Rod-pumped well group control method based on flexible load

Abstract

The invention discloses a rod-pumped well group control method based on a flexible load, and belongs to the technical field of rod-pumped well group control. The method is characterized by comprising the following steps that 1, modeling is conducted on an oil pumping system; step 2, establishing a flexible load model suitable for a rod-pumped well group; 3, taking the minimum difference value between the well group power and the input power as an objective function, considering the power constraint of each pumping unit and the frequency constraint of a motor, and solving the power constraint and the frequency constraint to obtain a pumping adjustment degree scheme between pumping unit well groups; and 4, forming a rod-pumped well group prediction control algorithm. According to the rod-pumped well group control method based on the flexible load, the flexible load characteristic of the rod-pumped well group is fully utilized, and green electric power resources can be efficiently absorbed and utilized. Dynamic matching of load and energy supply is realized by constructing an accurate flexible load model of the rod-pumped well group and performing scheduling optimization control on the system.

Description

Technical Field

[0001] The invention discloses a pumping well group control method based on flexible load, belonging to the technical field of pumping well group control. Background Art

[0002] The importance of clean energy in daily life and industrial production is becoming increasingly prominent. Clean energy generation represented by wind and solar energy is affected by weather and seasonal changes, and there is a certain instability. Flexible control methods have become a key strategy to deal with this instability. The core advantage of flexible load technology is that its electricity demand is highly flexible within a certain time range. This flexibility enables flexible loads to be dynamically adjusted according to the power supply status of the power grid, increasing electricity consumption when the power supply is sufficient, and reducing electricity consumption when the power supply is tight. In the oil field, the pumping well group is a system with significant flexible load characteristics, and its energy consumption accounts for more than 40% of the total energy consumption of the oil field. Significant progress has been made in using flexible control methods to absorb green electricity, so the application of flexible control methods to pumping units has broad development prospects.

[0003] The traditional control method of a group of pumping wells relies on manual operation, which has limited efficiency and is difficult to cope with the variability of reservoir conditions. To improve efficiency, automation and intelligent control technologies have emerged. In the current mainstream system design, model predictive control (MPC) is usually used for nonlinear process control with disturbances and constraints such as a group of pumping wells. In essence, model predictive control achieves the control target by solving an open-loop optimal control problem. The idea of ​​model predictive control is independent of the specific model, but its implementation is highly dependent on the accuracy of the model.

[0004] In the prior art, there are conventional technical solutions for controlling a group of pumping wells:

[0005] Literature: Yang Xuefeng. Research on the Co-optimization of Peak-shifting and Intermittent Pumping Operation System of Pumping Well Groups [D]. China University of Petroleum (Beijing), 2023. A technical solution is disclosed. In this technical solution, for cluster wells with insufficient fluid supply, the optimal single-well intermittent pumping system is taken as the premise, and the lowest energy consumption cost of the pumping unit is taken as the objective function. The single-well production capacity of single-well intermittent pumping is guaranteed by combining the power data characteristics of the pumping wells and the changing law of the pumping degree. Considering the valley and peak electricity price constraints of industrial electricity, the characteristic value change rate constraints, the minimum number of wells opened and the maximum number of wells opened, etc., a cluster well group peak-shifting well opening and intermittent pumping coordinated scheduling model is established to achieve the coordination of single-well intermittent pumping and well group peak-shifting well opening. However, in this technical solution, the relationship between the pumping degree and the power peak is obtained by analyzing the working status of the pumping unit and the characteristics of the electrical parameter data. The particle swarm optimization algorithm is combined with the genetic algorithm to establish and solve the intermittent pumping and well group peak-shifting coordinated optimization model, and the optimal intermittent pumping system for the oil well is obtained. However, the problem of green electricity consumption is not considered.

[0006] The Chinese invention patent with application number 202410523927.7 and application date April 29, 2024, and patent name "A method for inter-pumping control of oil pumping units using wireless measurement" discloses a technical solution, in which an oil well production simulation model is established based on the geological, engineering and production data of the oil well: first, the control parameter relationship of the oil pumping unit is determined; then the current oil well status information and the current oil pumping unit status information are clarified to determine the response complexity level and formulate a rough inter-pumping control strategy; further, the response detection time is determined according to the response complexity level, and the start and stop and control parameters of the oil pumping unit are controlled by the rough inter-pumping control strategy within this response detection time; finally, according to the control effect, a fine inter-pumping control strategy is formulated to further refine the start and stop and controllable parameters of the oil pumping unit. However, the constructed oil pumping unit production simulation model and the determination of the inter-pumping system of the oil pumping unit do not consider the problem of flexible regulation.

[0007] The Chinese invention patent with application number 202111423156.7 and application date of November 26, 2021, and the patent name of "A method and system for controlling the pumping screen of a pumping well without stopping the machine" discloses a technical solution, in which the efficiency of the pumping unit can be effectively improved, so that the oil well can achieve the best state of supply and drainage coordination. However, this technical solution only considers a single pumping unit, and does not consider the coordinated control of the well group.

[0008] The Chinese invention patent with application number 202110588435.2 and application date May 28, 2021, and the patent name "A self-learning based method for adjusting the stroke frequency of a rod pumping unit well" discloses a technical solution. In this technical solution, a set of self-learning based parameter optimization methods is provided for oil field production in combination with oil field production technology to achieve one policy for one well; the oil well parameter optimization process is automatically evaluated and verified to achieve the effect after adjustment, which is more reliable, and combined with historical data analysis, the optimal value of the stroke frequency is selected to achieve optimization. The multi-objective optimization strategy is suitable for different production needs of oil fields, and can reduce costs and increase efficiency of oil wells, providing a technical basis for realizing green oil field construction. However, this technical solution also only considers a single pumping unit, does not consider the coordinated control of the well group, and does not consider the problem of system constraints. Summary of the invention

[0009] The technical problem to be solved by the present invention is: to overcome the shortcomings of the prior art and provide a flexible load control method for a pumping well group that fully utilizes the flexible load characteristics of a pumping well group, can efficiently absorb and utilize green power resources, and constructs an accurate flexible load model of the pumping well group and performs scheduling and optimization control on the system to achieve dynamic matching of load and energy supply.

[0010] The technical solution adopted by the present invention to solve the technical problem is: the flexible load-based pumping well group control method is characterized by comprising the following steps:

[0011] Step 1, modeling the oil pumping system;

[0012] Step 2, establishing a flexible load model suitable for the pumping well group;

[0013] Step 3, taking the minimum difference between the well group power and the input power as the objective function, considering the power constraint of each pumping unit and the motor frequency constraint, and solving them to obtain the pumping scheduling plan between the well groups;

[0014] Step 4, forming a prediction control algorithm for a group of pumping wells.

[0015] Preferably, in step 2, the flexible load model defines three operating conditions of the oil pump: operating condition one, the power is not adjustable. Under this operating condition, the power of the oil pump cannot be adjusted due to factors such as liquid production and dynamic liquid level, and always remains a constant; operating condition two, the power is adjustable, and the power consumed per day is a fixed value. Under this operating condition, the power consumed per day is a fixed value, but it is adjusted in each time period; operating condition three, the power is adjustable, but the power is limited. Under this operating condition, the power of the oil pump is adjusted, but does not exceed its limit value.

[0016] Preferably, in step 3, the pumping scheduling scheme between pumping well groups includes the following steps:

[0017] Step 3-1, establish an optimization scheduling objective function to minimize the difference between the system power of the pumping well group and the input power;

[0018]

[0019] Where P0(t) represents the real-time output power of the power supply side, which is known; V = [v 1 ,…,v i ,…,v N ] T , v i is a 0-1 variable, representing the switch state of the i-th pumping unit, to be determined; P = [P 1 ,…,P i ,…,P N ], P i represents the predicted power after the i-th pumping unit is turned on;

[0020] Step 3-2, setting constraints to ensure safe operation of the well group system;

[0021] Step 3-3, describing the scheduling optimization problem as a mixed integer nonlinear programming model, and using the Gurobi solver to solve the model, thereby obtaining a pumping scheduling scheme among pumping well groups.

[0022] Preferably, in step 3-2, the constraints specifically include:

[0023] (1) Power constraints:

[0024]

[0025] in, is the instantaneous input power of the i-th pumping unit;

[0026] (2) Single well operation time constraints:

[0027] t min ≤t i ≤t max

[0028] Among them, t min is the minimum required well opening time; t max is the maximum allowable well opening time; t i is the single well operation time;

[0029] (3) Constraints on well group operation status:

[0030] O min ≤N L ≤O max

[0031] Among them, O min is the minimum number of wells to be opened; O max is the maximum number of wells to be opened; N L The number of wells opened for the pumping well group;

[0032] (4) Constraints on single well fluid production:

[0033]

[0034] in, represents the liquid production of the i-th oil well; T is the sampling period; Δt is the time interval between two adjacent samplings; represents the minimum liquid production of the i-th oil well;

[0035] (5) Dynamic liquid surface constraint:

[0036]

[0037] in, represents the dynamic liquid level height of the i-th oil well; are the maximum and minimum dynamic liquid level heights allowed for the i-th oil well respectively.

[0038] Preferably, in step 2, the flexible load model applicable to the pumping well group is:

[0039]

[0040] in, It means that the power of the i-th pumping unit cannot be adjusted and is always P1; It means that the power of the i-th pumping unit is adjustable, but the total power consumed in a single day is a fixed value P2; Indicates that the power of the i-th pumping unit is adjustable (flexible load), but there are upper and lower limits on the power during the adjustment process

[0041] And the overall power load of the pumping well group is obtained as follows:

[0042]

[0043] Among them, N1, N2, and N3 represent the number of pumping units that meet the three operating conditions respectively, N is the total number of pumping units in the well group, N=N1+N2+N3, let: i=1,2,…,N1,…,N1+N2,…,N, that is, the first N1 pumping units are in the first operating condition, the N1+1 to N1+N2 pumping units are in the second operating condition, and the N1+N2+1 to N pumping units are in the third operating condition.

[0044] Preferably, in step 4, the pumping well group prediction control algorithm comprises the following steps:

[0045] Step 4-1, determine the optimization control objective function to ensure that the difference between the power consumed by the well group and the input power is minimized:

[0046]

[0047] Where P0(t) represents the real-time output power of the power supply side, which is known; V = [v 1 ,…,v i ,…,v N ] T , v i is a 0-1 variable, representing the switch status of the i-th pumping unit, which has been obtained in step 3; represents the real-time power of the i-th pumping unit obtained by the power model in step 1, from which the control law of the i-th pumping unit, i.e., the motor frequency, is obtained;

[0048] Step 4-2, setting constraints to ensure safe and stable operation of the well group system;

[0049] Step 4-3, design a predictive control algorithm.

[0050] Preferably, the constraints in step 4-2 specifically include:

[0051] (1) Constraints on total fluid production of a well group:

[0052]

[0053] Where N is the number of pumping units in the well group; represents the liquid production of the i-th oil well; T is the sampling period; Δt is the time interval between two adjacent samplings; Q min Indicates the minimum liquid production required for the well group;

[0054] (2) Constraints on single well fluid production:

[0055]

[0056] in, represents the liquid production of the i-th oil well; T is the sampling period; Δt is the time interval between two adjacent samplings; represents the minimum liquid production of the i-th oil well;

[0057] (2) Dynamic liquid surface constraint:

[0058]

[0059] in, represents the dynamic liquid level height of the i-th oil well; are the maximum dynamic liquid level height and the minimum dynamic liquid level height allowed by the i-th oil well respectively;

[0060] (3) Dynamometer diagram constraints:

[0061] ΔS min <ΔS k <ΔS max

[0062] ΔS k =S k -S k-1

[0063] Among them, S k is the area of ​​the dynamometer diagram at the current moment, S k-1 is the area of ​​the dynamometer diagram at the previous moment.

[0064] Preferably, step 4-3 comprises the following steps:

[0065] Step 4-3-1, the mechanism model of the pumping well group model established in step 1 (including the four-bar linkage model, the pump load model, the dynamic liquid level model, the liquid production model and the electric power model of the pumping well group) is as follows:

[0066]

[0067] Step 4-3-2, according to the mechanism model established in step 1, set the control time domain and prediction time domain, and predict N at time k. p Output in range:

[0068]

[0069] Step 4-3-3, under the given constraints, the designed prediction algorithm is used to solve the objective function to obtain the optimal real-time control strategy for each pumping unit:

[0070]

[0071] ΔS min <ΔS k <ΔS max

[0072] Among them, x1…x7 represent the suspension point displacement, oil well pump load, pump displacement, suspension point displacement, liquid production, dynamic liquid level height, and instantaneous motor input power respectively; U is the input of the system; and Y represents the system output.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] In this flexible load-based control method for pumping well groups, the flexible load characteristics of the pumping well group are fully utilized, and green power resources can be efficiently absorbed and utilized. By constructing an accurate flexible load model of the pumping well group and performing scheduling optimization control on the system, dynamic matching of load and energy supply can be achieved.

[0075] In this flexible load-based control method for pumping well groups, a flexible load model suitable for pumping well groups is designed. Three working conditions of the pumping wells are defined according to the actual conditions of the oil field pumping wells, solving the problem of flexible expression of the pumping wells.

[0076] In this flexible load-based control method for a pumping well group, a pumping well group model under flexible load is first established; then, an optimization scheduling method is designed based on the model to obtain the start and stop time of each pumping unit in the well group; finally, based on the above content and the constraints of the well group system, an optimization control strategy is developed to further ensure the stability of the well group system and the maximum consumption of green electricity.

[0077] In this flexible load-based control method for pumping wells, the input power curve of green electricity is tracked to determine the start and stop time of pumping units under different working conditions while ensuring system stability and safety, so as to realize the consumption of green electricity power. The pumping well system will be able to use green electricity more efficiently, reduce the pressure on the power grid, and improve the stability and operation efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 Flowchart of the control method for a group of pumping wells based on flexible load. DETAILED DESCRIPTION

[0079] Figure 1 The best embodiment of the present invention is shown below in conjunction with the attached Figure 1 The present invention is further described.

[0080] like Figure 1 As shown, a control method for a pumping well group based on flexible load (hereinafter referred to as the control method) comprises the following steps:

[0081] Step 1, modeling the oil pumping system;

[0082] In this step, the following model is established for the oil pumping system:

[0083] 1. By analyzing the motion law and structural parameters of the pumping unit, the kinematic equation of the pumping unit and the four-bar linkage model are established:

[0084] θ=2π·u·t

[0085]

[0086] θ2=2π-θ+α

[0087]

[0088] ψ=χ+β

[0089]

[0090] x1(t)=A(ψ-ψ min )

[0091] Among them, u is the control cabinet frequency, which is also the control input; x1(t) represents the suspension point displacement; is the torque factor; R is the crank radius; P is the connecting rod length; A is the length of the front arm of the walking beam; C is the length of the rear arm of the walking beam; K is the base rod length; I is the horizontal distance from the center of the output shaft of the reduction gearbox to the center of the bracket shaft; L kis the length from the crank end to the swing beam shaft; θ2 is the angle between the base rod and the crank, with the counterclockwise direction being the positive direction; ψ is the angle between the swing beam rear arm and the base rod; α is the angle between the base rod and the vertical direction; β is the angle between the base rod and the line connecting the crank and the swing beam shaft; ψ min It is the minimum value of the angle between the rear arm of the rocker and the base rod, and t represents the crank running time.

[0092] 2. By analyzing the structure and force of the sucker rod string, the sucker rod string wave equation and the pump load model of the sucker pump are established:

[0093]

[0094] p d =p o +[w0ρ w +(1-w0)ρ0]gL p

[0095] p s =p c +[w0ρ w +(1-w0)ρ0]g(L p -H)

[0096] W0=A p (p d -p s )

[0097]

[0098] Where x2 represents the oil well pump load; p d is the discharge pressure of the pump, p s is the sinking pressure; p o is the wellhead oil pressure; w0 is the water content; ρ w is the density of water; ρ0 is the density of crude oil; L p is the pumping depth; p c is the casing pressure at the wellhead; H is the dynamic liquid level; A p is the cross-sectional area of ​​the plunger; L is the total length of the sucker rod string; E r is the elastic modulus; A r is the cross-sectional area of ​​the sucker rod; S p is the pump stroke; t m is the time when the plunger reaches the top dead center, W0 is the difference between the displacement pressure and the sinking pressure acting on the plunger, and g is the acceleration due to gravity.

[0099] Sucker rod wave equation:

[0100]

[0101] Where s(l,t) represents the displacement of the sucker rod section with a sucker rod length of l at time t; x1 is the displacement of the suspension point; x2 represents the pump load of the oil well pump; A r is the cross-sectional area of ​​the sucker rod string; E r is the elastic modulus; L is the total length of the sucker rod string; g is the acceleration due to gravity; is the speed of sound in the sucker rod; ρ r is the density of the sucker rod; υ is the rod fluid damping coefficient:

[0102] The differential solution method is used to solve the pump rod wave equation to obtain the pump displacement and suspension point load:

[0103]

[0104] Where x3 represents the pump displacement, x4 represents the suspension load, and W r is the weight of the sucker rod string in air.

[0105] 3. Establish dynamic liquid surface model and liquid production model.

[0106] The dynamic liquid surface model is:

[0107]

[0108] In the formula, x5 represents the liquid production; x6 represents the dynamic liquid level; u represents the control cabinet frequency; D is the pump diameter of the oil well pump; S is the suspension stroke; n is the stroke number; α p is the pump efficiency; s1 is the slip rate; k is the reduction ratio; n p is the pole pair number; Q in is the amount of liquid flowing from the reservoir into the oil well; D ci , D te are the inner diameter of the casing and the outer diameter of the tubing, Represents the derivative of the dynamic liquid level height.

[0109] The liquid production model is:

[0110] α p =η S η F η L η V

[0111]

[0112] Among them, η S is the plunger stroke coefficient; η F is the filling coefficient of the pump; η L is the leakage coefficient; η V is the volume coefficient of the mixed liquid; A r is the cross-sectional area of ​​the sucker rod string; ρ lis the density of the well fluid; L is the pump hanging depth; n is the number of strokes; E is the elastic modulus of steel; A p is the cross-sectional area of ​​the plunger; w0 is the water content; ρ w is the density of water; ρ o is the density of crude oil; ΔP is the pressure difference of the liquid column at both ends of the plunger; S is the stroke; n represents the number of strokes; A t is the weighted average cross-sectional area of ​​the oil pipe; R x is the solubility coefficient; p s is the sinking pressure; p h is the saturation pressure; Z is the gas compressibility coefficient; B l is the volume coefficient of the liquid in the pump; δ is the gap between the plunger and the pump barrel; L c is the effective plunger length; δ l is the relative density of the liquid; B o ,B w They are the volume coefficient of crude oil and the volume coefficient of water in the pump respectively.

[0113] 4. Establish the electrical power model of the pumping well group;

[0114] The electrical power model of the pumping well group is:

[0115]

[0116] Among them, x7(t) represents the instantaneous motor input power; x4 is the suspension point load; P0 is the motor no-load power; P N is the rated power of the motor; η N is the rated efficiency of the motor; β=N MO / P N is the instantaneous power utilization of the motor. N MO is the instantaneous output power of the motor; ω is the crank angular velocity of the pumping unit; is the transmission efficiency between the pumping unit belt and the reduction box, k1=±1 (when v A >0, k1 = -1; when v A <0, k1=1, v A is the suspension point speed). N is the net crankshaft torque; is the torque factor of the pumping unit; B W It is the structural unbalanced weight of the pumping unit; is the mechanical transmission efficiency from the crank to the suspension point, k2 = ±1 (when N MO >0, k2 = -1; when N MO <0,k2=1); M C is the maximum balancing torque of the crank counterweight; θ is the crank angle; τ is the offset angle of the crank counterweight.

[0117] Step 2, establishing a flexible load model suitable for the oil pumping well group;

[0118] A flexible load model suitable for a group of pumping wells is established. In this control method, the flexible load model defines three operating conditions of the pumping unit according to actual conditions: The first is that the power is not adjustable. Under this operating condition, the power of the pumping unit cannot be adjusted due to various factors such as liquid production and dynamic liquid level, and it always remains a constant. The second is that the power is adjustable, but the power consumed per day is a fixed value. In this case, although the power consumed by the pumping unit per day is a fixed value, it can be adjusted according to production needs in each time period. The third is that the power is adjustable, but the power is limited. The power of the pumping unit under this operating condition can be appropriately adjusted according to production needs, but it cannot exceed its limit value. The model is as follows:

[0119]

[0120] in, It means that the power of the i-th pumping unit cannot be adjusted and is always P1; It means that the power of the i-th pumping unit is adjustable, but the total power consumed in a single day is a fixed value P2; Indicates that the power of the i-th pumping unit is adjustable (flexible load), but there are upper and lower limits on the power during the adjustment process

[0121] According to the specific working conditions of the pumping well group, the overall power load of the pumping well group is obtained as follows:

[0122]

[0123] Among them, N1, N2, and N3 respectively represent the number of pumping units that meet the above three working conditions, and N is the total number of pumping units in the well group. For the convenience of subsequent expression, N=N1+N2+N3.

[0124] To facilitate subsequent expression, the pumping units in the well group are sorted according to three working conditions, and the following are made:

[0125] i=1,2,…,N1,…,N1+N2,…,N, that is, the first N1 pumping units are in the first operating condition, the N1+1 to N1+N2 pumping units are in the second operating condition, and the N1+N2+1 to N pumping units are in the third operating condition.

[0126] Step 3, obtaining a pumping scheduling plan among pumping well groups;

[0127] In order to meet the purpose of flexible load and green electricity consumption, it is necessary to stagger the opening of wells and conduct intermittent pumping for the pumping well group. However, the scheduling plan based on manual experience has great uncertainty, which easily leads to increased energy consumption and cost. Therefore, in this control method, the minimum difference between the well group power and the input power is taken as the objective function, and the power constraints of each pumping unit and the motor frequency constraints are considered and solved to obtain the inter-pumping scheduling plan for the pumping well group. The algorithm flow is as follows:

[0128] Step 3-1, establish an optimization scheduling objective function to minimize the difference between the system power of the pumping well group and the input power;

[0129]

[0130] Where P0(t) represents the real-time output power of the power supply side, which is known; V = [v 1 ,…,v i ,…,v N ] T , v i is a 0-1 variable, representing the switch state of the i-th pumping unit; P = [P 1 ,…,P i ,…,P N ], P i represents the predicted power after the i-th pumping unit is turned on, J is the objective function; T is the prediction time domain;

[0131] Step 3-2, setting constraints to ensure safe operation of the well group system;

[0132] The constraints include:

[0133] (1) Power constraints:

[0134]

[0135] in, is the instantaneous input power of the i-th pumping unit.

[0136] (2) Single well operation time constraints:

[0137] t min ≤t i ≤t max

[0138] Among them, t min is the minimum required well opening time; t max is the maximum allowable well opening time; t i is the operation time of a single well; the total scheduling time is 24 hours, assuming that the minimum scheduling unit is 2 hours.

[0139] (3) Constraints on well group operation status:

[0140] O min ≤N L ≤O max

[0141] Among them, O min is the minimum number of wells to be opened; O max is the maximum number of wells to be opened; N L The number of wells opened for the pumping well group.

[0142] (4) Constraints on single well fluid production:

[0143]

[0144] in, represents the liquid production of the i-th oil well; T is the sampling period; Δt is the time interval between two adjacent samplings; Represents the minimum liquid production of the i-th oil well.

[0145] (5) Dynamic liquid surface constraint:

[0146]

[0147] in, represents the dynamic liquid level height of the i-th oil well; are the maximum and minimum dynamic liquid level heights allowed for the i-th oil well respectively.

[0148] Step 3-3, describing the scheduling optimization problem as a mixed integer nonlinear programming model, and using the Gurobi solver to solve the model, thereby obtaining a pumping scheduling scheme among pumping well groups.

[0149] Step 4, forming a prediction control algorithm for a pumping well group;

[0150] In order to better absorb green electricity and realize refined control of the pumping well group, in this control method, a pumping well group prediction control algorithm is formed. The specific steps are as follows:

[0151] Step 4-1, determine the optimization control objective function to ensure that the difference between the power consumed by the well group and the input power is minimized:

[0152]

[0153] Where P0(t) represents the real-time output power of the power supply side, which is known; V = [v 1 ,…,v i ,…,v N ] T , v i is a 0-1 variable, representing the switch status of the i-th pumping unit, which has been obtained in step 3; represents the real-time power of the i-th pumping unit obtained by the power model in step 1, from which the control law of the i-th pumping unit, that is, the motor frequency, is obtained; T is the prediction time domain.

[0154] Step 4-2, setting constraints to ensure safe and stable operation of the well group system;

[0155] The constraints include:

[0156] (1) Constraints on total fluid production of a well group:

[0157]

[0158] Where N is the number of pumping units in the well group; represents the liquid production of the i-th oil well; T is the sampling period; Δt is the time interval between two adjacent samplings; Q min Indicates the minimum liquid production required for the well group.

[0159] (2) Constraints on single well fluid production:

[0160]

[0161] in, represents the liquid production of the i-th oil well; T is the sampling period; Δt is the time interval between two adjacent samplings; Represents the minimum liquid production of the i-th oil well.

[0162] (2) Dynamic liquid surface constraint:

[0163]

[0164] in, represents the dynamic liquid level height of the i-th oil well; are the maximum and minimum dynamic liquid level heights allowed for the i-th oil well respectively.

[0165] (3) Dynamometer diagram constraints:

[0166] ΔS min <ΔS k <ΔS max

[0167] ΔS k =S k -S k-1

[0168] Among them, S k is the area of ​​the dynamometer diagram at the current moment, S k-1 is the area of ​​the dynamometer diagram at the previous moment. ΔS min , ΔS max They represent the minimum and maximum values ​​of the difference between the area of ​​the dynamometer diagram at the current moment and the area of ​​the dynamometer diagram at the previous moment respectively.

[0169] Step 4-3, designing a predictive control algorithm;

[0170] The algorithm can predict the future output of the system based on the historical information and future input of the controlled object. The prediction model can be in the form of convolution model, mechanism model, neural network model, etc.

[0171] The mechanism model of the pumping well group model established in step 1 (including the four-bar linkage model, the pump load model, the dynamic liquid level model, the liquid production model and the electric power model of the pumping well group) is as follows:

[0172]

[0173] According to the mechanism model established in step 1, set the control time domain and prediction time domain to predict N at time k. p Output in range:

[0174]

[0175] Under the given constraints, the designed prediction algorithm is used to solve the objective function and obtain the optimal real-time control strategy for each pumping unit:

[0176]

[0177] ΔS min <ΔS k <ΔS max

[0178] Among them, x1…x7 represent the suspension point displacement, oil well pump load, pump displacement, suspension point displacement, liquid production, dynamic liquid level height, and instantaneous motor input power respectively; U is the input of the system; and Y represents the system output.

[0179] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A method for controlling a group of pumping wells based on flexible load, characterized in that: The steps include: Step 1, modeling the oil pumping system; Step 2, establishing a flexible load model suitable for the oil pumping well group; Step 3, taking the minimum difference between the well group power and the input power as the objective function, considering the power constraint of each pumping unit and the motor frequency constraint, and solving them to obtain the pumping scheduling plan between the well groups; Step 4, forming a prediction control algorithm for a group of pumping wells.

2. The method for controlling a group of pumping wells based on flexible load according to claim 1, characterized in that: In step 2, the flexible load model defines three operating conditions of the oil pump: operating condition 1, the power is not adjustable. Under this operating condition, the power of the oil pump cannot be adjusted due to factors such as liquid production and dynamic liquid level, and always remains a constant; operating condition 2, the power is adjustable, and the power consumed per day is a fixed value. Under this operating condition, the power consumed per day is a fixed value, but it is adjusted in each time period; operating condition 3, the power is adjustable, but the power is limited. Under this operating condition, the power of the oil pump is adjusted, but does not exceed its limit.

3. The method for controlling a group of pumping wells based on flexible load according to claim 1, characterized in that: In step 3, the pumping scheduling plan between the pumping well groups includes the following steps: Step 3-1, establish an optimization scheduling objective function to minimize the difference between the system power of the pumping well group and the input power; Where P0(t) represents the real-time output power of the power supply side, which is known; V = [v 1 ,...,v i ,...,v N ] T , v i is a 0-1 variable, representing the switch state of the i-th pumping unit, to be determined; P = [P 1 ,…,P i ,…,P N ], P i represents the predicted power after the i-th pumping unit is turned on; Step 3-2, setting constraints to ensure safe operation of the well group system; Step 3-3, describing the scheduling optimization problem as a mixed integer nonlinear programming model, and using the Gurobi solver to solve the model, thereby obtaining a pumping scheduling scheme among pumping well groups.

4. The method for controlling a group of pumping wells based on flexible load according to claim 3 is characterized in that: In step 3-2, the constraints specifically include: (1) Power constraints: in, is the instantaneous input power of the i-th pumping unit; (2) Single well operation time constraints: t min ≤t i ≤t max Among them, t min is the minimum required well opening time; t max is the maximum allowable well opening time; t i is the single well operation time; (3) Constraints on well group operation status: THE min ≤N L ≤O max Among them, O min is the minimum number of wells to be opened; O max is the maximum number of wells to be opened; N L The number of wells opened for the pumping well group; (4) Constraints on single well fluid production: in, represents the liquid production of the i-th oil well; T is the sampling period; Δt is the time interval between two adjacent samplings; represents the minimum liquid production of the i-th oil well; (5) Dynamic liquid surface constraint: in, represents the dynamic liquid level height of the i-th oil well; are the maximum and minimum dynamic liquid level heights allowed for the i-th oil well respectively.

5. The method for controlling a group of pumping wells based on flexible load according to claim 2, characterized in that: In step 2, the flexible load model applicable to the pumping well group is: in, It means that the power of the i-th pumping unit cannot be adjusted and is always P1; It means that the power of the i-th pumping unit is adjustable, but the total power consumed in a single day is a fixed value P2; Indicates that the power of the i-th pumping unit is adjustable (flexible load), but there are upper and lower limits on the power during the adjustment process And the overall power load of the pumping well group is obtained as follows: Among them, N1, N2, and N3 represent the number of pumping units that meet the three operating conditions respectively, N is the total number of pumping units in the well group, N=N1+N2+N3, let: i=1,2,…,N1,…,N1+N2,…,N, that is, the first N1 pumping units are in the first operating condition, the N1+1 to N1+N2 pumping units are in the second operating condition, and the N1+N2+1 to N pumping units are in the third operating condition.

6. The method for controlling a group of pumping wells based on flexible load according to claim 2, characterized in that: In step 4, the prediction control algorithm for the pumping well group includes the following steps: Step 4-1, determine the optimization control objective function to ensure that the difference between the power consumed by the well group and the input power is minimized: Where P0(t) represents the real-time output power of the power supply side, which is known; V = [v 1 ,…,v i ,…,v N ] T , v i is a 0-1 variable, representing the switch status of the i-th pumping unit, which has been obtained in step 3; represents the real-time power of the i-th pumping unit obtained by the power model in step 1, from which the control law of the i-th pumping unit, i.e., the motor frequency, is obtained; Step 4-2, setting constraints to ensure safe and stable operation of the well group system; Step 4-3, design a predictive control algorithm.

7. The method for controlling a group of pumping wells based on flexible load according to claim 6, characterized in that: The constraints in step 4-2 specifically include: (1) Constraints on total fluid production of a well group: Where N is the number of pumping units in the well group; represents the liquid production of the i-th oil well; T is the sampling period; Δt is the time interval between two adjacent samplings; Q min Indicates the minimum liquid production required for the well group; (2) Constraints on single well fluid production: in, represents the liquid production of the i-th oil well; T is the sampling period; Δt is the time interval between two adjacent samplings; represents the minimum liquid production of the i-th oil well; (2) Dynamic liquid surface constraint: in, represents the dynamic liquid level height of the i-th oil well; are the maximum dynamic liquid level height and the minimum dynamic liquid level height allowed by the i-th oil well respectively; (3) Dynamometer diagram constraints: ΔS min <ΔS k <ΔS max ΔS k =S k -S k-1 Among them, S k is the area of ​​the dynamometer diagram at the current moment, S k-1 is the area of ​​the dynamometer diagram at the previous moment.

8. The method for controlling a group of pumping wells based on flexible load according to claim 6, characterized in that: Step 4-3 includes the following steps: Step 4-3-1, the mechanism model of the pumping well group model established in step 1 (including the four-bar linkage model, the pump load model, the dynamic liquid level model, the liquid production model and the electric power model of the pumping well group) is as follows: Step 4-3-2, according to the mechanism model established in step 1, set the control time domain and prediction time domain, and predict N at time k. p Output in range: Step 4-3-3, under the given constraints, the designed prediction algorithm is used to solve the objective function to obtain the optimal real-time control strategy for each pumping unit: ΔS min <ΔS k <ΔS max Among them, x1…x7 represent the suspension point displacement, oil well pump load, pump displacement, suspension point displacement, liquid production, dynamic liquid level height, and instantaneous motor input power respectively; U is the input of the system; and Y represents the system output.

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