Collaborative optimization design method and system for infilled well location parameters and injection-production parameters

Abstract

The invention relates to the field of oil and gas field development, and particularly discloses a collaborative optimization design method and system for infilled well position parameters and injection-production parameters. The method comprises the following steps: step 1, constructing a collaborative optimization mathematical model of infilled well location parameters and injection-production parameters; step 2, constructing a remaining oil recoverable potential map of the target block; 3, collaborative optimization preparation work of the infilled well position, the track, the well type / switch state and the injection-production parameters is completed; step 4, adding the combination scheme of the infilled well position, the track, the well type / switch state and the injection-production parameters and the corresponding accumulated oil production into a cache scheme set; 5, an improved whale optimization algorithm is adopted to repeatedly optimize and update to generate a new encrypted well position, track, well type / switch state and injection-production parameter combination scheme, and the evaluation, caching and updating process is repeatedly executed until the termination condition is met; and step 6, outputting an optimal combination scheme of the infilled well position, the track, the well type and the injection-production parameters. The method can greatly improve the recovery efficiency of the water-drive oil reservoir.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas field development, and in particular relates to a method and system for collaboratively optimizing design of infill well location parameters and injection and production parameters, for maximizing the recovery rate of water-driven oil reservoirs. Background Art

[0002] Well location selection and trajectory design for infill wells, as well as injection and production parameter control for existing wells, significantly impact oilfield production. Determining optimal well location parameters and injection and production parameters for each well is crucial for maximizing oilfield production. Improper infill well location selection and / or injection and production parameter design can lead to premature water breakthrough or a rapid increase in water cut in producing wells, or make it difficult to achieve high liquid production while maintaining reservoir pressure.

[0003] Currently, most academic literature treats the optimization of infill well placement parameters and injection-production parameters as two separate problems. However, the optimal placement of infill wells is critically dependent on the injection-production control strategy employed. Therefore, determining optimal infill well placement parameters also requires adjusting the injection-production parameters of existing wells. An integrated approach combining well placement and injection-production optimization may offer advantages over separate approaches that address these issues separately.

[0004] Patent application number CN107829718 B discloses a method for optimizing the design of reservoir well patterns and injection-production schemes based on the concept of balanced water flooding. This method combines a water flooding development index calculation method based on reservoir engineering theory with optimization theory. Using a global random search algorithm, it optimizes and generates new well location and injection-production parameters, predicts and evaluates displacement conditions in various injection-production directions, and determines the optimal well pattern injection-production design. Patent application number CN117521219 A discloses an efficient optimization decision-making method for well locations and well control parameters based on a reduced-order model. This method leverages the high efficiency and precision of reduced-order models and the global optimization capabilities of the chaotic grey wolf algorithm to directly achieve efficient joint optimization of reservoir well locations and well control parameters. Patent application number CN 118036477 B discloses a method for optimizing well location and well control parameters based on a spatiotemporal graph neural network. This method couples the spatiotemporal graph neural network with a multi-objective particle swarm optimization algorithm to achieve multi-objective collaborative optimization of cumulative oil production and CO2 storage while delaying CO2 gas crossover. Patent application number CN 115906675 B discloses a method for jointly optimizing well location and injection and production parameters based on a time-series multi-objective prediction model. This method couples a long-short-term memory neural network time-series multi-objective prediction model with the NSGA-II multi-objective optimization algorithm to establish a joint optimization model for well location and injection and production parameters, achieving accurate prediction of water flooding production dynamics and joint optimization of well control parameters based on a model-driven architecture. At present, the well location and injection-production coordinated optimization problem in the existing technologies such as the above-mentioned patent documents is generally limited to the case of vertical wells, that is, for the well location parameters, only the (x, y) coordinates need to be optimized, and the optimization of the injection-production parameters is completely achieved through random combination of parameters under the flow and pressure constraints of each well. Through iterative calculations of a large number of numerical simulators or proxy models, an optimal parameter combination with the maximum corresponding objective function value is found in the feasible domain based on an intelligent algorithm. There are a large number of invalid parameter random combination scheme calculations, resulting in low optimization calculation efficiency, and the overall production and injection volume constraints of the oil field are usually not taken into consideration, which sometimes deviates greatly from the actual needs of oil field development.

[0005] Well placement optimization involves optimizing the location, trajectory, and well-specific parameters of infill wells, while injection-production optimization involves optimizing the injection-production parameters of both infill wells and existing wells. Therefore, an integrated approach combining well placement optimization and injection-production optimization poses a challenge to oilfield production optimization. This is because the location, well-specific / switch-state parameters, trajectory parameters, and injection-production parameters of infill wells are inherently different from each other, involving the simultaneous optimization of both integer and continuous variables, and the potential number of possible combinations is enormous. Consequently, more efficient intelligent optimization algorithms and optimization acceleration strategies are needed to assist decision-making. Summary of the Invention

[0006] To overcome the defects of existing optimization methods, the present invention provides a collaborative optimization design method and system for infill well location parameters and injection and production parameters, which realizes the integrated intelligent optimization design of the well location, trajectory, well type / switch status, injection and production parameters of infill wells and the injection and production parameters of existing wells, further improving the oil reservoir recovery rate.

[0007] The object of the present invention can be achieved by the following technical solution: a method for collaborative optimization design of infill well location parameters and injection and production parameters, comprising:

[0008] Step 1: Construct a mathematical model for collaborative optimization of infill well location parameters and injection and production parameters;

[0009] Step 2: Collect and organize geological reservoir model data for the target block, construct a map of the target block's remaining oil recoverable potential, determine the maximum number of infill wells, the length of the underground well section, the upper and lower constraints on the azimuth and inclination angles of the infill wells, the infill range and boundary constraints, the upper and lower constraints on the injection and production parameters of the wells to be optimized, and the total daily production and injection volume;

[0010] Step 3: Given the relevant control parameters of the optimization algorithm, the Latin hypercube sampling method is used to initialize the discrete variables of the infilled well location and well type / switch status. The Sine mapping is used to initialize the continuous variables of the trajectory parameters of the infilled well. The linear programming method is used to initialize the continuous variables of the injection and production parameters of the infilled wells and the injection and production parameters of the existing wells to be optimized to meet the daily production and injection allocation constraints. The initial combination of infilled well location, trajectory, well type / switch status, and injection and production parameters is generated, completing the preparation for the coordinated optimization of infilled well location, trajectory, well type / switch status, and injection and production parameters.

[0011] Step 4: Calling a reservoir numerical simulator to evaluate the cumulative oil production corresponding to the infilled well location, trajectory, well type / switch status, and injection-production parameter combination scheme, and adding the generated infilled well location, trajectory, well type / switch status, and injection-production parameter combination scheme and the corresponding cumulative oil production to a cached scheme set;

[0012] Step 5: Use the improved Whale Optimization Algorithm (WOA) to repeatedly optimize and update the generated new infill well locations, trajectories, well types / switch states, and injection / production parameter combinations. Repeat the evaluation, caching, and updating process until the termination condition is met.

[0013] Step 6: Output the optimal combination of infill well locations, trajectories, well types, and injection and production parameters.

[0014] Preferably, in step 1, the mathematical model for collaborative optimization of infill well location parameters and injection and production parameters includes an optimization objective function, optimization variables, and constraint conditions;

[0015] The expression of the optimization objective function is:

[0016] (1),

[0017] (2),

[0018] (3),

[0019] In formulas (1), (2), and (3), is the objective function; is the maximum value of the objective function; f penalty is the penalty coefficient; is the cumulative oil production of the oil field; is the cumulative oil production threshold of the infill wells; is the cumulative oil production of the nth infill well; N is the maximum number of infill wells; X is a vector of discrete optimization variables; Y is a vector of continuous optimization variables;

[0020] The optimization variables include the well location, trajectory, well type / switch status, injection and production parameters of the infill well and the injection and production parameters of the existing wells to be optimized;

[0021] The well location includes x, y, and z coordinates, the trajectory includes azimuth angle α and well inclination angle θ, and the well classification / switch state optimization variables of the infill well are:

[0022] (4),

[0023] In formula (4), c n is the well type / switch status of the nth infill well, c n =0 means the infill well is an injection well and is in the open well state, c n =1 means the infill well is an oil well and is in the open well state, c n = 2 means the infill well is shut in;

[0024] The injection and production parameters of the infilled wells are: if the infilled wells are water injection wells, the injection and production parameters of the infilled wells are the water injection volume; if the infilled wells are oil production wells, the injection and production parameters of the infilled wells are the liquid production volume. The optimized variables of the injection and production parameters of the infilled wells are:

[0025] (5),

[0026] In formula (5), q in,n is the injection and production parameter of the nth infill well;

[0027] The injection and production parameters of the well to be optimized are: if the well to be optimized is a water injection well, the injection and production parameters of the well to be optimized are the water injection rate; if the well to be optimized is an oil production well, the injection and production parameters of the well to be optimized are the liquid production rate, and the optimization variables of the injection and production parameters of the well to be optimized are:

[0028] (6),

[0029] In formula (6), q ex,m is the injection and production parameter of the mth well to be optimized, and the subscript M is the number of wells to be optimized;

[0030] The vector X composed of discrete optimization variables = [ c 1, x 1, y 1, z 1, c 2, x 2, y 2, z 2,…, c n , x n , y n , z n ,…, c N , x N , y N , z N ]; the vector Y composed of the continuous optimization variables = [ α 1, θ 1, q in,1 , α 2, θ 2, q in,2 ,…, α n , θ n , q in,n ,…, α N , θ N , q in,N ,Q exist ];

[0031] The constraints include the azimuth and upper and lower limit constraints of the infill wells, the infill range and boundary constraints, the upper and lower limit constraints of the injection and production parameters, and the daily production and injection total amount constraints.

[0032] Preferably, in step 2,

[0033] The construction of the remaining oil recoverable potential map of the target block includes:

[0034] Step 2.1: Collect and organize geological reservoir model data for the target area. Taking each grid cell divided by the reservoir numerical simulation model as the research object, extract the distribution data of permeability, porosity, effective reservoir thickness, oil saturation, and attribute parameters such as crude oil viscosity, formation water viscosity, residual oil saturation, and oil-water relative permeability. Based on the relative permeability curve, use the nonlinear relationship of the phase permeability curve to obtain the fitting coefficients a, b, and c:

[0035] (7),

[0036] In formula (7), K ro is the relative permeability of crude oil; K rw is the relative permeability of formation water; S w is the water saturation; a, b, c are the fitting coefficients of the nonlinear relationship of the phase permeability curve;

[0037] Step 2.2: Calculate the conductivity coefficient, remaining oil potential abundance, and oil content evaluation index of each grid cell according to equations (8)-(12):

[0038] (8),

[0039] (9),

[0040] (10),

[0041] In formulas (8), (9), and (10):

[0042] (11),

[0043] (12),

[0044] In formulas (8), (9), and (10), COI is the conductivity coefficient; VOI is the abundance of remaining oil potential; f o is the oil content; K is the reservoir permeability; μ o is the crude oil viscosity; μ w is the formation water viscosity; φ is the porosity; h is the effective thickness of the reservoir; S o is the oil saturation; S oris the residual oil saturation; subscripts i, j, k represent the (i, j, k)th grid cell; subscripts i±½,j,k represent the connection between the (i, j, k)th grid cell and the (i±1, j, k)th grid cell; subscripts i,j±½,k represent the connection between the (i, j, k)th grid cell and the (i, j±1, k)th grid cell;

[0045] In step 2.3, the three evaluation indicators of unit conductivity, unit remaining oil potential abundance, and unit oil content are weighted and multiplied together to calculate the remaining oil recoverable potential coefficient ROI of each grid cell in the target block:

[0046] (13);

[0047] Step 2.4, drawing a residual oil recoverable potential map of the target block based on the calculated residual oil recoverable potential coefficient of each grid cell in the target block;

[0048] The upper and lower limit constraints of the azimuth and inclination angles of the infill wells are:

[0049] (14),

[0050] (15),

[0051] In formulas (14) and (15), α inf,n is the azimuth of the nth infill well; α lb,n is the lower limit constraint of the azimuth angle of the nth infill well; α ub,n is the upper limit constraint of the azimuth angle of the nth infill well; θ inf,n is the inclination angle of the nth infill well; θ lb,n is the lower limit constraint of the well inclination angle of the nth infill well; θ ub,n is the upper limit constraint of the well inclination angle of the nth infill well; subscript n=1,2,…,N represents the nth infill well; subscript lb represents the lower limit, and subscript ub represents the upper limit;

[0052] The encryption range and boundary constraint range of the infill well are:

[0053] (16),

[0054] (17),

[0055] In formulas (16) and (17), x in,n is the X-direction coordinate of the nth infill well; x lb,n is the lower limit constraint of the X-direction coordinate of the nth infill well; ub,n The upper limit constraint of the X-direction coordinate of the nth infill well;in,n is the Y-direction coordinate of the nth infill well; lb,n The Y-direction coordinate lower limit constraint of the nth infill well; y ub,n is the upper limit constraint of the Y coordinate of the nth infill well; z in,n is the Z-direction coordinate of the nth infill well; z lb,n is the lower limit constraint of the Z-direction coordinate of the nth infill well; ub,n is the upper limit constraint of the Z-direction coordinate of the nth infill well; Ω act is the set of valid grid cells;

[0056] The upper and lower limit constraints of the injection and production parameters of the well to be optimized are:

[0057] (18),

[0058] In formula (18), the superscript exist represents the wells to be optimized, the subscript m=1,2,…,M represents the mth well to be optimized, and q ex,m is the injection and production parameter of the mth well to be optimized; is the lower limit constraint of the injection and production parameters of the mth well to be optimized; is the upper limit constraint of the injection and production parameters of the mth well to be optimized;

[0059] The total daily production and injection volume is:

[0060] (19),

[0061] (20),

[0062] In formulas (19) and (20), N inj is the number of infill water injection wells; N pro is the number of infilled oil-producing wells; M inj is the number of water injection wells to be optimized; M pro is the number of oil-producing wells to be optimized; Q inj is the daily injection volume; Q pro The total daily production.

[0063] Preferably, step 3 comprises the following steps:

[0064] Step 3.1, giving the optimization algorithm related control parameters, said optimization algorithm related control parameters including the maximum number of iterations, population size, population adjustment starting generation, and population adjustment percentage;

[0065] In step 3.2, the Latin hypercube sampling method is used to generate normalized samples in the range of [0, 1]. Based on the upper and lower limit constraints of the well location, well type / switch status of the infilled wells, the following formula is used to map to the specified discrete range to initialize the generated discrete variables of the well location, well type / switch status of the infilled wells. The mapping relationship is:

[0066] (twenty one),

[0067] In formula (21), X in,n They represent the discrete variables corresponding to the well location and well type / switch status of the infilled wells; lhs is the normalized sample generated by the Latin hypercube sampling method; the subscripts lb and up represent the upper and lower limit constraints of the discrete variables, respectively;

[0068] In step 3.3, the Sine map is used to generate the chaotic positions of the initial continuous variables of the azimuth and inclination angle of the infill wells. The mathematical expression of the Sine map is as follows:

[0069] (twenty two),

[0070] In formula (22), raw is the chaotic value generated by the chaotic mapping; subscripts l and l+1 represent the lth and l+1th iteration sequence values,

[0071] The following formula is used to map the generated chaotic position to the range of the specified continuous variable. The mapping relationship is:

[0072] (twenty three),

[0073] In formula (23), raw is the chaotic value generated by chaotic mapping; Y in,n Y is the initial continuous variable of the azimuth and inclination of the nth infill well; lb,n Y is the lower limit constraint of the initial continuous variables of the azimuth and inclination of the nth infill well; ub,n is the upper limit constraint of the initial continuous variables of the azimuth and inclination of the nth infill well;

[0074] Step 3.4: Based on the generated discrete variables of the well type / switch status of the infilled wells, determine whether the infilled wells are to be infilled and the well type of the infilled wells. Count the number of infilled injection wells, set the number of retries, generate random perturbations for the objective function of the linear programming, perform small random transformations on the upper and lower limits of the injection and production parameters, use linear programming to solve the infilled wells that meet the daily production and injection allocation constraints, and initialize the injection and production parameters of the infilled wells and the injection and production parameters of the existing wells to be optimized as continuous variables.

[0075] In step 3.5, the initialized discrete variables of encrypted well locations, well types / switch statuses are combined with the trajectory of the encrypted wells, injection and production parameters, and the continuous variables of the injection and production parameters of the existing wells to be optimized. The dimensions of the discrete variables and the dimensions of the continuous variables are counted to complete the preparation work for the coordinated optimization of encrypted well locations, trajectories, well types / switch statuses, and injection and production parameters.

[0076] Preferably, in step 5, an improved whale optimization algorithm (WOA) is used to repeatedly optimize and update to generate a new encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme, and determine whether the new encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme meets the parameter constraint conditions. If the encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme does not meet the parameter constraint conditions, then directly return an infinite value and go to step 5. If the encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme meets the parameter constraint conditions, then further determine whether the parameter combination scheme exists in the cache solution set. If so, go to step 5; if not, go to step 4, and repeat the evaluation, caching and update process until the termination condition is met.

[0077] The object of the present invention can be achieved by another technical solution as follows: a collaborative optimization design system for infill well location parameters and injection and production parameters, comprising an optimization model construction module, an optimization parameter control module, an algorithm parameter input module, a combination scheme optimization module, and an optimization scheme generation module, wherein:

[0078] The optimization model construction module is used to collaboratively optimize the mathematical model based on the constructed infill well location parameters and injection and production parameters to determine the objective function and optimization variables;

[0079] The optimization parameter control module is used to collect and organize geological reservoir model data of the target block, construct a residual oil recoverable potential map of the target block, determine the maximum number of infill wells, the length of the underground well section, the azimuth angle and upper and lower limit constraints of the well inclination angle of the infill wells, the infill range and boundary constraint range, the well names of the wells to be optimized, the upper and lower limit constraints of the injection and production parameters, and the total daily production and injection volume;

[0080] The algorithm parameter input module is used to give the relevant control parameters of the optimization algorithm, initialize the discrete variables of the infilled well location and well type / switch status using the Latin hypercube sampling method, initialize the continuous variables of the trajectory parameters of the infilled well using the Sine mapping method, and initialize the injection and production parameters of the infilled wells and the injection and production parameters of the existing wells to be optimized using the linear programming method to generate the initial combination of infilled well location, trajectory, well type / switch status and injection and production parameters, thereby completing the preparation work for the coordinated optimization of infilled well location, trajectory, well type / switch status and injection and production parameters.

[0081] The combination scheme optimization module is used to call a reservoir numerical simulator to evaluate the cumulative oil production corresponding to the above-mentioned combination scheme of infilled well locations, trajectories, well types / switch states, and injection and production parameters, and add the generated combination scheme of infilled well locations, trajectories, well types / switch states, and injection and production parameters and the corresponding cumulative oil production to a cached scheme set; and is used to use an improved whale optimization algorithm (WOA) to repeatedly optimize and update to generate new combination schemes of infilled well locations, trajectories, well types / switch states, and injection and production parameters, and repeat the evaluation, caching, and updating process until a termination condition is met;

[0082] The optimization scheme generation module is used to output the optimal combination scheme of encrypted well locations, trajectories, well types / switch states and injection and production parameters.

[0083] Preferably, in the optimization model construction module,

[0084] The mathematical model for collaborative optimization of infill well location parameters and injection and production parameters includes an optimization objective function, optimization variables, and constraint conditions;

[0085] The expression of the optimization objective function is:

[0086] (1),

[0087] (2),

[0088] (3),

[0089] In formulas (1), (2), and (3), is the objective function; is the maximum value of the objective function; f penalty is the penalty coefficient; is the cumulative oil production of the oil field; is the cumulative oil production threshold of the infill wells; is the cumulative oil production of the nth infill well; N is the maximum number of infill wells; X is a vector of discrete optimization variables; Y is a vector of continuous optimization variables;

[0090] The optimization variables include the well location, trajectory, well type / switch status, injection and production parameters of the infill well and the injection and production parameters of the existing wells to be optimized;

[0091] The well location includes x, y, and z coordinates, the trajectory includes azimuth angle α and well inclination angle θ, and the well classification / switch state optimization variables of the infill well are:

[0092] (4),

[0093] In formula (4), c nis the well type / switch status of the nth infill well, c n =0 means the infill well is an injection well and is in the open well state, c n =1 means the infill well is an oil well and is in the open well state, c n = 2 means the infill well is shut in;

[0094] The injection and production parameters of the infilled wells are: if the infilled wells are water injection wells, the injection and production parameters of the infilled wells are the water injection volume; if the infilled wells are oil production wells, the injection and production parameters of the infilled wells are the liquid production volume. The optimized variables of the injection and production parameters of the infilled wells are:

[0095] (5),

[0096] In formula (5), q in,n is the injection and production parameter of the nth infill well;

[0097] The injection and production parameters of the well to be optimized are: if the well to be optimized is a water injection well, the injection and production parameters of the well to be optimized are the water injection rate; if the well to be optimized is an oil production well, the injection and production parameters of the well to be optimized are the liquid production rate, and the optimization variables of the injection and production parameters of the well to be optimized are:

[0098] (6),

[0099] In formula (6), q ex,m is the injection and production parameter of the mth well to be optimized, and the subscript M is the number of wells to be optimized;

[0100] The vector X composed of discrete optimization variables = [ c 1, x 1, y 1, z 1, c 2, x 2, y 2, z 2,…, c n , x n , y n , z n ,…, c N , x N , y N , z N ]; the vector Y composed of the continuous optimization variables = [ α 1, θ 1,q in,1 , α 2, θ 2, q in,2 ,…, α n , θ n , q in,n ,…, α N , θ N , q in,N ,Q exist ];

[0101] The constraints include the azimuth and upper and lower limit constraints of the infill wells, the infill range and boundary constraints, the upper and lower limit constraints of the injection and production parameters, and the daily production and injection total amount constraints.

[0102] Preferably, in the optimization parameter control module,

[0103] The construction of the remaining oil recoverable potential map of the target block includes:

[0104] Step 2.1: Collect and organize geological reservoir model data for the target area. Taking each grid cell divided by the reservoir numerical simulation model as the research object, extract the distribution data of permeability, porosity, effective reservoir thickness, oil saturation, and attribute parameters such as crude oil viscosity, formation water viscosity, residual oil saturation, and oil-water relative permeability. Based on the relative permeability curve, use the nonlinear relationship of the phase permeability curve to obtain the fitting coefficients a, b, and c:

[0105] (7),

[0106] In formula (7), K ro is the relative permeability of crude oil; K rw is the relative permeability of formation water; S w is the water saturation; a, b, c are the fitting coefficients of the nonlinear relationship of the phase permeability curve;

[0107] Step 2.2: Calculate the conductivity coefficient, remaining oil potential abundance, and oil content evaluation index of each grid cell according to equations (8)-(12):

[0108] (8),

[0109] (9),

[0110] (10),

[0111] In formulas (8), (9), and (10):

[0112] (11),

[0113] (12),

[0114] In formulas (8), (9), and (10), COI is the conductivity coefficient; VOI is the abundance of remaining oil potential; f o is the oil content; K is the reservoir permeability; μ o is the crude oil viscosity; μ w is the formation water viscosity; φ is the porosity; h is the effective thickness of the reservoir; S o is the oil saturation; S or is the residual oil saturation; subscripts i, j, k represent the (i, j, k)th grid cell; subscripts i±½,j,k represent the connection between the (i, j, k)th grid cell and the (i±1, j, k)th grid cell; subscripts i,j±½,k represent the connection between the (i, j, k)th grid cell and the (i, j±1, k)th grid cell;

[0115] In step 2.3, the three evaluation indicators of unit conductivity, unit remaining oil potential abundance, and unit oil content are weighted and multiplied together to calculate the remaining oil recoverable potential coefficient ROI of each grid cell in the target block:

[0116] (13);

[0117] Step 2.4, drawing a residual oil recoverable potential map of the target block based on the calculated residual oil recoverable potential coefficient of each grid cell in the target block;

[0118] The upper and lower limit constraints of the azimuth and inclination angles of the infill wells are:

[0119] (14),

[0120] (15),

[0121] In formulas (14) and (15), α inf,n is the azimuth of the nth infill well; α lb,n is the lower limit constraint of the azimuth angle of the nth infill well; α ub,n is the upper limit constraint of the azimuth angle of the nth infill well; θ inf,n is the inclination angle of the nth infill well; θ lb,n is the lower limit constraint of the well inclination angle of the nth infill well; θ ub,nis the upper limit constraint of the well inclination angle of the nth infill well; subscript n=1,2,…,N represents the nth infill well; subscript lb represents the lower limit, and subscript ub represents the upper limit;

[0122] The encryption range and boundary constraint range of the infill well are:

[0123] (16),

[0124] (17),

[0125] In formulas (16) and (17), x in,n is the X-direction coordinate of the nth infill well; x lb,n is the lower limit constraint of the X-direction coordinate of the nth infill well; ub,n The upper limit constraint of the X-direction coordinate of the nth infill well; in,n is the Y-direction coordinate of the nth infill well; lb,n The Y-direction coordinate lower limit constraint of the nth infill well; y ub,n is the upper limit constraint of the Y coordinate of the nth infill well; z in,n is the Z-direction coordinate of the nth infill well; z lb,n is the lower limit constraint of the Z-direction coordinate of the nth infill well; ub,n is the upper limit constraint of the Z-direction coordinate of the nth infill well; Ω act is the set of valid grid cells;

[0126] The upper and lower limit constraints of the injection and production parameters of the well to be optimized are:

[0127] (18),

[0128] In formula (18), the superscript exist represents the wells to be optimized, the subscript m=1,2,…,M represents the mth well to be optimized, and q ex,m is the injection and production parameter of the mth well to be optimized; is the lower limit constraint of the injection and production parameters of the mth well to be optimized; is the upper limit constraint of the injection and production parameters of the mth well to be optimized;

[0129] The total daily production and injection volume is:

[0130] (19),

[0131] (20),

[0132] In formulas (19) and (20), N inj is the number of infill water injection wells; N pro is the number of infilled oil-producing wells; Minj is the number of water injection wells to be optimized; M pro is the number of oil-producing wells to be optimized; Q inj is the daily injection volume; Q pro The total daily production.

[0133] Preferably, the algorithm parameter input module is used to:

[0134] Given optimization algorithm related control parameters, the optimization algorithm related control parameters include maximum number of iterations, population size, population adjustment starting generation, and population adjustment percentage;

[0135] The Latin hypercube sampling method is used to generate normalized samples in the range of [0, 1]. According to the upper and lower limit constraints of the well location, well type / switch status of the infilled wells, the following formula is used to map to the specified discrete range to initialize the discrete variables of the well location, well type / switch status of the infilled wells. The mapping relationship is:

[0136] (twenty one),

[0137] In formula (21), X in,n They represent the discrete variables corresponding to the well location and well type / switch status of the infilled wells; lhs is the normalized sample generated by the Latin hypercube sampling method; the subscripts lb and up represent the upper and lower limit constraints of the discrete variables, respectively;

[0138] Sine mapping is used to generate the chaotic positions of the initial continuous variables of the azimuth and inclination angle of the infill wells. The mathematical expression of the Sine mapping is as follows:

[0139] (twenty two),

[0140] In formula (22), raw is the chaotic value generated by the chaotic mapping; subscripts l and l+1 represent the lth and l+1th iteration sequence values,

[0141] The following formula is used to map the generated chaotic position to the range of the specified continuous variable. The mapping relationship is:

[0142] (twenty three),

[0143] In formula (23), raw is the chaotic value generated by chaotic mapping; Y in,n Y is the initial continuous variable of the azimuth and inclination of the nth infill well; lb,n Y is the lower limit constraint of the initial continuous variables of the azimuth and inclination of the nth infill well; ub,n is the upper limit constraint of the initial continuous variables of the azimuth and inclination of the nth infill well;

[0144] Based on the generated discrete variables of the well type / switch status of the infilled wells, determine whether the infilled wells are to be infilled and the well type of the infilled wells, count the number of infilled water injection wells, set the number of retries, generate random perturbations for the objective function of linear programming, and perform small random transformations on the upper and lower limits of the injection and production parameters. Use linear programming to solve the infilled wells that meet the daily production and injection allocation total volume constraints, and initialize the injection and production parameters of the infilled wells and the injection and production parameters of the existing wells to be optimized as continuous variables;

[0145] Combine the initialized discrete variables of encrypted well locations, well types / switch status, encrypted well trajectories, injection and production parameters with the continuous variables of injection and production parameters of existing wells to be optimized, and count the dimensions of discrete variables and continuous variables to complete the preparation work for the coordinated optimization of encrypted well locations, trajectories, well types / switch status and injection and production parameters.

[0146] Preferably, the algorithm parameter input module is used to use the improved whale optimization algorithm (WOA) to repeatedly optimize and update to generate a new encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme, and judge whether the new encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme meets the parameter constraint conditions. If the encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme does not meet the parameter constraint conditions, then directly return an infinite value and then use the improved whale optimization algorithm (WOA) to repeatedly optimize and update to generate a new encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme. If the encrypted well location, trajectory, well type / switch status If the state and injection-production parameter combination scheme meets the parameter constraint conditions, it is further determined whether the parameter combination scheme exists in the cached scheme set. If so, the improved whale optimization algorithm (WOA) is used to repeatedly optimize and update to generate a new encrypted well location, trajectory, well type / switch state and injection-production parameter combination scheme. If not, the reservoir numerical simulator is called to evaluate the cumulative oil production corresponding to the encrypted well location, trajectory, well type / switch state and injection-production parameter combination scheme. The generated encrypted well location, trajectory, well type / switch state and injection-production parameter combination scheme and the corresponding cumulative oil production are added to the cached scheme set, and the evaluation, caching and updating process is repeated until the termination condition is met.

[0147] The beneficial technical effects brought about by the present invention are:

[0148] The present invention quickly screens and determines the maximum number of infill wells and infill areas based on the residual oil recoverable potential map, takes maximizing cumulative oil production as the objective function, and, under the constraints of the underground well section length of the given infill well and the total daily production and injection volume of the oil field, combines the established improved whale optimization algorithm (WOA) to achieve integrated intelligent collaborative optimization design of different infill well types (vertical wells, highly deviated wells, horizontal wells), the well locations of infill wells, discrete variables of well type / switch status, the trajectory of infill wells, injection and production parameters, and continuous variables of injection and production parameters of existing wells to be optimized. The number of infill wells and the switch status of existing wells to be optimized are automatically determined by design; and a cache strategy for optimization results is adopted to greatly reduce the number of invalid calls to the numerical simulator, significantly improving optimization efficiency. This can provide guidance for the optimal design of water flooding development plans and has positive significance for oil reservoir development and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0149] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0150] Figure 1 This is a flow chart of an embodiment of the present invention.

[0151] Figure 2 The permeability distribution diagram.

[0152] Figure 3 The porosity distribution diagram.

[0153] Figure 4 This is the distribution map of effective reservoir thickness.

[0154] Figure 5 This is the oil saturation distribution map.

[0155] Figure 6 is the oil-water relative permeability curve.

[0156] Figure 7 This is the distribution map of the recoverable potential of remaining oil.

[0157] Figure 8 Diagram of the convergence process of collaborative optimization of well locations and injection-production parameters for the PUNQ-S3 reservoir model.

[0158] Figure 9 This is a comparison chart of the liquid production parameters of existing wells in the PUNQ-S3 reservoir model before and after optimization.

[0159] Figure 10 Comparison of cumulative oil production and comprehensive water cut before and after optimization of the PUNQ-S3 reservoir model. DETAILED DESCRIPTION

[0160] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0161] The purpose of the present invention is to provide a method and system for collaborative optimization design of infill well location parameters and injection and production parameters, which can improve the comprehensiveness and pertinence of oilfield development adjustment parameter optimization, realize the integrated intelligent collaborative optimization design of the number of infill wells, different infill well types (vertical wells, highly deviated wells, horizontal wells), the location of different infill wells, well type / switch status, trajectory, injection and production parameters and the switch status and injection and production parameters of existing wells to be optimized, and maximize the improvement of oil reservoir development effects.

[0162] like Figure 1 As shown, an embodiment of the present invention provides a collaborative optimization design method for infill well location parameters and injection and production parameters, including the following steps:

[0163] Step 1: Construct a mathematical model for collaborative optimization of infill well location parameters and injection-production parameters.

[0164] Specifically, in step 1, the mathematical model for collaborative optimization of infill well location parameters and injection and production parameters includes an optimization objective function, optimization variables, and constraint conditions.

[0165] Preferably, the expression of the optimization objective function is:

[0166] (1),

[0167] (2),

[0168] (3),

[0169] In formulas (1), (2), and (3), is the objective function; is the maximum value of the objective function; f penalty is the penalty coefficient; is the cumulative oil production of the oil field; is the cumulative oil production threshold of the infill wells; is the cumulative oil production of the nth infill well; N is the maximum number of infill wells; X is a vector of discrete optimization variables; and Y is a vector of continuous optimization variables.

[0170] Preferably, the optimization variables include the well location, trajectory, well type / switch status, injection and production parameters of the infill well and the injection and production parameters of the existing wells to be optimized.

[0171] Among them, for the well location, trajectory, well type / switch status, and injection and production parameters of the infilled well, the well location includes x, y, and z coordinates, the trajectory includes azimuth angle α and well inclination angle θ, and the well type / switch status optimization variables of the infilled well are:

[0172] (4),

[0173] In formula (4), c n is the well type / switch status of the nth infill well, c n = 0 means the infill well is an injection well and is in the open state, c n =1 means the infill well is an oil well and is in the open well state, c n = 2 means the infill well is in shut-in status.

[0174] Regarding the injection and production parameters of the infilled wells, if the infilled wells are water injection wells, the injection and production parameters of the infilled wells are the water injection rate; if the infilled wells are oil production wells, the injection and production parameters of the infilled wells are the liquid production rate. The optimized variables of the injection and production parameters of the infilled wells are:

[0175] (5),

[0176] In formula (5), q in,n The injection and production parameters of the nth infill well.

[0177] The injection and production parameters of the well to be optimized are: if the well to be optimized is a water injection well, the injection and production parameters of the well to be optimized are the water injection rate; if the well to be optimized is an oil production well, the injection and production parameters of the well to be optimized are the liquid production rate, and the optimization variables of the injection and production parameters of the well to be optimized are:

[0178] (6),

[0179] In formula (6), q ex,m is the injection-production parameter of the mth well to be optimized. The subscripts exist and ex both represent wells to be optimized. The subscript M is the number of wells to be optimized.

[0180] The vector X composed of discrete optimization variables = [ c 1, x 1, y 1, z 1, c 2, x 2, y 2, z 2,…,c n , x n , y n , z n ,…, c N , x N , y N , z N ]; the vector Y composed of the continuous optimization variables = [ α 1, θ 1, q in,1 , α 2, θ 2, q in,2 ,…, α n , θ n , q in,n ,…, α N , θ N , q in,N ,Q exist ].

[0181] The constraints include the azimuth and upper and lower limit constraints of the infill wells, the infill range and boundary constraints, the upper and lower limit constraints of the injection and production parameters, and the daily production and injection total amount constraints.

[0182] Step 2: Collect and organize the geological reservoir model data of the target block, construct the remaining oil recoverable potential map of the target block, determine the maximum number of infill wells N, the length of the underground well section L, the azimuth angle of the infill wells, the upper and lower limit constraints of the well inclination angle, the infill range and boundary constraint range, the well names of the wells to be optimized, the upper and lower limit constraints of the injection and production parameters, and the total daily production and injection volume.

[0183] Specifically, the construction of the remaining oil recoverable potential map of the target block includes:

[0184] Step 2.1: Collect and organize geological reservoir model data for the target area. Taking each grid cell divided by the reservoir numerical simulation model as the research object, extract the distribution data of permeability, porosity, effective reservoir thickness, oil saturation, and attribute parameters such as crude oil viscosity, formation water viscosity, residual oil saturation, and oil-water relative permeability. Based on the relative permeability curve, use the nonlinear relationship of the phase permeability curve to obtain the fitting coefficients a, b, and c:

[0185] (7),

[0186] In formula (7), K ro is the relative permeability of crude oil; K rw is the relative permeability of formation water; S w is the water saturation; a, b, c are the fitting coefficients of the nonlinear relationship of the phase permeability curve;

[0187] Step 2.2: Calculate the conductivity coefficient, remaining oil potential abundance, and oil content evaluation index of each grid cell according to equations (8)-(12):

[0188] (8),

[0189] (9),

[0190] (10),

[0191] In formulas (8), (9), and (10):

[0192] (11),

[0193] (12),

[0194] In formulas (8), (9), and (10), e represents the exponential function; a, b, and c are the fitting coefficients of the nonlinear relationship of the phase permeability curve mentioned above; COI is the conductivity coefficient; VOI is the abundance of the remaining oil potential; f o is the oil content; K is the reservoir permeability; μ o is the crude oil viscosity; μ w is the formation water viscosity; φ is the porosity; h is the effective thickness of the reservoir; S o is the oil saturation; S or is the residual oil saturation; subscripts i, j, k represent the (i, j, k)th grid cell; subscripts i±½,j,k represent the connection between the (i, j, k)th grid cell and the (i±1, j, k)th grid cell; subscripts i,j±½,k represent the connection between the (i, j, k)th grid cell and the (i, j±1, k)th grid cell;

[0195] In step 2.3, the three evaluation indicators of unit conductivity, unit remaining oil potential abundance, and unit oil content are weighted and multiplied together to calculate the remaining oil recoverable potential coefficient ROI of each grid cell in the target block:

[0196] (13);

[0197] Step 2.4: Draw a residual oil recoverable potential map of the target block based on the calculated residual oil recoverable potential coefficient of each grid cell in the target block.

[0198] Specifically, the upper and lower limit constraints of the azimuth and inclination angles of the infill wells are:

[0199] (14),

[0200] (15),

[0201] In formulas (14) and (15), α inf,n is the azimuth of the nth infill well; α lb,n is the lower limit constraint of the azimuth angle of the nth infill well; α ub,n is the upper limit constraint of the azimuth angle of the nth infill well; θ inf,n is the inclination angle of the nth infill well; θ lb,n is the lower limit constraint of the well inclination angle of the nth infill well; θ ub,n is the upper limit constraint of the well inclination angle of the nth infill well; subscript n=1,2,…,N represents the nth infill well; subscript lb represents the lower limit, and subscript ub represents the upper limit;

[0202] Specifically, the encryption range and boundary constraint range of the encrypted well are:

[0203] (16),

[0204] (17),

[0205] In formulas (16) and (17), x in,n is the X-direction coordinate of the nth infill well; x lb,n is the lower limit constraint of the X-direction coordinate of the nth infill well; ub,n The upper limit constraint of the X-direction coordinate of the nth infill well; in,n is the Y-direction coordinate of the nth infill well; lb,n The Y-direction coordinate lower limit constraint of the nth infill well; y ub,n is the upper limit constraint of the Y coordinate of the nth infill well; z in,n is the Z-direction coordinate of the nth infill well; z lb,n is the lower limit constraint of the Z-direction coordinate of the nth infill well; ub,n is the upper limit constraint of the Z-direction coordinate of the nth infill well; Ω act is the set of valid grid cells;

[0206] Specifically, the upper and lower limit constraints of the injection and production parameters of the well to be optimized are:

[0207] (18),

[0208] In formula (18), the superscript exist represents the wells to be optimized, the subscript m=1,2,…,M represents the mth well to be optimized, and q ex,m is the injection and production parameter of the mth well to be optimized; is the lower limit constraint of the injection and production parameters of the mth well to be optimized; is the upper limit constraint of the injection and production parameters of the mth well to be optimized;

[0209] Specifically, the total daily production and injection volume is:

[0210] (19),

[0211] (20),

[0212] In formulas (19) and (20), N inj is the number of infill water injection wells; N pro is the number of infilled oil-producing wells; M inj is the number of water injection wells to be optimized; M pro is the number of oil-producing wells to be optimized; Q inj is the daily injection volume; Q pro The total daily production.

[0213] Step 3: Given the relevant control parameters of the optimization algorithm, the Latin hypercube sampling method is used to initialize the discrete variables of the infilled well location and well type / switch status, the Sine mapping is used to initialize the continuous variables of the trajectory parameters of the infilled well, and the linear programming method is used to initialize the injection and production parameters of the infilled wells that meet the daily production and injection total volume constraints and the injection and production parameters of the existing wells to be optimized. The initial combination scheme of the infilled well location, trajectory, well type / switch status and injection and production parameters is generated, completing the preparation work for the coordinated optimization of the infilled well location, trajectory, well type / switch status and injection and production parameters.

[0214] Specifically, step 3 includes the following steps:

[0215] Step 3.1, giving the optimization algorithm related control parameters, said optimization algorithm related control parameters including the maximum number of iterations, population size, population adjustment starting generation, and population adjustment percentage;

[0216] In step 3.2, the Latin hypercube sampling method is used to generate normalized samples in the range of [0, 1]. Based on the upper and lower limit constraints of the well location, well type / switch status of the infilled wells, the following formula is used to map to the specified discrete range to initialize the generated discrete variables of the well location, well type / switch status of the infilled wells. The mapping relationship is:

[0217] (twenty one),

[0218] In formula (21), X in,n They represent the discrete variables corresponding to the well location and well type / switch status of the infilled wells; lhs is the normalized sample generated by the Latin hypercube sampling method; the subscripts lb and up represent the upper and lower limit constraints of the discrete variables, respectively;

[0219] In step 3.3, the Sine map is used to generate the chaotic positions of the initial continuous variables of the azimuth and inclination angle of the infill wells. The mathematical expression of the Sine map is as follows:

[0220] (twenty two),

[0221] In formula (22), raw is the chaotic value generated by the chaotic mapping; subscripts l and l+1 represent the lth and l+1th iteration sequence values,

[0222] The following formula is used to map the generated chaotic position to the range of the specified continuous variable. The mapping relationship is:

[0223] (twenty three),

[0224] In formula (23), raw is the chaotic value generated by chaotic mapping; Y in,n Y is the initial continuous variable of the azimuth and inclination of the nth infill well; lb,n Y is the lower limit constraint of the initial continuous variables of the azimuth and inclination of the nth infill well; ub,n is the upper limit constraint of the initial continuous variables of the azimuth and inclination of the nth infill well;

[0225] Step 3.4: Based on the generated discrete variables of the well type / switch status of the infilled wells, determine whether the infilled wells are to be infilled and the well type of the infilled wells. Count the number of infilled injection wells, set the number of retries, generate random perturbations for the objective function of the linear programming, perform small random transformations on the upper and lower limits of the injection and production parameters, use linear programming to solve the infilled wells that meet the daily production and injection allocation constraints, and initialize the injection and production parameters of the infilled wells and the injection and production parameters of the existing wells to be optimized as continuous variables.

[0226] In step 3.5, the initialized discrete variables of encrypted well locations, well types / switch statuses are combined with the trajectory of the encrypted wells, injection and production parameters, and the continuous variables of the injection and production parameters of the existing wells to be optimized. The dimensions of the discrete variables and the dimensions of the continuous variables are counted to complete the preparation work for the coordinated optimization of encrypted well locations, trajectories, well types / switch statuses, and injection and production parameters.

[0227] Step 4: Call the reservoir numerical simulator to evaluate the cumulative oil production corresponding to the encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme, and add the generated encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme and the corresponding cumulative oil production to the cache scheme set.

[0228] In step 5, the improved whale optimization algorithm (WOA) is used to repeatedly optimize and update the generated new encrypted well locations, trajectories, well types / switch states, and injection and production parameter combinations. The evaluation, caching, and updating processes are repeated until the termination conditions are met.

[0229] Specifically, in step 5, an improved whale optimization algorithm (WOA) is used to repeatedly optimize and update the generated new encrypted well locations, trajectories, well types / switch states, and injection and production parameter combinations. A determination is made as to whether the new encrypted well locations, trajectories, well types / switch states, and injection and production parameter combinations satisfy parameter constraints. If the encrypted well locations, trajectories, well types / switch states, and injection and production parameter combinations do not satisfy the parameter constraints, the algorithm directly returns an infinite value and proceeds to step 5. If the encrypted well locations, trajectories, well types / switch states, and injection and production parameter combinations satisfy the parameter constraints, the algorithm further determines whether the parameter combination exists in the cached solution set. If so, the algorithm proceeds to step 5; if not, the algorithm proceeds to step 4. The evaluation, caching, and updating processes are repeated until a termination condition is met. The termination condition is the maximum number of iterations described in step 3.

[0230] Step 6: Output the optimal combination of infill well locations, trajectories, well types / switch states, and injection / production parameters. The optimal solution that satisfies the termination conditions and reaches the maximum number of iterations described in Step 3 is the optimal combination.

[0231] In another embodiment of the present invention, a collaborative optimization design system for infill well location parameters and injection and production parameters includes: an optimization model construction module, an optimization parameter control module, an algorithm parameter input module, a combination scheme optimization module, and an optimization scheme generation module;

[0232] The optimization model construction module is used to collaboratively optimize the mathematical model based on the constructed infill well location parameters and injection and production parameters to determine the objective function and optimization variables;

[0233] The optimization parameter control module is used to collect and organize geological reservoir model data of the target block, construct a residual oil recoverable potential map of the target block, determine the maximum number of infill wells, the length of the underground well section, the azimuth angle and upper and lower limit constraints of the well inclination angle of the infill wells, the infill range and boundary constraint range, the well names of the wells to be optimized, the upper and lower limit constraints of the injection and production parameters, and the total daily production and injection volume;

[0234] The algorithm parameter input module is used to give the relevant control parameters of the optimization algorithm, initialize the discrete variables of the infilled well location and well type / switch status using the Latin hypercube sampling method, initialize the continuous variables of the trajectory parameters of the infilled well using the Sine mapping method, and initialize the injection and production parameters of the infilled wells and the injection and production parameters of the existing wells to be optimized using the linear programming method to generate the initial combination of infilled well location, trajectory, well type / switch status and injection and production parameters, thereby completing the preparation work for the coordinated optimization of infilled well location, trajectory, well type / switch status and injection and production parameters.

[0235] The combination scheme optimization module is used to call a reservoir numerical simulator to evaluate the cumulative oil production corresponding to the above-mentioned combination scheme of infilled well locations, trajectories, well types / switch states, and injection and production parameters, and add the generated combination scheme of infilled well locations, trajectories, well types / switch states, and injection and production parameters and the corresponding cumulative oil production to a cached scheme set; and is used to repeatedly optimize and update the generation of new combination schemes of infilled well locations, trajectories, well types / switch states, and injection and production parameters using an improved whale optimization algorithm, and repeat the evaluation, caching, and updating process until a termination condition is met;

[0236] The optimization scheme generation module is used to output the optimal combination scheme of encrypted well locations, trajectories, well types / switch states and injection and production parameters.

[0237] Among them, the specific process of each module is the same as that of the above method embodiment and will not be repeated here.

[0238] A specific implementation of the method of the present invention is shown in Example 1.

[0239] In Example 1, the geological reservoir model data of the PUNQ-S3 reservoir model were collected and sorted, and the permeability, porosity, effective reservoir thickness, and oil saturation distribution were extracted. Figures 2 to 5 The crude oil viscosity of the PUNQ-S3 reservoir model is 2 mPs·s, the formation water viscosity is 0.5 mPs·s, the residual oil saturation is 0.1, and the oil-water relative permeability is as follows: Figure 6 As shown in Figure 2, the nonlinear relationship (7) of the relative permeability curve is used to fit the relative permeability curve, and the fitting coefficients a=-0.8118, b=-7.3645, and c=0.9608 are obtained.

[0240] In Example 1, based on the calculated remaining oil recoverable potential coefficient of each grid cell of the PUNQ-S3 reservoir model, the remaining oil recoverable potential distribution of the PUNQ-S3 reservoir model is plotted as follows: Figure 7As shown in the figure, the maximum number of infill wells N=2, and the infill wells are named W1H and W2H respectively; the length of the underground well section L=200; the upper and lower limits of the azimuth angle of the infill wells are constrained in the range of [0°, 360°]; the upper and lower limits of the well inclination angle are constrained in the range of [0°, 90°]; the upper and lower limits of the injection and production parameters of the infill wells W1H and W2H are both constrained in the range of [0, 400m 3 / d]; PUNQ-S3 reservoir model has 6 oil producing wells, including PRO-1, PRO-4, PRO-5, PRO-11, PRO-12, and PRO-15. The liquid production of the above 6 wells is optimized collaboratively, and the upper and lower limits of the liquid production parameters are both [0, 450 m 3 / d]; the total daily production of the PUNQ-S3 reservoir model is 2200m 3 / d, the total daily injection volume is 0 m 3 / d. The cumulative oil production threshold of the infill well is set at 50,000 m 3 / well, that is, the cumulative oil production of the infill well is less than the cumulative oil production threshold, and the drilling operation of the infill well cannot be carried out on site.

[0241] In Example 1, the maximum number of iterations of the optimization algorithm is set to 50, the population size is 12, the starting generation of population adjustment is 5, the population adjustment percentage is 20%, the dimension of discrete variables is 8, and the dimension of continuous variables is 12.

[0242] In Example 1, Figure 8 The improved whale optimization algorithm is used to optimize the infill well locations and injection and production parameters of the PUNQ-S3 reservoir model. Figure 8 It can be seen that the improved whale optimization algorithm converged after 50 iterations, with a total of 408 effective optimization iterative calculations, of which 368 were directly evaluated by calling the numerical simulator, and 40 were evaluated using the optimization result caching strategy, effectively reducing the number of numerical simulator calls by 232 times and improving the optimization efficiency by 40%. Finally, the optimal well location, azimuth, well inclination, production fluid volume, and predicted 10-year cumulative oil production parameters of the two infill wells were determined as shown in Table 1.

[0243] Table 1 Summary of optimization parameters for infill wells

[0244] Optimize well names Well type / switch status Well location coordinates Azimuth Well inclination angle Production liquid volume Cumulative oil production W1H 1 15,21,1 358.5° 41.5° <![CDATA[400m 3 / d]]> <![CDATA[72.8234×10 4 m 3 / d]]> W2H 1 10,19,1 358.5° 48.0° <![CDATA[365.8 m 3 / d]]> <![CDATA[91.1316×10 4 m 3 / d]]>

[0245] The injection and production parameters of the existing wells PRO-1, PRO-4, PRO-5, PRO-11, PRO-12, and PRO-15 are as follows before and after optimization: Figure 9 shown. Figure 10The comparison of cumulative oil production and comprehensive water cut before and after the optimization of the PUNQ-S3 reservoir model is given. Two wells are added to the PUNQ-S3 reservoir model and the injection and production parameters of each well are collaboratively optimized. It can be seen from the figure that the cumulative oil production increases by 134.79×10 4 m 3 The comprehensive water content dropped by up to 35.1 percentage points, and the optimization effect was significant.

Claims

1. A collaborative optimization design method for infill well location parameters and injection and production parameters, characterized in that: The following steps are involved: Step 1: construct a mathematical model for collaborative optimization of infill well location parameters and injection and production parameters; Step 2: Collect and organize the geological reservoir model data of the target block, construct the remaining oil recoverable potential map of the target block, determine the maximum number of infill wells, the length of the underground well section, the upper and lower limit constraints of the azimuth and well inclination of the infill wells, the infill range and boundary constraints, the upper and lower limit constraints of the injection and production parameters of the wells to be optimized, and the total daily production and injection volume; Step 3: Given the relevant control parameters of the optimization algorithm, the Latin hypercube sampling method is used to initialize the discrete variables of the well location and well type / switch status of the infilled wells, the Sine mapping is used to initialize the continuous variables of the trajectory parameters of the infilled wells, and the linear programming method is used to initialize the injection and production parameters of the infilled wells that meet the daily production and injection total volume constraints and the injection and production parameters of the existing wells to be optimized. The initial combination of infilled well location, trajectory, well type / switch status and injection and production parameters is generated, and the preparation work for the coordinated optimization of infilled well location, trajectory, well type / switch status and injection and production parameters is completed; Step 4, calling a reservoir numerical simulator to evaluate the cumulative oil production corresponding to the encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme, and adding the generated encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme and the corresponding cumulative oil production to a cache scheme set; Step 5: Use the improved whale optimization algorithm to repeatedly optimize and update to generate new encrypted well locations, trajectories, well types / switch states, and injection and production parameter combination plans, and repeat the evaluation, caching, and updating process until the termination condition is met; Step 6: Output the optimal combination of encrypted well locations, trajectories, well types, and injection and production parameters.

2. A collaborative optimization design method for infill well location parameters and injection and production parameters as claimed in claim 1, characterized in that: In step 1, the mathematical model for collaborative optimization of infill well location parameters and injection and production parameters includes an optimization objective function, optimization variables, and constraint conditions; The expression of the optimization objective function is: （1）， （2）， （3）， In formulas (1), (2), and (3), is the objective function; is the maximum value of the objective function; f penalty is the penalty coefficient; is the cumulative oil production of the oil field; is the cumulative oil production threshold of the infill wells; is the cumulative oil production of the nth infill well; N is the maximum number of infill wells; X is a vector of discrete optimization variables; Y is a vector of continuous optimization variables; The optimization variables include the well location, trajectory, well type / switch status, injection and production parameters of the infill wells and the injection and production parameters of the existing wells to be optimized; The well position includes x, y, and z direction coordinates, the trajectory includes azimuth angle α and well inclination angle θ, and the well type / switch state optimization variables of the infill well are: （4）， In formula (4), c n is the well type / switch status of the nth encrypted well, c n =0 means that the infill well is an injection well and is in the open well state, c n =1 means the infill well is an oil production well and is in the open well state, c n = 2 means the infill well is in shut-in status; The injection and production parameters of the infilled wells are, if the well type of the infilled wells is a water injection well, the injection and production parameters of the infilled wells are the water injection volume; if the well type of the infilled wells is an oil production well, the injection and production parameters of the infilled wells are the liquid production volume, and the injection and production parameters optimization variables of the infilled wells are: （5）， In formula (5), q in,n is the injection and production parameter of the nth infill well; The injection and production parameters of the well to be optimized are: if the well to be optimized is a water injection well, the injection and production parameters of the well to be optimized are the water injection volume; if the well to be optimized is an oil production well, the injection and production parameters of the well to be optimized are the liquid production volume, and the injection and production parameter optimization variables of the well to be optimized are: （6）， In formula (6), q ex,m is the injection-production parameter of the mth well to be optimized, and the subscript M is the number of wells to be optimized; The vector X composed of discrete optimization variables = [ c 1, x 1, y 1, z 1, c 2, x 2, y 2, z 2,…, c n , x n , y n , z n ,…, c N , x N , y N , z N ]; the vector Y composed of the continuous optimization variables = [ α 1, θ 1, q in,1 , α 2, θ 2, q in,2 ,…, α n , θ n , q in,n ,…, α N , θ N , q in,N ,Q exist ]; The constraints include the azimuth and upper and lower limit constraints of the well inclination, the infill range and boundary constraints, the upper and lower limit constraints of the injection and production parameters, and the total daily production and injection volume constraints.

3. A collaborative optimization design method for infill well location parameters and injection and production parameters as claimed in claim 2, characterized in that: In the step 2, The construction of the remaining oil recoverable potential map of the target block includes: Step 2.1, collect and organize the geological reservoir model data of the target block, take each grid unit divided by the reservoir numerical simulation model as the research object, extract the permeability, porosity, effective reservoir thickness, oil saturation distribution data and crude oil viscosity, formation water viscosity, residual oil saturation, oil-water relative permeability attribute parameters; according to the relative permeability curve, the nonlinear relationship of the phase permeability curve is used to fit the fitting coefficients a, b, c: （7）， In formula (7), K ro is the relative permeability of crude oil; K rw is the relative permeability of formation water; S w is water saturation; a, b, c are the fitting coefficients of the nonlinear relationship of the phase permeability curve; Step 2.2: Calculate the conductivity coefficient, remaining oil potential abundance and oil content evaluation index of each grid unit according to equations (8)-(12): （8）， （9）， （10）， In formulas (8), (9), and (10): （11）， （12）， In formulas (8), (9) and (10), COI is the conductivity coefficient; VOI is the potential abundance of remaining oil; f o is the oil content; K is the reservoir permeability; μ o is the viscosity of crude oil; μ w is the formation water viscosity; φ is the porosity; h is the effective thickness of the reservoir; S o is the oil saturation; S or is the residual oil saturation; subscripts i, j, k represent the (i, j, k)th grid cell; subscript i±½,j,k represents the connection position between the (i, j, k)th grid cell and the (i±1, j, k)th grid cell; subscript i,j±½,k represents the connection position between the (i, j, k)th grid cell and the (i, j±1, k)th grid cell; Step 2.3, weighted multiplication of the three evaluation indicators of unit conductivity, unit remaining oil potential abundance and unit oil content is performed to calculate the remaining oil recoverable potential coefficient ROI of each grid unit in the target block: （13）； Step 2.4, drawing a residual oil recoverable potential map of the target block according to the calculated residual oil recoverable potential coefficient of each grid unit of the target block; The upper and lower limits of the azimuth and inclination angles of the infill wells are: （14）， （15）， In formulas (14) and (15), α inf,n is the azimuth of the nth infill well; α lb,n is the lower limit constraint of the azimuth angle of the nth infill well; α ub,n is the upper limit constraint of the azimuth angle of the nth infill well; θ inf,n is the well inclination angle of the nth infill well; θ lb,n is the lower limit constraint of the well inclination angle of the nth infill well; θ ub,n is the upper limit constraint of the well inclination angle of the nth infill well; subscript n=1,2,…,N represents the nth infill well; subscript lb represents the lower limit, and subscript ub represents the upper limit; The encryption range and boundary constraint range of the encrypted well are: （16）， （17）， In formula (16) and (17), x in,n is the X-direction coordinate of the nth infill well; lb,n is the lower limit constraint of the X-direction coordinate of the nth infill well; ub,n is the upper limit constraint of the X-direction coordinate of the nth infill well; in,n is the Y-direction coordinate of the nth infill well; lb,n is the lower limit constraint of the Y direction coordinate of the nth infill well; ub,n z is the upper limit constraint of the Y coordinate of the nth infill well; in,n is the Z-direction coordinate of the nth infill well; lb,n is the lower limit constraint of the Z direction coordinate of the nth infill well; ub,n is the upper limit constraint of the Z-direction coordinate of the nth infill well; Ω act is the set of valid grid cells; The upper and lower limit constraints of the injection and production parameters of the existing wells to be optimized are: （18）， In formula (18), the superscript exist represents the wells to be optimized, the subscript m=1,2,…,M represents the mth well to be optimized, and q ex,m is the injection and production parameters of the mth well to be optimized; is the lower limit constraint of the injection and production parameters of the mth well to be optimized; is the upper limit constraint of the injection and production parameters of the mth well to be optimized; The total daily production and injection volume is: （19）， （20）， In formula (19) and (20), N inj N is the number of infilled water injection wells; pro is the number of infilled oil-producing wells; M inj is the number of water injection wells in the wells to be optimized; M pro is the number of oil producing wells to be optimized; Q inj Q is the total daily injection volume; pro The total daily production.

4. A collaborative optimization design method for infill well location parameters and injection and production parameters as claimed in claim 3, characterized in that: The step 3 comprises the following steps: Step 3.1, giving the optimization algorithm related control parameters, wherein the optimization algorithm related control parameters include the maximum number of iterations, the population size, the population adjustment starting generation, and the population adjustment percentage; Step 3.2, use the Latin hypercube sampling method to generate normalized samples in the range of [0,1]. According to the upper and lower limit constraints of the well location, well type / switch status of the encrypted well, the following formula is used to map to the specified discrete range to initialize the generated discrete variables of the well location, well type / switch status of the encrypted well. The mapping relationship is: （21）， In formula (21), X in,n They represent the discrete variables corresponding to the well location and well type / switch status of the infilled wells; lhs is the normalized sample generated by the Latin hypercube sampling method; the subscripts lb and up represent the upper and lower limit constraints of the discrete variables, respectively; Step 3.3, Sine mapping is used to generate the chaotic positions of the initial continuous variables of the azimuth and inclination angle of the infill wells. The mathematical expression of Sine mapping is as follows: （22）， In formula (22), raw is the chaotic value generated by chaotic mapping; subscripts l and l+1 represent the lth and l+1th iteration sequence values. The following formula is used to map the generated chaotic position to the range of the specified continuous variable. The mapping relationship is: （23）， In formula (23), raw is the chaotic value generated by chaotic mapping; Y in,n Y is the initial continuous variable of the azimuth and inclination angle of the nth infill well; lb,n Y is the lower limit constraint of the initial continuous variables of the azimuth and inclination of the nth infill well; ub,n The upper limit constraint of the initial continuous variables of the azimuth and inclination of the nth infill well; Step 3.4, based on the generated discrete variables of the well type / switch state of the encrypted well, determine whether the encrypted well is to be encrypted and the well type of the encrypted well, count the number of encrypted water injection wells, set the number of retries, generate random perturbations for the objective function of linear programming, and perform small random changes on the upper and lower limits of the injection and production parameters, use linear programming to solve the encrypted wells that meet the constraints of the total daily production and injection volume, and initialize the injection and production parameters of the encrypted wells and the injection and production parameters of the existing wells to be optimized. Continuous variables; Step 3.5, combine the initialized generated discrete variables of encrypted well locations, well types / switch states, encrypted well trajectories, injection and production parameters, and injection and production parameter continuous variables of existing wells to be optimized, count the dimensions of discrete variables and continuous variables, and complete the preparation work for the coordinated optimization of encrypted well locations, trajectories, well types / switch states, and injection and production parameters.

5. A collaborative optimization design method for infill well location parameters and injection and production parameters as claimed in claim 4, characterized in that: In the step 5, the improved whale optimization algorithm is used to repeatedly optimize and update to generate new encrypted well locations, trajectories, well types / switch states and injection and production parameter combination schemes, and it is determined whether the new encrypted well locations, trajectories, well types / switch states and injection and production parameter combination schemes meet the parameter constraints. If the encrypted well locations, trajectories, well types / switch states and injection and production parameter combination schemes do not meet the parameter constraints, then the infinite value is directly returned and step 5 is performed. If the encrypted well locations, trajectories, well types / switch states and injection and production parameter combination schemes meet the parameter constraints, then it is further determined whether the parameter combination scheme exists in the cached solution set. If so, then step 5 is performed; if not, then step 4 is performed and the evaluation, caching and updating process is repeated until the termination condition is met.

6. A system for collaborative optimization design of well location parameters and injection and production parameters, characterized in that: It includes: optimization model building module, optimization parameter control module, algorithm parameter input module, combination scheme optimization module, optimization scheme generation module; The optimization model building module is used to coordinately optimize the mathematical model based on the constructed infill well location parameters and injection and production parameters to determine the objective function and optimization variables; The optimization parameter control module is used to collect and organize the geological reservoir model data of the target block, construct the remaining oil recoverable potential map of the target block, determine the maximum number of infill wells, the length of the underground well section, the azimuth angle of the infill well, the upper and lower limit constraint range of the well inclination angle, the infill range and the boundary constraint range, the well names of the wells to be optimized, the upper and lower limit constraint ranges of the injection and production parameters, and the total daily production and injection allocation; The algorithm parameter input module is used to give the optimization algorithm related control parameters, use Latin hypercube sampling method to initialize the well location, well type / switch status discrete variables of the infilled wells, use Sine mapping to initialize the trajectory parameter continuous variables of the infilled wells, use linear programming method to initialize the injection and production parameters of the infilled wells that meet the daily production and injection total amount constraint and the injection and production parameters of the existing wells to be optimized, generate the initial infilled well location, trajectory, well type / switch status and injection and production parameter combination scheme, and complete the preparation work for the coordinated optimization of the infilled well location, trajectory, well type / switch status and injection and production parameters; The combination scheme optimization module is used to call the reservoir numerical simulator to evaluate the cumulative oil production corresponding to the above-mentioned encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme, and add the generated encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme and the corresponding cumulative oil production to the cache scheme set; and is used to use the improved whale optimization algorithm to repeatedly optimize and update to generate new encrypted well location, trajectory, well type / switch status and injection and production parameter combination scheme, and repeat the evaluation, caching and updating process until the termination condition is met; The optimization scheme generation module is used to output the optimal combination scheme of encrypted well locations, trajectories, well types / switch states and injection and production parameters.

7. A system for collaborative optimization design of infill well location parameters and injection and production parameters as claimed in claim 6, characterized in that: In the optimization model building module, The mathematical model for collaborative optimization of infill well location parameters and injection and production parameters includes an optimization objective function, optimization variables, and constraint conditions; The expression of the optimization objective function is: （1）， （2）， （3）， In formulas (1), (2), and (3), is the objective function; is the maximum value of the objective function; f penalty is the penalty coefficient; is the cumulative oil production of the oil field; is the cumulative oil production threshold of the infill wells; is the cumulative oil production of the nth infill well; N is the maximum number of infill wells; X is a vector of discrete optimization variables; Y is a vector of continuous optimization variables; The optimization variables include the well location, trajectory, well type / switch status, injection and production parameters of the infill wells and the injection and production parameters of the existing wells to be optimized; The well position includes x, y, and z direction coordinates, the trajectory includes azimuth angle α and well inclination angle θ, and the well type / switch state optimization variables of the infill well are: （4）， In formula (4), c n is the well type / switch status of the nth encrypted well, c n =0 means that the infill well is an injection well and is in the open well state, c n =1 means the infill well is an oil production well and is in the open well state, c n = 2 means the infill well is in shut-in status; The injection and production parameters of the infilled wells are, if the well type of the infilled wells is a water injection well, the injection and production parameters of the infilled wells are the water injection volume; if the well type of the infilled wells is an oil production well, the injection and production parameters of the infilled wells are the liquid production volume, and the injection and production parameters optimization variables of the infilled wells are: （5）， In formula (5), q in,n is the injection and production parameter of the nth infill well; The injection and production parameters of the well to be optimized are: if the well to be optimized is a water injection well, the injection and production parameters of the well to be optimized are the water injection volume; if the well to be optimized is an oil production well, the injection and production parameters of the well to be optimized are the liquid production volume, and the injection and production parameter optimization variables of the well to be optimized are: （6）， In formula (6), q ex,m is the injection-production parameter of the mth well to be optimized, and the subscript M is the number of wells to be optimized; The vector X composed of discrete optimization variables = [ c 1, x 1, y 1, z 1, c 2, x 2, y 2, z 2,…, c n , x n , y n , z n ,…, c N , x N , y N , z N ]; the vector Y composed of the continuous optimization variables = [ α 1, θ 1, q in,1 , α 2, θ 2, q in,2 ,…, α n , θ n , q in,n ,…, α N , θ N , q in,N ,Q exist ]; The constraints include the azimuth and upper and lower limit constraints of the well inclination, the infill range and boundary constraints, the upper and lower limit constraints of the injection and production parameters, and the total daily production and injection volume constraints.

8. A system for collaborative optimization design of encrypted well location parameters and injection and production parameters as claimed in claim 7, characterized in that: In the optimization parameter control module, The construction of the remaining oil recoverable potential map of the target block includes: Step 2.1, collect and organize the geological reservoir model data of the target block, take each grid unit divided by the reservoir numerical simulation model as the research object, extract the permeability, porosity, effective reservoir thickness, oil saturation distribution data and crude oil viscosity, formation water viscosity, residual oil saturation, oil-water relative permeability attribute parameters; according to the relative permeability curve, the nonlinear relationship of the phase permeability curve is used to fit the fitting coefficients a, b, c: （7）， In formula (7), K ro is the relative permeability of crude oil; K rw is the relative permeability of formation water; S w is water saturation; a, b, c are the fitting coefficients of the nonlinear relationship of the phase permeability curve; Step 2.2: Calculate the conductivity coefficient, remaining oil potential abundance and oil content evaluation index of each grid unit according to equations (8)-(12): （8）， （9）， （10）， In formulas (8), (9), and (10): （11）， （12）， In formulas (8), (9) and (10), COI is the conductivity coefficient; VOI is the potential abundance of remaining oil; f o is the oil content; K is the reservoir permeability; μ o is the viscosity of crude oil; μ w is the formation water viscosity; φ is the porosity; h is the effective thickness of the reservoir; S o is the oil saturation; S or is the residual oil saturation; subscripts i, j, k represent the (i, j, k)th grid cell; subscript i±½,j,k represents the connection position between the (i, j, k)th grid cell and the (i±1, j, k)th grid cell; subscript i,j±½,k represents the connection position between the (i, j, k)th grid cell and the (i, j±1, k)th grid cell; Step 2.3, weighted multiplication of the three evaluation indicators of unit conductivity, unit remaining oil potential abundance and unit oil content is performed to calculate the remaining oil recoverable potential coefficient ROI of each grid unit in the target block: （13）； Step 2.4, drawing a residual oil recoverable potential map of the target block according to the calculated residual oil recoverable potential coefficient of each grid unit of the target block; The upper and lower limits of the azimuth and inclination angles of the infill wells are: （14）， （15）， In formulas (14) and (15), α inf,n is the azimuth of the nth infill well; α lb,n is the lower limit constraint of the azimuth angle of the nth infill well; α ub,n is the upper limit constraint of the azimuth angle of the nth infill well; θ inf,n is the well inclination angle of the nth infill well; θ lb,n is the lower limit constraint of the well inclination angle of the nth infill well; θ ub,n is the upper limit constraint of the well inclination angle of the nth infill well; subscript n=1,2,…,N represents the nth infill well; subscript lb represents the lower limit, and subscript ub represents the upper limit; The encryption range and boundary constraint range of the encrypted well are: （16）， （17）， In formula (16) and (17), x in,n is the X-direction coordinate of the nth infill well; lb,n is the lower limit constraint of the X-direction coordinate of the nth infill well; ub,n is the upper limit constraint of the X-direction coordinate of the nth infill well; in,n is the Y-direction coordinate of the nth infill well; lb,n is the lower limit constraint of the Y direction coordinate of the nth infill well; ub,n z is the upper limit constraint of the Y coordinate of the nth infill well; in,n is the Z-direction coordinate of the nth infill well; lb,n is the lower limit constraint of the Z direction coordinate of the nth infill well; ub,n is the upper limit constraint of the Z-direction coordinate of the nth infill well; Ω act is the set of valid grid cells; The upper and lower limit constraints of the injection and production parameters of the existing wells to be optimized are: （18）， In formula (18), the superscript exist represents the wells to be optimized, the subscript m=1,2,…,M represents the mth well to be optimized, and q ex,m is the injection and production parameters of the mth well to be optimized; is the lower limit constraint of the injection and production parameters of the mth well to be optimized; is the upper limit constraint of the injection and production parameters of the mth well to be optimized; The total daily production and injection volume is: （19）， （20）， In formula (19) and (20), N inj N is the number of infilled water injection wells; pro is the number of infilled oil-producing wells; M inj is the number of water injection wells in the wells to be optimized; M pro is the number of oil producing wells to be optimized; Q inj Q is the total daily injection volume; pro The total daily production.

9. A system for collaborative optimization design of infill well location parameters and injection and production parameters as claimed in claim 8, characterized in that: The algorithm parameter input module is used for: Given optimization algorithm related control parameters, the optimization algorithm related control parameters include maximum number of iterations, population size, population adjustment starting generation, population adjustment percentage; The Latin hypercube sampling method is used to generate normalized samples in the range of [0,1]. According to the upper and lower limit constraints of the well location, well type / switch status of the encrypted well, the following formula is used to map to the specified discrete range to initialize the generated discrete variables of the well location, well type / switch status of the encrypted well. The mapping relationship is: （21）， In formula (21), X in,n They represent the discrete variables corresponding to the well location and well type / switch status of the infilled wells; lhs is the normalized sample generated by the Latin hypercube sampling method; the subscripts lb and up represent the upper and lower limit constraints of the discrete variables, respectively; Sine mapping is used to generate the chaotic positions of the initial continuous variables of the azimuth and well inclination of the infill wells. The mathematical expression of Sine mapping is as follows: （22）， In formula (22), raw is the chaotic value generated by chaotic mapping; subscripts l and l+1 represent the lth and l+1th iteration sequence values. The following formula is used to map the generated chaotic position to the range of the specified continuous variable. The mapping relationship is: （23）， In formula (23), raw is the chaotic value generated by chaotic mapping; Y in,n Y is the initial continuous variable of the azimuth and inclination angle of the nth infill well; lb,n Y is the lower limit constraint of the initial continuous variables of the azimuth and inclination of the nth infill well; ub,n The upper limit constraint of the initial continuous variables of the azimuth and inclination of the nth infill well; According to the generated discrete variables of the well type / switch status of the encrypted well, determine whether the encrypted well is implemented and the well type of the encrypted well, count the number of encrypted water injection wells, set the number of retries, generate random perturbations for the objective function of linear programming, and perform slight random transformations on the upper and lower limits of the injection and production parameters. Use linear programming to solve the encrypted wells that meet the constraints of the total daily production and injection volume, and initialize the injection and production parameters of the encrypted wells and the injection and production parameters of the existing wells to be optimized. Continuous variables; Combine the initialized generated discrete variables of encrypted well locations, well types / switch states with the trajectories of encrypted wells, injection and production parameters and the continuous variables of injection and production parameters of existing wells to be optimized, count the dimensions of discrete variables and continuous variables, and complete the preparation work for the coordinated optimization of encrypted well locations, trajectories, well types / switch states and injection and production parameters.

10. A system for collaborative optimization design of infill well location parameters and injection and production parameters as claimed in claim 9, characterized in that: The algorithm parameter input module is used to use the improved whale optimization algorithm to repeatedly optimize and update to generate new encrypted well locations, trajectories, well types / switch states and injection and production parameter combination schemes, and to determine whether the new encrypted well locations, trajectories, well types / switch states and injection and production parameter combination schemes meet the parameter constraints. If the encrypted well locations, trajectories, well types / switch states and injection and production parameter combination schemes do not meet the parameter constraints, then directly return an infinite value and then use the improved whale optimization algorithm to repeatedly optimize and update to generate new encrypted well locations, trajectories, well types / switch states and injection and production parameter combination schemes. If the encrypted well locations, trajectories, well types / switch states and injection and production parameter combination schemes do not meet the parameter constraints, then directly return an infinite value and then use the improved whale optimization algorithm to repeatedly optimize and update to generate new encrypted well locations, trajectories, well types / switch states and injection and production parameter combination schemes. If the parameter combination scheme satisfies the parameter constraint conditions, it is further determined whether the parameter combination scheme exists in the cache scheme set. If so, the improved whale optimization algorithm is used to repeatedly optimize and update to generate new encrypted well locations, trajectories, well types / switch states and injection-production parameter combination schemes. If not, the reservoir numerical simulator is called to evaluate the cumulative oil production corresponding to the encrypted well locations, trajectories, well types / switch states and injection-production parameter combination schemes. The generated encrypted well locations, trajectories, well types / switch states and injection-production parameter combination schemes and the corresponding cumulative oil production are added to the cache scheme set, and the evaluation, caching and updating process is repeated until the termination conditions are met.

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