Oil reservoir injection-production parameter optimization method based on improved particle swarm optimization algorithm and related device

Abstract

The invention discloses an oil reservoir injection and production parameter optimization method based on an improved particle swarm optimization algorithm and a related device, and belongs to the technical field of oil reservoir injection and production. Firstly, a particle swarm is created according to preset parameters, the particle swarm is initialized, and particles are oil reservoir injection-production parameters; then based on the position of each particle in the particle population, calculating an individual optimal value pbest and a population global optimal value gbest of each particle through a preset objective function; calculating the position of the next iteration of each particle through the position of each particle, the individual optimal value pbest and the population global optimal value gbest; and S3, updating all particles in the population by adopting a particle swarm optimization algorithm to obtain a population of next iteration, returning to the second step until a preset iteration termination condition is met, and outputting the particles corresponding to the global optimal value gbest as optimal oil reservoir injection-production parameters. The convergence speed of the improved particle swarm optimization algorithm is greatly improved, and the solving accuracy of the improved particle swarm optimization algorithm is also effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil reservoir injection-production, and relates to an oil reservoir injection-production parameter optimization method and related device that improve the particle swarm optimization algorithm. Background Art

[0002] During the water injection development process, how to accurately determine the injection-production parameters of injection wells and production wells has always been a difficult problem. Because of the subtle changes in these parameters, it will have a great impact on the production of production wells, the water breakthrough time, and the ultimate recovery rate, etc. In reservoirs with strong heterogeneity, due to the uneven distribution of oil layers and large differences in reservoir permeability, etc., these conditions make the influence of injection-production parameters particularly prominent and cannot be ignored.

[0003] Since the optimization of the injection-production plan is a complex non-linear dynamic optimization problem affected by the interaction of multiple factors, currently, large-scale numerical simulation software is mainly used to determine the optimal injection-production parameters through simulation calculations. However, such numerical simulation software takes into account many complex factors, is complex to operate, and has poor optimization efficiency. Based on this, intelligent optimization algorithms have been applied, but the convergence speed and accuracy of the commonly used intelligent algorithms are still slightly insufficient at present, and it is difficult to fully meet the current requirements for injection-production development. Summary of the Invention

[0004] The purpose of the present invention is to solve the technical problem of insufficient convergence speed and accuracy of the existing algorithms, and provide an oil reservoir injection-production parameter optimization method and related device that improve the particle swarm optimization algorithm.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] In the first aspect, the present invention provides an oil reservoir injection-production parameter optimization method that improves the particle swarm optimization algorithm, including the following steps:

[0007] S1, create a particle population according to preset parameters and initialize the particle population, where the particles are oil reservoir injection-production parameters;

[0008] S2, based on the position of each particle in the particle population, calculate the individual optimal value pbest and the global optimal value gbest of the group of each particle through a preset objective function;

[0009] S3, calculate the position of the particle in the next iteration through the position of each particle, the individual optimal value pbest, and the global optimal value gbest of the group;

[0010] S4, use the particle swarm optimization algorithm to update all the particles in the population to obtain the population in the next iteration, return to S2, until the preset termination iteration condition is met, and output the particle corresponding to the global optimal value gbest as the optimal oil reservoir injection-production parameter.

[0011] In a second aspect, the present invention provides a reservoir injection-production parameter optimization system that improves the particle swarm optimization algorithm, including:

[0012] A population creation module, configured to create a particle population according to preset parameters and initialize the particle population, where the particles are reservoir injection-production parameters;

[0013] An optimal value calculation module, configured to calculate the individual optimal value pbest and the global optimal value gbest of each particle based on the position of each particle in the particle population through a preset objective function;

[0014] An iteration module, configured to calculate the position of the particle in the next iteration through the position of each particle, the individual optimal value pbest, and the global optimal value gbest of the population;

[0015] An output module, configured to update all the particles in the population by using the particle swarm optimization algorithm to obtain the population in the next iteration, return to S2, and until a preset termination iteration condition is met, output the particle corresponding to the global optimal value gbest as the optimal reservoir injection-production parameter.

[0016] In a third aspect, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The present invention discloses an improved particle swarm optimization algorithm for reservoir injection-production parameter optimization method and related device, which improves the particle swarm optimization algorithm and uses the cosine formula to improve the inertia weight parameter w tThe descending speed is then utilized, and based on the principle of simulated annealing, the particles are updated. Subsequently, the improved algorithm is applied to the optimization of reservoir injection-production parameters to achieve efficient control of reservoir injection-production parameters. The convergence speed of the improved particle swarm optimization algorithm of the present invention has been greatly improved, and its solution accuracy has also been effectively enhanced. This is because the simulated annealing algorithm is used to optimize the position of the current particle in each iteration process. As the number of particle iterations increases, the range selection of the random solution of the particle in simulated annealing decreases, which can greatly improve the development ability of the particle in the later stage, thereby improving the convergence accuracy of the improved particle swarm algorithm. The results show that overall, the algorithm of the present invention has a high convergence accuracy, and the curve adaptability can better balance the transition process from exploration to development. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can be obtained based on these drawings.

[0021] Figure 1 is a flowchart of the method of the present invention;

[0022] Figure 2 is a schematic diagram of the system of the present invention;

[0023] Figure 3 is an optimization flowchart of the algorithm of the present invention;

[0024] Figure 4 is a curve showing the variation of the parameter w of the optimization algorithm of the present invention with the number of iterations;

[0025] Figure 5 is a schematic diagram of the structure of the computer device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0027] Accordingly, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts fall within the scope of protection of the present invention.

[0028] It should be noted that like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it is not necessary to further define and explain it in subsequent drawings.

[0029] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper", "lower", "horizontal", "inner", etc. are used to indicate the orientation or positional relationship, it is based on the orientation or positional relationship shown in the drawings or the orientation or positional relationship in which the product of the invention is usually placed during use. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention. In addition, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0030] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but it can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but it can be slightly inclined.

[0031] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and limited, if terms such as "set", "installed", "connected", "coupled" are used, they should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0032] The present invention will be further described in detail below with reference to the accompanying drawings:

[0033] See Figure 1 , embodiments of the present invention disclose an optimized method and related device for reservoir injection-production parameters using an improved particle swarm optimization algorithm, including the following steps:

[0034] S1. Create a particle population according to preset parameters and initialize the particle population, where the particles are reservoir injection-production parameters;

[0035] S2. Calculate the individual optimal value pbest and the global optimal value gbest of each particle through a preset objective function based on the position of each particle in the particle population.

[0036] S3. Calculate the position of the particle in the next iteration through the position of each particle, the individual optimal value pbest, and the global optimal value gbest of the population.

[0037] S4. Use the particle swarm optimization algorithm to update all particles in the population to obtain the population in the next iteration, return to S2, and continue until the preset termination iteration condition is met. Then output the particle corresponding to the global optimal value gbest as the optimal reservoir injection-production parameter.

[0038] See Figure 3 In a feasible implementation manner of the present invention, the initialization of the particle population in S1 specifically includes: determining the particle population size n, dimension d, particle position x i t , particle initial velocity v i t , maximum inertia weight w max , minimum inertia weight w min , maximum particle velocity v max , minimum particle velocity v min and the number of iterations iter, and then update the particles to obtain the new positions and velocities of the particles.

[0039] In a feasible implementation manner of the present invention, S2 specifically includes:

[0040] S201. Calculate the objective function value of each particle according to the position of each particle in the particle population and the preset objective function.

[0041] S202. Determine the individual optimal value pbest of each particle in this iteration according to the comparison result between the objective function value of each particle and the objective function value of the corresponding individual optimal value pbest in the previous iteration.

[0042] S203. Solve the global optimal value gbest of the population according to the objective function value of each particle, and determine the global optimal value gbest of the population in this iteration according to the comparison result between the global optimal value gbest of the population and the global optimal value gbest in the previous iteration.

[0043] In a feasible embodiment of the present invention, the S202 specifically includes: respectively comparing the objective function value of each particle with the objective function value of the corresponding individual best value pbest in the previous iteration. If the objective function value of the particle is better than the objective function value of the corresponding individual best value pbest in the previous iteration, the current position of the particle is used as the corresponding individual best value pbest for this iteration; if the objective function value of the particle is not better than the objective function value of the corresponding individual best value pbest in the previous iteration, the corresponding individual best value pbest in the previous iteration is used as the corresponding individual best value pbest for this iteration.

[0044] In a feasible embodiment of the present invention, the S203 specifically includes: based on the objective function value of each particle, performing a quick sort on the population to find the global best value gbest of the population, comparing the global best value gbest of the population with the global best value gbest in the previous iteration. If the global best value gbest in the previous iteration is the optimal solution, the global best value gbest in the previous iteration is used as the global best value for this iteration. If the global best value gbest in the previous iteration is not the optimal solution, the global best value gbest for this iteration is saved as the global best value gbest.

[0045] In a feasible embodiment of the present invention, in the S4, the particle swarm optimization algorithm is used to update all the particles in the population, which specifically includes: first, using the cosine formula to improve the descent speed of the inertia weight parameter w t Then, using the principle of simulated annealing to update the particles, and judging whether the fitness value of the x i t +v i t+1 position becomes worse. If it becomes worse, use the principle of simulated annealing to make the particles update again; otherwise, the position x i t +v i t+1 of the particle to be updated is the new solution and no processing is done.

[0046] In a feasible embodiment of the present invention, using the cosine formula to improve the descent speed of the inertia weight parameter w t specifically includes: optimizing the particle swarm optimization algorithm using the cosine formula, setting an adaptive non-linear inertia weight descent function, accelerating the descent speed of the inertia weight in the middle stage of iteration, and slowing down the descent speed of the inertia weight in the early and late stages of iteration. The expression for parameter update is:

[0047]

[0048] where Iter_max is the maximum number of iterations; iter is the current number of iterations; w max and w min are the maximum and minimum values of the initial inertia weight, respectively;

[0049] Using the principle of simulated annealing to update the particles, specifically including:

[0050] (1) Determine the upper and lower bounds of the coordinates of the injection-production parameter variables, the maximum value w max of the initial inertia weight and the minimum value w min of the initial inertia weight, and randomly initialize the particle swarm parameters, including the particle position vector and the velocity vector;

[0051] (2) Use the existing optimal fitness function formula to calculate the fitness function value of each particle, determine the particle individual historical optimal value pbest and the population global optimal value gbest, and update the position and velocity of the particles;

[0052] (3) Calculate the fitness values of each particle;

[0053] (4) Compare the fitness value tmp of the current position of each particle with the fitness value pbest of its best historical position; if the fitness value tmp of the current position of the particle is better than the fitness value pbest of the historical position, then the fitness value tmp of the current position is used as the fitness value of the historical best position; if the fitness value tmp of the current position of the particle is better than the global optimal fitness value, then the new fitness value tmp is used as the global optimal fitness value, and the particle position of the global optimal fitness value is recorded;

[0054] (5) If the number of iterations reaches the upper limit, or a particle reaches the target value, then exit the loop; otherwise, jump to step (2) to start the iteration process again to find the optimal solution;

[0055] The fitness value of the particle is the fitness function of the reservoir exploitation effect.

[0056] See Figure 2 , this embodiment of the present invention discloses a reservoir injection-production parameter optimization system using an improved particle swarm optimization algorithm, including:

[0057] A population creation module for creating a particle population according to preset parameters and initializing the particle population, where the particles are reservoir injection-production parameters;

[0058] An optimal value calculation module for calculating the individual optimal value pbest and the population global optimal value gbest of each particle based on the position of each particle in the particle population through a preset objective function;

[0059] An iterative module, which is used to calculate the position of each particle in the next iteration based on the position of each particle, the individual optimal value pbest, and the global optimal value gbest of the population.

[0060] An output module, which is used to update all the particles in the population by using the particle swarm optimization algorithm to obtain the population in the next iteration, return to S2, and until the preset termination iteration condition is satisfied, output the particle corresponding to the global optimal value gbest as the optimal reservoir injection-production parameter.

[0061] See Figure 5 , an embodiment of the present invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the reservoir injection-production parameter optimization method such as the improved particle swarm optimization algorithm when executing the computer program.

[0062] The reservoir injection-production parameter optimization method of the improved particle swarm optimization algorithm includes the following steps:

[0063] S1, create a particle population according to preset parameters and initialize the particle population, where the particles are reservoir injection-production parameters;

[0064] S2, calculate the individual optimal value pbest and the global optimal value gbest of each particle through a preset objective function based on the position of each particle in the particle population;

[0065] S3, calculate the position of each particle in the next iteration through the position of each particle, the individual optimal value pbest, and the global optimal value gbest of the population;

[0066] S4, update all the particles in the population by using the particle swarm optimization algorithm to obtain the population in the next iteration, return to S2, and until the preset termination iteration condition is satisfied, output the particle corresponding to the global optimal value gbest as the optimal reservoir injection-production parameter.

[0067] An embodiment of the present invention discloses a computer-readable storage medium, the computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the reservoir injection-production parameter optimization method such as the improved particle swarm optimization algorithm when executed by a processor.

[0068] The reservoir injection-production parameter optimization method of the improved particle swarm optimization algorithm includes the following steps:

[0069] S1, create a particle population according to preset parameters and initialize the particle population, where the particles are reservoir injection-production parameters;

[0070] S2. Based on the positions of each particle in the particle population, calculate the individual optimal value pbest of each particle and the global optimal value gbest of the population through a preset objective function.

[0071] S3. Calculate the position of the particle in the next iteration through the position of each particle, the individual optimal value pbest, and the global optimal value gbest of the population.

[0072] S4. Use the particle swarm optimization algorithm to update all the particles in the population to obtain the population in the next iteration, and return to S2 until the preset termination iteration condition is met. Output the particle corresponding to the global optimal value gbest as the optimal reservoir injection-production parameter.

[0073] The working principle of the present invention is as follows:

[0074] Based on the comprehensive analysis of various intelligent algorithms, the present invention focuses on the particle swarm algorithm. Particle swarm optimization has an important feature: particles have memory, which makes the information sharing mechanism of the particle swarm optimization algorithm different from that of the genetic algorithm. During the process of the genetic algorithm, chromosomes share information with each other to find the optimal solution, so the movement of the entire population moves relatively evenly towards the optimal region. In the movement of the particle swarm optimization algorithm, only the globally optimal particle transmits information to other particles, and there is no information transmission between other particles at all. Each time a single particle is updated, it moves in the direction of the global optimal solution and is not affected by other particles outside the globally optimal. The particle swarm algorithm formulates simpler behavior rules for each particle compared to the genetic algorithm. In most optimization problems, particles can search for the optimal solution faster.

[0075] The particle swarm optimization algorithm is one of the commonly used algorithms for solving optimization problems, which imitates the way birds search for food in a cooperative manner. By abstracting each bird as a solution, the search space of the optimization problem is similar to the flight space of birds, and the flight process is the search process. Each particle represents a solution, and the fitness value can be calculated by substituting it into the fitness function. The fitness function is an index for judging the quality of individuals in the population. As the iteration progresses, the entire particle population, driven by the optimal solution, completes the search for the optimal solution in the decision space. This algorithm can be used to solve constrained optimization problems, and for target optimization problems, the particle swarm optimization algorithm can achieve good results.

[0076] After three different optimization methods are used for continuous gas injection and water injection through experiments, compared with the results of the standard particle swarm optimization method, the net present value of the improved particle swarm optimization algorithm has increased by 3.28%. As Figure 4 shown, the curve of the parameter w of the particle swarm optimization algorithm of the present invention changing with the number of iterations has good adaptability to different models.

[0077] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0078] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0079] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still can modify the specific implementation manners of the present invention or make equivalent substitutions. Any modification or equivalent substitution that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. An optimization method for reservoir injection-production parameters by improving the particle swarm optimization algorithm, characterized in that, It includes the following steps: S1. Create a particle population according to preset parameters and initialize the particle population, where the particles are reservoir injection-production parameters; S2. Based on the positions of each particle in the particle population, calculate the individual optimal value pbest and the global optimal value gbest of the population for each particle through a preset objective function; S3. Calculate the position of the particle in the next iteration through the position of each particle, the individual optimal value pbest, and the global optimal value gbest of the population; S4. Use the particle swarm optimization algorithm to update all particles in the population to obtain the population in the next iteration, return to S2 until the preset termination iteration condition is met, and output the particle corresponding to the global optimal value gbest as the optimal reservoir injection-production parameter.

2. An optimization method for reservoir injection-production parameters by improving the particle swarm optimization algorithm according to claim 1, characterized in that, The initialization of the particle swarm in S1 specifically includes: determining the particle swarm size n, dimension d, particle position x i t , the initial particle velocity v i t , the maximum inertia weight w max , the minimum inertia weight w min , the maximum particle velocity v max , the minimum particle velocity v min and the number of iterations iter, and then updating the particles to obtain the new positions and velocities of the particles.

3. An optimization method for reservoir injection-production parameters by improving the particle swarm optimization algorithm according to claim 1, characterized in that The specific content of S2 includes: S201. Calculate the objective function value of each particle according to the position of each particle in the particle population and the preset objective function; S202. Determine the individual optimal value pbest of each particle in this iteration according to the comparison result between the objective function value of each particle and the objective function value of the corresponding individual optimal value pbest in the previous iteration; S203. Solve the global optimal value gbest of the population according to the objective function value of each particle, and determine the global optimal value gbest of the population in this iteration according to the comparison result between the global optimal value gbest of the population and the global optimal value gbest in the previous iteration.

4. An optimization method for reservoir injection-production parameters by improving the particle swarm optimization algorithm according to claim 3, characterized in that The specific content of S202 includes: Compare the objective function value of each particle with the objective function value of the corresponding individual optimal value pbest in the previous iteration respectively. If the objective function value of the particle is better than the objective function value of the corresponding individual optimal value pbest in the previous iteration, take the current position of the particle as the individual optimal value pbest corresponding to this iteration; if the objective function value of the particle is not better than the objective function value of the corresponding individual optimal value pbest in the previous iteration, then take the individual optimal value pbest corresponding to the previous iteration as the individual optimal value pbest corresponding to this iteration.

5. An optimization method for reservoir injection-production parameters by improving the particle swarm optimization algorithm according to claim 4, characterized in that, The specific content of S203 includes: Based on the objective function value of each particle, perform a quick sort on the population to find the global optimal value gbest of the population, and compare the global optimal value gbest of the population with the global optimal value gbest in the previous iteration. If the global optimal value gbest in the previous iteration is the optimal solution, take the global optimal value gbest in the previous iteration as the global optimal value of the population in this iteration; if the global optimal value gbest in the previous iteration is not the optimal solution, then save the global optimal value gbest of the population in this iteration as the global optimal value gbest.

6. An optimization method for reservoir injection-production parameters by improving the particle swarm optimization algorithm according to claim 1, characterized in that In S4, the particle swarm optimization algorithm is used to update all particles in the population, specifically including: first, the cosine formula is used to improve the inertia weight parameter w t 's descent speed, and then, based on the principle of simulated annealing, the particles are updated. It is judged whether the fitness value of the position of x i t +v i t+1 becomes worse. If it becomes worse, the particles are updated again based on the principle of simulated annealing; otherwise, the position of the particle to be updated x i t +v i t+1 is the new solution and no processing is done.

7. An optimization method for reservoir injection-production parameters by improving the particle swarm optimization algorithm according to claim 6, characterized in that, Improving the descent speed of the inertia weight parameter w using the cosine formula t Specifically, it includes: optimizing the particle swarm optimization algorithm using the cosine formula, setting an adaptive non-linear inertia weight descent function to accelerate the descent speed of the inertia weight in the middle stage of iteration and slow down the descent speed of the inertia weight in the early and late stages of iteration. The expression is as follows: Among them, Iter_max is the maximum number of iterations; iter is the current number of iterations; w max and w min are the maximum and minimum values of the initial inertia weight, respectively; Using the principle of simulated annealing to update the particles specifically includes: (1) Determine the upper and lower bounds of the coordinates of the injection-production parameter variables, the maximum initial inertia weight w max and the minimum initial inertia weight w min , and randomly initialize the particle swarm parameters, including the particle position vector and velocity vector; (2) Use the existing optimal fitness function formula to calculate the fitness function value of each particle, determine the individual historical optimal value pbest and the global optimal value gbest of the population, and update the position and velocity of the particles; (3) Calculate the fitness values of each particle; (4) Compare the fitness value tmp of each particle's current position with the fitness value pbest of its best historical position; if the fitness value tmp of the particle's current position is better than the fitness value pbest of the historical position, then the fitness value tmp of the current position is used as the fitness value of the historical best position; if the fitness value tmp of the particle's current position is better than the global optimal fitness value, then the new fitness value tmp is used as the global optimal fitness value, and record the particle position corresponding to the global optimal fitness value. (5) If the number of iterations reaches the upper limit, or a particle reaches the target value, then exit the loop; otherwise, jump to step (2) to restart the iteration process to find the optimal solution. The fitness value of the particle is the fitness function of the oil reservoir production effect.

8. An optimized system for reservoir injection and production parameters of an improved particle swarm optimization algorithm, characterized in that It includes: A population creation module, which is used to create a particle population according to preset parameters and initialize the particle population, where the particles are oil reservoir injection-production parameters. An optimal value calculation module, which is used to calculate the individual optimal value pbest and the population global optimal value gbest of each particle based on the position of each particle in the particle population through a preset objective function. An iteration module, which is used to calculate the position of the particle in the next iteration through the position of each particle, the individual optimal value pbest, and the population global optimal value gbest. An output module, which is used to update all the particles in the population using the particle swarm optimization algorithm to obtain the population in the next iteration, return to S2, until the preset termination iteration condition is met, and output the particle corresponding to the global optimal value gbest as the optimal oil reservoir injection-production parameter.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-7.

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