An oilfield multi-cycle partial pressure water injection optimization method based on improved butterfly algorithm

Abstract

The present invention provides an oilfield multi-cycle partial pressure water injection optimization method based on an improved butterfly algorithm, the method comprising: obtaining basic data of a target water injection network; constructing an objective function of an oilfield water injection optimization model considering multi-cycle partial pressure water injection; constructing constraint conditions of an oilfield water injection optimization model considering multi-cycle partial pressure water injection; using an improved butterfly algorithm to solve a water injection scheme optimization model considering multi-cycle partial pressure water injection, and generating a multi-cycle partial pressure water injection optimization scheme. The present invention divides water injection wells in different pressure ranges into groups and divides pressure, performs water injection in multiple cycles, and determines an optimal water injection scheme based on solving the optimization model using the improved butterfly algorithm, which can effectively reduce throttling losses caused by conventional water injection methods in oil fields, improve the energy efficiency of water injection technology, and reduce the operating costs of oilfield water injection networks.

Description

Technical Field

[0001] The invention relates to the field of oilfield water injection optimization, and in particular to an oilfield multi-cycle partial pressure water injection optimization method based on an improved butterfly algorithm. Background Art

[0002] Oilfield water injection is one of the most important tasks in oil production and is of great significance in the development of oil fields. Compared with other substances, water injection has certain advantages. On the one hand, the source of water is easier to solve, and injecting water into the oil layer is economically feasible; on the other hand, water is an ideal medium for draining water from an oil layer. Energy consumption in oil production is an important part of the cost of oil development.

[0003] The power consumption of water injection network generally accounts for 33%-56% of the total power consumption of oil fields. In addition to the effective energy injected into the formation, the energy of water injection network is also consumed in motors, water injection pumps, regulating valves, pipe networks and wellheads. In the energy consumption distribution of typical domestic water injection network, the energy loss of motors accounts for about 4% of the total energy; the energy loss of water injection pumps accounts for about 20% of the total energy; the energy loss of water injection network accounts for about 20% of the total energy, while the effective energy actually injected into the formation accounts for only about 56% of the total energy. Among them, conventional water injection adopts the operation mode of average water injection within the water injection cycle. However, the water injection pressure of each water injection well in the water injection area is uneven and the difference is too large. In order to meet the highest water injection pressure demand in the system, the outlet pressure of the water injection station is too different from the required pressure of each well, which can easily lead to huge throttling losses and serious waste of water injection energy. Multi-cycle pressure-dividing water injection refers to combining water injection wells with similar water injection pressures, equipping them with suitable water injection pumps and corresponding water injection pipelines, and injecting water at different time periods for water injection well groups with different pressure ranges. The use of multi-cycle pressure-dividing water injection can effectively reduce the throttling loss caused by the large difference between the outlet pressure of the water injection station and the required pressure of each well, and reduce the operating cost of the water injection pipeline network.

[0004] Therefore, it is urgent to propose an oilfield multi-cycle partial pressure water injection optimization method based on the improved butterfly algorithm to further reduce the water injection cost of the oilfield water injection network and improve the economy of the water injection network operation plan. Summary of the invention

[0005] Based on the overall optimization strategy, the present invention proposes an oilfield multi-cycle partial pressure water injection optimization method based on the improved butterfly algorithm, which reduces the system energy consumption of the water injection network and reduces the total cost of the water injection network operation. The method comprises the following steps:

[0006] S1: Obtain basic data of the target water injection network;

[0007] S2: Construct the objective function of the water injection scheme optimization model considering multi-cycle partial pressure water injection;

[0008] S3: Construct constraint conditions to form an optimization model for water injection scheme considering multi-cycle partial pressure water injection;

[0009] S4: The improved butterfly algorithm is used to solve the water injection scheme optimization model considering multi-cycle partial pressure water injection, and a multi-cycle partial pressure water injection optimization scheme is generated.

[0010] Preferably, the multi-cycle partial pressure water injection refers to a method of combining water injection wells with similar water injection pressures, equipping them with suitable water injection pumps and corresponding water injection pipe networks, and injecting water at different time periods for water injection well groups with different pressure ranges.

[0011] Preferably, the basic data described in step S1 include pipe network structure data, water injection well operation parameters, water injection well location parameters, water injection station configuration parameters, water injection station location parameters, water injection cycle data, water injection pump operation parameters, and electricity prices.

[0012] Preferably, the objective function described in step S2 is to minimize the energy consumption cost of the water injection network pump unit, and the relationship is derived from the work done by the pump unit on the injected water in each period, various efficiencies of the pump unit, electricity prices, etc.:

[0013]

[0014] In the formula, i is the period number within the cycle, I 1 / I 2 / I 3 is the set of injection time periods of the injection well groups in the low-pressure, medium-pressure, and high-pressure ranges within the cycle, j is the number of the injection pump in the station, J is the set of injection pump numbers in the station, and p ele is the electricity price, ΔT i is the unit duration of each water injection period, γ ij is the start and stop status of the j-th water injection pump in the i-th period, Q i is the outlet flow rate of the jth water injection pump in the i-th period, H ij is the outlet head of the j-th water injection pump in the i-th period, ρ is the density of injected water, g represents the acceleration of gravity, α is the unit conversion coefficient, η bij is the operating efficiency of the j-th water injection pump in the i-th period, η cij is the transmission efficiency of the j-th water injection pump in the i-th period, η dij The efficiency of the motor equipped for the j-th water injection pump in the i-th period, η pij The frequency conversion efficiency of the motor equipped for the j-th water injection pump in the i-th period.

[0015] Preferably, the constraints described in step S3 can be divided into in-station and out-station constraints, the in-station constraints include water injection pump head constraints, water injection pump efficiency constraints, variable frequency speed ratio constraints, single pump flow constraints, parallel head constraints, parallel flow constraints, water injection cycle constraints, and the out-station constraints include water injection well minimum head constraints, pipeline friction constraints, node flow constraints, and total water injection balance constraints;

[0016] The head constraint of the water injection pump stipulates the relationship between the head and flow rate of the water injection pump under variable frequency speed regulation, and the relationship is:

[0017]

[0018] In the formula, H ij is the head of the j-th injection pump in the i-th period, Q ij is the flow rate of the j-th water injection pump in the i-th period, k ij is the speed ratio of the j-th water injection pump in the i-th period, H 0 is the virtual total head when the flow rate is 0, S j is the internal virtual resistance coefficient of the j-th water injection pump;

[0019] The efficiency constraint of the water injection pump stipulates the relationship between the efficiency and flow rate of the water injection pump under variable frequency speed regulation, and the relationship is:

[0020]

[0021] Where η bij is the operating efficiency of the j-th water injection pump in the i-th period, Q ij is the flow rate of the j-th water injection pump in the i-th period, k ij is the speed ratio of the j-th water injection pump in the i-th period, a / b / c is the pump efficiency coefficient;

[0022] The variable frequency speed ratio constraint stipulates that the speed ratio of each pump speed regulating device should meet a certain range, and the relationship is:

[0023]

[0024] In the formula, is the lowest speed ratio of the j-th water injection pump in the i-th period; is the maximum speed ratio of the j-th water injection pump in the i-th period, k ij is the speed ratio of the j-th water injection pump in the i-th period;

[0025] The single pump flow constraint stipulates that each pump meets the flow interval in the high efficiency zone when running, and the relationship is:

[0026]

[0027] In the formula, is the critical point of the left flow rate in the high-efficiency zone of the j-th water injection pump in the i-th period, is the critical point of the right flow rate in the high-efficiency zone of the j-th water injection pump in the i-th period, Q ij is the flow rate of the j-th water injection pump in the i-th period;

[0028] The parallel head constraint stipulates that each water injection pump meets the principle of equal pressure of parallel pumps. The pressure of each pump should be equal to the outlet pressure. The relationship is:

[0029]

[0030] In the formula, is the outlet head of the water injection station in the i-th period, H ij is the outlet head of the j-th water injection pump in the i-th period;

[0031] The parallel flow constraint stipulates that for each water injection pump, according to the principle of superposition of parallel pump flow, the flow of each pump should be equal to the sum of the outlet flow, and the relationship is:

[0032]

[0033] In the formula, is the outlet flow of the injection station in the i-th period, Q ij is the outlet flow of the jth water injection pump in the i-th period, and J is the set of water injection pump numbers in the station;

[0034] The injection cycle constraint stipulates that the injection period of each pressure range injection well group should meet the injection period set of low pressure, medium pressure and high pressure range injection well groups. The union of the injection period sets is the entire injection cycle, and the relationship is:

[0035] I 1 UI 2 UI 3 =I

[0036] In the formula, I 1 is the injection period set of the injection well group in the low pressure range, I 2 is the water injection period set of the medium pressure range water injection well group, I 3 is the set of injection time periods of the injection well group in the high-pressure range, and I is the entire injection cycle;

[0037] The minimum head constraint of the injection well stipulates that each single well must meet the requirements of the highest head injection well, and the relationship is:

[0038]

[0039] In the formula, is the discharge head of the water injection station in the i-th period, is the injection head requirement of the w-th injection well, H wf is the total friction loss along the w-th injection well, Δhw is the height difference between the wth injection well and the injection station;

[0040] The friction constraint along the pipeline stipulates that for any pipe element, its nodes are m and n respectively. If its flow direction is m→n, it should satisfy the energy equation, and the relationship is:

[0041]

[0042] In the formula, ΔH l is the absolute value of the friction loss along the pipe section l, H m is the lift at node m, H n is the lift at node n, Q l is the absolute value of the flow rate in pipe section l, d l is the diameter of pipe section l, L l is the length of pipe section l, C is the calculation coefficient;

[0043] The node flow constraint stipulates that any node must satisfy the node flow balance, that is, the flow flowing to the node at any time in the pipeline network is equal to the flow flowing out of the node. The relationship is:

[0044]

[0045] In the formula, q m is the traffic of node m, Q l is the flow rate of the pipe segment l connected to the node m, β ml is the flow connection coefficient of the pipe element connected to the node m, and L is the set of pipe elements connected to the node m;

[0046] The total injection volume balance constraint stipulates that the entire water injection network must meet the system supply and demand flow balance: that is, during the entire water injection cycle, the sum of the outlet flow of the water injection station in each period should be equal to the sum of the daily demand and daily leakage of each well, and the relationship is:

[0047]

[0048] In the formula, Q i is the outlet flow of the injection station in the i-th period, Q wi is the required injection volume of the wth injection well in the ith period, I is the set of time periods in the cycle, W is the set of injection wells in the system, and ΔQ is the total flow loss in the cycle.

[0049] Preferably, the oilfield multi-cycle partial pressure water injection optimization model described in step S4 belongs to a mixed integer nonlinear programming model, and an improved butterfly algorithm is used when solving the model. The solving steps include:

[0050] S401: According to the basic data and constraints of the water injection network, the water injection time, number of operating pumps, flow distribution of each pump, speed ratio of each pump, efficiency of each pump, total power of the pump unit, total energy consumption cost of the pump unit, total power of the pump unit within the cycle, and total energy consumption cost of the pump unit within the cycle of each water injection well in the water injection network are initialized based on the Circle chaotic map, which is the initial population of the improved butterfly algorithm. The Circle chaotic map can be expressed as:

[0051]

[0052] S402: randomly generate the initial position of the butterfly individual in the search space, and calculate its fitness value and initial fragrance intensity according to the spatial position of the butterfly individual. The fitness value is the energy consumption cost of the water injection network pump unit, and the initial fragrance intensity calculation formula is as follows:

[0053] f=cI α

[0054] S403: Set the switching frequency P to control the algorithm for global search or local mining. During the iteration process, a random number rand of 0 to 1 needs to be generated for comparison with the switching frequency P. If P>rand, the algorithm performs a global search, otherwise it performs local mining. The improved butterfly algorithm introduces an adaptive weight coefficient to improve the update formula for global search and local mining. The adaptive weight coefficient, global search and local mining position update formula are as follows:

[0055]

[0056]

[0057]

[0058] S404: Update individual and global optimal solutions based on an iterative process until the model reaches a preset maximum number of iterations, and output the optimal solution obtained by the improved butterfly algorithm.

[0059] Preferably, the water injection optimization plan for partial pressure water injection in the water injection network described in step S4 includes the injection time of the water injection well, the number of operating pumps, the flow distribution of each pump, the speed ratio of each pump, the efficiency of each pump, the total power of the pump unit, the total energy consumption cost of the pump unit, the total power of the pump unit within the cycle, and the total energy consumption cost of the pump unit within the cycle.

[0060] This manual provides an oilfield multi-cycle partial pressure water injection optimization method based on the improved butterfly algorithm. Focusing on the multi-cycle partial pressure water injection strategy, an oilfield water injection optimization model is established. The improved butterfly algorithm with high optimization accuracy is used to solve the model to obtain a water injection plan that takes into account both applicability and economy, effectively reducing throttling losses and improving the system energy efficiency level. It has very important research significance and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of the present invention;

[0062] Figure 2 To improve the butterfly algorithm flow chart;

[0063] Figure 3 It is the pipe network layout diagram of the water injection network;

[0064] Figure 4 It is the layout diagram of the water injection pipe network after pressure division;

[0065] Figure 5 Results of percentage reduction in throttling losses for low-pressure water injection wells;

[0066] Figure 6 Results for percentage reduction of throttling losses for medium pressure water injection wells. DETAILED DESCRIPTION

[0067] 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 clearly and completely described 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, 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.

[0068] like Figure 1 As shown, the present invention provides an oil field multi-cycle partial pressure water injection optimization method based on an improved butterfly algorithm, comprising the following steps:

[0069] S1: Obtain basic data of the target water injection network;

[0070] S2: Construct the objective function of the water injection scheme optimization model considering multi-cycle partial pressure water injection;

[0071] S3: Construct constraint conditions to form an optimization model for water injection scheme considering multi-cycle partial pressure water injection;

[0072] S4: The improved butterfly algorithm is used to solve the water injection scheme optimization model considering multi-cycle partial pressure water injection, and a multi-cycle partial pressure water injection optimization scheme is generated.

[0073] In one embodiment, the multi-cycle pressure-dividing water injection refers to a method of combining water injection wells with similar water injection pressures, equipping them with suitable water injection pumps and corresponding water injection pipe networks, and injecting water at different time periods for water injection well groups with different pressure ranges.

[0074] In one embodiment, the basic data described in step S1 includes pipe network structure data, water injection well operation parameters, water injection well location parameters, water injection station configuration parameters, water injection station location parameters, water injection cycle data, water injection pump operation parameters, and electricity prices.

[0075] In one embodiment, the objective function described in step S2 is to minimize the energy consumption cost of the water injection network pump unit, and the relationship is derived from the work done by the pump unit on the injected water in each period, various efficiencies of the pump unit, electricity prices, etc.:

[0076]

[0077] In one embodiment, the constraints described in step S3 can be divided into in-station and out-station constraints. The in-station constraints include water injection pump head constraints, water injection pump efficiency constraints, variable frequency speed ratio constraints, single pump flow constraints, parallel head constraints, parallel flow constraints, and water injection cycle constraints. The out-station constraints include water injection well minimum head constraints, pipeline friction constraints, node flow constraints, and total water injection balance constraints.

[0078] The head constraint of the water injection pump stipulates the relationship between the head and flow rate of the water injection pump under variable frequency speed regulation, and the relationship is:

[0079]

[0080] The efficiency constraint of the water injection pump stipulates the relationship between the efficiency and flow rate of the water injection pump under variable frequency speed regulation, and the relationship is:

[0081]

[0082] The variable frequency speed ratio constraint stipulates that the speed ratio of each pump speed regulating device should meet a certain range, and the relationship is:

[0083]

[0084] The single pump flow constraint stipulates that each pump meets the flow interval in the high efficiency zone when running, and the relationship is:

[0085]

[0086] The parallel head constraint stipulates that each water injection pump meets the principle of equal pressure of parallel pumps. The pressure of each pump should be equal to the outlet pressure. The relationship is:

[0087]

[0088] The parallel flow constraint stipulates that for each water injection pump, according to the principle of superposition of parallel pump flow, the flow of each pump should be equal to the sum of the outlet flow, and the relationship is:

[0089]

[0090] The injection cycle constraint stipulates that the injection period of each pressure range injection well group should meet the injection period set of low pressure, medium pressure and high pressure range injection well groups. The union of the injection period sets is the entire injection cycle, and the relationship is:

[0091] I 1 UI 2 UI 3 =I (8)

[0092] The minimum head constraint of the injection well stipulates that each single well must meet the requirements of the highest head injection well, and the relationship is:

[0093]

[0094] The friction constraint along the pipeline stipulates that for any pipe element, its nodes are m and n respectively. If its flow direction is m→n, it should satisfy the energy equation, and the relationship is:

[0095]

[0096] The node flow constraint stipulates that any node must satisfy the node flow balance, that is, the flow flowing to the node at any time in the pipeline network is equal to the flow flowing out of the node. The relationship is:

[0097]

[0098] The total injection volume balance constraint stipulates that the entire water injection network must meet the system supply and demand flow balance: that is, during the entire water injection cycle, the sum of the outlet flow of the water injection station in each period should be equal to the sum of the daily demand and daily leakage of each well, and the relationship is:

[0099]

[0100] The symbols of formula (1) to formula (12) are shown in Table 1 and Table 2.

[0101] Table 1 Index and set of oilfield water injection optimization models considering multi-cycle partial pressure water injection

[0102] j∈J Station injection pump number collection l∈L A collection of pipe elements m / n The flow direction between two nodes of any pipe element is m→n w∈W Injection well number collection <![CDATA[i∈I 1 / I 2 / I 3 ]]> The injection time period of the injection well group in the low, medium and high pressure ranges within the cycle

[0103] Table 2 Known parameters of the oilfield water injection optimization model considering multi-cycle partial pressure water injection

[0104]

[0105]

[0106] In one embodiment, the oilfield water injection optimization model considering the multi-cycle partial pressure formula described in step S4 belongs to a mixed integer nonlinear programming model. The improved butterfly algorithm is used when solving the model. The solving steps include:

[0107] In one embodiment, the oil field multi-cycle partial pressure water injection optimization model described in step S4 belongs to a mixed integer nonlinear programming model. The improved butterfly algorithm is used when solving the model. The solving steps include:

[0108] S401: According to the basic data and constraints of the water injection network, the water injection time, number of operating pumps, flow distribution of each pump, speed ratio of each pump, efficiency of each pump, total power of the pump unit, total energy consumption cost of the pump unit, total power of the pump unit within the cycle, and total energy consumption cost of the pump unit within the cycle of each water injection well in the water injection network are initialized based on the Circle chaotic map, which is the initial population of the improved butterfly algorithm. The Circle chaotic map can be expressed as:

[0109]

[0110] S402: randomly generate the initial position of the butterfly individual in the search space, and calculate its fitness value and initial fragrance intensity according to the spatial position of the butterfly individual. The fitness value is the energy consumption cost of the water injection network pump unit, and the initial fragrance intensity calculation formula is as follows:

[0111] f=cI α (14)

[0112] S403: Set the switching frequency P to control the algorithm for global search or local mining. During the iteration process, a random number rand of 0 to 1 needs to be generated for comparison with the switching frequency P. If P>rand, the algorithm performs a global search, otherwise it performs local mining. The improved butterfly algorithm introduces an adaptive weight coefficient to improve the update formula for global search and local mining. The adaptive weight coefficient, global search and local mining position update formula are as follows:

[0113]

[0114]

[0115]

[0116] S404: Update individual and global optimal solutions based on an iterative process until the model reaches a preset maximum number of iterations, and output the optimal solution obtained by the improved butterfly algorithm.

[0117] In one embodiment, the water injection optimization plan for the divided-pressure water injection in the water injection network described in step S4 includes the injection time of the water injection well, the number of operating pumps in the water injection well, the flow distribution of each pump in the water injection well, the speed ratio of each pump, the pressure efficiency of each pump, the total power of the pump unit, the total energy consumption cost of the pump unit, the total power of the pump unit within the cycle, and the total energy consumption cost of the pump unit within the cycle.

[0118] In order to prove that the multi-cycle partial pressure type oilfield water injection optimization can further reduce the total energy consumption of the water injection network and further reduce the total cost of the water injection network, a domestic oilfield water injection network was taken as a research example to compare and analyze the total energy consumption of the oilfield water injection network under the conventional mode and the multi-cycle partial pressure type.

[0119] Taking a water injection network of an oil field as an example, the system includes 1 water injection station, 8 water distribution rooms, 54 water injection wells and several water injection trunk lines connecting the units. The basic data of the network are shown in Table 3, the operating parameters of each water injection well are shown in Table 4, and the configuration parameters of the water injection station are shown in Table 5.

[0120] Table 3 Pipeline network basic data

[0121]

[0122]

[0123]

[0124] Table 4 Water injection well operation data

[0125]

[0126]

[0127] Table 5 Water injection station configuration parameters

[0128]

[0129] Specific application example 1: Conventional mode water injection network

[0130] By solving the conventional water injection optimization model, the optimization results are shown in Table 6. In one water injection cycle (24h), the average water injection volume is 501.67m 3 / h, the total water injection cost reached 58198.73 yuan.

[0131] Table 6 Water injection station parameter optimization results

[0132] parameter result Number of running pumps 2 <![CDATA[1# Pump flow rate (m 3 / h)]]> 249.77 <![CDATA[2# Pump flow rate (m 3 / h)]]> 251.90 1# pump speed ratio 0.97 2# pump speed ratio 0.97 1# Pump efficiency (%) 72.0％ 2# Pump efficiency (%) 72.6％ Total power of pump unit (kW) 3233.26 Total energy consumption cost of pump unit (yuan) 58198.73

[0133] Specific application example 2: Multi-cycle pressure-dividing water injection network

[0134] (1) Optimization results

[0135] By solving the multi-cycle partial pressure water injection optimization model, the optimization results are shown in Table 7. It can be seen that in one water injection cycle (24h), the low-pressure water injection well carried out 10.618h of water injection work, with an average water injection volume of 455.83m 3 / h; The medium-pressure water injection well carried out water injection for 5.909 hours, with an average water injection volume of 502.28m 3 / h; The high-pressure water injection well carried out water injection for 7.473 hours, with an average water injection volume of 566.31m 3 / h. During the entire water injection cycle, the total energy consumption cost was reduced to 44,631.54 yuan. Compared with the conventional water injection method, the cost of the multi-cycle partial pressure water injection method was reduced by 13,567.19 yuan.

[0136] Table 7 Parameter optimization results of low, medium and high pressure water injection stations

[0137]

[0138] (2) Comparison of throttling losses

[0139] After multi-cycle pressure-dividing treatment and water injection in the water injection network, the throttling loss caused by pressure mismatch in the network was effectively reduced. Among them, low, medium and high pressure water injection well groups are injected with independent system pressures. Compared with conventional water injection, the pressure difference before and after the valve of low and medium pressure water injection wells has been reduced, while the high pressure water injection wells are still injected with the highest system pressure, so it remains unchanged. Among them, the pressure difference before and after the valve of low pressure water injection wells has been reduced by an average of 82.95%, and the pressure difference before and after the valve of medium pressure water injection wells has been reduced by an average of 74.27%. Figure 4 , Figure 5 shown.

[0140] The above description is only an example of a specific implementation method in this specification and is not intended to limit the embodiments of this specification. For those skilled in the art, the embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of this specification should be included in the scope of the claims of the embodiments of this specification.

Claims

1. An oilfield multi-cycle partial pressure water injection optimization method based on an improved butterfly algorithm, characterized in that: The following steps are involved: S1: Obtain basic data of the target water injection network; S2: Construct the objective function of the water injection scheme optimization model considering multi-cycle partial pressure water injection; The objective function is to minimize the energy consumption cost of the water injection network pump unit. The relationship is derived from the work done by the pump unit on the injected water in each period, the various efficiencies of the pump unit, and the electricity price: Where F is the total energy consumption cost; i is the period number within the cycle, I1 / I2 / I3 is the injection period set of the low-pressure, medium-pressure, and high-pressure injection well groups within the cycle, j is the injection pump number within the station, J is the injection pump number set within the station, and p ele is the electricity price, ΔT i is the unit duration of each water injection period, γ ij is the start and stop status of the j-th water injection pump in the i-th period, Q ij is the outlet flow rate of the jth water injection pump in the i-th period, H ij is the outlet head of the j-th water injection pump in the i-th period, ρ is the density of injected water, g represents the acceleration of gravity, α is the unit conversion coefficient, η bij is the operating efficiency of the j-th water injection pump in the i-th period, η cij is the transmission efficiency of the j-th water injection pump in the i-th period, η dij is the efficiency of the motor equipped for the j-th water injection pump in the i-th period, η pij The frequency conversion efficiency of the motor equipped for the j-th water injection pump in the i-th period; S3: Construct constraint conditions to form an optimization model for water injection scheme considering multi-cycle partial pressure water injection; S4: Using the improved butterfly algorithm to solve the water injection scheme optimization model considering multi-cycle partial pressure water injection, and generate a multi-cycle partial pressure water injection optimization scheme; The oilfield multi-cycle partial pressure water injection optimization model is a mixed integer nonlinear programming model. The improved butterfly algorithm is used to solve the model. The solution steps include: S401: According to the basic data and constraints of the water injection network, the water injection time, number of operating pumps, flow distribution of each pump, speed ratio of each pump, efficiency of each pump, total power of the pump unit, total energy consumption cost of the pump unit, total power of the pump unit within the cycle, and total energy consumption cost of the pump unit within the cycle of each water injection well in the water injection network are initialized based on the Circle chaotic map, which is the initial population of the improved butterfly algorithm. The Circle chaotic map is expressed as: S402: randomly generate the initial position of the butterfly individual in the search space, and calculate its fitness value and initial fragrance intensity according to the spatial position of the butterfly individual. The fitness value is the energy consumption cost of the water injection network pump unit, and the initial fragrance intensity calculation formula is as follows: f=cI α S403: Set the switching frequency P to control the algorithm for global search or local mining. During the iteration process, a random number rand of 0 to 1 needs to be generated for comparison with the switching frequency P. If P>rand, the algorithm performs a global search, otherwise it performs local mining. The improved butterfly algorithm introduces an adaptive weight coefficient to improve the update formula for global search and local mining. The adaptive weight coefficient, global search and local mining position update formula are as follows: S404: Update individual and global optimal solutions based on an iterative process until the model reaches a preset maximum number of iterations, and output the optimal solution obtained by the improved butterfly algorithm.

2. The oilfield multi-cycle partial pressure water injection optimization method based on the improved butterfly algorithm according to claim 1, characterized in that: The multi-cycle partial pressure water injection refers to a method of combining water injection wells with similar water injection pressures, equipping them with suitable water injection pumps and corresponding water injection pipe networks, and injecting water at different time periods for water injection well groups with different pressure ranges.

3. The oilfield multi-cycle partial pressure water injection optimization method based on the improved butterfly algorithm according to claim 1, characterized in that: The basic data described in step S1 include pipe network structure data, water injection well operation parameters, water injection well location parameters, water injection station configuration parameters, water injection station location parameters, water injection cycle data, water injection pump operation parameters, and electricity prices.

4. The oilfield multi-cycle partial pressure water injection optimization method based on the improved butterfly algorithm according to claim 1, characterized in that: The constraints described in step S3 are divided into in-station constraints and out-station constraints. The in-station constraints include water injection pump head constraints, water injection pump efficiency constraints, variable frequency speed ratio constraints, single pump flow constraints, parallel head constraints, parallel flow constraints, and water injection cycle constraints. The out-station constraints include water injection well minimum head constraints, pipeline friction constraints, node flow constraints, and total water injection balance constraints. The head constraint of the water injection pump stipulates the relationship between the head and flow rate of the water injection pump under variable frequency speed regulation, and the relationship is: In the formula, H ij is the outlet head of the j-th injection pump in the i-th period, Q ij is the outlet flow rate of the jth water injection pump in the i-th period, k ij is the speed ratio of the j-th water injection pump in the i-th period, H0 is the virtual total head when the flow rate is 0, S j is the internal virtual resistance coefficient of the j-th water injection pump; The efficiency constraint of the water injection pump stipulates the relationship between the efficiency and flow rate of the water injection pump under variable frequency speed regulation, and the relationship is: Where η bij is the operating efficiency of the j-th water injection pump in the i-th period, Q ij is the outlet flow rate of the jth water injection pump in the i-th period, k ij is the speed ratio of the j-th water injection pump in the i-th period, a / b / c is the pump efficiency coefficient; The variable frequency speed ratio constraint stipulates that the speed ratio of each pump speed regulating device should meet a certain range, and the relationship is: In the formula, is the lowest speed ratio of the j-th water injection pump in the i-th period; is the maximum speed ratio of the j-th water injection pump in the i-th period, k ij is the speed ratio of the j-th water injection pump in the i-th period; The single pump flow constraint stipulates that each pump meets the flow interval in the high efficiency zone when running, and the relationship is: In the formula, is the critical point of the left flow rate in the high-efficiency zone of the j-th water injection pump in the i-th period, is the critical point of the right flow rate in the high-efficiency zone of the j-th water injection pump in the i-th period, Q ij is the outlet flow rate of the j-th water injection pump in the i-th period; The parallel head constraint stipulates that each water injection pump meets the principle of equal pressure of parallel pumps. The pressure of each pump should be equal to the outlet pressure. The relationship is: In the formula, is the discharge head of the water injection station in the i-th period, H ij is the outlet head of the j-th water injection pump in the i-th period; The parallel flow constraint stipulates that for each water injection pump, according to the principle of superposition of parallel pump flow, the flow of each pump should be equal to the sum of the outlet flow, and the relationship is: In the formula, is the outlet flow of the injection station in the i-th period, Q ij is the outlet flow of the jth injection pump in the i-th period, and J is the set of injection pump numbers in the station; The injection cycle constraint stipulates that the injection period of each pressure range injection well group should meet the injection period set of low pressure, medium pressure and high pressure range injection well groups. The union of the injection period sets is the entire injection cycle, and the relationship is: I1∪I2∪I3=I Where, I1 is the injection period set of the low-pressure range injection well group, I2 is the injection period set of the medium-pressure range injection well group, I3 is the injection period set of the high-pressure range injection well group, and I is the entire injection cycle; The minimum head constraint of the injection well stipulates that each single well must meet the requirements of the highest head injection well, and the relationship is: In the formula, is the discharge head of the water injection station in the i-th period, is the injection head requirement of the w-th injection well, H wf is the total friction loss along the w-th injection well, Δh w is the height difference between the wth injection well and the injection station; The friction constraint along the pipeline stipulates that for any pipe element, its nodes are m and n respectively. If its flow direction is m→n, it should satisfy the energy equation, and the relationship is: In the formula, ΔH l is the absolute value of the friction loss along the pipe section l, H m is the lift at node m, H n is the lift at node n, Q l is the flow rate of pipe section l, d l is the diameter of pipe section l, L l is the length of pipe section l, C is the calculation coefficient; The node flow constraint stipulates that any node must satisfy the node flow balance, that is, the flow flowing to the node at any time in the pipeline network is equal to the flow flowing out of the node. The relationship is: In the formula, q m is the traffic of node m, Q l is the flow rate of pipe section l, β ml is the flow connection coefficient of the pipe element connected to the node m, and L is the set of pipe elements connected to the node m; The total injection volume balance constraint stipulates that the entire water injection network must meet the system supply and demand flow balance: that is, during the entire water injection cycle, the sum of the outlet flow of the water injection station in each period should be equal to the sum of the daily demand and daily leakage of each well, and the relationship is: In the formula, Q i is the outlet flow of the injection station in the i-th period, Q wi is the required injection volume of the wth injection well in the ith period, I is the set of time periods in the cycle, W is the set of injection wells in the system, and ΔQ is the total flow loss in the cycle.

5. The oilfield multi-cycle partial pressure water injection optimization method based on the improved butterfly algorithm according to claim 1, characterized in that: The water injection optimization plan for partial pressure water injection in the water injection network described in step S4 includes the injection time of the water injection well, the number of operating pumps, the flow distribution of each pump, the speed ratio of each pump, the efficiency of each pump, the total power of the pump unit, the total energy consumption cost of the pump unit, the total power of the pump unit within the cycle, and the total energy consumption cost of the pump unit within the cycle.

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