Plunger gas lift well group peak shifting well opening and closing scheduling optimization method

Abstract

The invention discloses a plunger gas lift well group peak shifting well opening and closing scheduling optimization method. The method comprises the following steps: acquiring plunger gas lift well group field data; a plunger gas lift well group production operation scheduling collaborative optimization mixed integer linear programming model described based on discrete time, namely an MI LP model; a plunger gas lift well group production operation scheduling collaborative optimization M ILP model solving method based on a genetic algorithm; obtaining a plunger gas lift well group peak shifting scheduling feasibility scheme under different objective functions; and evaluating and optimizing the effect of the plunger gas lift well group peak shifting scheduling feasibility scheme. According to the method, an automatic optimization method is adopted, the intensity of manual decision making and possible errors are reduced, and the scheduling efficiency and accuracy are improved; the genetic algorithm is adopted for optimization solution, an approximate optimal solution can be obtained in a short time, and the method is suitable for the scheduling problem of large-scale well groups.

Description

Technical Field

[0001] The invention relates to the technical field of natural gas exploitation, and in particular to a method for optimizing the scheduling of peak-shifted switching wells in a plunger gas lift well group. Background Art

[0002] As the degree of natural gas exploitation continues to increase, the production of many gas wells continues to decrease, and the flow rate continues to decrease, making the bottom hole energy insufficient to carry all the accumulated liquid out of the wellbore, greatly affecting the production of gas wells. To address this problem, plunger gas lift technology is widely used as a solution at home and abroad.

[0003] Plunger gas lift is a technology that improves the efficiency of liquid lifting in oil and gas wells. It forms a solid interface between gas and liquid by moving the plunger up and down in the oil pipe, and uses gas pressure to push the plunger upward to carry the liquid to the ground, reducing gas crossflow and liquid fallback. This technology is particularly suitable for low-yield gas wells and gas wells that cannot carry liquid autonomously. By optimizing the shut-in time, plunger upward speed and production system, it can significantly improve the wellbore liquid lifting efficiency and gas well production capacity.

[0004] When several wells in a certain block adopt the plunger gas lift technology, a plunger gas lift well group is formed. If a large number of plunger gas wells are opened at the same time in a certain period of time, it may cause excessive pressure in the trunk line, affecting the production of other wells, and at the same time increase the load on the ground equipment and affect the service life of the equipment; if a large number of plunger gas wells are closed at the same time in a certain period of time, the flow in the trunk line is reduced and cannot reach the liquid carrying flow, which will cause liquid accumulation in the pipeline, and ice blockage may form at a certain temperature, seriously affecting normal production. In actual work, the production and operation scheduling of plunger gas well groups is mostly based on manual decision-making. This scheduling method is intensive and inefficient, and it is difficult to meet the actual production needs when the scale of the well group increases. At the same time, there is no professional scheduling software to assist. Therefore, a plunger gas lift well group staggered opening and closing well scheduling optimization method is needed to effectively solve the scheduling problems encountered by plunger gas lift well groups in the production process and realize efficient, safe and stable production management. Summary of the invention

[0005] If a large number of plunger gas lift wells are opened or closed in the same period of time, it will cause excessive mainline pressure or insufficient flow, which will affect the equipment life and production efficiency, and may cause pipeline liquid accumulation and ice blockage. In actual production, the scheduling method of manual decision-making has problems such as high intensity and low efficiency. To solve the above problems, a scheduling optimization method for staggered opening and closing of plunger gas lift wells is proposed.

[0006] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical scheme:

[0007] The present invention provides a method for optimizing the scheduling of peak-shifted switching wells in a plunger gas lift well group, comprising the following steps:

[0008] S1. Acquire the field data of the plunger gas lift well group, including but not limited to the production system of the plunger gas lift well, the state of the switch well, the wellhead pressure, and the wellhead flow rate;

[0009] S2. Taking the minimum number of wells opened and closed in the same time period as the objective function, considering the scheduling constraints of pressure difference between gas wells, staggered well opening time, and well opening and closing status, a mixed integer linear programming model for collaborative optimization of production operation scheduling of plunger gas lift well group based on discrete time description, namely, MILP model;

[0010] S3. Using genetic algorithm, solve the MILP model for collaborative optimization of production operation scheduling of plunger gas lift well group;

[0011] S4. Obtaining the feasibility scheme of peak-shifting scheduling of plunger gas lift well groups under different objective functions;

[0012] S5. Evaluate the feasibility of peak-shifting scheduling for plunger gas lift well groups and select the best peak-shifting well switching scheduling plan.

[0013] Further technology of the present invention:

[0014] Preferably, the acquired field data needs to be cleaned in S1, and data cleaning includes removing duplicate data, correcting erroneous data, filling missing values, and processing abnormal values.

[0015] Preferably, the steps of establishing the MILP model in S2 are:

[0016] S2.1 Model assumptions are described as:

[0017] (1) Considering the periodicity of the well opening and closing system, the scheduling scheme is a 24-hour gas well scheduling Gantt chart with a 24-hour scheduling cycle.

[0018] (2) The opening and closing system of a single well remains unchanged, and only the first opening time of a gas well within the scheduling cycle is considered to be changed;

[0019] (3) Each well has three variables in terms of time: ΔT, T1, and T2; ΔT represents the time interval from the zero time of the scheduling cycle to the first opening of a single well; T1 represents the opening time of a single well in the well switching system; T2 represents the closing time of a single well in the well switching system. T1 and T2 of each well are known parameters, and the variable solved by the model is ΔT.

[0020] (4) At the beginning of the cycle, all wells are in the closed state by default. After ΔT, they are changed to the open state. Subsequently, the wells are kept open for T1 and then closed. After the closed state lasts for T2, they are changed to the open state and kept open for T1... and so on, and the cycle repeats until the end of the scheduling cycle;

[0021] (5) In the operation of plunger gas lift wells, only the opening and closing well operations are considered. The basis for judging the simultaneous opening of different gas wells is: the transition from the closed well state to the open well state within the same discrete time;

[0022] (6) The scheduling work time axis of the plunger gas lift well group, that is, the scheduling cycle, is uniformly discretized into time slices of the same fixed length, and the well opening and closing operations occur on specific time slices;

[0023] S2.2 model constraints include: inter-well pressure constraints, staggered well opening time constraints, and switch status constraints;

[0024] (1) Interwell pressure constraints:

[0025] When the group of plunger gas lift wells opened at the same time includes a pair of wells with a large wellhead oil pressure difference, the high-pressure wells are likely to block the low-pressure wells, resulting in a decrease in the production of the low-pressure wells. Therefore, the first opening time of the high-pressure wells should be less than the first opening time of the low-pressure wells:

[0026] ΔT h ≤ΔT l (Formula 1);

[0027] Where, ΔT h Indicates the first opening time of the high-pressure well, h; ΔT l represents the first opening time of the low-pressure well, h;

[0028] (2) Peak well opening time constraints:

[0029] The time interval ΔT from the start of cycle zero to the first opening of a single well shall not be greater than the shut-in time T2 of the well;

[0030] ΔT≤T2 (Formula 2);

[0031] (3) Switch state constraints:

[0032] Let the cycle start at time zero. As the discrete time period increases, the switch status of each gas well should change according to,,:

[0033]

[0034] Among them, z i,t Indicates the on / off state of single well i in period t, which is a binary variable. If it is 0, it means that the well is in the closed state in period t, and if it is 1, it means that the well is in the open state in period t; ΔT i is a decision variable, which represents the time interval from the zero time of the cycle to the first opening of the well i; T 1i represents the opening time of single well i in the well opening and closing system; T 2i It represents the shut-in time of single well i in the well opening and closing system;

[0035] The objective function of the S2.3 model is:

[0036] The objective function is to minimize the number of wells opened simultaneously in each discrete time period within the scheduling interval under the premise of satisfying all the above constraints. The expression function is as follows:

[0037] minf={m0,m1,...m t} max (Formula 5);

[0038] Among them, m0 represents the number of wells opened simultaneously in time period 0, m1 represents the number of wells opened simultaneously in time period 1, and so on. t represents the number of wells opened simultaneously in time period t. t The maximum value in the set is obtained to obtain the maximum number of wells opened simultaneously in each time period, and the minimum optimization is performed to minimize the number of wells opened simultaneously in each time period; m t The expression is:

[0039]

[0040] Preferably, the specific process of solving the S3 model is:

[0041] S3.1 First, chromosome coding is designed. A chromosome is defined as the ΔT of each well in a scheduling scheme. Assuming there are Tn time periods and Wn gas wells, the length of a chromosome is Wn. Taking 24 time periods and 5 gas wells as an example, the length of each chromosome in the generated chromosomes is 5, and the value range of each point is 0~T 2imax ;

[0042] S3.2 Since the solution target of the model is the number of wells opened simultaneously in each time period, the number of wells opened simultaneously is defined as the chromosome fitness function:

[0043] Fitness={m0,m1,...m t} max +f(x) (Equation 7);

[0044] Among them, f(x) is the penalty function, and the specific penalty function is defined in S3.3; if the chromosome fitness evaluation value is smaller, the scheduling scheme is better, otherwise it is worse;

[0045] S3.3 randomly generates initial solutions according to the chromosome encoding design in S3.1, and obtains an initial solution set consisting of ΔT of each well; at the same time, the following constraints are processed:

[0046] (1) Satisfy the interwell pressure constraint:

[0047] Set the penalty function as a filter condition:

[0048]

[0049] Among them, P hd Indicates the maximum oil pressure difference of the wells opened in the same time period;

[0050] (2) Meeting the time constraints for staggered well opening:

[0051] Since the decision variable ΔT has different ranges for different gas wells, range conversion should be performed. The conversion formula is as follows:

[0052]

[0053] Relative value of ΔT to (T 2i +1) modulo, then when the value range of ΔT is 0 to 10, ΔT i The range is 0~T 2i ;

[0054] (3) Satisfy the switch state constraints:

[0055] As the discrete time period increases, the switch state z of each gas well i,t Should be based on z i,t 、T 1i 、T 2i Make changes, and refer to formulas (3) and (4) for the assignment process;

[0056] S3.4 adopts a uniform mutation strategy, selecting loci in the individual coding string one by one as potential mutation sites. For each selected mutation site, a value is randomly drawn from the value range of the corresponding gene with a set mutation probability Pm to replace the original gene value; a uniform crossover strategy is adopted, involving two paired individuals, each of which exchanges genes at each locus with the same crossover probability Pr, thereby deriving two new individuals.

[0057] Preferably, the specific process of S4 is:

[0058] S4.1 sets the objective function, sets the fitness function in S3.2 according to the objective function, sets the mutation probability Pm and crossover probability Pr in S3.4, and sets the number of iterations GEN;

[0059] S4.2 The counter variable G is set to zero. When G=1, the chromosome fitness is calculated and the current solution is retained;

[0060] S4.3 performs crossover and mutation operations to generate a new population, G = 2, calculates chromosome fitness, and selects to retain the historical optimal solution; repeats the above operations until G = GEN, outputs the global optimal solution, and forms a feasible plan for staggered peak scheduling of plunger gas lift well groups under the objective function;

[0061] S4.4 sets different objective functions, repeats S4.1 to S4.3, and obtains feasible solutions for peak-shifting scheduling of plunger gas lift well groups under different objective functions.

[0062] Preferably, the specific process of S5 is:

[0063] S5.1 First, compare the number of wells opened simultaneously in each period before and after the peak-shifting scheduling, and make a preliminary evaluation of the plan;

[0064] S5.2 Establish an evaluation index for the peak-shifting scheduling scheme of plunger gas lift well groups. The evaluation index consists of two parts: the standard deviation of oil pressure between wells and the gas production of the well group. The expression is as follows:

[0065]

[0066] In the formula, σ represents the standard deviation of oil pressure between wells, which reflects the degree of dispersion between the oil pressure values ​​of all open wells and their average value within a certain period of time. The smaller the dispersion of this value, the closer the oil pressure between wells is, which means that the pipeline pressure fluctuation is also smaller; q represents the gas production of the well group;

[0067] S5.3 uses simulation software to establish an integrated simulation model of the actual block, and inputs the scheduling plan set obtained under different parameters in S4.4 into the integrated model, and sorts the simulation results according to this evaluation index. The higher the index value, the better the effect, thereby obtaining the best peak-shifting well switching scheduling plan.

[0068] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0069] 1. The present invention adopts an automated optimization method, which reduces the intensity of manual decision-making and possible errors, and improves scheduling efficiency and accuracy.

[0070] 2. The present invention significantly reduces pipeline pressure fluctuations and increases the overall gas production of the well group by reducing the number of wells opened in the same period.

[0071] 3. The genetic algorithm is used for optimization and solving, which can obtain the approximate optimal solution in a relatively short time and is suitable for the scheduling problems of large-scale well groups.

[0072] 4. Through simulation and verification of comprehensive evaluation indicators, the optimal solution selected not only has advantages in economic benefits, but also excels in production stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of 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 creative work.

[0074] Figure 1 This is a flow chart of the optimization method for scheduling peak-shifted switching wells of a plunger gas lift well group according to the present invention;

[0075] Figure 2 It is a Gantt chart of the feasibility scheme 1 for staggered peak scheduling of plunger gas lift well groups in the embodiment;

[0076] Figure 3 It is a Gantt chart of the feasibility scheme 2 for staggered peak scheduling of plunger gas lift well groups in the embodiment;

[0077] Figure 4 It is a Gantt chart of the feasibility scheme 3 of the peak shifting scheduling of the plunger gas lift well group in the embodiment;

[0078] Figure 5 It is a Gantt chart of the feasibility scheme 4 for staggered peak scheduling of plunger gas lift well groups in the embodiment;

[0079] Figure 6 It is a curve diagram of the number of wells opened simultaneously before the optimization of the peak-shifting scheduling of the plunger gas lift well group in the embodiment;

[0080] Figure 7 This is a graph showing the number of wells being opened simultaneously for a feasible solution 1 for staggered peak scheduling of a plunger gas lift well group in the embodiment. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be further described in detail below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention but not all. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0082] Example:

[0083] The optimization method for scheduling the peak-shifting wells of the plunger gas lift well group proposed in this embodiment is as follows: Figure 1 As shown, the following steps are included:

[0084] S1. There are 80 plunger gas lift wells in a block of an oil field. The field data of the plunger gas lift well group is obtained, including but not limited to the production system of the plunger gas lift well (well opening time, well closing time), well opening and closing status, wellhead pressure, wellhead flow, etc. The acquired field data needs to be cleaned to remove duplicate data, correct erroneous data, fill in missing values, and process abnormal values.

[0085] S2. Taking the minimum number of wells opened and closed in the same time period as the objective function, considering the scheduling constraints of pressure difference between gas wells, staggered well opening time, and well opening and closing status, a mixed integer linear programming (MILP) model for collaborative optimization of production operation scheduling of plunger gas lift well group based on discrete time description is established. The specific process is as follows:

[0086] S2.1 Model assumptions are described as:

[0087] (1) Considering the periodicity of the well opening and closing system, the scheduling scheme is a 24-hour gas well scheduling Gantt chart with a 24-hour scheduling cycle.

[0088] (2) The opening and closing system of a single well remains unchanged, and only the first opening time of a gas well within the scheduling cycle is considered to be changed.

[0089] (3) Each well has three variables in terms of time: ΔT, T1, and T2. ΔT represents the time interval from the zero time of the scheduling cycle to the first opening of a single well; T1 represents the opening time of a single well in the well switching system; and T2 represents the closing time of a single well in the well switching system. T1 and T2 of each well are known parameters, and the variable solved by the model is ΔT.

[0090] (4) At the beginning of the cycle, all wells are in the closed state by default. After ΔT, they are changed to the open state. Subsequently, the wells are kept open for T1 and then closed. After T2, the closed state is changed to the open state and the wells are kept open for T1... and so on, and the cycle repeats until the end of the scheduling cycle.

[0091] (5) In the operation of plunger gas lift wells, only the opening and closing well operations are considered. The basis for judging whether different gas wells are opened at the same time is: the wells are changed from the closed state to the open state within the same discrete time.

[0092] (6) The scheduling work time axis of the plunger gas lift well group, that is, the scheduling cycle, is uniformly discretized into time slices of the same fixed length, and the well opening and closing operations occur on specific time slices.

[0093] The constraints of the S2.2 model include: inter-well pressure constraints, staggered well opening time constraints, and switch status constraints.

[0094] (1) Interwell pressure constraints

[0095] When the group of plunger gas lift wells opened at the same time includes a pair of wells with a large wellhead oil pressure difference, the high-pressure wells are likely to block the low-pressure wells, resulting in a decrease in the production of the low-pressure wells. Therefore, the first opening time of the high-pressure wells should be shorter than the first opening time of the low-pressure wells:

[0096] ΔT h ≤ΔT l (Formula 1)

[0097] Where, ΔT h Indicates the first opening time of the high-pressure well, h; ΔT l Indicates the first opening time of the low-pressure well, h.

[0098] (2) Peak well opening time constraints

[0099] The time interval ΔT from the start of cycle zero to the first opening of a single well shall not be greater than the well shut-in time T2 of the well.

[0100] ΔT≤T2 (Formula 2)

[0101] (3) Switch state constraints

[0102] Let the cycle start at time zero. As the discrete time period increases, the switch status of each gas well should change according to,,:

[0103]

[0104] Among them, z i,t Indicates the on / off state of single well i in period t, which is a binary variable. If it is 0, it means that the well is in the closed state in period t, and if it is 1, it means that the well is in the open state in period t; ΔT i is a decision variable, which represents the time interval from the zero time of the cycle to the first opening of the well i; T 1i represents the opening time of single well i in the well opening and closing system; T 2i It indicates the shut-in time of single well i in the well opening and closing system.

[0105] The objective function of the S2.3 model is:

[0106] The objective function is to minimize the number of wells opened simultaneously in each discrete time period within the scheduling interval while satisfying all the above constraints. The expression function is as follows:

[0107] minf={m0,m1,...m t} max (Formula 5)

[0108] Among them, m0 represents the number of wells opened simultaneously in time period 0, m1 represents the number of wells opened simultaneously in time period 1, and so on. trepresents the number of wells opened simultaneously in time period t. t The maximum value in the set is obtained to obtain the maximum number of wells opened simultaneously in each time period, and the minimum optimization is performed to minimize the number of wells opened simultaneously in each time period. t The expression is:

[0109]

[0110] S3. Using genetic algorithm, solve the MILP model for collaborative optimization of production operation scheduling of plunger gas lift well group;

[0111] The solution method of the mixed integer linear programming (MILP) model for the coordinated optimization of the production operation scheduling of the S3 plunger gas lift well group is the genetic algorithm. The genetic algorithm is the product of the combination of the random algorithm and the local search algorithm, which can more effectively find the approximate optimal solution. In the multi-objective solution problem, the genetic algorithm has the characteristics of high robustness and strong search ability, and is more suitable for solving more complex scheduling optimization problems. It has significant advantages in solving large-scale scheduling problems of plunger gas lift well group production. The specific process of model solution is:

[0112] S3.1 First, chromosome coding is designed. The chromosome is defined as the ΔT of each well in a scheduling scheme. There are 144 time periods and 80 gas wells. The length of a chromosome is 80, and the value range of each point is 0~T 2imax .

[0113] S3.2 Since the solution target of the model is the number of wells opened simultaneously in each time period, the number of wells opened simultaneously is defined as the chromosome fitness function:

[0114] Fitness={m0,m1,...m t} max +f(x) (Equation 7)

[0115] Among them, f(x) is the penalty function, and the specific penalty function is defined in S3.3. If the chromosome fitness evaluation value is smaller, the scheduling scheme is better, otherwise it is worse.

[0116] S3.3 According to the chromosome coding design in S3.1, the initial solution can be randomly generated, and the initial solution set consisting of ΔT of each well is obtained. However, the first opening time of the obtained gas well is random, resulting in most chromosomes failing to meet the constraints in S2.2. The following constraint processing is required to ensure that the chromosomes in the population meet the inter-well pressure constraints, staggered opening time constraints, and switch state constraints, thereby improving the solution quality.

[0117] (1) Satisfying the interwell pressure constraints

[0118] In order to ensure that the oil pressure difference of the gas wells opened in the same period is small and to avoid the situation where high-pressure wells and low-pressure wells are opened at the same time, a penalty function is set as the screening condition:

[0119]

[0120] Among them, P hd Indicates the maximum oil pressure difference of wells opened in the same time period.

[0121] (2) Meeting the time constraints for staggered well opening

[0122] Since the decision variable ΔT has different ranges for different gas wells, range conversion should be performed. The conversion formula is as follows:

[0123]

[0124] Relative value of ΔT to (T 2i +1) modulo, then when the value range of ΔT is 0 to 10, ΔT i The range is 0~T 2i .

[0125] (3) Satisfy switch state constraints

[0126] As the discrete time period increases, the switch state z of each gas well i,t Should be based on z i,t 、T 1i 、T 2i Make changes, and refer to formulas (3) and (4) for the assignment process.

[0127] S3.4 adopts a uniform mutation strategy, selecting loci in the individual coding string one by one as potential mutation sites. For each selected mutation site, a value is randomly drawn from the value range of the corresponding gene with a set mutation probability Pm to replace the original gene value. The uniform crossover strategy is adopted, which involves two paired individuals, each of which exchanges genes at each locus with the same crossover probability Pr, thereby deriving two new individuals.

[0128] S4. Obtain the feasibility scheme of peak-shifting scheduling of plunger gas lift well groups under different objective functions. The specific process is as follows:

[0129] S4.1 sets the objective function to open a maximum of 5 wells in the same period, sets the fitness function in S3.2 according to the objective function, sets the mutation probability Pm = 0.0005, the crossover probability Pr = 0.7 in S3.4, and sets the number of iterations GEN = 100;

[0130] S4.2 The counter variable G is set to zero. When G=1, the chromosome fitness is calculated and the current solution is retained;

[0131] S4.3 performs crossover and mutation operations to generate a new population (G=2), calculates chromosome fitness, and selects to retain the historical optimal solution. Repeat the above operations until G=GEN, outputs the global optimal solution, and forms a feasible plan for staggered scheduling of plunger gas lift wells under this objective function (Scheme 1). The Gantt chart under Scheme 1 is shown as follows: Figure 2 As shown, the vertical axis represents the plunger gas lift well number, the horizontal axis represents the scheduling time, the green rectangular box represents the well opening production state, and the red rectangular box represents the well closing recovery state.

[0132] S4.4 sets different objective functions and repeats S4.1 to S4.3 to obtain the feasibility schemes for peak-shifting scheduling of plunger gas lift well groups under different objective functions. The Gantt charts for peak-shifting scheduling under schemes 2, 3, and 4 are shown in the figure. Figure 3 , 4 , as shown in Figure 5.

[0133] S5. Evaluate the feasibility of the peak-shifting scheduling of plunger gas lift wells. The specific process is as follows:

[0134] S5.1 First, compare the number of wells opened simultaneously in each period before and after the peak shifting scheduling, and make a preliminary evaluation of the plan. The curve of the number of wells opened simultaneously in each period before optimization is as follows: Figure 6 As shown in the figure, after optimization (Scheme 1), the number of wells opened simultaneously in each period is as follows Figure 7 As shown in the figure, where the horizontal axis is time and the vertical axis is the number of wells opened at the same time, it can be seen that before optimization, the number of wells opened at the same time fluctuated significantly, and even reached 9 wells opened at the peak, showing great instability. After optimization, the number of wells opened at the same time was effectively controlled, the number was significantly reduced, and it was stably maintained below 5 wells, and the optimization effect was good.

[0135] S5.2 Establish an evaluation index for the peak-shifting scheduling scheme of plunger gas lift well groups. The evaluation index consists of two parts: the standard deviation of oil pressure between wells and the gas production of the well group. The expression is as follows:

[0136]

[0137] In the formula, σ represents the standard deviation of oil pressure between wells, which reflects the degree of dispersion between the oil pressure values ​​of all open wells and their average value within a certain period of time. The smaller the dispersion of this value, the closer the oil pressure between wells is, which means that the pipeline pressure fluctuation is also smaller; q represents the gas production of the well group.

[0138] S5.3 uses simulation software to establish a simulation integrated model of the block, and inputs the scheduling scheme set obtained under different parameters in S4.4 into the integrated model, and sorts the simulation results according to this evaluation index. The evaluation indexes of different schemes in this embodiment are shown in Table 1.

[0139] Table 1 Scheme evaluation table

[0140]

[0141] As can be seen from Table 1, there is a significant negative correlation between the standard deviation of oil pressure between wells and the gas production of the well group. When the standard deviation of oil pressure between wells increases, the gas production of the well group will decrease accordingly. Further analysis shows that by reducing the number of wells opened at the same time, the standard deviation of oil pressure between wells can be effectively reduced, thereby indirectly increasing the gas production of the well group. Based on the comprehensive evaluation of the gas production of the well group and the standard deviation of oil pressure between wells, Scheme 3 is the best peak-shifting well opening and closing scheme.

[0142] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for optimizing the scheduling of staggered opening and closing wells in a plunger gas lift well group, characterized in that: The following steps are involved: S1. Acquire the field data of the plunger gas lift well group, including but not limited to the production system of the plunger gas lift well, the state of the switch well, the wellhead pressure, and the wellhead flow rate; S2. Taking the minimum number of wells opened and closed in the same time period as the objective function, considering the scheduling constraints of pressure difference between gas wells, staggered well opening time, and well opening and closing status, a mixed integer linear programming model for collaborative optimization of production operation scheduling of plunger gas lift well group based on discrete time description, namely, MILP model; S3. Using genetic algorithm, solve the MILP model for collaborative optimization of production operation scheduling of plunger gas lift well group; S4. Obtaining the feasibility scheme of peak-shifting scheduling of plunger gas lift well groups under different objective functions; S5. Evaluate the feasibility of peak-shifting scheduling for plunger gas lift well groups and select the best peak-shifting well switching scheduling plan.

2. The method for optimizing the scheduling of staggered opening and closing wells of a plunger gas lift well group according to claim 1, characterized in that: In S1, the acquired field data needs to be cleaned. Data cleaning includes removing duplicate data, correcting erroneous data, filling missing values, and processing outliers.

3. The method for optimizing the scheduling of the peak-shifting wells of a plunger gas lift well group according to claim 1, characterized in that: The steps for establishing the MILP model in S2 are: S2.1 Model assumptions are described as follows: (1) Considering the periodicity of the well opening and closing system, the scheduling scheme is a 24-hour gas well scheduling Gantt chart with a 24-hour scheduling cycle. (2) The opening and closing system of a single well remains unchanged, and only the first opening time of a gas well within the scheduling cycle is considered to be changed; (3) Each well has three variables in terms of time: ΔT, T1, and T2; ΔT represents the time interval from the zero time of the scheduling cycle to the first opening of a single well; T1 represents the opening time of a single well in the well switching system; T2 represents the closing time of a single well in the well switching system. T1 and T2 of each well are known parameters, and the variable solved by the model is ΔT. (4) At the beginning of the cycle, all wells are in the closed state by default. After ΔT, they are changed to the open state. Subsequently, the wells are kept open for T1 and then closed. After the closed state lasts for T2, they are changed to the open state and kept open for T1... and so on, and the cycle repeats until the end of the scheduling cycle; (5) In the operation of plunger gas lift wells, only the opening and closing well operations are considered. The basis for judging the simultaneous opening of different gas wells is: the transition from the closed well state to the open well state within the same discrete time; (6) The scheduling work time axis of the plunger gas lift well group, that is, the scheduling cycle, is uniformly discretized into time slices of the same fixed length, and the well opening and closing operations occur on specific time slices; S2.2 model constraints include: inter-well pressure constraints, staggered well opening time constraints, and switch status constraints; (1) Interwell pressure constraints: When the group of plunger gas lift wells opened at the same time includes a pair of wells with a large wellhead oil pressure difference, the high-pressure wells are likely to block the low-pressure wells, resulting in a decrease in the production of the low-pressure wells. Therefore, the first opening time of the high-pressure wells should be less than the first opening time of the low-pressure wells: ΔT h ≤ΔT l (Formula 1); Where, ΔT h Indicates the first opening time of the high-pressure well, h; ΔT l represents the first opening time of the low-pressure well, h; (2) Peak well opening time constraints: The time interval ΔT from the start of cycle zero to the first opening of a single well shall not be greater than the shut-in time T2 of the well; ΔT≤T2 (Formula 2); (3) Switch state constraints: Let the cycle start at time zero. As the discrete time period increases, the switch status of each gas well should change according to,,: Among them, z i,t Indicates the on / off state of single well i in period t, which is a binary variable. If it is 0, it means that the well is in the closed state in period t, and if it is 1, it means that the well is in the open state in period t; ΔT i is a decision variable, which represents the time interval from the zero time of the cycle to the first opening of the well i; T 1i represents the opening time of single well i in the well opening and closing system; T 2i It represents the shut-in time of single well i in the well opening and closing system; The objective function of the S2.3 model is: The objective function is to minimize the number of wells opened simultaneously in each discrete time period within the scheduling interval under the premise of satisfying all the above constraints. The expression function is as follows: minf={m0,m1,...m t } max (Formula 5); Among them, m0 represents the number of wells opened simultaneously in time period 0, m1 represents the number of wells opened simultaneously in time period 1, and so on. t represents the number of wells opened simultaneously in time period t. t The maximum value in the set is obtained to obtain the maximum number of wells opened simultaneously in each time period, and the minimum optimization is performed to minimize the number of wells opened simultaneously in each time period; m t The expression is:

4. The method for optimizing the scheduling of staggered opening and closing wells of a plunger gas lift well group according to claim 3, characterized in that: The specific process of solving the S3 model is as follows: S3.1 First, chromosome coding is designed. A chromosome is defined as the ΔT of each well in a scheduling scheme. Assuming there are Tn time periods and Wn gas wells, the length of a chromosome is Wn. Taking 24 time periods and 5 gas wells as an example, the length of each chromosome in the generated chromosomes is 5, and the value range of each point is 0~T 2imax ; S3.2 Since the solution target of the model is the number of wells opened simultaneously in each time period, the number of wells opened simultaneously is defined as the chromosome fitness function: Fitness={m0,m1,...m t } max +f(x) (Equation 7); Among them, f(x) is the penalty function, and the specific penalty function is defined in S3.3; if the chromosome fitness evaluation value is smaller, the scheduling scheme is better, otherwise it is worse; S3.3 randomly generates initial solutions according to the chromosome encoding design in S3.1, and obtains an initial solution set consisting of ΔT of each well; at the same time, the following constraints are processed: (1) Satisfy the interwell pressure constraint: Set the penalty function as a filter condition: Among them, P hd Indicates the maximum oil pressure difference of the wells opened in the same time period; (2) Meeting the time constraints for staggered well opening: Since the decision variable ΔT has different ranges for different gas wells, range conversion should be performed. The conversion formula is as follows: Relative value of ΔT to (T 2i +1) modulo, then when the value range of ΔT is 0 to 10, ΔT i The range is 0~T 2i ; (3) Satisfy the switch state constraints: As the discrete time period increases, the switch state z of each gas well i,t Should be based on z i,t 、T 1i 、T 2i Make changes, and refer to formulas (3) and (4) for the assignment process; S3.4 adopts a uniform mutation strategy, selecting loci in the individual coding string one by one as potential mutation sites. For each selected mutation site, a value is randomly drawn from the value range of the corresponding gene with a set mutation probability Pm to replace the original gene value; a uniform crossover strategy is adopted, involving two paired individuals, each of which exchanges genes at each locus with the same crossover probability Pr, thereby deriving two new individuals.

5. The method for optimizing the scheduling of staggered opening and closing wells of a plunger gas lift well group according to claim 4, characterized in that: The specific process of S4 is as follows: S4.1 sets the objective function, sets the fitness function in S3.2 according to the objective function, sets the mutation probability Pm and crossover probability Pr in S3.4, and sets the number of iterations GEN; S4.2 The counter variable G is set to zero. When G=1, the chromosome fitness is calculated and the current solution is retained; S4.3 performs crossover and mutation operations to generate a new population, G = 2, calculates chromosome fitness, and selects to retain the historical optimal solution; repeats the above operations until G = GEN, outputs the global optimal solution, and forms a feasible plan for staggered peak scheduling of plunger gas lift well groups under the objective function; S4.4 sets different objective functions, repeats S4.1 to S4.3, and obtains feasible solutions for peak-shifting scheduling of plunger gas lift well groups under different objective functions.

6. The method for optimizing the scheduling of staggered opening and closing wells of a plunger gas lift well group according to claim 5, characterized in that: The specific process of S5 is as follows: S5.1 First, compare the number of wells opened simultaneously in each period before and after the peak-shifting scheduling, and make a preliminary evaluation of the plan; S5.2 Establish an evaluation index for the peak-shifting scheduling scheme of plunger gas lift well groups. The evaluation index consists of two parts: the standard deviation of oil pressure between wells and the gas production of the well group. The expression is as follows: In the formula, σ represents the standard deviation of oil pressure between wells, which reflects the degree of dispersion between the oil pressure values ​​of all open wells and their average value within a certain period of time. The smaller the dispersion of this value, the closer the oil pressure between wells is, which means that the pipeline pressure fluctuation is also smaller; q represents the gas production of the well group; S5.3 uses simulation software to establish an integrated simulation model of the actual block, and inputs the scheduling plan set obtained under different parameters in S4.4 into the integrated model, and sorts the simulation results according to this evaluation index. The higher the index value, the better the effect, thereby obtaining the best peak-shifting well switching scheduling plan.

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