the precise adjustment amount ΔK. p (k),ΔK i (k),ΔK d (k), the parameters of the PID controller are updated in real time based on the adjustment amount:

[0048]

[0049] In the formula, These are the initial parameter values ​​for the PID controller;

[0050] S44: Output PID control quantity, using an incremental PID algorithm to calculate the increment Δu(k) of the control quantity and update the control signal u(k). The expressions for the increment and the control signal are as follows:

[0051]

[0052]

[0053] In the formula, Let k be the PID parameter value at sampling time k. This represents the error values ​​between the current time, the previous time, and the two time points before that. These are the control signals for the previous and current moments.

[0054] In one example of the present invention, the model prediction control in step S40 includes the following steps:

[0055] S401: A controlled autoregressive integral moving average model is used as the internal prediction model, and its expression is:

[0056]

[0057] In the formula, For the polynomial of the shift operator, For the controlled variables, To control the input signal, For difference operators, It is an uncorrelated random noise sequence;

[0058] S402: Within the finite time domain at each time k, solve the open-loop optimal control problem that minimizes the objective function J(k), and obtain the optimal control increment sequence that makes the system output as close as possible to the desired trajectory in the future prediction time domain. The expression for the optimal control increment sequence is:

[0059]

[0060] In the formula, P is the prediction time domain length, and L is the control time domain length. The predicted output value at time k+j from time k. As a reference trajectory for future time k+j, The control increment from time k to k+j;

[0061] S403: Constraining control variables, control increments, and output variables within permissible ranges ensures the system operates within safe limits. The expressions for the constraint control variables, control increments, and output variables are as follows:

[0062]

[0063] In the formula, To determine the value of the control variable, the control increment, and the predicted value of the output variable at time k+j, To control the variable u, the control increment, and the minimum allowable value of the output variable y, This corresponds to the maximum allowed value;

[0064] S404: Apply the first element of the optimized control sequence to update the system control variable, and refresh the prediction with the new measured value y(k+1). Repeat the optimization to form a closed-loop feedback control. The closed-loop feedback formula is:

[0065]

[0066] In the formula, To control the values ​​of the variables at times k and k-1, The optimal control increment is calculated at time k.

[0067] In one example of the present invention, step S50 specifically includes the following steps:

[0068] S51: Determine if the mineralization rate has reached the expected level. When the mineralization rate is lower than the target value, R... t R min / R t <α, where α is the efficiency threshold coefficient, and fracturing and permeability enhancement are implemented;

[0069] S52: Based on the spatial distribution of dominant seepage channels and the volume effect of fracture networks, quantitatively evaluate its seepage enhancement effect and identify the weak areas of the current seepage network; artificially create new high-conductivity fracture channels in this area to enable them to efficiently connect with the natural fracture network.

[0070] S53: Further evaluate its modification effect, input the updated reservoir seepage characteristic parameters into the multi-objective enhancement and control module, reconstruct the Pareto optimal solution set, and output the optimized injection parameters that match the modified reservoir characteristics to maximize the reservoir's CO2 storage potential.

[0071] In one example of the present invention, step S52, the quantitative evaluation of its seepage enhancement effect, includes the following steps:

[0072] S521: Utilize an underground microseismic monitoring system to collect microseismic signals induced by hydraulic fracturing in real time, perform source localization processing on the signals, and obtain a set of spatiotemporal parameters for each microseismic event;

[0073] S522: Identify the main fracture surfaces based on the set of microseismic event points, calculate the geometric parameters of the fracture surfaces characterized by each cluster of microseismic events, and thereby construct a discrete fracture network model after hydraulic fracturing.

[0074] S523: Quantitative calculation of the seepage enhancement effect in reservoirs after fracturing based on a discrete fracture network model:

[0075] S524: Input the output quantitative evaluation results, such as the modified volume, equivalent permeability tensor, and conductivity index, into the multi-objective enhancement and control module to update the reservoir attribute parameters of the optimization algorithm.

[0076] In one example of the present invention, in step S523, based on the discrete fracture network model, a quantitative calculation of the seepage enhancement effect of the reservoir after fracturing is performed, including the following:

[0077] 1) Calculation of fracturing volume: The convex hull algorithm is used to perform three-dimensional volume wrapping on the set of microseismic event points to calculate the extent of the fracturing and fracturing zone, and output the fracturing volume V. srv ;

[0078] 2) Calculation of equivalent permeability tensor: Based on the DFN model, the contribution of the fracture network to the equivalent permeability of the rock mass is calculated, and the permeability tensor of the fracture system is calculated. :

[0079]

[0080] In the formula, r represents the total number of cracks. j ,d j For crack radius and aperture, For the Kronecker function, Let p be the unit normal vector in the directions p and q;

[0081] 3) Calculation of the flow guidance capacity index: The DFN model is abstracted into a graph theory network, utilizing the edge weights. Define the conductivity of the fracture segment An improved Dijkstra algorithm was used to calculate the dominant seepage channels, and the conductivity of all fracture segments in the dominant channels was obtained. The harmonic mean is defined as the average conductivity index. ,

[0082] ,

[0083] In the formula, d is the length of the crack segment. j Let n be the crack aperture and n be the number of dominant channel cracks.

[0084] Another objective of this invention is to propose a dynamic intelligent control and permeation optimization system for carbon dioxide mineralization and storage, comprising:

[0085] The data acquisition module is configured to determine the target area for mineralization and storage, collect relevant data on injection parameters and reaction products during the mineralization and storage process in real time, take samples periodically to complete data acquisition and analysis, and deploy a microseismic monitoring system to monitor microseismic events during the fracturing stage.

[0086] The objective function module is configured to calculate the amount of CO2 mineralization per unit time using a geochemical reaction kinetic model based on the relevant data of injection parameters and reaction products monitored on-site, and to estimate the remaining volume of free CO2 by combining pore volume and reservoir pressure, thus forming two optimized objective functions: mineralization rate and remaining volume of free CO2.

[0087] The optimal parameter module is configured to calculate the Pareto optimal solution set based on two optimization objective functions using the NSGA-II multi-objective evolutionary algorithm. Combining the actual reservoir conditions and safety constraints, a multi-objective optimization model for mineralization and storage is constructed. The optimal injection parameter settings are selected from the Pareto optimal solution set to obtain the optimal field implementation condition parameters.

[0088] The optimization and adjustment module is configured to build an enhanced control module based on a combination of fuzzy PID control and model predictive control. The optimal injection parameters are input into the field enhanced control module, and the control variables of CO2 injection are adjusted in real time according to the real-time changes of reservoir response, mineralization reaction process and injection conditions, so as to achieve continuous optimization and adaptive adjustment of the injection scheme.

[0089] The mineralization control module is configured to combine the optimized injection parameter settings output by the enhancement control module with the mineralization fracturing and permeability enhancement method to perform fracturing and transformation on the target storage rock mass, quantitatively evaluate its seepage enhancement effect, and dynamically update the injection scheme based on the transformed reservoir parameters, thereby realizing an enhanced mineralization control system that combines dynamic control of injection parameters with artificial permeability enhancement.

[0090] Compared with the prior art, the present invention has the following beneficial effects:

[0091] This invention combines three-dimensional CT scanning technology and graph theory models with a discrete crack network model to perform flow simulation, providing more accurate crack network structure and flow simulation results, and supporting the identification of dominant seepage channels and the evaluation of seepage enhancement effects.

[0092] This invention utilizes the lattice Boltzmann method for flow simulation and calculates the optimal seepage channel path using an improved Dijkstra algorithm. This method improves the accuracy of simulation results and provides more reliable technical support for evaluating the permeability enhancement effect of CO2 mineralization and storage by accurately calculating the equivalent permeability tensor.

[0093] This invention establishes a quantitative evaluation model that comprehensively considers injection parameters such as injection pressure, temperature, and CO2 concentration, as well as mineralization and storage permeability enhancement indicators. This multi-parameter coupled model can more comprehensively evaluate the seepage enhancement effect and mineralization reaction rate under different operating conditions, providing a predictive model and quantitative evaluation method for optimizing storage strategies. Evaluating the seepage enhancement effect lays the foundation for improving formation permeability and can be applied not only to CO2 geological storage but also to enhancing oil and gas recovery.

[0094] The method provided by this invention uses numerical simulation and quantitative evaluation models to predict the sequestration effect and seepage enhancement effect at a lower cost. This method helps to reduce the economic burden of CO2 sequestration, improve the scalability and cost-effectiveness of the technology, and has application value in the fields of energy extraction and environmental management.

[0095] The preferred embodiments of the invention will be described in more detail below with reference to the accompanying drawings, so as to facilitate an understanding of the features and advantages of the invention. Attached Figure Description

[0096] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. The drawings are merely illustrative of some embodiments of the present invention and are not intended to limit the scope of the present invention to all embodiments.

[0097] Figure 1 This is a flowchart of a dynamic intelligent control and permeation optimization method for carbon dioxide mineralization and storage according to an embodiment of the present invention.

[0098] Figure 2 The flowchart of the NSGA-II optimization algorithm according to an embodiment of the present invention is shown below;

[0099] Figure 3 The flowchart of fuzzy PID and model predictive control according to an embodiment of the present invention is shown below;

[0100] Figure 4 This is a flowchart for evaluating the fracturing and permeability enhancement effect according to an embodiment of the present invention. Detailed Implementation

[0101] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The same reference numerals in the drawings represent the same components. It should be noted that the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0102] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, “an” or “a” and similar terms do not necessarily indicate a quantity limitation. Terms such as “comprising” or “including” mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. Terms such as “connected” or “linked” are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as “upper,” “lower,” “left,” and “right” are used only to indicate relative positional relationships; these relative positional relationships may change accordingly when the absolute position of the described object changes.

[0103] According to a first aspect of the present invention, a method for dynamic intelligent regulation and permeation optimization of carbon dioxide mineralization and storage is provided, such as... Figure 1 As shown, it includes the following steps:

[0104] S10: Determine the target area for mineralization and storage, collect relevant data on injection parameters and reaction products during the mineralization and storage process in real time, including downhole pressure, temperature, CO2 injection flow rate, CO2 reflux rate, mineral cation concentration, and pH value, take samples regularly to complete data collection and analysis, and deploy a microseismic monitoring system to monitor microseismic events during the fracturing stage;

[0105] S20: Based on the injection parameters and relevant data of reaction products monitored on site, the amount of CO2 mineralization per unit time is calculated using a geochemical reaction kinetic model, and the remaining volume of free CO2 is estimated by combining pore volume and reservoir pressure, forming two optimization objective functions: mineralization rate and remaining volume of free CO2.

[0106] S30: Based on the dual objective function of maximizing the mineralization rate and minimizing the free CO2 volume, the Pareto optimal solution set is calculated using the NSGA-II multi-objective evolutionary algorithm. Combined with the actual reservoir conditions and safety constraints, a multi-objective optimization model for mineralization and storage is constructed. The optimal injection parameter settings are selected from the Pareto solution set to obtain the field implementation condition parameters of optimal injection pressure, flow rate and concentration.

[0107] S40: Based on the construction of an enhanced control module by combining fuzzy PID control and model predictive control, the optimal injection parameters in the Pareto optimal solution set are input into the field enhanced control module. According to the real-time changes in reservoir response, mineralization reaction process and injection conditions, the control variables of CO2 injection flow rate, pressure and temperature are adjusted in real time to achieve continuous optimization and adaptive adjustment of the injection scheme.

[0108] S50: Combines the optimized injection parameter settings output by the enhanced control module with the mineralization fracturing and permeability enhancement method to perform fracturing and transformation on the target storage rock mass, quantitatively evaluates its seepage enhancement effect, and dynamically updates the injection scheme based on the transformed reservoir parameters, thereby realizing an enhanced mineralization control system that combines dynamic control of injection parameters with artificial permeability enhancement.

[0109] This optimization method combines 3D CT scanning technology and graph theory models with a discrete crack network model for flow simulation, providing more accurate crack network structure and flow simulation results, and supporting the identification of dominant seepage channels and the evaluation of seepage enhancement effects.

[0110] This optimization method utilizes the lattice Boltzmann method for flow simulation and calculates the optimal seepage channel path using an improved Dijkstra algorithm. This method improves the accuracy of simulation results and provides more reliable technical support for evaluating the permeability enhancement effect of CO2 mineralization and storage by accurately calculating the equivalent permeability tensor.

[0111] This optimization method establishes a quantitative evaluation model that comprehensively considers injection parameters such as injection pressure, temperature, and CO2 concentration, as well as mineralization and storage permeability enhancement indicators. This multi-parameter coupled model can more comprehensively evaluate the seepage enhancement effect and mineralization reaction rate under different operating conditions, providing a predictive model and quantitative evaluation method for optimizing storage strategies. Evaluating the seepage enhancement effect lays the foundation for improving formation permeability and can be applied not only to CO2 geological storage but also to enhancing oil and gas recovery.

[0112] The optimization method provides a way to predict the sequestration effect and seepage enhancement effect at a lower cost through numerical simulation and quantitative evaluation model. This method helps to reduce the economic burden of CO2 sequestration, improve the scalability and cost-effectiveness of the technology, and has application value in the fields of energy extraction and environmental management.

[0113] In one example of the present invention, step S20 specifically includes the following steps:

[0114] S21: The cumulative mineralization amount is calculated by inverting and fitting the relevant data of the injection parameters and reaction products using a geochemical reaction kinetic model. The mineralization rate is used as the first objective function F1, and its expression is as follows:

[0115]

[0116] In the formula, Reaction time The quality of internally cured CO2;

[0117] S22: To reduce the risk of CO2 leakage and lower the pressure within the storage unit, the pore volume occupied by unmineralized free CO2 is calculated and minimized as a second objective function F2, where F2 represents the gas volume stored in the form of free CO2. :

[0118]

[0119] In the formula, The total mass of CO2 injected. To accumulate the mass of CO2 used in the mineralization reaction, Let be the density of CO2 under the temperature and pressure conditions P and T.

[0120] In one example of the present invention, in step S30, the Pareto optimal solution set is calculated using the NSGA-II multi-objective evolutionary algorithm, such as... Figure 2 As shown, it includes the following steps:

[0121] S31: Population evolution operation, generating an initial population representing different combinations of injection parameters, including pressure, temperature, and flow rate. Through a tournament selection mechanism, the individual with the best fitness is chosen from the parent population as the parent; using a simulated binary crossover operation, the selected parents are crossovered to generate new offspring individuals; the formula for calculating offspring individuals is as follows:

[0122]

[0123]

[0124] In the formula, For random variables in binary crossover operations, These are a pair of parent individuals, representing combinations of parameters such as pressure, temperature, and flow rate. It consists of two offspring individuals;

[0125] The offspring individuals are subjected to polynomial mutation, using the following formula:

[0126]

[0127] In the formula, These are the values ​​of the parameter before and after the mutation. These are the upper and lower bounds of the parameters. This represents the perturbation quantity in the polynomial mutation operation;

[0128] S32: Non-dominated ordination and density assessment. The parent and offspring populations are merged, and non-dominated ordination ranks are determined based on dominance relationships. Within the same non-dominated rank, crowding is calculated to assess distribution density. The crowding degree of an individual is... The expression is:

[0129]

[0130] In the formula, For individual i in the objective function Adjacent function values ​​on, These are the maximum and minimum values ​​of the objective function in the current non-dominated layer;

[0131] S33: Elite retention screening, selecting new populations from merged populations based on higher priority non-dominance level and lower priority crowding density;

[0132] S34: Iterative convergence. Repeat steps S31 to S33 until the iteration termination condition is met. Finally, output the first non-dominated level as the Pareto optimal solution set of the bi-objective optimization problem.

[0133] In one example of the present invention, in step S30, a multi-objective optimization model for mineralization and storage is constructed by combining actual reservoir conditions and safety constraints. The optimal injection parameter settings are then selected from the Pareto solution set to obtain the optimal field implementation conditions parameters for injection pressure, flow rate, and concentration. This includes the following steps:

[0134] S301: The Pareto optimal solution set obtained by the NSGA-II algorithm Its expression is:

[0135]

[0136] In the formula, This represents the optimal combination of injection pressure, temperature, and flow rate corresponding to the i-th Pareto optimal solution. For the corresponding objective function value, Let m be the objective function for mineralization rate and free CO2 volume, and m be the number of nondominated solutions.

[0137] S302: To achieve dynamic control, set the optimal setpoint. The mapping is expressed as:

[0138]

[0139] In the formula, For mapping functions, This is a vector representing the system state; it includes real-time downhole monitoring parameters and cumulative mineralization. Free CO2 volume Reservoir condition indicators, etc.

[0140] S303: Execute the decision-making process in each control cycle to select the optimal setpoint. The data is sent to the enhanced control module in real time; the execution output and new monitoring data are used as feedback inputs to the enhanced control module and the NSGA-II optimization steps for short-term adjustment and medium-to-long-term updates, forming a closed-loop adaptive system.

[0141] In one example of the present invention, the fuzzy PID control described in step S40 is as follows: Figure 3 As shown, it includes the following steps:

[0142] S41: Input ambiguity, reception error and error change rate The key input variable has the following expression:

[0143]

[0144]

[0145] In the formula, Let k be the target value and the actual value at sampling time k. The sampling period is defined as follows: these two precise input variables are fuzzified into fuzzy linguistic variables through a membership function, enabling the controller to handle nonlinear problems.

[0146] S42: Construct a fuzzy rule base. Based on the IF-THEN rule, output the adjustment amount of the PID parameters according to the fuzzy state of the current error and the rate of change of error. When the error is large and the rate of change of error is large, the fuzzy rule increases the proportional gain K. p Adjust the integral gain K appropriately. i Simultaneously reduce the differential gain K d ;

[0147] S43: Convert the fuzzy output into a precise value, and use the centroid method to calculate the centroid position of the fuzzy output set to obtain the precise adjustment amount ΔK. p (k),ΔK i (k),ΔK d (k), the parameters of the PID controller are updated in real time based on the adjustment amount:

[0148]

[0149] In the formula, These are the initial parameter values ​​for the PID controller;

[0150] S44: Output PID control quantity, using an incremental PID algorithm to calculate the increment Δu(k) of the control quantity and update the control signal u(k). The expressions for the increment and the control signal are as follows:

[0151]

[0152]

[0153] In the formula, Let k be the PID parameter value at sampling time k. This represents the error values ​​between the current time, the previous time, and the two time points before that. These are the control signals for the previous and current moments.

[0154] In one example of the present invention, the model prediction control described in step S40, such as... Figure 3 As shown, it includes the following steps:

[0155] S401: A controlled autoregressive integral moving average model is used as the internal prediction model, and its expression is:

[0156]

[0157] In the formula, For the polynomial of the shift operator, These are the controlled variables, including temperature, location, and pressure. To control the input signal, For difference operators, It is an uncorrelated random noise sequence;

[0158] S402: Within the finite time domain at each time k, solve the open-loop optimal control problem that minimizes the objective function J(k), and obtain the optimal control increment sequence that makes the system output as close as possible to the desired trajectory in the future prediction time domain. The expression for the optimal control increment sequence is:

[0159]

[0160] In the formula, P is the prediction time domain length, and L is the control time domain length. The predicted output value at time k+j from time k. As a reference trajectory for future time k+j, The control increment from time k to k+j;

[0161] S403: Constraining control variables, control increments, and output variables within permissible ranges ensures the system operates within safe limits. The expressions for the constraint control variables, control increments, and output variables are as follows:

[0162]

[0163] In the formula, To determine the value of the control variable, the control increment, and the predicted value of the output variable at time k+j, To control the variable u, the control increment, and the minimum allowable value of the output variable y, This corresponds to the maximum allowed value;

[0164] S404: Apply the first element of the optimized control sequence to update the system control variable, and refresh the prediction with the new measured value y(k+1). Repeat the optimization to form a closed-loop feedback control. The closed-loop feedback formula is:

[0165]

[0166] In the formula, To control the values ​​of the variables at times k and k-1, The optimal control increment is calculated at time k.

[0167] In one example of the present invention, step S50 specifically includes the following steps:

[0168] S51: Determine if the mineralization rate has reached the expected level. When the mineralization rate is lower than the target value, R... t R min / R t <α, where α is the efficiency threshold coefficient, and fracturing and permeability enhancement are implemented;

[0169] S52: Based on the spatial distribution of superior seepage channels and the volume effect of fracture network, quantitatively evaluate its seepage enhancement effect and identify the weak areas of the current seepage network; artificially create new high-conductivity fracture channels in this area to efficiently connect them with the natural fracture network, significantly increasing the modification volume and effective permeability.

[0170] S53: Further evaluate its modification effect, input the updated reservoir seepage characteristic parameters into the multi-objective enhancement and control module, reconstruct the Pareto optimal solution set, and output the optimized injection parameters that match the modified reservoir characteristics to maximize the reservoir's CO2 storage potential.

[0171] In one example of the present invention, in step S52, as Figure 4 As shown, the quantitative evaluation of its seepage enhancement effect includes the following steps:

[0172] S521: Deploy a microseismic sensor network monitoring system to collect microseismic signals induced by fracturing in real time using underground microseismic monitoring systems, filter, denoise and locate the source of the signals to obtain the spatiotemporal parameter set of each microseismic event;

[0173] S522: Based on the set of microseismic event points, identify the main fracture surfaces and calculate the geometric parameters of the fracture surfaces characterized by each cluster of microseismic events, including the center coordinates, normal vector, radius, and aperture of the fracture surfaces, thereby constructing a discrete fracture network model after hydraulic fracturing.

[0174] S523: Quantitative calculation of the seepage enhancement effect in reservoirs after fracturing based on a discrete fracture network model:

[0175] S524: Input the output quantitative evaluation results, such as the modified volume, equivalent permeability tensor, and conductivity index, into the multi-objective enhancement and control module to update the reservoir attribute parameters of the optimization algorithm.

[0176] In one example of the present invention, in step S523, based on the discrete fracture network model, a quantitative calculation of the seepage enhancement effect of the reservoir after fracturing is performed, including the following:

[0177] 1) Calculation of fracturing volume: The convex hull algorithm is used to perform three-dimensional volume wrapping on the set of microseismic event points to calculate the extent of the fracturing and fracturing zone, and output the fracturing volume V. srv ;

[0178] 2) Calculation of equivalent permeability tensor: Based on the DFN model, the contribution of the fracture network to the equivalent permeability of the rock mass is calculated, and the permeability tensor of the fracture system is calculated. :

[0179]

[0180] In the formula, r represents the total number of cracks. j ,d j For crack radius and aperture, For the Kronecker function, Let p be the unit normal vector in the directions p and q;

[0181] 3) Calculation of the flow guidance capacity index: The DFN model is abstracted into a graph theory network, utilizing the edge weights. Define the conductivity of the fracture segment An improved Dijkstra algorithm was used to calculate the dominant seepage channels, and the conductivity of all fracture segments in the dominant channels was obtained. The harmonic mean is defined as the average conductivity index. ,

[0182] ,

[0183] In the formula, d is the length of the crack segment. j Let n be the crack aperture and n be the number of dominant channel cracks.

[0184] In one example of the present invention, step S52, identifying weak areas in the current seepage network, includes:

[0185] Based on the quantitative assessment results, target areas with weak seepage capacity are identified for priority modification. The priority modification areas are mainly: the proposed storage area outside the volumetric space inclusion, the area corresponding to the minimum permeability direction determined by the equivalent permeability tensor, and the crack segment with a significantly lower conductivity index than the average level identified by the spatial distribution of the dominant seepage channels, or the proposed storage area that was not selected as a dominant channel.

[0186] According to a second aspect of the present invention, a dynamic intelligent control and permeation enhancement optimization system for carbon dioxide mineralization and storage includes:

[0187] The data acquisition module is configured to determine the target area for mineralization and storage, and to collect relevant data on injection parameters and reaction products during the mineralization and storage process in real time, including downhole pressure, temperature, CO2 injection flow rate, CO2 reflux rate, mineral cation concentration, and pH value. It also takes samples periodically to complete data acquisition and analysis, and deploys a microseismic monitoring system to monitor microseismic events during the fracturing stage.

[0188] The objective function module is configured to calculate the amount of CO2 mineralization per unit time using a geochemical reaction kinetic model based on the relevant data of injection parameters and reaction products monitored on-site, and to estimate the remaining volume of free CO2 by combining pore volume and reservoir pressure, thus forming two optimized objective functions: mineralization rate and remaining volume of free CO2.

[0189] The optimal parameter module is configured as a dual objective function based on maximizing the mineralization rate and minimizing the free CO2 volume. The Pareto optimal solution set is calculated using the NSGA-II multi-objective evolutionary algorithm. Combined with the actual reservoir conditions and safety constraints, a multi-objective optimization model for mineralization and storage is constructed. The optimal injection parameter settings are selected from the Pareto solution set to obtain the optimal injection pressure, flow rate and concentration on-site implementation conditions parameters.

[0190] The optimization and adjustment module is configured to build an enhanced control module based on a combination of fuzzy PID control and model predictive control. The optimal injection parameters in the Pareto optimal solution set are input into the field enhanced control module. Based on the real-time changes in reservoir response, mineralization reaction process and injection conditions, the control variables of CO2 injection flow rate, pressure and temperature are adjusted in real time to achieve continuous optimization and adaptive adjustment of the injection scheme.

[0191] The mineralization control module is configured to combine the optimized injection parameter settings output by the enhancement control module with the mineralization fracturing and permeability enhancement method to perform fracturing and transformation on the target storage rock mass, quantitatively evaluate its seepage enhancement effect, and dynamically update the injection scheme based on the transformed reservoir parameters, thereby realizing an enhanced mineralization control system that combines dynamic control of injection parameters with artificial permeability enhancement.

[0192] This optimization system combines 3D CT scanning technology and graph theory models with a discrete crack network model to perform flow simulation, providing more accurate crack network structure and flow simulation results, and supporting the identification of dominant seepage channels and the evaluation of seepage enhancement effects.

[0193] This optimization system utilizes the lattice Boltzmann method for flow simulation and calculates the optimal seepage path using an improved Dijkstra algorithm. This method improves the accuracy of the simulation results and provides more reliable technical support for evaluating the permeability enhancement effect of CO2 mineralization and storage by accurately calculating the equivalent permeability tensor.

[0194] This optimization system establishes a quantitative evaluation model that comprehensively considers injection parameters such as injection pressure, temperature, and CO2 concentration, as well as mineralization and storage permeability enhancement indicators. This multi-parameter coupled model can more comprehensively evaluate the seepage enhancement effect and mineralization reaction rate under different operating conditions, providing a predictive model and quantitative evaluation method for optimizing storage strategies. Evaluating the seepage enhancement effect lays the foundation for improving formation permeability, and can be applied not only to CO2 geological storage but also to enhancing oil and gas recovery.

[0195] The optimization system provides a method that uses numerical simulation and quantitative evaluation models to predict the effects of CO2 sequestration and seepage enhancement at a lower cost. This method helps to reduce the economic burden of CO2 sequestration, improve the scalability and cost-effectiveness of the technology, and has application value in the fields of energy extraction and environmental management.

[0196] It should be noted that the carbon dioxide mineralization storage dynamic intelligent control and permeation optimization system of the present invention can also perform any of the processes described in the previously described carbon dioxide mineralization storage dynamic intelligent control and permeation optimization method, and the specific details are not repeated here.

[0197] The foregoing description, with reference to preferred embodiments, details the exemplary implementation of the dynamic intelligent regulation and permeation optimization method and system for carbon dioxide mineralization and storage proposed in this invention. However, those skilled in the art will understand that various modifications and alterations can be made to the above specific embodiments without departing from the concept of this invention, and various combinations can be made to the various technical features and structures proposed in this invention without exceeding the protection scope of this invention, which is determined by the appended claims.

Claims

1. A dynamic intelligent regulation and control and permeability enhancement optimization method for carbon dioxide mineralization sequestration, characterized in that, Comprise the following steps: S10: Determine the mineralization sealing target area, real-time collection of injection parameters and reaction products related data in the process of mineralization sealing, periodic sampling to complete data collection and analysis, deployment of microseismic monitoring system for monitoring microseismic events in the fracturing stage; S20: According to the injection parameters and reaction products related data monitored in the field, the CO2 mineralization amount per unit time is calculated by using the geochemical reaction kinetics model, and the remaining volume of free CO2 is estimated by combining the pore volume and reservoir pressure to form two optimization objective functions of mineralization rate and remaining volume of free CO2; S30: Based on the two optimization objective functions, the Pareto optimal solution set is calculated by using NSGA-II multi-objective evolutionary algorithm, and the actual conditions and safety constraints of the reservoir are combined to construct a multi-objective optimization model of mineralization sealing, and the optimal injection parameter setting value is selected from the Pareto optimal solution set to obtain the optimal field implementation condition parameter; wherein, the actual conditions and safety constraints of the reservoir are combined to construct a multi-objective optimization model of mineralization sealing, and the optimal injection parameter setting value is selected from the Pareto optimal solution set to obtain the optimal field implementation condition parameter, comprising the following steps: S301: Obtain the Pareto optimal solution set by solving the NSGA-II algorithm The expression is: wherein is the number of Pareto optimal solutions, i is the optimal combination of injection pressure, temperature and flow rate corresponding to the is the corresponding objective function value, is the mineralization rate and free CO2 volume objective function, m is the number of non-dominated solutions; S302: To realize dynamic regulation and control, set the optimal set point The mapping is expressed as: wherein is a mapping function, is a vector of system states; S303: Perform decision-making process in each control cycle to select the optimal set point and send to the enhanced control module in real time; the execution output and new monitoring data are input as feedback to the enhanced control module and NSGA-II optimization step for short-term regulation and medium and long-term update to form a closed-loop adaptive system; S40: Based on the combination of fuzzy PID control and model predictive control, an enhanced control module is constructed, the optimal injection parameter is input into the field enhanced control module, the control variables of CO2 injection are adjusted in real time according to the real-time changes of reservoir response, mineralization reaction process and injection conditions, and continuous optimization and adaptive adjustment of the injection scheme are realized; S50: The optimal injection parameter setting value output by the enhanced control module is combined with the mineralization fracturing and permeability enhancement method to perform fracturing reconstruction on the target storage rock mass, the seepage enhancement effect is quantitatively evaluated, and based on the reconstructed reservoir parameters, the injection scheme is dynamically updated to realize the dynamic regulation and control of the injection parameters combined with the artificial permeability enhancement; wherein, the step S50 specifically comprises the following steps: S51: judge whether the mineralization rate reaches the expectation, when the mineralization rate is lower than the target value, R t , i.e. R min / R t <α, wherein, α is the efficiency threshold coefficient, implement the fracturing permeability reconstruction; S52: Based on the spatial distribution of the dominant seepage channel and the volume effect of the fracture network, the seepage enhancement effect is quantitatively evaluated, and the weak area of the current seepage network is identified; new high-conductivity fracture channels are artificially created in this area to make them efficiently connected with the natural fracture network; wherein, the quantitative evaluation of the seepage enhancement effect comprises the following steps: S521: Real-time acquisition of microseismic signals generated by fracturing by using the microseismic monitoring system laid underground, source location processing of the signals, and acquisition of the space-time parameters of each microseismic event; S522: Based on the microseismic event point set, the main fracture surface is identified, the fracture surface geometric parameters represented by each cluster of microseismic events are calculated, and a discrete fracture network model after fracturing is constructed; S523: Based on the discrete fracture network model, the quantitative calculation of the seepage enhancement effect of the reservoir after fracturing reconstruction is performed; wherein, based on the discrete fracture network model, the quantitative calculation of the seepage enhancement effect of the reservoir after fracturing reconstruction comprises the following: 1) Reconstruction volume calculation: adopt the convex hull algorithm to three-dimensional volume package of microseismic event point set, calculate the range of fracturing reconstruction area, output reconstruction volume V srv ; 2) Equivalent permeability tensor calculation: Calculate the contribution of fracture network to the equivalent permeability of rock mass based on DFN model, the permeability tensor of fracture system : wherein is the total number of cracks, r j , d j is the crack radius and opening, is the Kronecker function, is the unit normal vector in p and q directions; 3) Calculation of the flow guidance capacity index: The DFN model is abstracted into a graph theory network, utilizing the edge weights. Define the conductivity of the fracture segment An improved Dijkstra algorithm was used to calculate the dominant seepage channels, and the conductivity of all fracture segments in the dominant channels was obtained. The harmonic mean is defined as the average conductivity index. , ， wherein is the length of the fracture segment, d j is the fracture opening, n is the number of dominant channel fractures; S524: The quantitative evaluation results of the reconstruction volume, equivalent permeability tensor and conductivity index are input into the enhanced control module to update the reservoir attribute parameters of the optimization algorithm; S53: Further evaluate the effect of its modification, input the updated reservoir seepage characteristics parameters into the enhanced control module, reconstruct the Pareto optimal solution set, output the optimized injection parameters matching the reservoir characteristics after modification, maximize the potential of CO2 storage in the reservoir.

2. The dynamic intelligent control and enhanced permeability optimization method for carbon dioxide mineralization storage according to claim 1, characterized in that, S20 specifically comprises the following steps: S21: the injection parameters and the related data of the reaction products are calculated by the geochemical reaction kinetics model to perform inversion fitting calculation of the cumulative mineralization amount, and the mineralization rate is taken as a first objective function F 1, whose expression is: wherein reaction time mass of CO2 internalized S22: calculating the pore volume occupied by the free CO2 not mineralized, which is minimized as a second objective function F 2, second objective function F 2 volume of gas stored in the form of free CO2 : wherein is the total mass of CO2 injected, is the mass of CO2 mineralized, is the density of CO2 at the conditions of P, T.

3. The dynamic intelligent control and enhanced permeability optimization method for carbon dioxide mineralization storage according to claim 1, characterized in that, In step S30, the NSGA-II multi-objective evolutionary algorithm is used to calculate the Pareto optimal solution set, including the following steps: S31: Population evolution operation, generate an initial population representing different injection parameter combinations, select the individual with the best fitness as the parent from the parent population through the tournament selection mechanism; use the simulated binary crossover operation to combine the selected parents, generate new offspring individuals; wherein the offspring individual calculation formula is: wherein is a random variable in the binary crossover operation, is a pair of parent individuals, representing a combination of parameters such as pressure, temperature and flow rate, are two child individuals; Polynomial mutation is performed on the offspring individuals, and the formula is as follows: wherein is the value of the parameter before and after variation, is the upper and lower bound of the parameter, is the amount of perturbation in the polynomial variation operation; S32: non-dominated sorting and density estimation, merging the parent and offspring populations, non-dominated sorting based on the dominance relationship to divide the ranks, and calculating the crowding distance of the individuals in the same non-dominated rank to evaluate the distribution density, wherein the crowding distance of the individual is The expression is: wherein is the individual i The objective function is the adjacent function value, is the maximum and minimum value of the objective function in the current non-dominated layer. S33: Elite retention screening, select a new population from the merged population according to the higher priority non-dominated level and lower priority higher congestion of the higher priority non-dominated level; S34: Iterative convergence, repeat steps S31 to S33 until the iteration termination condition is met, and finally output the first non-dominated level as the Pareto optimal solution set of the two optimization objective function optimization problems.

4. The dynamic intelligent control and enhanced permeability optimization method for carbon dioxide mineralization storage according to claim 1, characterized in that, In step S40, the fuzzy PID control includes the following steps: S41: input fuzzification, receive error and error rate of change key input variable, expressed as: wherein is the target value and the actual value at the sampling instant k is the target value and the actual value at the sampling instant is the sampling period; S42: Constructing fuzzy rule base, outputting adjustment amount of PID parameter according to current error and error change rate based on IF-THEN rule, when error is large and error change rate is large, fuzzy rule increases proportional gain K p , appropriately adjusting integral gain K i , while reducing differential gain K d ; S43: convert the fuzzy output to a precise numerical value, use the barycenter method to calculate the barycenter position of the fuzzy output set, and obtain the precise adjustment amount Δ K p ( k ), Δ K i ( k ), Δ K d ( k ), based on the adjustment amount, real-time update the parameters of the PID controller: In the formula, PID controller initial parameter values; S44: outputting the PID control quantity, calculating the increment Δ of the control quantity by using the incremental PID algorithm u k updating the control signal u k , wherein the expressions of the increment and the control signal are respectively​​ wherein is the PID parameter value at the sampling instant k is the PID parameter value at the sampling instant is the error value at the current instant, the previous instant and the two previous instants, is the control signal at the previous instant and the current instant.

5. The dynamic intelligent control and enhanced permeability optimization method for carbon dioxide mineralization storage according to claim 1, characterized in that, In step S40, the model predictive control includes the following steps: S401: Use the controlled autoregressive integrated moving average model as the internal prediction model, and its expression is: wherein is a polynomial of the backward shift operator, is a controlled variable, is a control input signal, is a difference operator, is an uncorrelated random noise sequence; S402: At each time instant k , solve the open-loop optimal control problem of minimizing the objective function J ( k ) over a finite time horizon, the optimal control increment sequence that brings the system output as close as possible to the desired trajectory over the future prediction horizon, the expression of the optimal control increment sequence is: wherein P is a prediction horizon length, L is a control horizon length, at time k for future k+j time, is a reference trajectory for future k+j time, is a control increment from time k to k + j . S403: Restrict the control variables, control increments and output variables within the allowed range to ensure that the system operates within a safe range, wherein the expressions of the control variables, control increments and output variables are: wherein is the control variable value at time k + j the control increment and the output variable prediction value, is the control variable u , the control increment, the minimum allowed value of the output variable y , is the corresponding maximum allowed value; S404: apply the first element of the optimized optimal control sequence to the system to update the control variable, and use the new measurement value y ( k +1) refresh the prediction, repeat the optimization to form a closed-loop feedback control, and the closed-loop feedback formula is: In the formula, is the optimal control increment calculated at time k , k -1 control variable value, is the optimal control increment calculated at time k .

6. An optimization system for using the dynamic intelligent regulation and optimization method for carbon dioxide mineralization sequestration according to any one of claims 1 to 5, characterized in that, including: A data acquisition module configured to determine a mineralization storage target area, real-time acquisition of relevant data of injection parameters and reaction products in the mineralization storage process, periodic sampling for data acquisition and analysis, and deployment of a microseismic monitoring system for monitoring microseismic events in the fracturing stage; A target function module configured to calculate the CO2 mineralization amount per unit time using the geochemical reaction kinetics model based on the relevant data of the injection parameters and reaction products monitored in the field, and estimate the remaining volume of free CO2 combined with the pore volume and reservoir pressure to form two optimization objective functions of mineralization rate and remaining volume of free CO2; The optimal parameter module is configured to calculate a Pareto optimal solution set by using an NSGA-II multi-objective evolutionary algorithm based on two optimization objective functions, to construct a mineralization storage multi-objective optimization model in combination with actual conditions and safety constraints of a reservoir, to screen optimal injection parameter set values from the Pareto optimal solution set, and to obtain optimal field implementation condition parameters. The optimization and adjustment module is configured to construct an enhanced control module in a manner of combination of fuzzy PID control and model predictive control, to input the optimal injection parameters into the field enhanced control module, to adjust the control variables of CO2 injection in real time according to real-time changes of reservoir responses, mineralization reaction processes and injection conditions, and to realize continuous optimization and adaptive adjustment of the injection scheme. The mineralization regulation module is configured to combine the optimal injection parameter set values output by the enhanced control module with the mineralization fracturing and permeability enhancement method, to perform fracturing reconstruction on the target storage rock mass, to quantitatively evaluate the permeation enhancement effect, to dynamically update the injection scheme based on the reconstructed reservoir parameters, and to realize the enhanced mineralization regulation system combining dynamic regulation of injection parameters and artificial permeability enhancement.

Images