A step-by-step optimization method for injection and production parameters in gas-driven oil reservoirs

Abstract

The invention discloses a step-by-step optimization method for injection and production parameters of a gas-driven oil reservoir, comprising the following steps: the first step is to determine the daily liquid production rate of each oil well and the daily gas injection rate of the gas injection well based on an intelligent optimization algorithm and black oil numerical simulation, with the maximum recovery rate as the objective function; the second step is to establish an optimization mathematical model with the maximum economic net present value (NPV) as the objective function based on the liquid volume of each well determined in step one as the initial value, and to re-optimize the liquid volume of each well in the oil reservoir based on the intelligent optimization algorithm and the optimal injection and production parameters obtained in the first step by using component numerical simulation, so as to obtain the maximum NPV optimal gas injection rate and the daily liquid production rate of each production well. The invention can make full use of a small amount of conventional experimental test data, greatly shorten the time consumed by numerical simulation, realize simple, fast and accurate determination of gas-driven injection and production parameters, significantly improve the optimization time of gas-driven injection and production parameters, and reduce related economic costs.

Description

Technical Field

[0001] The invention relates to the technical field of oilfield gas injection, and in particular to a step-by-step optimization method for injection and production parameters of a gas-driven oil reservoir. Background Art

[0002] Gas injection development has now developed into a very important technology for improving oil recovery, which has been widely used at home and abroad and has achieved good development results. Studies have shown that gas injection development is of great significance for improving the development effects of different types of oil reservoirs such as high water content, low permeability, and shale oil. In recent years, after large-scale gas injection tests in multiple oil fields, my country has made major breakthroughs in gas injection development. Gas injection to improve oil recovery has gradually formed a complete set of development theories, supporting technologies and operation models. Gas injection to improve oil recovery has shown to be one of the most promising technologies. Injection and production parameters are important factors affecting the effect of gas drive, but currently the main method is to compare and select artificial design schemes, and pay more attention to injection parameters while ignoring the working system of production wells, resulting in poor synergy between injection and production. In addition, most of the current optimization methods have the problems of long time consumption and complex processes. Therefore, how to construct a set of efficient gas drive injection and production parameter fast methods is of great significance to improving the effect of gas drive development.

[0003] At present, the optimization methods of gas drive injection and production parameters mainly include empirical methods, indoor experimental methods and numerical simulation methods. However, these methods all have their own limitations. The empirical method can only determine the injection and production parameters qualitatively, with large errors and strong subjectivity, and has poor adaptability and generalization for reservoirs with different geological characteristics; although the current indoor experimental method can intuitively observe the seepage law during the gas drive development process, it is difficult to simulate the real reservoir development process due to the limitation of experimental equipment. The main steps of the core drive experiment include core preparation, water drive, gas drive and other processes. Among them, the gas drive process is complicated and has high requirements for experimental equipment. In addition, for flammable and explosive dangerous gases, the experimental process has great safety hazards. It can be seen that the core drive experiment process is extremely cumbersome and complex, time-consuming and high experimental cost. In addition, since core drive is a one-dimensional displacement process, the gas drive recovery rate obtained by the long core drive experiment essentially reflects the gas drive oil recovery efficiency, while the actual reservoir gas drive is a three-dimensional displacement process and is affected by factors such as reservoir heterogeneity. Therefore, the values ​​of the core drive experiment are usually significantly higher than the actual values ​​of the mine;

[0004] There are mainly the following methods for optimizing injection and production parameters of gas drive based on numerical simulation. The first is to optimize injection parameters only. During optimization, only the total gas injection volume and injection rate of the injection parameters are optimized, and the best solution is selected by comparing the recovery rate. This optimization is performed through artificial design schemes, which only pays attention to injection parameters and ignores the influence of the working system of production wells. It is not the real optimization of injection and production parameters. The second is to use the orthogonal design method to optimize the injection and production parameters of a single well group in a gas drive reservoir. However, the orthogonal design scheme is limited, and it is difficult to meet the actual needs of large-scale gas injection well groups in actual reservoirs. The third is the method of successive optimization of injection and production parameters. Based on the historical fitting basis of numerical simulation, the best solution is selected. On this basis, according to the method of successive optimization of injection and production parameters, the key parameters affecting the effect of gas injection development are studied in turn, and then the final recovery rate or cumulative oil production under different schemes are compared to select the best gas injection development parameters. However, this successive optimization method cannot take into account the coordinated development effect of injection and production parameters, and cannot achieve balanced gas drive, which is very easy to form gas channeling and reduce the gas injection utilization rate; the fourth is to optimize the injection and production parameters by combining numerical simulation with optimization algorithm. The biggest drawback of this method is that the optimization algorithm needs to call the numerical simulator multiple times. The optimization effect is better for smaller gas injection well groups, but it takes a lot of time to optimize larger actual oil reservoirs, and multiple optimization and real-time optimization are difficult. Summary of the invention

[0005] The purpose of the present invention is to provide a step-by-step optimization method for injection and production parameters of a gas-driven oil reservoir, which can make full use of a small amount of conventional experimental test data, greatly shorten the time consumed by numerical simulation, and achieve simple, fast and accurate determination of gas-driven injection and production parameters, significantly improve the optimization time of gas-driven injection and production parameters, reduce related economic costs, and provide rapid and scientific technical support for on-site development decisions.

[0006] To achieve the above object, the present invention provides a step-by-step optimization method for injection and production parameters of a gas drive reservoir, comprising the following steps:

[0007] (1) Collect and organize geological and development data of the target area: Collect and organize geological and development data of the reservoir, including porosity, permeability, oil layer thickness, net-to-gross ratio, saturation, pressure, viscosity, relative permeability curve, reservoir well location, injection well and production well historical working system information;

[0008] (2) Setting the optimization-related parameters as the injection-production parameters of each well and completing the preparation for injection-production optimization: On the basis of injection-production balance, a black oil simulator is used to establish a mathematical model for gas drive injection-production parameter optimization with maximum recovery rate as the objective function, and relevant optimization parameters and boundary conditions are set. Under the condition of predicting the simulation time and the total amount of gas injection, the daily gas injection rate of each gas injection well and the daily liquid production rate of each production well are optimized;

[0009] (3) Determine the fluid volume of each well when the economic net present value is the maximum at the current well location: Based on the fluid volume of each well when the recovery rate is the maximum obtained in step (2), a component simulator is used to establish a gas drive injection and production parameter optimization mathematical model with the maximum economic net present value (NPV) as the objective function. The intelligent optimization algorithm CMA-ES is used for the second step optimization. The total gas injection volume of the injection well is the same as that in step (2). The optimization variables are the injection and production parameters of each well. The initial value of the optimization parameter is the fluid volume of each well when the recovery rate is the maximum in step (2). The upper and lower limits of the fluid volume of each well are ±20% of the initial value. The parameters such as the number of populations, the number of iterations, the boundary constraints, the equality constraints and the simulation time of the intelligent optimization algorithm are set to be the same as those in step (2). The fluid volume of each well in the reservoir is re-optimized to obtain the fluid volume of each well when the economic net present value is the maximum at the current well location.

[0010] Preferably, the boundary conditions in step (2) are constrained by a penalty function method combined with an intelligent optimization algorithm. The boundary condition constraints include the gas-oil ratio constraint of the production well and the recovery factor constraint. The equation constraints include the total gas injection volume and the total liquid production volume of the reservoir. The global search intelligent optimization algorithm CMA-ES is used for optimization to obtain the gas injection rate of the injection well and the daily liquid production rate of each production well when the total gas injection volume is constant and the recovery factor is maximum at the current well location:

[0011] Taking the recovery rate as the objective function:

[0012] F max =FOE (1)

[0013] The optimization variables are injection and production parameters:

[0014] x={I g1 ,…I gn ;Q L1 ,…Q Lm} (2)

[0015] Constraints and solutions:

[0016] Daily gas injection rate of each gas injection well:

[0017]

[0018] Daily fluid production rate of each production well:

[0019]

[0020] Production well gas-oil ratio limit:

[0021] GOR≤GOR max (5)

[0022] Recovery factor constraints:

[0023] FOE≥FOE min(6)

[0024] Total gas injection volume in reservoir:

[0025]

[0026] Total daily liquid production rate of the reservoir:

[0027]

[0028] Under injection-production balance conditions:

[0029] Q Lt =I gt (9)

[0030] Where: I gi is the gas injection rate m of the i-th gas injection well 3 / d(underground volume);Q Li is the daily liquid production rate of the i-th production well m 3 / d (underground volume); GOR is the gas-oil ratio of the production well, unit: m 3 / m 3 ;GOR max is the maximum gas-oil ratio constraint, unit: m 3 / m 3 ; FOE is recovery factor, %; FOE max is the maximum recovery factor constraint, %; I gt is the total daily gas injection rate of the reservoir m 3 / d(underground volume);Q Lt is the total daily liquid production rate of the reservoir m 3 / d (underground volume); n is the number of gas injection wells, and m is the number of production wells.

[0031] Preferably, in step (3), based on the fluid volume of each well when the recovery factor is maximum obtained in step (2), a component simulator is used to establish a gas drive injection and production parameter optimization mathematical model with the maximum economic net present value (NPV) as the objective function, and the intelligent optimization algorithm CMA-ES is used for the second step optimization. The total gas injection volume of the gas injection well is the same as that in step (2), and the optimization variables are the injection and production parameters of each well. The initial value of the optimization parameter is the fluid volume of each well when the recovery factor is maximum in step (2), and the upper and lower limits of the fluid volume of each well are respectively ±20% of the initial value. The relevant population number, number of iterations, boundary constraints, equality constraints and simulation time parameters of the intelligent optimization algorithm are set to be the same as those in step (2), and the fluid volume of each well in the reservoir is re-optimized to obtain the fluid volume of each well when the economic net present value is maximum at the current well location:

[0032] Taking the economic net present value as the objective function:

[0033]

[0034] The optimization variables are injection and production parameters:

[0035] x={I g1 ,…I gn ;Q L1 ,…Q Lm} (11)

[0036] Production well gas-oil ratio limit:

[0037] GOR≤GOR max (12)

[0038] Recovery factor constraints:

[0039] FOE≥FOE min (13)

[0040] Total gas injection volume in reservoir:

[0041]

[0042] Total daily liquid production rate of the reservoir:

[0043]

[0044] Under injection-production balance conditions:

[0045] Q Lt =I gt (16)

[0046] In the formula, NPV max is the economic net present value, yuan; are the oil, gas and water production of the i-th well in the k-th year, in tons / year or cubic meters / year, which can be obtained through numerical simulation of components; r gp 、r op are the sales prices of oil and gas, RMB / ton or RMB / cubic meter respectively; c wp is the treatment cost of produced water, RMB / ton; c gi is the gas injection cost, yuan / cubic meter; c wi is the water injection cost, yuan / cubic meter; N t is the evaluation period, years; b is the discount rate, %.

[0047] Therefore, the present invention adopts the above-mentioned step-by-step optimization method for injection and production parameters of gas-drive oil reservoirs, which can make full use of a small amount of conventional experimental test data, greatly shorten the time consumed by numerical simulation, and realize simple, fast and accurate determination of gas-drive injection and production parameters, significantly improve the optimization time of gas-drive injection and production parameters, reduce related economic costs, and provide fast and scientific technical support for on-site development decisions.

[0048] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of an embodiment of a method for step-by-step optimization of injection and production parameters in a gas drive reservoir according to the present invention;

[0050] Figure 2 A flow chart of step (2) of an embodiment of a method for stepwise optimization of injection and production parameters in a gas drive reservoir according to the present invention;

[0051] Figure 3 It is a flow chart of step (3) of an embodiment of a method for stepwise optimization of injection and production parameters in a gas drive reservoir according to the present invention;

[0052] Figure 4 The permeability distribution diagram of the model in the present invention in the X direction;

[0053] Figure 5 is the porosity distribution diagram of the model of the present invention;

[0054] Figure 6 is the initial oil saturation distribution diagram of the present invention;

[0055] Figure 7 It is the first step of optimizing the iteration graph of the present invention;

[0056] Figure 8 This is a comparison diagram of the convergence of the step-by-step optimization and direct optimization of the present invention;

[0057] Fig. 9 This is a time-consuming comparison diagram of step-by-step optimization and direct optimization of the present invention;

[0058] Fig.10 This is a comparison chart of the optimal cumulative oil production of different optimization methods of the present invention. DETAILED DESCRIPTION

[0059] The present invention provides a method for optimizing injection and production parameters of a gas-driven oil reservoir in steps, comprising the following steps:

[0060] (1) Collect and organize geological and development data of the target area: Collect and organize geological and development data of the reservoir, including porosity, permeability, oil layer thickness, net-to-gross ratio, saturation, pressure, viscosity, relative permeability curve, reservoir well location, injection well and production well historical working system information;

[0061] (2) Set the optimization-related parameters as the injection-production parameters of each well and complete the preparation work for injection-production optimization: On the basis of injection-production balance, a black oil simulator is used to establish a mathematical model for gas drive injection-production parameter optimization with recovery maximization as the objective function. Set the relevant optimization parameters and boundary conditions, and predict the simulation time. Under the condition of a certain total amount of gas injection, optimize the daily gas injection rate of each gas injection well and the daily liquid production rate of each production well. Among them, the boundary conditions are constrained by the penalty function method combined with the intelligent optimization algorithm. The boundary condition constraints include the gas-oil ratio constraint of the production well and the recovery constraint. The equation constraints include the total gas injection volume and the total liquid production volume of the reservoir. The global search intelligent optimization algorithm CMA-ES is used for optimization to obtain the injection rate of the injection well and the daily liquid production rate of each production well when the recovery rate is maximized under the current well position and the total amount of gas injection.

[0062] Taking the recovery rate as the objective function:

[0063] F max =FOE (1)

[0064] The optimization variables are injection and production parameters:

[0065] x={I g1 ,…I gn ;Q L1 ,…Q Lm} (2)

[0066] Constraints and solutions:

[0067] Daily gas injection rate of each gas injection well:

[0068]

[0069] Daily fluid production rate of each production well:

[0070]

[0071] Production well gas-oil ratio limit:

[0072] GOR≤GOR max (5)

[0073] Recovery factor constraints:

[0074] FOE≥FOE min (6)

[0075] Total gas injection volume in reservoir:

[0076]

[0077] Total daily liquid production rate of the reservoir:

[0078]

[0079] Under injection-production balance conditions:

[0080] Q Lt =I gt (9)

[0081] Where: I gi is the gas injection rate m of the i-th gas injection well 3 / d(underground volume);Q Li is the daily liquid production rate of the i-th production well m 3 / d (underground volume); GOR is the gas-oil ratio of the production well, unit: m 3 / m 3 ;GOR max is the maximum gas-oil ratio constraint, unit: m 3 / m 3 ; FOE is recovery factor, %; FOE max is the maximum recovery factor constraint, %; I gt is the total daily gas injection rate of the reservoir m 3 / d(underground volume);Q Lt is the total daily liquid production rate of the reservoir m 3 / d (underground volume); n is the number of gas injection wells, and m is the number of production wells.

[0082] (3) Determine the fluid volume of each well when the economic net present value is the maximum at the current well location: Based on the fluid volume of each well when the recovery rate is the maximum obtained in step (2), a component simulator is used to establish a gas drive injection and production parameter optimization mathematical model with the maximum economic net present value (NPV) as the objective function. The intelligent optimization algorithm CMA-ES is used for the second step optimization. The total gas injection volume of the injection well is the same as that in step (2). The optimization variables are the injection and production parameters of each well. The initial value of the optimization parameter is the fluid volume of each well when the recovery rate is the maximum in step (2). The upper and lower limits of the fluid volume of each well are ±20% of the initial value. The parameters such as the number of populations, the number of iterations, the boundary constraints, the equality constraints and the simulation time of the intelligent optimization algorithm are set to be the same as those in step (2). The fluid volume of each well in the reservoir is re-optimized to obtain the fluid volume of each well when the economic net present value is the maximum at the current well location.

[0083] Based on the fluid volume of each well when the recovery rate is maximum obtained in step (2), a component simulator is used to establish a gas drive injection and production parameter optimization mathematical model with the maximum economic net present value (NPV) as the objective function. The intelligent optimization algorithm CMA-ES is used for the second step optimization. The total gas injection volume of the gas injection well is the same as that in step (2). The optimization variables are the injection and production parameters of each well. The initial value of the optimization parameter is the fluid volume of each well when the recovery rate is maximum in step (2). The upper and lower limits of the fluid volume of each well are ±20% of the initial value respectively. The relevant population number, iteration number, boundary constraint, equality constraint and simulation time parameters of the intelligent optimization algorithm are set to be the same as those in step (2). The fluid volume of each well in the reservoir is re-optimized to obtain the fluid volume of each well when the economic net present value is maximum at the current well location:

[0084] Taking the economic net present value as the objective function:

[0085]

[0086] The optimization variables are injection and production parameters:

[0087] x={I g1 ,…I gn ;Q L1 ,…Q Lm} (11)

[0088] Production well gas-oil ratio limit:

[0089] GOR≤GOR max (12)

[0090] Recovery factor constraints:

[0091] FOE≥FOE min (13)

[0092] Total gas injection volume in reservoir:

[0093]

[0094] Total daily liquid production rate of the reservoir:

[0095]

[0096] Under injection-production balance conditions:

[0097] Q Lt =I gt (16)

[0098] In the formula, NPV max is the economic net present value, yuan; are the oil, gas and water production of the i-th well in the k-th year, in tons / year or cubic meters / year, which can be obtained through numerical simulation of components; r gp 、r opare the sales prices of oil and gas, RMB / ton or RMB / cubic meter respectively; c wp is the treatment cost of produced water, RMB / ton; c gi is the gas injection cost, yuan / cubic meter; c wi is the water injection cost, yuan / cubic meter; N t is the evaluation period, years; b is the discount rate, %.

[0099] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0100] Example

[0101] Based on the actual oil reservoir in Tarim 4 Taking the drive as an example, the process of this method is explained with an example.

[0102] In step 1, the basic parameters of the geological and development data of the H block of the actual Tarim oil reservoir are collected and sorted out: porosity, permeability, saturation, oil layer thickness, pressure, viscosity, relative permeability curve, reservoir well location, historical working system of injection wells and production wells, etc. The daily gas injection rate of a single well in this reservoir is about 8000m 3 (Underground volume is about 300m 3 ),like Figure 4 The permeability distribution is shown as Figure 5 The porosity distribution is shown in Figure 6 Saturation distribution shown.

[0103] In step 2, if Figure 2 Shown is the second step of the present invention.

[0104] (1) In step (2), set the optimization-related parameters as the injection-production parameters of each well to complete the injection-production optimization preparation. Setting the optimization-related parameters includes: specifying 8 wells to be optimized, including 2 gas injection wells and 6 production wells;

[0105] (2) Set the constraints of each optimization variable. For production wells, the constraint is the lower limit of the single well production velocity 0m 3 / d, the upper limit of single well fluid production rate is 100m 3 / d; For gas injection wells, the lower limit of single well gas injection velocity is 0m 3 / d, upper limit of gas injection speed in a single well: 300m 3 / d (underground volume); total gas injection volume is 300m 3 / d; Under the condition of injection-production balance, the sum of the daily liquid production rates of the six production wells is equal to the sum of the daily gas injection rates of the two gas injection wells;

[0106] (3) According to the actual needs of the site, in order to prevent gas channeling, the gasoline ratio constraint is set to 3000m 3 / m 3 When the gas-oil ratio of the production well reaches 3000m3 / m 3 The optimization objective function is the recovery rate of the last time step at the end of the simulation; the minimum recovery rate constraint is set to 10%, and when the recovery rate of the offspring is less than 10%, the algorithm will impose corresponding penalties;

[0107] (4) Select the global search algorithm CMA-ES, and optimize the injection and production parameters based on the black oil numerical simulator. The simulation time is 10 years, the population size is 20, the number of iterations is 400, and the optimization objective function is the recovery rate. Generate a new generation of injection and production schemes through the offspring generation strategy in the global random search algorithm. Determine whether the newly generated injection and production scheme meets the constraints. If not, recalculate until the constraints are met; calculate the recovery rate under each scheme in the current iteration step, iterate and determine whether the optimization termination condition is met. If not, repeat the steps until the optimization termination condition is met. Table 1 shows the injection and production parameters of each well when the optimal recovery rate is obtained based on the first step of optimization of the black oil simulator;

[0108] Table 1 Fluid volume of each well at the first step of optimal recovery

[0109]

[0110] In step (3), if Figure 3 Shown is the third step of the present invention.

[0111] (1) Based on the daily liquid production rate of each production well when the recovery factor is maximum obtained in step (2), on the basis of injection-production balance, the daily gas injection rate (underground) of the two wells is equal to the daily liquid production rate of the six wells:

[0112] I gt =Q Lt (1)

[0113] (2) Based on the injection and production parameters of each well when the recovery factor is maximum obtained in step (2), a component simulator is used to establish a mathematical model for optimizing gas drive injection and production parameters with the economic net present value (NPV) as the objective function. The intelligent optimization algorithm CMA-ES algorithm is used to perform the second step optimization of the injection and production parameters of each well. The total gas injection volume of the gas injection well is the same as that of the first step optimization. The optimization variables are the injection and production parameters of each well. The initial value of the optimization variable is the fluid volume of each well when the recovery factor is maximum in step 2. The upper and lower limits of the optimization variables are ±20% of the initial value, as shown in Table 2. The number of populations related to the intelligent optimization algorithm is set to 20, the number of iterations is 400, the boundary constraints and equality constraints are the same as those in step 2, and the simulation time is 10 years. The fluid volume of each well in the reservoir is optimized, and it is iterated to determine whether the optimization termination condition is met. If not, the steps are repeated until the optimization termination condition is met to obtain the fluid volume of each well when the economic net present value is maximum at the current well location.

[0114] Table 2 Constraints of injection and production parameters for each well in the second step

[0115]

[0116] A mathematical model for optimizing injection and production parameters of continuous gas drive with maximum economic net present value as the objective function is established:

[0117]

[0118] In the formula, are the oil and gas production of the i-th well in the k-th year, in tons / year or cubic meters / year, which can be obtained through numerical simulation of components; r gp The sales gas price is 1 yuan / cubic meter; op is the oil sales price, 2500 yuan / ton; c gi is the gas injection cost, 1.5 yuan / m3; N t is the evaluation period, 10 years; b is the discount rate, 0.1; c is the discount rate, 0.1; wi The cost of water injection is 30 yuan per cubic meter.

[0119] (4) Sorting out the optimization results, forming a gas drive injection and production parameter optimization scheme, as shown in Table 3, for CH block H of the Tarim oil reservoir. 4 The injection and production parameters of the best wells obtained by step-by-step optimization and direct optimization by component simulation. By comparing the injection and production schemes obtained by step-by-step optimization and direct optimization by component simulation, it can be found that the error of the optimal liquid volume of each well is basically less than 10%; Figure 7 and Figure 8 The optimization process of step-by-step optimization and direct optimization using component simulator is compared. As can be seen from the figure, step-by-step optimization can significantly improve the convergence speed of the optimization process. In the second step of optimization, the optimal NPV is achieved after about 100 iterations. Fig. 9 It can be seen that compared with direct optimization using the component simulator, the total time consumption was reduced by 540 minutes and the optimization efficiency was improved by more than 60%. Fig.10 It can be seen that the comparison of the cumulative oil production results of different optimization methods shows that although the effect of step-by-step optimization is slightly worse than that of direct optimization by component simulation (the error is less than 3%), the optimization efficiency is greatly improved.

[0120] Table 3 Comparison of optimal injection-production schemes using different optimization methods

[0121]

[0122] Therefore, the present invention adopts the above-mentioned step-by-step optimization method for injection and production parameters of gas-drive oil reservoirs, which can make full use of a small amount of conventional experimental test data, greatly shorten the time consumed by numerical simulation, and realize simple, fast and accurate determination of gas-drive injection and production parameters, significantly improve the optimization time of gas-drive injection and production parameters, reduce related economic costs, and provide fast and scientific technical support for on-site development decisions.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A step-by-step optimization method for injection and production parameters of a gas-driven oil reservoir, characterized in that: The following steps are involved: (1) Collect and organize geological and development data of the target area: Collect and organize geological and development data of the reservoir, including porosity, permeability, oil layer thickness, net-to-gross ratio, saturation, pressure, viscosity, relative permeability curve, reservoir well location, injection well and production well historical working system information; (2) Set the optimization-related parameters as the injection-production parameters of each well and complete the preparation work for injection-production optimization: On the basis of injection-production balance, use a black oil simulator to establish a mathematical model for gas drive injection-production parameter optimization with maximum recovery rate as the objective function, set relevant optimization parameters and boundary conditions, and predict the simulation time and total gas injection volume to optimize the daily gas injection rate of each gas injection well and the daily liquid production rate of each production well under certain conditions; (3) Determine the fluid volume of each well when the economic net present value is the maximum at the current well location: Based on the fluid volume of each well when the recovery factor is the maximum obtained in step (2), a component simulator is used to establish a mathematical model for optimizing gas drive injection and production parameters with the maximum economic net present value (NPV) as the objective function. The intelligent optimization algorithm CMA-ES is used for the second step optimization. The total gas injection volume of the injection well is the same as that in step (2). The optimization variables are the injection and production parameters of each well. The initial value of the optimization parameter is the fluid volume of each well when the recovery factor is the maximum in step (2). The upper and lower limits of the fluid volume of each well are ±20% of the initial value. The relevant population number, number of iterations, boundary constraints, equality constraints and simulation time parameters of the intelligent optimization algorithm are set to be the same as those in step (2). The fluid volume of each well in the reservoir is re-optimized to obtain the fluid volume of each well when the economic net present value is the maximum at the current well location.

2. A method for optimizing injection and production parameters of a gas-driven oil reservoir according to claim 1, characterized in that: The boundary conditions in step (2) are constrained by a penalty function method combined with an intelligent optimization algorithm. The boundary condition constraints include the gas-oil ratio constraint of the production well and the recovery factor constraint. The equation constraints include the total gas injection volume and the total liquid production volume of the reservoir. The global search intelligent optimization algorithm CMA-ES is used for optimization to obtain the gas injection rate of the injection well and the daily liquid production rate of each production well when the total gas injection volume is constant and the recovery factor is maximized at the current well location: Taking the recovery rate as the objective function: （1） The optimization variables are injection and production parameters: （2） Constraints and solutions: Daily gas injection rate of each gas injection well: （3） Daily fluid production rate of each production well: （4） Production well gas-oil ratio limit: （5） Recovery factor constraints: （6） Total gas injection volume in reservoir: （7） Total daily liquid production rate of the reservoir: （8） Under injection-production balance conditions: （9） Where: I gi For the i Gas injection rate of gas injection wells m 3 / d underground volume; Q Li For the i Daily production rate of production wells m 3 / d underground volume; GOR is the gas-oil ratio of the production well, unit: m 3 / m 3 ; GOR max is the maximum gas-oil ratio constraint, unit: m 3 / m 3 ; FOE is the recovery factor, %; FOE max is the maximum recovery factor constraint, %; I gt is the total daily gas injection rate of the reservoir m 3 / d underground volume; Q Lt is the total daily liquid production rate of the reservoir m 3 / d underground volume; n is the number of gas injection wells, m is the number of producing wells.

3. A method for optimizing injection and production parameters of a gas-driven oil reservoir according to claim 1, characterized in that: In step (3), based on the fluid volume of each well at the maximum recovery rate obtained in step (2), a component simulator is used to establish a gas drive injection and production parameter optimization mathematical model with the maximum economic net present value (NPV) as the objective function, and the intelligent optimization algorithm CMA-ES is used for the second step optimization. The total gas injection volume of the gas injection well is the same as that in step (2). The optimization variables are the injection and production parameters of each well. The initial value of the optimization parameter is the fluid volume of each well at the maximum recovery rate in step (2). The upper and lower limits of the fluid volume of each well are ±20% of the initial value, respectively. The relevant population number, number of iterations, boundary constraints, equality constraints and simulation time parameters of the intelligent optimization algorithm are set to be the same as those in step (2). The fluid volume of each well in the reservoir is re-optimized to obtain the fluid volume of each well at the maximum economic net present value at the current well location: Taking the economic net present value as the objective function: （10） The optimization variables are injection and production parameters: （11） Production well gas-oil ratio limit: （12） Recovery factor constraints: （13） Total gas injection volume in reservoir: (14) Total daily liquid production rate of the reservoir: (15) Under injection-production balance conditions: (16) In the formula, NPV max is the economic net present value, yuan; , , , respectively i The well is in k Annual oil, gas and water production, tons / year or cubic meters / year, can be obtained through component numerical simulation; r gp 、r op The sales prices of oil and gas are respectively, RMB / ton or RMB / cubic meter; c wp is the treatment cost of produced water, RMB / ton; c gi is the gas injection cost, yuan / cubic meter; c wi is the water injection cost, RMB / cubic meter; N t is the evaluation time, year; b is the discount rate, %.

Images