Oil reservoir production optimization method and system based on expensive optimization

Abstract

The invention relates to an oil reservoir production optimization method and system based on expensive optimization, and the method comprises the steps: obtaining a plurality of oil reservoir production parameters of a target oil field, and constructing an expensive optimization problem model according to the plurality of oil reservoir production parameters; the oil reservoir production parameters comprise water injection cost, oil price and water removal cost; determining a test function and test parameters of the expensive optimization problem model; on the basis of the multiple oil reservoir production parameters, performing multiple iterations on an expensive optimization problem model through an agent model and a grey prediction evolutionary algorithm until the number of iterations reaches a preset value, and obtaining an optimal solution of the expensive optimization problem model; and simulating the expensive optimization problem model based on a test function and a test parameter, and verifying the effectiveness of the expensive optimization problem model. According to the method, the proxy model and the grey prediction evolutionary algorithm are applied to the expensive optimization problem, and the optimization effect and the convergence speed of the proxy auxiliary evolutionary algorithm in solving the expensive optimization problem are further improved.

Description

Technical Field

[0001] The invention belongs to the technical field of oil reservoir production and intelligent algorithm, and in particular relates to an oil reservoir production optimization method and system based on expensive optimization. Background Art

[0002] Reservoir production optimization regards the reservoir production system as a complex system, and uses mathematical methods, numerical simulation technology and optimization algorithms to regulate various parameters in the production process (such as water injection volume, gas injection volume, well location layout, production speed, etc.) to seek the optimal production plan.

[0003] In the field of engineering applications, many optimization problems require expensive calculations of objective functions, indicators, and constraints, which are often referred to as expensive optimization problems. With the continuous progress of society and the emerging challenges brought by smart cities, the Internet of Things, and the era of big data, more efficient solutions to expensive optimization problems are becoming a necessary condition for promoting the prosperity of various fields.

[0004] At present, agent-assisted evolutionary algorithm has become the mainstream technology for solving expensive optimization problems. Its basic idea is to use agent models instead of expensive functions for evaluation, define pre-screening strategies, and select individuals that need to be evaluated with expensive functions from the search population of the evolutionary algorithm, thereby reducing the computational cost. Commonly used agent models include Kriging model, polynomial regression surface (PRS), radial basis function (RBF) and support vector regression (SVR).

[0005] Although existing agent-assisted evolutionary algorithms show superior performance in solving expensive optimization problems, for high-dimensional expensive optimization problems, the balance between the convergence speed and optimization effect of agent-assisted evolutionary algorithms needs to be further enhanced.

[0006] Disadvantages of existing technology: (1) Traditional evolutionary algorithms search the population by performing mutation and crossover between current individuals and historical individuals. The proxy model affects the update of the next generation of population by providing predicted fitness values ​​for offspring. When the proxy model cannot provide accurate prediction values, it may mislead the population search.

[0007] (2) Existing agent-assisted evolution algorithms are overly dependent on agent models. Most algorithms still require a lot of computing resources to obtain sufficient sample data to build a high-precision agent model before obtaining a high-quality solution. Summary of the invention

[0008] In order to improve the accuracy and convergence speed of the expensive optimization model in reservoir production optimization, a first aspect of the present invention provides a reservoir production optimization method based on expensive optimization, including: obtaining multiple reservoir production parameters of the target oil field, and constructing an expensive optimization problem model according to the multiple reservoir production parameters; the reservoir production parameters include water injection cost, oil price and water removal cost; determining the test function and test parameters of the expensive optimization problem model; based on the multiple reservoir production parameters, iterating the expensive optimization problem model multiple times through the proxy model and the grey prediction evolutionary algorithm until the number of iterations reaches a preset value, and obtaining the optimal solution of the expensive optimization problem model; simulating the expensive optimization problem model based on the test function and the test parameters to verify the effectiveness of the expensive optimization problem model.

[0009] In some embodiments of the present invention, determining the test function and test parameters of the expensive optimization problem model includes: using multiple benchmark functions as test functions; determining population parameters, sample parameters, problem dimension and maximum evaluation times of each test function.

[0010] In some embodiments of the present invention, the expensive optimization problem model is iterated multiple times by the proxy model and the grey prediction evolutionary algorithm based on the multiple reservoir production parameters until the number of iterations reaches a preset value to obtain the optimal solution of the expensive optimization problem model, including: randomly generating the first three generations of populations in the feasible region of the expensive optimization problem model based on the multiple reservoir production parameters; performing a global search in the search space based on the first three generations of populations; adding the individual with the best fitness value to the sample set to be selected to participate in the next iteration; from the sample set to be selected, selecting multiple samples with the best fitness value through the proxy model to generate multiple offspring individuals; performing a local search among the multiple offspring individuals to screen out the individual with the best fitness value; and performing a global search and a local search alternately based on the optimal function value to screen out the individual with the best fitness value until the number of iterations reaches a preset value.

[0011] Furthermore, the randomly generating the first three generations of populations in the feasible region of the expensive optimization problem model based on the multiple reservoir production parameters includes: generating the first generation population using random number distribution in the search space; and generating the second generation population and the third generation population through a heuristic algorithm based on the first generation population.

[0012] Furthermore, the global search in the search space based on the first three generations of populations through a global proxy model and a grey prediction evolutionary algorithm includes: generating an initial sample through Latin hypercube sampling; constructing a global radial basis function proxy model to predict the fitness value of the population; generating offspring through a grey prediction evolutionary algorithm, comparing the generated offspring with the predicted fitness value of the parent generation, and retaining the individuals with the best predicted fitness value in the offspring.

[0013] In the above-mentioned embodiment, constructing an expensive optimization problem model according to the multiple reservoir production parameters includes: constructing an objective function based on multiple reservoir production parameters; determining multiple constraint parameters of the expensive optimization problem model based on a synthetic reservoir model and multiple reservoir production parameters; and constructing an expensive optimization problem model according to the objective function and multiple constraint parameters.

[0014] According to a second aspect of the present invention, there is provided a reservoir production optimization system based on expensive optimization, comprising: an acquisition module for acquiring a plurality of reservoir production parameters of a target oil field, and constructing an expensive optimization problem model according to the plurality of reservoir production parameters; the reservoir production parameters comprising water injection cost, oil price and water removal cost; a determination module for determining a test function and test parameters of the expensive optimization problem model; an iteration module for iterating the expensive optimization problem model multiple times through an agent model and a grey prediction evolutionary algorithm based on the plurality of reservoir production parameters until the number of iterations reaches a preset value, thereby obtaining an optimal solution to the expensive optimization problem model; and a verification module for simulating the expensive optimization problem model based on the test function and the test parameters, and verifying the validity of the expensive optimization problem model.

[0015] The third aspect of the present invention provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the reservoir production optimization method based on expensive optimization provided in the first aspect of the present invention.

[0016] A fourth aspect of the present invention provides a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for optimizing reservoir production based on costly optimization provided in the first aspect of the present invention is implemented.

[0017] The beneficial effects of the present invention are: (1) The present invention uses the grey prediction evolutionary algorithm to perform global search to find the optimal value with the assistance of the proxy model. It inherits the advantages of the grey prediction evolutionary algorithm in that it converges quickly and requires fewer parameters, thus reducing its technical complexity. (2) The present invention optimizes the method of finding the optimal value by alternating between global search and local search during the iteration process, thereby reducing the computational cost and improving the computational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A basic flow chart of a method for optimizing oil reservoir production based on costly optimization in some embodiments of the present invention; Figure 2It is a schematic diagram of the principle of the oil reservoir production optimization method based on costly optimization in some embodiments of the present invention; Figure 3 It is a schematic structural diagram of an oil reservoir production optimization system based on costly optimization in some embodiments of the present invention; Figure 4 It is a schematic diagram of the structure of an electronic device in some embodiments of the present invention. DETAILED DESCRIPTION

[0019] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0020] refer to Figure 1 and Figure 2 In a first aspect of the present invention, a method for optimizing reservoir production based on expensive optimization is provided, comprising: S100. Acquire multiple reservoir production parameters of a target oil field, and construct an expensive optimization problem model according to the multiple reservoir production parameters; the reservoir production parameters include water injection cost, oil price and water removal cost; S200. Determine a test function and test parameters of the expensive optimization problem model; S300. Based on the multiple reservoir production parameters, perform multiple iterations on the expensive optimization problem model through an agent model and a grey prediction evolutionary algorithm until the number of iterations reaches a preset value, and obtain an optimal solution to the expensive optimization problem model; S400. Simulate the expensive optimization problem model based on the test function and the test parameters to verify the effectiveness of the expensive optimization problem model.

[0021] In step S100 of some embodiments of the present invention, constructing a costly optimization problem model according to the plurality of reservoir production parameters comprises: S101. constructing an objective function based on multiple reservoir production parameters; Specifically, the calculation formula for economic benefits during water drive reservoir production is as follows: , Where J is the decision variable x and the state variable The objective function can be calculated by a numerical simulator . is the time step, , and are the oil price, water removal cost, and water injection cost, all in USD / bbl. , and In time step The oil production rate of all production wells at the time step The water production rate of all production wells and the water production rate of all production wells in the time step The injection flow rates for all injection wells are in barrels per day. represents the annual discount rate, Indicates The elapsed time of a time step in years.

[0022] A synthetic reservoir model, Egg Model, was used for simulation experiments. Random sampling is to randomly select 200 samples in the designed water injection interval, the population size of GPE is 200, and the maximum number of iterations is t max The initial value of is set to 1000. The other parameters used in the present invention are set according to the parameters generally given in the existing literature. In order to reduce the error caused by randomness, each group of experiments is independently run 30 times. The simulation experiment sets the water injection cost to be 5 US dollars per barrel, the oil price to be 80 US dollars per barrel, and the water removal cost to be 19 US dollars per barrel.

[0023] S102. Determine multiple constraint parameters of the expensive optimization problem model based on the synthetic reservoir model and multiple reservoir production parameters; S103. Construct an expensive optimization problem model based on the objective function and multiple constraint parameters.

[0024] Based on the economic benefits generated during the production of the above water-driven reservoir as the objective function and the synthetic reservoir model as the constraint parameter, an expensive optimization problem model is constructed.

[0025] In step S200 of some embodiments of the present invention, determining the test function and test parameters of the expensive optimization problem model includes: S201. Using multiple benchmark functions as test functions; Specifically, 12 benchmark functions are used to test the proposed method. Among them, Ellipsoid, Rosenbrock, Ackley, Griewank, and Rastrigin are commonly used benchmark functions. Three very complex multimodal functions are selected from the CEC2005 test set, and four complex benchmark functions are selected from the CEC2017 test set. These test functions are widely used in the performance testing of agent-assisted evolutionary algorithms.

[0026] S202. Determine the population parameters, sample parameters, problem dimension and maximum number of evaluations for each test function. Specifically, the parameters used include the size of the population, the initial sample size, and the optimal sample size used to construct the local proxy model. , are set to twice the dimension of the problem, and the maximum number of evaluations is set to 1000. Other parameters used in the present invention are set according to the parameters generally given in existing literature. In order to reduce the error caused by randomness, each group of experiments is run independently 30 times.

[0027] In step S300 of some embodiments of the present invention, the expensive optimization problem model is iterated multiple times by using a proxy model and a grey prediction evolutionary algorithm based on the multiple reservoir production parameters until the number of iterations reaches a preset value, and obtaining the optimal solution of the expensive optimization problem model includes: S301. Based on the multiple reservoir production parameters, randomly generate the first three generations of populations in the feasible region of the expensive optimization problem model; Specifically, during the initialization process, three generations of populations must be randomly generated in the feasible region. t The first generation of the population i The individual j Wei Jizuo , can be generated by the following formula: , in D It is the dimension of the problem; and They are j The upper and lower bounds of the dimension; represents a random number generated by a uniform distribution in the range [0,1]. , , ( t≥ 2) Perform boundary processing.

[0028] S302. Based on the first three generations of populations, a global search is performed in the search space through a global proxy model and a grey prediction evolutionary algorithm; individuals with the best fitness values ​​are added to the sample set to be selected to participate in the next iteration; Specifically, the global proxy model focuses on exploring the optimal solution in the entire search space, using samples in the database to train the global RBF proxy model to evaluate the offspring to obtain the predicted fitness value. The function expression of RBF is: , in and Represents n input points in n-dimensional space and their corresponding output values ​​in output space. represents the function value calculated by the RBF interpolation function, represents the weight coefficient, is the kernel function. This method uses a cubic function as a kernel function. For c and The Euclidean distance between . Represents a polynomial.

[0029] The reproduction operation of the grey prediction evolutionary algorithm regards the population sequence as a time series, converts the three disordered original data into a sequence with approximate exponential function properties, and predicts the offspring by constructing an exponential function. , , ( t ≥ 2) Considered as a population sequence. , , ( t≥ 2) Randomly select three individuals , and .make , , , , Assuming that the absolute value of the difference between the maximum and minimum values ​​of the three individuals is less than the difference threshold set by the grey prediction evolutionary algorithm, random perturbation is used to generate new individuals; if the absolute value of the difference between two random values ​​of the three selected individuals is less than the difference threshold, linear fitting is used to generate new individuals, otherwise, the even grey model is used to generate new individuals.

[0030] Test subjects It can be generated by the following formula: , in As a parameter to control the predicted differential threshold, the present invention is set to . , and They are grey development coefficient, grey control parameter and disturbance coefficient. , and The formula is as follows: , Each individual in the generated experimental population must be subject to constraint processing. And the experimental individuals that meet the constraints Perform a prediction fitness evaluation to obtain the prediction fitness value .

[0031] Next, by comparing the target individual and experimental individuals The predicted fitness value of , retaining the better individuals to enter the next generation, the selection operation can be performed by the following formula: ; Then, use the expensive function f The individual with the best predicted fitness value in the experimental population Perform a true evaluation to obtain the objective function value , and the assessed individuals are added to the database.

[0032] S303. From the sample set to be selected, multiple samples with the best fitness value are selected through the proxy model to generate multiple offspring individuals; local search is performed from the multiple offspring individuals to screen out the individual with the best fitness value; Specifically, the local proxy model can improve the search speed within promising areas. The local RBF proxy model is trained to predict the true function value of the offspring. Similarly, the local proxy model uses the cubic function As the kernel function of the RBF model. The even gray model in the gray pre-evolutionary algorithm is used to generate offspring, and the best individuals in the offspring are selected for real evaluation and added to the database. The method is the same as the global agent model. When the maximum number of iterations is reached, the algorithm ends and the optimal solution is output.

[0033] S304. Based on the optimal function value, perform global search and local search alternately to select individuals with the best fitness value until the number of iterations reaches a preset value.

[0034] In step S400 of some embodiments of the present invention, the expensive optimization problem model is simulated based on the test function and the test parameters to verify the effectiveness of the expensive optimization problem model. Specifically, the performance of the model on 12 benchmark functions is tested. The results show that the algorithm of the present invention is generally better than other algorithms, verifying that the solution obtained by the present invention is better, and showing the feasibility, effectiveness and superiority of the present invention.

[0035] Example 2 refer to Figure 3 According to a second aspect of the present invention, a reservoir production optimization system 1 based on expensive optimization is provided, comprising: an acquisition module 11, used to acquire multiple reservoir production parameters of a target oil field, and construct an expensive optimization problem model according to the multiple reservoir production parameters; the reservoir production parameters include water injection cost, oil price and water removal cost; a determination module 12, used to determine a test function and test parameters of the expensive optimization problem model; an iteration module 13, used to iterate the expensive optimization problem model multiple times through an agent model and a grey prediction evolutionary algorithm based on the multiple reservoir production parameters until the number of iterations reaches a preset value, so as to obtain an optimal solution to the expensive optimization problem model; a verification module 14, used to simulate the expensive optimization problem model based on the test function and the test parameters, and verify the validity of the expensive optimization problem model.

[0036] Furthermore, the iteration module 13 includes: a generation unit, which is used to randomly generate the first three generations of populations in the feasible area of ​​the expensive optimization problem model based on the multiple reservoir production parameters; a global unit, which is used to perform a global search in the search space based on the first three generations of populations through a global proxy model and a grey prediction evolutionary algorithm; adding the individual with the best fitness value to the sample set to be selected to participate in the next iteration; a local unit, which is used to select multiple samples with the best fitness value from the sample set to be selected through a proxy model to generate multiple offspring individuals; performing a local search from the multiple offspring individuals to screen out the individual with the best fitness value; an iteration unit, which is used to perform a global search and a local search alternately based on the optimal function value to screen out the individual with the best fitness value until the number of iterations reaches a preset value.

[0037] Example 3 refer to Figure 4 According to the third aspect of the present invention, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the reservoir production optimization method based on expensive optimization in the first aspect of the present invention.

[0038] The electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0039] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 4 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0040] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, the above functions defined in the method of the embodiment of the present disclosure are executed. It should be noted that the computer-readable medium described in the embodiment of the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In an embodiment of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, an apparatus, or a device. In an embodiment of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, an apparatus, or a device. The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wire, optical cable, RF (radio frequency), etc., or any suitable combination of the foregoing.

[0041] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device. The computer-readable medium carries one or more computer programs. When the one or more programs are executed by the electronic device, the electronic device: Computer program code for performing the operations of embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, Python, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as a separate software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0042] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0043] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing oil reservoir production based on costly optimization, characterized in that: include: Acquire multiple reservoir production parameters of the target oil field, and construct an expensive optimization problem model according to the multiple reservoir production parameters; the reservoir production parameters include water injection cost, oil price and water removal cost; Determining a test function and test parameters of the expensive optimization problem model; Based on the multiple reservoir production parameters, the expensive optimization problem model is iterated multiple times by using a proxy model and a grey prediction evolutionary algorithm until the number of iterations reaches a preset value, thereby obtaining an optimal solution to the expensive optimization problem model; The expensive optimization problem model is simulated based on the test function and the test parameters to verify the validity of the expensive optimization problem model.

2. The method for optimizing oil reservoir production based on costly optimization according to claim 1, characterized in that: The test function and test parameters for determining the expensive optimization problem model include: Use multiple benchmark functions as test functions; Determine the population parameter, sample parameter, problem dimension, and maximum number of evaluations for each test function.

3. The method for optimizing oil reservoir production based on costly optimization according to claim 1, characterized in that: The expensive optimization problem model is iterated multiple times based on the multiple reservoir production parameters by using the proxy model and the grey prediction evolutionary algorithm until the number of iterations reaches a preset value, and obtaining the optimal solution of the expensive optimization problem model includes: Based on the plurality of reservoir production parameters, randomly generating first three generations of populations within a feasible region of the expensive optimization problem model; Based on the first three generations of populations, a global search is performed in the search space through a global proxy model and a grey prediction evolutionary algorithm; individuals with the best fitness value are added to the sample set to be selected to participate in the next iteration; From the candidate sample set, multiple samples with the best fitness values ​​are selected through the proxy model to generate multiple offspring individuals; local search is performed among the multiple offspring individuals to screen out the individual with the best fitness value; Based on the optimal function value, global search and local search are performed alternately to screen the individuals with the best fitness value until the number of iterations reaches the preset value.

4. The method for optimizing oil reservoir production based on costly optimization according to claim 3, characterized in that: The randomly generating the first three generations of populations in the feasible region of the expensive optimization problem model based on the multiple reservoir production parameters comprises: Generate the first generation population using random number distribution in the search space; Based on the first generation population, the second generation population and the third generation population are generated through a heuristic algorithm.

5. The method for optimizing oil reservoir production based on costly optimization according to claim 3, characterized in that: The global search in the search space based on the first three generations of populations by using a global proxy model and a grey prediction evolutionary algorithm includes: Generate the initial sample through Latin hypercube sampling; Construct a global radial basis function surrogate model to predict the fitness value of the population; The offspring are generated by the grey prediction evolutionary algorithm, the predicted fitness values ​​of the generated offspring are compared with those of the parent generation, and the individuals with the best predicted fitness values ​​among the offspring are retained.

6. The method for optimizing oil reservoir production based on costly optimization according to claim 1, characterized in that: The constructing of the expensive optimization problem model according to the plurality of reservoir production parameters comprises: Constructing an objective function based on multiple reservoir production parameters; determining multiple constraint parameters of an expensive optimization problem model based on a synthetic reservoir model and multiple reservoir production parameters; Construct a model of an expensive optimization problem based on an objective function and multiple constraints.

7. A reservoir production optimization system based on costly optimization, characterized in that: include: An acquisition module is used to acquire multiple reservoir production parameters of a target oil field and construct an expensive optimization problem model according to the multiple reservoir production parameters; the reservoir production parameters include water injection cost, oil price and water removal cost; A determination module, used to determine a test function and test parameters of the expensive optimization problem model; An iteration module, used to perform multiple iterations on the expensive optimization problem model based on the multiple reservoir production parameters through a proxy model and a grey prediction evolutionary algorithm until the number of iterations reaches a preset value, thereby obtaining an optimal solution to the expensive optimization problem model; The verification module is used to simulate the expensive optimization problem model based on the test function and the test parameters to verify the validity of the expensive optimization problem model.

8. The reservoir production optimization system based on costly optimization according to claim 7, characterized in that: The iteration module includes: A generation unit, configured to randomly generate first three generations of populations within a feasible region of the expensive optimization problem model based on the plurality of reservoir production parameters; A global unit is used to perform a global search in the search space based on the first three generations of populations through a global agent model and a grey prediction evolutionary algorithm; and add individuals with the best fitness value to a sample set to be selected so as to participate in the next iteration; The local unit is used to select multiple samples with the best fitness value from the candidate sample set through the proxy model to generate multiple offspring individuals; perform local search among the multiple offspring individuals to screen out the individual with the best fitness value; The iteration unit is used to alternately perform global search and local search based on the optimal function value to screen the individual with the best fitness value until the number of iterations reaches a preset value.

9. An electronic device, comprising: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the reservoir production optimization method based on costly optimization as described in any one of claims 1 to 6.

10. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method for optimizing oil reservoir production based on costly optimization according to any one of claims 1 to 6 is implemented.

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