CO2 enhanced petroleum recovery and underground synchronous storage system for old oil field reutilization

Abstract

The invention relates to the technical field of oilfield development, and discloses a CO2 enhanced petroleum recovery and underground synchronous storage system for old oilfield reutilization, which comprises a data acquisition module for acquiring geological data of a target old oilfield; the mechanism model creating module creates a mechanism model according to the geological data of the target old oil field to obtain initial injection-production parameters; the prediction model creating module obtains and matches historical geological data of the oil reservoir subjected to carbon dioxide flooding, and creates a prediction model based on the oil reservoir meeting the matching degree; the injection-production parameter optimization module is used for obtaining optimal injection-production parameters through iterative updating of a grey wolf optimization algorithm based on the prediction model; and the verification module is used for comparing and analyzing the initial injection-production parameters and the optimal injection-production parameters to obtain final injection-production parameters. According to the method, the injection-production parameters can be optimized, so that the efficiency of old oil field recovery and carbon dioxide sequestration is improved.

Description

Technical Field

[0001] The invention relates to the technical field of oil field development, and in particular to a CO2 enhanced oil recovery and underground synchronous storage system for reusing old oil fields. Background Art

[0002] Old oil fields generally have the characteristics of low initial recovery rate. When developing, they adopt primary oil recovery (relying on formation energy) and secondary extraction (water injection) methods. The recovery rate is generally between 20% and 40%, and the remaining reserves are large, so they have the prospect of secondary reuse. The reuse of old oil fields refers to improving the recovery rate of remaining oil and extending the production time of oil fields by improving the technology and management of oil fields that have been developed for many years.

[0003] The reuse of old oil fields has the following characteristics: (1) Complex distribution of residual oil: The geological structure of old oil fields is heterogeneous, such as uneven distribution of fractures, porosity and permeability. The residual oil is often distributed in inefficient areas of the geological structure, such as dead oil zones, low permeability layers and fracture-undeveloped areas; (2) Rich geological data: Old oil fields have accumulated a large amount of geological, production and monitoring data, which provide valuable reference materials for subsequent development. Through historical data, more accurate geological models can be established to provide support for optimizing development plans; (3) Great technical challenges: Advanced tertiary oil recovery technologies (such as carbon dioxide flooding, polymer flooding, thermal recovery, etc.) are needed to increase the recovery rate of residual oil. The geological conditions of different oil fields vary greatly, and it is necessary to select appropriate technical solutions in a targeted manner. The cost of tertiary oil recovery technology is relatively high, and a detailed economic feasibility analysis is required to ensure the economic benefits of the project.

[0004] In the prior art, physical models are often used to test carbon dioxide flooding in oil fields to obtain injection and production parameters; however, traditional physical models have certain limitations, such as the inability to simulate the well network injection process, poor pressure bearing capacity of the casting model, and the inability to simulate the inter-well flow process of carbon dioxide after fracture sealing, etc., and the efficiency is low. Therefore, it is urgent to optimize the injection and production parameters to improve the efficiency of recovery and carbon dioxide storage in old oil fields. Summary of the invention

[0005] The present invention aims to provide a CO2 enhanced oil recovery and underground synchronous storage system for the reuse of old oil fields, and optimizes the injection and production parameters to improve the efficiency of old oil field recovery and carbon dioxide storage.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme:

[0007] CO2 enhanced oil recovery and underground simultaneous storage system for the reuse of old oil fields, including:

[0008] Data acquisition module, used to obtain geological data of target old oil fields;

[0009] The mechanism model creation module is used to create a mechanism model based on the geological data of the target old oil field and obtain the initial injection and production parameters through the mechanism model;

[0010] The prediction model creation module is used to obtain the historical geological data of the oil reservoirs that have completed the CO2 flooding, match them with the geological data of the target old oil fields, and create a prediction model based on the oil reservoirs that meet the matching degree;

[0011] The injection and production parameter optimization module is used to obtain the optimal injection and production parameters based on the prediction model and the injection and production parameters as the optimization target through the iterative update of the Grey Wolf Optimization Algorithm;

[0012] The verification module is used to compare and analyze the initial injection and production parameters with the optimal injection and production parameters. When the difference exceeds the threshold, the key parameters of the mechanism model are adjusted based on the prediction model results, and the final injection and production parameters are obtained based on the adjusted mechanism model; when the difference does not exceed the threshold, the initial injection and production parameters are used as the final injection and production parameters.

[0013] The principle and advantages of this scheme are: in actual application, the mechanism model is highly interpretable and authentic. The mechanism model is based on scientific principles such as physical laws, chemical reaction kinetics, thermodynamics, etc., and has high requirements for data integrity. The reused oil reservoirs have accumulated a large amount of geological, production and monitoring data. Through these geological data, it is easy to quickly and accurately create a mechanism model, so as to obtain accurate injection and production parameters based on the mechanism model for carbon dioxide flooding to improve the recovery rate; however, the internal mechanism of the old oil field is too complicated, and the laws that can be reflected by geological data are limited. Therefore, it is impossible to fully grasp the internal mechanism of the old oil field, and it still has certain limitations. The old oil field that has completed carbon dioxide flooding can make up for this defect. The old oil field that has completed carbon dioxide flooding can reflect the real carbon dioxide flooding process. By matching historical geological data, it can reduce the interference caused by the differences in geological conditions of different oil fields and improve the accuracy of the injection and production parameter prediction values. Based on the Grey Wolf optimization algorithm and prediction model, the injection and production parameters are optimized to obtain the optimal injection and production parameters, and the final injection and production parameters are verified, which can further improve the accuracy of the injection and production parameters, thereby further improving the recovery rate, extending the production life of old oil fields, and increasing the economic value of oil fields.

[0014] Preferably, as an improvement, the injection and production parameters include injection volume, injection mode, injection pressure, injection time, injection speed, injection and production temperature, oil production volume, and bottom hole pressure.

[0015] Technical effect: It is easy to clarify the optimization target to ensure the best injection effect.

[0016] Preferably, as an improvement, the prediction model creation module includes:

[0017] The geological data matching submodule is used to perform similarity analysis between the historical geological data of the oil reservoirs that have completed carbon dioxide flooding and the geological data of the target old oil field. The similarity analysis includes geological similarity, fluid similarity, temperature and pressure similarity, injection and production parameter similarity, and production data similarity; each similarity is assigned a value and weighted to obtain a matching degree;

[0018] A screening submodule is used to obtain a matching degree threshold and screen the oil reservoirs that meet the matching degree threshold;

[0019] The model creation submodule is used to train the neural network model with reservoirs that meet the matching degree, using geological data as input and injection and production parameters as output to train the prediction model.

[0020] Technical effect: It makes it easier for predicted results to be close to actual conditions.

[0021] Preferably, as an improvement, the injection-production parameter optimization module includes:

[0022] The objective function definition submodule is used to define the objective function and constraints based on the recovery factor and storage factor;

[0023] Algorithm initialization submodule, used to initialize the gray wolf population;

[0024] The algorithm iteration submodule is used to iterate the gray wolf algorithm until the preset maximum number of iterations is reached and output the final optimization result.

[0025] Technical effect: The gray wolf algorithm is used to simulate the leadership hierarchy and hunting mechanism of gray wolves in nature, so as to accurately obtain the optimization results of injection and production parameters.

[0026] Preferably, as an improvement, the objective function is:

[0027]

[0028] Among them, τ is the fitness adjustment constant, w1 and w2 are the weight values ​​of oil recovery rate and carbon dioxide storage rate, respectively, S y is the oil recovery factor, f c is the carbon dioxide storage rate.

[0029] Technical effect: It is easy to ensure the recovery rate and storage rate of old oil fields after the objective function is optimized.

[0030] Preferably, as an improvement, the constraint condition is:

[0031] P y ≤P z ≤P d

[0032] vq ≤v z ≤v c

[0033] Among them, P z is the injection pressure, P d is the formation fracture pressure, P y is the reservoir pressure, v z is the injection velocity, v q is the effective oil displacement rate, P c The maximum velocity capacity of equipment and pipelines.

[0034] Technical effect: It is easy to ensure that oil recovery can be carried out and the oil field structure, facilities and equipment are safe.

[0035] Preferably, as an improvement, the initialization of the gray wolf population includes the initialization of the gray wolf population size, the individual gray wolf positions, and the parameter convergence factor, the distance coefficient vector, the position coefficient vector and the maximum number of iterations, and the initialization is completed using a roulette method.

[0036] Technical effect: Initialization makes it easier to integrate the optimization problem of injection and production parameters into the Grey Wolf optimization algorithm.

[0037] Preferably, as an improvement, the algorithm iteration submodule further includes:

[0038] The fitness update molecular module is used to update the fitness of the gray wolf based on the fitness function and obtain new α wolf, β wolf and δ wolf;

[0039] Gray wolf position update molecular module, used to update the current gray wolf position;

[0040] The memory wolf pack acquisition submodule is used to introduce the memory wolf pack, store the current position and objective function of the wolves in the explorer wolf pack in the memory wolf pack, retain the initial optimal injection and production parameters, select a better wolf from the updated current gray wolf position to update the memory wolf pack, and perform random local search.

[0041] Technical effect: It is easy to improve the development and exploration capabilities during the operation of the injection and production parameter optimization algorithm and maintain a balance between the two.

[0042] Preferably, as an improvement, the memory wolf pack acquisition submodule is also used to delete individuals with poor fitness through a linear population size reduction technique.

[0043] Technical effect: It is easy to reduce the impact of interference data on optimization speed and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic diagram of the structure of the CO2 enhanced oil recovery and underground synchronous storage system for the reuse of old oil fields. DETAILED DESCRIPTION

[0045] The following is further described in detail through specific implementation methods:

[0046] The embodiment is basically as shown in the attached Figure 1 As shown:

[0047] CO2 enhanced oil recovery and underground simultaneous storage system for the reuse of old oil fields, including:

[0048] The data acquisition module is used to obtain geological data of the target old oil field; the geological data of the old oil field is the basis for oil field development and management. The geological data includes geological structure (stratum distribution, faults and fractures, fold structure), lithology (rock type, mineral composition), reservoir characteristics (porosity, permeability, saturation), fluid properties (crude oil properties, natural gas properties, formation water properties), formation pressure and temperature, production data and historical data, etc. By combining data from geological surveys, drilling data, seismic exploration, production data, historical data, remote sensing technology and laboratory analysis, the geological data of the target old oil field can be fully obtained. For example, geological mapping and geological profiles in geological surveys record information on surface and shallow geological structures, lithology, and other information. Geological profile measurements are carried out at key locations to understand the vertical changes in geological structures. In drilling data, drilling logs record geological information during the drilling process, such as lithology, rock layer thickness, drilling speed, etc. Core analysis conducts detailed analysis of lithology, porosity, permeability, etc., and logging data obtains physical parameters such as resistivity, density, and acoustic wave velocity of the formation. In seismic exploration, two-dimensional seismic obtains the longitudinal profile information of the strata, and three-dimensional seismic obtains the three-dimensional structural information of the strata; in production data, production well data records the pressure, temperature, output and other production data of the production wells, and injection-production well data records the injection volume, pressure, temperature and other data of the injection-production wells; in historical archives, historical reports record the historical geological reports, development reports, production reports, etc. of the oil field, and database queries access the historical geological data in the geological database of the oil field company; in remote sensing technology, satellite remote sensing obtains surface and shallow geological information, and aerial photography obtains high-resolution surface images; in laboratory analysis, rock and mineral analysis determines the lithology and mineral composition, and fluid analysis determines the physical and chemical properties of oil, gas and water.

[0049] The mechanism model creation module is used to create a mechanism model based on the geological data of the target old oil field and obtain the initial injection and production parameters through the mechanism model. Specifically, PETREL software is used to establish a stratigraphic distribution and lithology distribution model based on geological data; ECLIPSE software is used to establish a physical model that describes the flow, diffusion and dissolution of CO2 in the reservoir; a model that describes the chemical reaction between CO2 and crude oil, formation water and rock is established; and a model that describes the effect of temperature and pressure on the behavior of CO2 and crude oil is established. Numerical simulation is run based on the mechanism model to generate the flow and distribution results of CO2 in the reservoir, and the initial injection and production parameters are obtained through the mechanism model.

[0050] The prediction model creation module is used to obtain the historical geological data of the oil reservoirs that have completed carbon dioxide flooding, match them with the geological data of the target old oil fields, and create a prediction model based on the oil reservoirs that meet the matching degree; the prediction model creation module includes a geological data matching submodule, a screening submodule and a model creation submodule.

[0051] The geological data matching submodule is used to perform similarity analysis between the historical geological data of the oil reservoirs that have completed carbon dioxide flooding and the geological data of the target old oil fields. The similarity analysis includes geological similarity, fluid similarity, temperature and pressure similarity, injection and production parameter similarity, and production data similarity; each similarity is assigned a value and weighted to obtain a matching degree; specifically, geological similarity includes geological structure, lithology, porosity and permeability; geological structure includes stratum distribution (such as stratum levels and distribution), faults and fractures, and fold structures; lithology includes rock type (such as sandstone, mudstone, limestone) and mineral composition; porosity The porosity and permeability include the distribution of porosity and permeability. In this embodiment, the method for obtaining geological similarity includes but is not limited to: drawing a geological profile of the oil field through geological survey, recording information such as rock layer type, thickness, and inclination, comparing geological profiles, and evaluating the similarity of geological structures; conducting seismic exploration through geophysical detection methods, obtaining seismic reflection profiles of the oil field, comparing seismic reflection characteristics, and evaluating the similarity of geological structures; obtaining gravity and magnetic anomaly maps through gravity and magnetic exploration methods, comparing gravity and magnetic anomaly characteristics, and evaluating the similarity of geological structures; using geochemical analysis methods, using X-ray diffraction to analyze the similarity of geological structures. (XRD) is used to analyze the rock mineral composition of the oil, and the atomic absorption spectroscopy (AAS) is used to analyze the element content in the rock, compare the rock chemical characteristics, and evaluate the similarity of geochemistry; by calculating the thickness, inclination, porosity and other parameters of the oil field, the correlation coefficient between the parameters is calculated, and the correlation between the parameters is evaluated. By using clustering algorithms (such as K-means), the geological data is clustered and analyzed, the clustering results are evaluated, and the similarity of the geological data is determined. The geographic information system (GIS) is used to integrate geological, geophysical, geochemical and other data, and the hierarchical analysis method (AHP) is combined to construct a multi-level Evaluation system, for comprehensive evaluation; fluid similarity includes similarity of crude oil, formation water and carbon dioxide, such as density, viscosity and chemical composition of crude oil, mineralization and pH value of formation water, density, viscosity and solubility of carbon dioxide; temperature and pressure similarity includes distribution and gradient of formation temperature and pressure; injection and production parameter similarity includes injection pressure (such as injection pressure range and change trend), injection rate (such as injection rate range and change trend), injection cycle (such as injection cycle length and cycle change trend); also includes production data similarity, such as production well layout, production well production, injection and production well layout, injection and production well production. Screening submodule, for obtaining matching threshold, screening reservoirs that meet the matching threshold. Model creation submodule, for using reservoirs that meet the matching threshold to train neural network model, with geological data as input and injection and production parameters as output, training to obtain prediction model, the injection and production parameters include but are not limited to injection volume, injection mode, injection pressure, injection time, injection rate, injection and production temperature, oil production, bottom hole pressure.

[0052] The injection and production parameter optimization module is used to obtain the optimal injection and production parameters based on the prediction model and injection and production parameters as the optimization target through iterative update of the gray wolf optimization algorithm. The injection and production parameter optimization module includes:

[0053] The objective function definition submodule is used to define the objective function and constraints based on the recovery factor and the sealing factor. Specifically, the objective function is:

[0054]

[0055] Among them, τ is the fitness adjustment constant, w1 and w2 are the weight values ​​of oil recovery rate and carbon dioxide storage rate, respectively, S y is the crude oil recovery factor, which refers to the percentage of crude oil recovered from the reservoir after CO2 flooding to the original reservoir reserves. c The storage rate of carbon dioxide refers to the proportion of injected CO2 that remains permanently in the reservoir.

[0056] The constraints are:

[0057] P y ≤P z ≤P d

[0058] v q ≤v z ≤v c

[0059] Among them, P z is the injection pressure, P d is the formation fracture pressure, P y is the reservoir pressure. The injection pressure must be higher than the reservoir pressure to ensure effective CO2 injection. z is the injection velocity, v q is the effective oil displacement velocity, v c The maximum velocity capacity of equipment and pipelines.

[0060] The algorithm initialization submodule is used to initialize the gray wolf population; the gray wolf population initialization includes the gray wolf population size, gray wolf individual position, parameter convergence factor, distance coefficient vector, position coefficient vector and the maximum number of iterations. The initialization is completed in a roulette way, and the initialization facilitates the integration of the optimization problem of injection and production parameters into the gray wolf optimization algorithm.

[0061] The algorithm iteration submodule is used to iterate the gray wolf algorithm until the preset maximum number of iterations is reached and output the final optimization result. Specifically, the algorithm iteration submodule also includes: a fitness update molecular module, which is used to update the gray wolf fitness based on the fitness function and obtain new α wolf, β wolf and δ wolf; a gray wolf position update molecular module, which is used to update the current gray wolf position; the gray wolf position update formula is:

[0062] X1=X α -A1D α ;

[0063] X2=X β -A2D β ;

[0064] X3=X δ -A3D δ ;

[0065]

[0066] Among them, A1, A2 and A3 are distance coefficient vectors, D α , D β , D δ is the distance between the wolf pack and the three best gray wolves α, β and δ, D α , D β , D δ for:

[0067] D α =|C1*X α -X|;

[0068] D β =|C2*X β -X|;

[0069] D δ =|C3*X δ -X|;

[0070] D α , D β , D δ represents the distance between other ω wolves and the best three gray wolves α, β and γ; X α , X β and X γ Represent the positions of α, β and γ wolves respectively, X represents the position of the current gray wolf individual; C1, C2 and C3 are position coefficient vectors.

[0071] Although the gray wolf optimization algorithm has certain advantages over other optimization algorithms in terms of global search, it still has the problem of slow convergence. Therefore, this embodiment also includes a memory wolf pack acquisition submodule, which is used to introduce a memory wolf pack to store better solutions during the iteration process. The size of the memory wolf pack is the same as that of the explorer wolf pack. After the algorithm is initialized, the fitness of each wolf is calculated, and the current position and objective function of the wolf in the explorer wolf pack are stored in the memory wolf pack. The initial optimal injection and production parameters are retained. In each iteration, the explorer wolf pack is updated, and a better wolf is selected from the updated current gray wolf position to update the memory wolf pack, and a random local search is performed. The memory wolf pack acquisition submodule is also used to delete individuals with poor fitness through linear population size reduction technology. Specifically, the gray wolf optimization algorithm introduces an evolutionary operator to simulate the evolution of organisms from low to high levels through inheritance, selection and mutation to enhance the algorithm search efficiency, and uses the differences between gray wolf individuals to rearrange the population, and a new generation is generated through competition between parent individuals. The evolutionary operator is:

[0072]

[0073] in, is the gray wolf mutation position vector, X α is the current position of wolf α, X i is the current gray wolf individual, f is the ratio parameter, and its value range is [0.5,1]. The target individual is crossed with the mutant individual, and the crossover model is:

[0074]

[0075] if rand(i)≤q c

[0076]

[0077] in, is the individual position vector of the experimental gray wolf, is the position vector of the mutant gray wolf, q c is the crossover probability, and its value range is [0.1, 0.9].

[0078] After updating the memory gray wolf, a random local search was performed near half of the randomly selected wolf pack. The steps of the random local search include: finding the nearest neighbor (with the shortest Euclidean distance) of a wolf in the memory wolf pack, randomly generating a local wolf, and the local search model is:

[0079] X t,i =X ij +c*rand(0,1)*(X ni -X ij )

[0080] Among them, Xij To remember the position of any wolf in the wolf pack, c is a constant, X ni is the location of the nearest neighbor.

[0081] Evaluate the generated local wolf X t,i If the value is lower than the current wolf, the local wolf replaces the current wolf in the memory wolf pack; if the value is higher than the current wolf, the local wolf is discarded.

[0082] In order to improve the effect of parameter adaptation based on the success history of the evolutionary algorithm, the size of the wolf pack is linearly reduced from the initial size Pint to the minimum size Pmin according to a function that depends on the number of fitness evaluations through the linear population size reduction (LPSR) technique:

[0083]

[0084] Among them, n g+1 is the gray wolf scale, P min is the minimum scale, P int is the initial scale, n fs Number of fitness evaluations, n f is the threshold of fitness evaluation times.

[0085] The verification module is used to compare and analyze the initial injection and production parameters with the optimal injection and production parameters. When the difference exceeds the threshold, the key parameters of the mechanism model are adjusted based on the prediction model results, and the final injection and production parameters are obtained based on the adjusted mechanism model; when the difference does not exceed the threshold, the initial injection and production parameters are used as the final injection and production parameters.

[0086] After the carbon dioxide displacement of the old oil field, the underground synchronous storage of carbon dioxide is the key link to achieve carbon capture and storage. In this embodiment, physical storage technology is preferred, and carbon dioxide is physically stored in the pores and cracks of the reservoir. Physical sealing also includes reservoir characteristics, cap rock sealing and geological structure assessment, assessing the porosity, permeability, thickness and other characteristics of the reservoir to ensure sufficient storage capacity; assessing the thickness, mineral composition, and crack development of the cap rock to ensure good sealing performance; assessing the faults, cracks and other geological structures in the area to avoid the risk of carbon dioxide leakage. When the physical storage assessment is not applicable, chemical storage and biological storage technologies are used. It also includes monitoring and management, setting up gas monitoring stations on the ground, regularly detecting carbon dioxide leakage, monitoring the carbon dioxide concentration in groundwater, ensuring that groundwater is not polluted, monitoring the temperature changes of the reservoir, assessing the distribution and storage of carbon dioxide, regularly analyzing monitoring data, assessing the storage effect, and generating monitoring reports, timely feedback on monitoring results, and guiding storage management.

[0087] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A CO2 enhanced oil recovery and underground synchronous storage system for the reuse of old oil fields, characterized in that: include: Data acquisition module, used to obtain geological data of target old oil fields; The mechanism model creation module is used to create a mechanism model based on the geological data of the target old oil field and obtain the initial injection and production parameters through the mechanism model; The prediction model creation module is used to obtain the historical geological data of the oil reservoirs that have completed the CO2 flooding, match them with the geological data of the target old oil fields, and create a prediction model based on the oil reservoirs that meet the matching degree; The injection and production parameter optimization module is used to obtain the optimal injection and production parameters based on the prediction model and the injection and production parameters as the optimization target through the iterative update of the Grey Wolf Optimization Algorithm; The verification module is used to compare and analyze the initial injection and production parameters with the optimal injection and production parameters. When the difference exceeds the threshold, the key parameters of the mechanism model are adjusted based on the prediction model results, and the final injection and production parameters are obtained based on the adjusted mechanism model; When the difference does not exceed the threshold, the initial injection and extraction parameters are used as the final injection and extraction parameters.

2. The CO2 enhanced oil recovery and underground synchronous storage system for the reuse of old oil fields according to claim 1 is characterized in that: The injection and production parameters include injection volume, injection mode, injection pressure, injection time, injection speed, injection and production temperature, oil production volume, and bottom hole pressure.

3. The CO2 enhanced oil recovery and underground synchronous storage system for the reuse of old oil fields according to claim 1 is characterized in that: The prediction model creation module includes: The geological data matching submodule is used to perform similarity analysis between the historical geological data of the oil reservoirs that have completed carbon dioxide flooding and the geological data of the target old oil field. The similarity analysis includes geological similarity, fluid similarity, temperature and pressure similarity, injection and production parameter similarity, and production data similarity; each similarity is assigned a value and weighted to obtain a matching degree; A screening submodule is used to obtain a matching degree threshold and screen the oil reservoirs that meet the matching degree threshold; The model creation submodule is used to train the neural network model with reservoirs that meet the matching degree, using geological data as input and injection and production parameters as output to train the prediction model.

4. The CO2 enhanced oil recovery and underground synchronous storage system for the reuse of old oil fields according to claim 1 is characterized in that: The injection-production parameter optimization module includes: The objective function definition submodule is used to define the objective function and constraints based on the recovery factor and storage factor; Algorithm initialization submodule, used to initialize the gray wolf population; The algorithm iteration submodule is used to iterate the gray wolf algorithm until the preset maximum number of iterations is reached and output the final optimization result.

5. The CO2 enhanced oil recovery and underground synchronous storage system for the reuse of old oil fields according to claim 4 is characterized in that: The objective function is: Among them, τ is the fitness adjustment constant, w1 and w2 are the weight values ​​of oil recovery rate and carbon dioxide storage rate, respectively, S y is the oil recovery factor, f c is the carbon dioxide storage rate.

6. The CO2 enhanced oil recovery and underground synchronous storage system for the reuse of old oil fields according to claim 4 is characterized in that: The constraints are: P y ≤P z ≤P d in q ≤in z ≤in c Among them, P z is the injection pressure, P d is the formation fracture pressure, P y is the reservoir pressure, v z is the injection velocity, v q is the effective oil displacement velocity, v c The maximum velocity capacity of equipment and pipelines.

7. The CO2 enhanced oil recovery and underground synchronous storage system for the reuse of old oil fields according to claim 4 is characterized in that: The initialization of the gray wolf population includes the initialization of the gray wolf population size, the individual gray wolf positions, the parameter convergence factor, the distance coefficient vector, the position coefficient vector and the maximum number of iterations, and the initialization is completed in a roulette manner.

8. The CO2 enhanced oil recovery and underground synchronous storage system for the reuse of old oil fields according to claim 4 is characterized in that: The algorithm iterator module also includes: The fitness update molecular module is used to update the fitness of the gray wolf based on the fitness function and obtain new α wolf, β wolf and δ wolf; Gray wolf position update molecular module, used to update the current gray wolf position; The memory wolf pack acquisition submodule is used to introduce the memory wolf pack, store the current position and objective function of the wolves in the explorer wolf pack in the memory wolf pack, retain the initial optimal injection and production parameters, select a better wolf from the updated current gray wolf position to update the memory wolf pack, and perform random local search.

9. The CO2 enhanced oil recovery and underground synchronous storage system for the reuse of old oil fields according to claim 8 is characterized in that: The memory wolf pack acquisition submodule is also used to delete individuals with poor fitness through a linear population size reduction technique.

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