Synergistic Optimization Method of Injection-Production Parameters Based on Water Flooding Matching Degree

Abstract

The present invention relates to the field of oil and gas exploration and development, and particularly to a collaborative optimization method for injection-production parameters based on water flooding matching degree. The method includes the following steps: Step 1, generate a restart model of the target reservoir, and give the initial injection-production parameters and optimization control parameters of the wells to be optimized in the target reservoir; Step 2, update the iteration number of the current optimization process, automatically generate injection-production parameter combination schemes, and call the restart model to simulate each injection-production parameter combination scheme; Step 3, calculate the water flooding matching degree values corresponding to each injection-production parameter combination scheme, and determine the optimal injection-production parameter combination scheme at the current iteration number; Step 4, judge whether the current iteration number reaches the maximum iteration number; Step 5, output the optimal injection-production parameter scheme. The present invention can accurately control the production allocation and injection allocation of water flooding reservoirs, improve the displacement degree of the reservoir, improve the reservoir development effect, and finally enhance the recovery factor.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas field development, and specifically to a collaborative optimization method for injection-production parameters based on water drive matching degree. Background Art

[0002] With the progress of oilfield waterflood development, the remaining oil reserves in old oilfields gradually decrease, and the production difficulty increases day by day. The oilfields enter the high water cut development stage. The traditional production methods are difficult to meet the demand of maximizing economic benefits. The injection-production optimization technology has become a key strategy to improve the oilfield development efficiency, improve the remaining oil distribution, and extend the oilfield life.

[0003] The objective functions of injection-production parameter optimization mainly include cumulative oil production, net present value, water cut, etc. In the waterflood development of oil reservoirs, ideally, the injection volume in each area should be reasonably matched with the movable oil reserves to achieve the best balanced displacement. However, in actual development, due to factors such as reservoir physical property differences, adjustment of injection-production relationships, and development strategies, it is difficult to achieve precise matching. Therefore, scientifically and quantitatively evaluating the degree of water drive balance and providing a basis for adjustment and optimization is an important research topic in oil reservoir development.

[0004] In the existing injection-production optimization technologies, the optimization objective functions mainly focus on evaluating the economic benefits and short-term development effects of oil reservoirs, lacking the reflection of the displacement process inside the oil reservoir, and cannot directly reflect the degree of balanced water drive displacement inside the reservoir. Summary of the Invention

[0005] To overcome the defects of the existing technology, the present invention provides a collaborative optimization design method for injection-production parameters of water drive oil reservoirs, aiming to solve the problem of unbalanced displacement caused by heterogeneity and unreasonable injection-production systems in water drive oil reservoirs. By quantitatively characterizing the spatial adaptation relationship between movable oil reserves and injection volume, based on the displacement matching degree, combined with intelligent optimization algorithms, an optimal injection-production parameter scheme that matches the actual movable oil reserves and injection volume of the oil reservoir can be quickly and accurately obtained, realizing the scientific optimization design of an injection-production parameter system with the maximum displacement matching degree in each area of the water drive oil reservoir, which is beneficial to improving the recovery factor of water drive oil reservoirs.

[0006] The technical solution adopted by the present invention to solve the above technical problems is: a collaborative optimization method for injection-production parameters based on water drive matching degree, comprising the following steps:

[0007] Step 1: Generate a restart model of the target oil reservoir, and give the initial injection-production parameters and optimization control parameters of the wells to be optimized in the target oil reservoir;

[0008] Step 2: Update the iteration number of the current optimization process, automatically generate an injection-production parameter combination scheme, and call the restart model to simulate each injection-production parameter combination scheme;

[0009] Step 3: Calculate the water flooding matching degree values corresponding to each injection-production parameter combination plan, and determine the optimal injection-production parameter combination plan at the current iteration number.

[0010] Step 4: Determine whether the current iteration number reaches the maximum iteration number.

[0011] Step 5: Output the optimal injection-production parameter plan.

[0012] Preferably, the said Step 1 includes: Step 101: Sort out the relevant data of the target reservoir and build a numerical simulation model of the target reservoir; Step 102: Run the numerical simulation model of the target reservoir until the moment to be optimized, and generate a restart model of the target reservoir; Step 103: Given the initial injection-production parameters and optimization control parameters of the wells to be optimized in the target reservoir. Among them, the relevant data of the target reservoir include geological data and development data; the geological data include reservoir structure parameters, reservoir physical property parameters, and fluid physical property parameters; the reservoir structure parameters include top structure data; the reservoir physical property parameters include formation pressure, formation temperature, oil layer thickness, saturation, permeability, porosity; the fluid physical property parameters include oil phase viscosity, oil phase density, water phase viscosity, water phase density, relative permeability curve; the development data include: wellhead coordinates, well trajectories, perforation horizons of injection wells, wellhead coordinates, well trajectories, perforation horizons of production wells; the initial injection-production parameters include: daily injection volume of injection wells, daily liquid production volume of production wells; the optimization control parameters include injection-production constraint conditions and optimization algorithm initialization parameters; the injection-production constraint conditions include: daily allocated injection volume, daily allocated liquid production volume of the target reservoir; upper limit of daily injection volume of injection wells, lower limit of daily injection volume of injection wells, upper limit of bottom hole pressure of injection wells, lower limit of bottom hole pressure of injection wells; upper limit of daily liquid production volume of production wells, lower limit of daily liquid production volume of production wells, upper limit of bottom hole pressure of production wells, lower limit of bottom hole pressure of production wells; the optimization algorithm initialization parameters include: population size, maximum iteration number, learning factor, and inertia weight, and the initial iteration number m = 0.

[0013] Preferably, the number pop of the population size takes a value from 20 to 50; the learning factor is set to 2.0; the inertia weight is set between 0.4 and 0.9.

[0014] Preferably, the optimization algorithm includes one or several of genetic algorithm, particle swarm algorithm, whale optimization algorithm, and sparrow optimization algorithm.

[0015] Preferably, step 2 includes: step 201, update the iteration number m of the current optimization process, let m = m + 1, and randomly generate pop injection-production parameter combination schemes using an optimization algorithm according to the preset population size pop on the premise of satisfying the injection-production constraint conditions; step 202, submit the pop injection-production parameter combination schemes in the current iteration process to the reservoir numerical simulator, call the restart model of the target reservoir, and perform reservoir numerical simulation on each injection-production parameter combination scheme respectively to obtain the corresponding numerical simulation response results; step 203, extract the corresponding grid cell characteristic parameters from the numerical simulation response results according to the numerical simulation response results of the pop injection-production parameter combination schemes.

[0016] Preferably, in step 202, a Python module is used to batch automatically submit the pop injection-production parameter combination schemes in the current iteration process through the interface between Python and the reservoir numerical simulator.

[0017] Preferably, the grid cell characteristic parameters are obtained by parsing the output file of the reservoir numerical simulator. The grid cell characteristic parameters include reservoir physical property parameters and fluid physical property parameters. The reservoir physical property parameters include: initial oil saturation, residual oil saturation, irreducible water saturation, water saturation, formation pressure, porosity, and comprehensive compressibility coefficient; the fluid physical property parameters include: oil phase viscosity, oil formation volume factor, water phase viscosity, and relative permeability curve.

[0018] Preferably, step 3 includes:

[0019] Step 301: According to the grid cell characteristic parameters extracted from each numerical simulation result, calculate the movable oil reserves of each grid cell using the following formula:

[0020] (1),

[0021] In formula (1): is the movable oil reserves of the grid cell; N is the geological reserves of the grid cell; S oi is the initial oil saturation of the grid cell; S or is the residual oil saturation;

[0022] Step 302: According to the water saturation, oil phase relative permeability, and water phase relative permeability data on the relative permeability curve, use the least squares method, nonlinear regression, or machine learning method to perform fitting based on the following formula to obtain the fitting coefficients a and b :

[0023] (2),

[0024] In formula (2): K ro is the relative permeability of the oil phase on the relative permeability curve; K rw is the relative permeability of the water phase on the relative permeability curve; S w is the water saturation on the relative permeability curve; a , b is the fitting coefficient;

[0025] Step 303: According to the grid cell characteristic parameters of each numerical simulation result, use the following formula to calculate the cumulative water injection volume of the grid cell:

[0026] (3)

[0027] In formula (3): W i is the cumulative water injection volume of the grid cell in a stage; V φ is the pore volume of the grid cell; μ o , μ w are the oil phase viscosity and water phase viscosity respectively; S wc is the irreducible water saturation; S w is the water saturation; B o is the formation volume factor of crude oil; C t is the comprehensive compressibility; ∆ P is the pressure difference; e is the base of the natural logarithm function;

[0028] Step 304: Use the following formula to calculate the water flooding matching degree value of each injection-production parameter combination scheme:

[0029] (4)

[0030] In formula (4): simi is the water flooding matching degree value; , is a vector composed of the movable oil reserve values of each grid cell of the target reservoir model; , is a vector composed of the cumulative water injection volume values of each grid cell of the target reservoir model; n is the number of grid cells included in the target reservoir model; N rj represents the j th grid cell of the target reservoir model corresponding to the movable oil reserve; W ij represents thej Cumulative water injection volume corresponding to a grid cell; N r ·W i Denote the vector N r and W i Dot product of; ||N r || denotes the modulus of the vector N r ; ; ||W i || denotes the modulus of the vector W i ; ;

[0031] Step 305: Obtain the injection-production parameter combination plan with the largest water drive matching degree value at the current iteration number m, and determine it as the optimal injection-production parameter combination plan at the current iteration number.

[0032] Preferably, in step 4, it is judged whether the current iteration number reaches the maximum iteration number: if the current iteration number m reaches the preset maximum iteration number, then go to step 5; otherwise, go to step 2; in step 5, output the optimal injection-production parameter combination plan as the injection-production parameter combination plan with the largest water drive matching degree value among all iteration numbers, including the injection volume of the injection well and the liquid production volume of the production well.

[0033] Beneficial technical effects brought by the present invention:

[0034] The present invention uses cosine similarity to construct a water drive matching degree index, establishes a collaborative optimization method for injection-production parameters based on the water drive matching degree, realizes the reasonable matching of movable oil reserves and water injection volume, guides oilfield production practice and improves oilfield development efficiency.

[0035] The present invention can accurately regulate the production and injection allocation of water drive reservoirs, realize the improvement of the displacement degree of the reservoir, improve the development effect of the reservoir, and finally increase the recovery rate. Brief Description of the Drawings

[0036] Figure 1 Is a flowchart of an embodiment of the present invention.

[0037] Figure 2 Is a permeability field and well location distribution map of the target reservoir.

[0038] Figure 3 Is an optimization iteration curve graph of injection-production parameters of the target reservoir.

[0039] Figure 4 Is a comparison result graph of reservoir production dynamics before and after optimization.

[0040] Figure 5 Is a saturation and streamline distribution map before optimization of the second smallest layer of the target reservoir.

[0041] Figure 6The optimized saturation and streamline distribution map for the second sub-layer of the target reservoir.

[0042] Figure 7 The saturation and streamline distribution map before optimization for the fourth sub-layer of the target reservoir.

[0043] Figure 8 The optimized saturation and streamline distribution map for the fourth sub-layer of the target reservoir.

[0044] Figure 9 The saturation and streamline distribution map before optimization for the sixth sub-layer of the target reservoir.

[0045] Figure 10 The optimized saturation and streamline distribution map for the sixth sub-layer of the target reservoir.

[0046] In the figure:

[0047] INJEC1 injection well; PROD1 production well; PROD2 production well; PROD3 production well; PROD4 production well. Detailed implementation manner

[0048] The detailed description and technical content of the present invention are described below in conjunction with the accompanying drawings. However, the accompanying drawings are only provided for reference and illustration, and are not used to limit the present invention.

[0049] Compared with the prior art, in the actual development process of waterflooding reservoirs, the present invention constructs a waterflooding matching degree index based on cosine similarity for the spatial adaptation relationship between the water injection volume and the movable oil reserve in each area of the reservoir; based on maximizing the displacement matching degree, a mathematical model for collaborative optimization of injection-production parameters in waterflooding reservoirs is established, and the optimal solution is obtained by combining intelligent optimization algorithms and numerical simulators, which better realizes the accurate production and injection allocation of the reservoir, effectively improves the balanced displacement condition of the waterflooding reservoir, and achieves the development goal of increasing oil production and reducing water cut.

[0050] Figures 1-10 It is a schematic flow of the method of an embodiment of the present invention and a schematic diagram of the optimization of a certain target reservoir by the method of this embodiment.

[0051] As Figure 1 shown, a method for collaborative optimization of injection-production parameters based on waterflooding matching degree in an embodiment of the present invention includes the following steps:

[0052] Step 1, generate a restart model of the target reservoir, and give the initial injection-production parameters and optimization control parameters of the wells to be optimized in the target reservoir;

[0053] Step 2, update the iteration number of the current optimization process, automatically generate a combination scheme of injection-production parameters, and call the restart model to simulate each combination scheme of injection-production parameters;

[0054] Step 3: Calculate the water flooding matching degree values corresponding to each injection-production parameter combination scheme, and determine the optimal injection-production parameter combination scheme at the current iteration number;

[0055] Step 4: Determine whether the current iteration number has reached the maximum iteration number;

[0056] Step 5: Output the optimal injection-production parameter scheme.

[0057] Step 1: Generate a restart model for the target reservoir, and specify the initial injection-production parameters and optimization control parameters for the wells to be optimized in the target reservoir. The specific steps are as follows:

[0058] Step 101: Organize the relevant data of the target reservoir and construct a numerical simulation model for the target reservoir; the relevant data of the target reservoir includes geological data and development data.

[0059] The geological data includes reservoir structure parameters, reservoir physical property parameters, and fluid physical property parameters; the reservoir structure parameters include top structure data; the reservoir physical property parameters include formation pressure, formation temperature, oil layer thickness, saturation, permeability, porosity; the fluid physical property parameters include oil phase viscosity, oil phase density, water phase viscosity, water phase density, relative permeability curve.

[0060] In the embodiment, a five-spot pattern of one injection and four production is adopted in the target reservoir. The development data includes the wellhead coordinates, well trajectories, and perforation intervals of the injection well INJEC1, the wellhead coordinates, well trajectories, and perforation intervals of the production wells PROD1, PROD2, PROD3, and PROD4. In other embodiments, the injection well and the production wells can each be one or more.

[0061] Step 102: Run the numerical simulation model of the target reservoir until the moment to be optimized, and generate a restart model for the target reservoir.

[0062] Step 103: Specify the initial injection-production parameters and optimization control parameters for the wells to be optimized in the target reservoir.

[0063] The initial injection-production parameters include: the daily injection volume I1 of the injection well INJEC1, the daily liquid production volume Q1 of the production well PROD1, the daily liquid production volume Q2 of the production well PROD2, the daily liquid production volume Q3 of the production well PROD3, and the daily liquid production volume Q4 of the production well PROD4.

[0064] The optimization control parameters include injection-production constraint conditions and initialization parameters of the optimization algorithm; the injection-production constraint conditions include: the daily allocated injection volume C I and the daily allocated liquid production volume C P, the upper limit I of the daily injection volume of injection well INJEC1 max , the lower limit I of the daily injection volume of injection well INJEC1 min , the upper limit of the bottom-hole pressure of injection well INJEC1 , the lower limit of the bottom-hole pressure of injection well INJEC1 ; the upper limit of the daily liquid production volume of each individual well of production wells PROD1 to PROD4 , the lower limit of the daily liquid production volume of each individual well of production wells PROD1 to PROD4 , production wells PROD1 to P ROD4's upper limit of bottom-hole pressure , the lower limit of the bottom-hole pressure of production wells PROD1 to PROD4 .

[0065] The optimization algorithms include, but are not limited to, genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA), and sparrow search algorithm (SSA).

[0066] The initialization parameters of the optimization algorithms include: population size pop, maximum number of iterations M, learning factor, and inertia weight. The population size pop usually takes values from 20 to 50. Initialize the iteration number m = 0. The learning factor is generally defaulted to 2.0; the inertia weight usually varies between 0.4 and 0.9.

[0067] Step 2, update the iteration number of the current optimization process, automatically generate injection-production parameter combination schemes, and call the restart model to simulate each injection-production parameter combination scheme. The specific steps are as follows:

[0068] Step 201, update the iteration number m of the current optimization process, let m = m + 1, and randomly generate pop injection-production parameter combination schemes using the optimization algorithm on the premise of meeting the injection-production constraint conditions according to the preset population size pop.

[0069] Step 202, submit the pop injection-production parameter combination schemes in the current iteration process to the reservoir numerical simulator, call the restart model of the target reservoir, and perform reservoir numerical simulation on each injection-production parameter combination scheme respectively to obtain the corresponding numerical simulation response results.

[0070] The Python module can be used to batch and automatically submit the pop injection-production parameter combination schemes in the current iteration process through the interface between Python and the reservoir numerical simulator.

[0071] The reservoir numerical simulator includes, but is not limited to, mainstream commercial reservoir simulation software such as Eclipse, CMG, tNavigator, etc., and can also be a self-developed reservoir simulation platform.

[0072] Step 203: According to the numerical simulation response results of the foregoing pop injection-production parameter combination schemes, extract the corresponding grid cell characteristic parameters from the numerical simulation response results.

[0073] The grid cell characteristic parameters are used for subsequent objective function evaluation, including reservoir physical property parameters and fluid physical property parameters, which are obtained by parsing the output files of the reservoir numerical simulator (such as the files with suffixes.UNRST,.GRID,.SMSPEC,.Fxxxx,.Xxxxx,.Sxxxx generated by Eclipse), or automatically batch processed, extracted, and structured by the Python interface. The reservoir physical property parameters include, but are not limited to, initial oil saturation, residual oil saturation, irreducible water saturation, water saturation, formation pressure, porosity, and comprehensive compressibility coefficient. The fluid physical property parameters include oil phase viscosity, crude oil volume coefficient, water phase viscosity, and relative permeability curve.

[0074] Step 3: Calculate the waterflood matching degree values corresponding to each injection-production parameter combination scheme, and determine the optimal injection-production parameter combination scheme at the current iteration. The specific steps are as follows:

[0075] Step 301: According to the grid cell characteristic parameters extracted from each numerical simulation result, calculate the movable oil reserves of each grid cell using the following formula:

[0076] (1)

[0077] In the formula: is the movable oil reserves of the grid cell, m 3 ; N is the geological reserves of the grid cell, m 3 ; S oi is the initial oil saturation of the grid cell; S or is the residual oil saturation.

[0078] Step 302: According to the water saturation, oil phase relative permeability, and water phase relative permeability data on the relative permeability curve, use the least squares method, nonlinear regression, or machine learning method to fit the data based on the following nonlinear relationship to obtain the fitting coefficients a and b :

[0079] (2)

[0080] In the formula: K ro is the oil phase relative permeability on the relative permeability curve; K rwis the relative permeability of the aqueous phase on the relative permeability curve; S w is the water saturation on the relative permeability curve; a , b is the fitting coefficient.

[0081] Step 303: According to the grid cell characteristic parameters of each numerical simulation result, use the following formula to calculate the cumulative water injection volume of the grid cell:

[0082] (3)

[0083] In the formula: W i is the stage cumulative water injection volume of the grid cell, m 3 ; V φ is the pore volume of the grid cell, m 3 ; μ o 、 μ w are the oil phase viscosity and water phase viscosity respectively, mPa·s; S wc is the irreducible water saturation, decimal; S w is the water saturation, decimal; B o is the oil formation volume factor, decimal; C t is the composite compressibility, decimal; ∆ P is the pressure difference, MPa; e is the base of the natural logarithm function, approximately 2.718281828459.

[0084] Step 304: Use the following formula to calculate the water flooding matching degree value of each injection-production parameter combination scheme:

[0085] (4)

[0086] where, simi is the water flooding matching degree value; , is the vector composed of the movable oil reserve values of each grid cell of the target reservoir model; , is the vector composed of the cumulative water injection volume values of each grid cell of the target reservoir model; n is the number of grid cells included in the target reservoir model; N rj represents the movable oil reserve corresponding to the j th grid cell of the target reservoir model, m 3 ; W ij represents the cumulative water injection volume corresponding to the j th grid cell of the target reservoir model, m3 ; N r ·W i represents the dot product of vectors N r and W i ; ||N r || represents the magnitude of vector N r , ; ||W i || represents the magnitude of vector W i , .

[0087] Step 305: Obtain the injection-production parameter combination plan with the largest water flooding matching degree value at the current iteration number m, and determine it as the optimal injection-production parameter combination plan at the current iteration number.

[0088] Step 4, determine whether the current iteration number reaches the maximum iteration number: If the current iteration number m reaches the preset maximum iteration number M, then go to Step 5; otherwise, go to Step 2.

[0089] Step 5, output the optimal injection-production parameter combination plan as the injection-production parameter combination plan with the largest water flooding matching degree value among all iteration numbers.

[0090] In the embodiment, the optimal injection-production parameter combination plan is output, including the injection volume I1 of the injection well INJEC1 and the liquid production volumes Q1, Q2, Q3, Q4 of the production wells PROD1 to PROD4.

[0091] As Figure 2 shown, the target reservoir adopts a five-spot well pattern with one injection well and four production wells, including one injection well INJEC1; four production wells PROD1, PROD2, PROD3, PROD4. The optimized range of the liquid volume of each production well is set to 0 - 80 m 3 / d, the total liquid production volume constraint is 200 m 3 / d. During the optimization process, injection-production balance is maintained, the optimization evaluation time is 10 years, and the particle swarm optimization algorithm is used to co-optimize the injection-production parameters of the reservoir model to achieve precise control of production allocation and injection allocation. Among them, the control parameters of the intelligent optimization algorithm in the target reservoir are preset with a population size of 25, a maximum iteration number of 200, a learning factor of 2, and an inertia weight of 0.5. As Figure 3 shown, the objective function gradually converges with the increase of the number of numerical simulations and stabilizes at about 260 times, indicating that the algorithm can achieve the optimization adjustment of injection-production parameters in a short time.

[0092] From Figure 4 the optimization results of the target reservoir, it can be seen that after 10 years of optimization, the cumulative oil production reaches 166.25×10 3 m 3, which has a significant improvement compared with the original scheme. At the same time, the optimized water cut curve is always lower than that before optimization, indicating that the optimization method effectively reduces the ineffective water cycle and improves the utilization efficiency of the injected water. From Figures 5-10 The comparison before and after optimization of the 2nd, 4th, and 6th sub-layers of the target reservoir shown, it can be seen that there are some un-displaced areas outside the production wells of the target reservoir before optimization. After optimization, the reservoir displacement is more uniform, indicating that the degree of balanced reservoir displacement has been effectively improved and the displacement matching degree has been significantly increased. The optimization results show that this method effectively realizes the accurate production and injection allocation of the reservoir, not only significantly improves the reservoir recovery factor, but also improves the production performance and injection-production efficiency, verifying the feasibility and practical value of the proposed optimization model.

[0093] The above-described embodiments are only preferred embodiments given to fully illustrate the present invention, and the protection scope of the present invention is not limited thereto. Equivalent substitutions or transformations made by those skilled in the art on the basis of the present invention are within the protection scope of the present invention.

Claims

1. A collaborative optimization method for injection-production parameters based on water flooding matching degree, characterized in that It includes the following steps: Step 1, generate a restart model of the target reservoir, and give the initial injection-production parameters and optimization control parameters of the wells to be optimized in the target reservoir; Step 2, update the iteration number of the current optimization process, automatically generate injection-production parameter combination schemes, and call the restart model to simulate each injection-production parameter combination scheme; Step 3, calculate the waterflooding matching degree values corresponding to each injection-production parameter combination scheme, and determine the optimal injection-production parameter combination scheme at the current iteration number; Step 4, judge whether the current iteration number reaches the maximum iteration number; Step 5, output the optimal injection-production parameter scheme; The said Step 1 includes: Step 101, sort out the relevant data of the target reservoir and construct a numerical simulation model of the target reservoir; Step 102, run the numerical simulation model of the target reservoir to the moment to be optimized, and generate a restart model of the target reservoir; Step 103, give the initial injection-production parameters and optimization control parameters of the wells to be optimized in the target reservoir; Among them, the relevant data of the target reservoir include geological data and development data; The geological data include reservoir structure parameters, reservoir physical property parameters and fluid physical property parameters; the reservoir structure parameters include top structure data; the reservoir physical property parameters include formation pressure, formation temperature, oil layer thickness, saturation, permeability, porosity; the fluid physical property parameters include oil-phase viscosity, oil-phase density, water-phase viscosity, water-phase density, relative permeability curve; The development data include: wellhead coordinates, well trajectories, perforation intervals of injection wells, wellhead coordinates, well trajectories, perforation intervals of production wells; The initial injection-production parameters include: daily injection volume of injection wells, daily liquid production volume of production wells; The optimization control parameters include injection-production constraint conditions and initialization parameters of the optimization algorithm; The injection-production constraint conditions include: daily allocated injection volume, daily allocated liquid production volume of the target reservoir; upper limit of daily injection volume of injection wells, lower limit of daily injection volume of injection wells, upper limit of bottom-hole pressure of injection wells, lower limit of bottom-hole pressure of injection wells; upper limit of daily liquid production volume of production wells, lower limit of daily liquid production volume of production wells, upper limit of bottom-hole pressure of production wells, lower limit of bottom-hole pressure of production wells; The initialization parameters of the optimization algorithm include: population size, maximum iteration number, learning factor and inertia weight, and the initialization iteration number m = 0; The said Step 3 includes: Step 301: According to the grid cell characteristic parameters extracted from each numerical simulation result, calculate the movable oil reserves of each grid cell by using the following formula: （1）， In Equation (1): is the movable oil reserves of the grid cell; N is the geological reserves of the grid cell; S oi is the initial oil saturation of the grid cell; S or is the residual oil saturation; Step 302: According to the water saturation, oil-phase relative permeability, and water-phase relative permeability data, use the least squares method, nonlinear regression, or machine learning method to perform fitting based on the following formula to obtain the fitting coefficients a and b : （2）， In Equation (2): K ro is the relative permeability of the oil phase; K rw is the relative permeability of the water phase; S w is the water saturation; a , b is the fitting coefficient; Step 303: According to the grid cell characteristic parameters of each numerical simulation result, calculate the cumulative injection volume of the grid cell by using the following formula: （3）， In Equation (3): W i is the cumulative water injection volume of the grid cell; V φ is the pore volume of the grid cell; μ o and μ w are the viscosities of the oil phase and the water phase, respectively; S wc is the irreducible water saturation; S w is the water saturation; B o is the formation volume factor of crude oil; C t is the composite compressibility; ∆ P is the pressure difference; e is the base of the natural logarithm function; Step 304: Calculate the waterflooding matching degree values of each injection-production parameter combination scheme by using the following formula: （4）， In formula (4): simi is the water flooding matching degree value; , which is a vector composed of the movable oil reserve values of each grid cell in the target reservoir model; , which is a vector composed of the cumulative water injection values of each grid cell in the target reservoir model; n is the number of grid cells included in the target reservoir model; N rj represents the movable oil reserve corresponding to the j th grid cell in the target reservoir model; W ij represents the cumulative water injection corresponding to the j th grid cell in the target reservoir model; N r ·W i represents the dot product of vectors N r and W i ; ||N r || represents the magnitude of vector N r , ; ||W i || represents the magnitude of vector W i , ; Step 305: Obtain the injection-production parameter combination scheme with the largest waterflooding matching degree value at the current iteration number m, and determine it as the optimal injection-production parameter combination scheme at the current iteration number.

2. The collaborative optimization method of injection-production parameters based on water flooding matching degree according to claim 1, characterized in that The value of the population size pop is taken as 20 to 50; the learning factor is set to 2.0; the inertia weight is set between 0.4 and 0.

9.

3. The collaborative optimization method of injection-production parameters based on water drive matching degree according to claim 1, characterized in that The optimization algorithm includes one or several of genetic algorithm, particle swarm algorithm, whale optimization algorithm, sparrow optimization algorithm.

4. The co - optimization method of injection - production parameters based on water - flooding matching degree according to any one of claims 1 to 3, characterized in that, Step 2 includes: Step 201: Update the iteration number m of the current optimization process, let m = m + 1. According to the preset population size pop, and on the premise of satisfying the injection-production constraint conditions, randomly generate pop injection-production parameter combination schemes by using an optimization algorithm; Step 202: Submit the pop injection-production parameter combination schemes in the current iteration process to the reservoir numerical simulator, call the restart model of the target reservoir, and perform reservoir numerical simulation on each injection-production parameter combination scheme respectively to obtain the corresponding numerical simulation response results; Step 203: Extract the corresponding grid cell characteristic parameters from the numerical simulation response results according to the numerical simulation response results of the pop injection-production parameter combination schemes.

5. The collaborative optimization method for injection-production parameters based on water flooding matching degree according to claim 4, wherein In Step 202, a Python module is used to batch automatically submit the pop injection-production parameter combination schemes in the current iteration process through the interface between Python and the reservoir numerical simulator.

6. The co - optimization method of injection - production parameters based on water - flooding matching degree according to claim 4, wherein The grid cell characteristic parameters are obtained by parsing the output file of the reservoir numerical simulator. The grid cell characteristic parameters include reservoir physical property parameters and fluid physical property parameters. The reservoir physical property parameters include: initial oil saturation, residual oil saturation, irreducible water saturation, water saturation, formation pressure, porosity, and comprehensive compressibility coefficient; the fluid physical property parameters include: oil phase viscosity, oil formation volume factor, water phase viscosity, and relative permeability curve.

7. The co - optimization method of injection - production parameters based on water - flooding matching degree according to claim 4, characterized in that In step 4, it is judged whether the current iteration number reaches the maximum iteration number: if the current iteration number m reaches the preset maximum iteration number, then go to step 5; Otherwise, go to Step 2; In Step 5, the optimal injection-production parameter combination scheme output is the injection-production parameter combination scheme with the largest water flooding matching degree value among all iteration times, including the injection volume of the injection well and the liquid production volume of the production well.

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