A pumping unit parameter optimization method and application thereof

Abstract

The application provides a pumping unit parameter optimization method and application thereof, and belongs to the technical field of pumping unit control, and comprises the following steps: performing energy efficiency evaluation on collected production operation data; performing parameter optimization on the production operation data according to the energy efficiency evaluation result; establishing a target function according to the environmental protection and energy saving demand; establishing a model constraint condition; establishing a pumping unit parameter optimization model according to the target function and the model constraint condition, and solving the optimal production operation data; and adjusting and optimizing the pumping unit parameters according to the optimal production operation data, so that the energy saving and consumption reduction of the pumping unit are realized. The motor rotating speed of the pumping unit is adjusted according to the optimal production operation data, the operation efficiency of the pumping unit is improved, the motor torque of the pumping unit is adjusted according to the optimal production operation data, the optimal control of the pumping unit suspension point movement track is realized, and the optimal balance degree setting value is obtained by solving the pumping unit parameter optimization model, so that the balance block position is adjusted.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of pumping unit control, and particularly relates to a pumping unit parameter optimization method and application thereof. BACKGROUND

[0002] Pumping units are the most commonly used mechanical systems in oilfield production, and are widely distributed and large in number. After energy efficiency monitoring, the working conditions of each link are diagnosed, problems are found in a timely manner, and then the control parameters of the pumping unit are optimized to realize economic and energy-saving operation of the pumping unit. Among them, the adjustment and control of the pumping unit's suspension point trajectory and the balance module position are relatively complex controlled objects. Changes in pumping unit production and electrical parameters and other factors will have a great impact on the pumping unit's suspension point trajectory and the balance module position. It is difficult to optimize the pumping unit's suspension point trajectory and the balance module position, whether in modeling or control.

[0003] In the prior art, optimization and adjustment are only performed from the perspective of the operation condition of a single well, and there is no optimization model and optimization and adjustment method from the perspective of the whole well group and block.

[0004] With the deepening of the construction of digital oilfields and the gradual popularization and application of Internet of Things technology in oilfield production, it is an urgent need for energy-saving management and control refinement to apply computer technology to more quickly, scientifically and comprehensively evaluate and intelligently diagnose the comprehensive energy efficiency of a large number of existing complex single-well pumping units and whole well group (block) units, and to timely optimize and control the working conditions.

[0005] Chinese patent application CN115566964A discloses a walking beam pumping unit variable frequency control system and method. A direct torque control unit is designed to generate an optimized inverter module driving mechanism. According to the voltage difference between the two capacitors on the direct current side of the inverter, a three-level hysteresis controller is used to realize the switching of three working modes of the system based on different switching vector tables. The above method cannot realize the parameter optimization process of the pumping unit, cannot determine the optimal production parameters, and cannot guide the economic and energy-saving operation of the pumping unit. SUMMARY

[0006] In view of the problems in the prior art, the present application provides a pumping unit parameter optimization method and application thereof. The technical problem to be solved by the present application is how to optimize the pumping unit parameter to realize economic and energy-saving operation of the pumping unit.

[0007] To solve the above technical problems, the present application provides a pumping unit parameter optimization method and application thereof, which comprises

[0008] Step S1: Energy efficiency evaluation is performed on the collected production operation data.

[0009] Step S2: Optimize the parameters of the production operation data based on the energy efficiency evaluation results;

[0010] Step S3: Establish a multi-objective function to minimize the carbon emissions of the pumping unit system within the block and maximize the economic benefits within the block;

[0011] Step S4: Establish model constraints corresponding to the operating parameters of the pumping unit and the production rate of the oil well;

[0012] Step S5: Based on the multi-objective function and model constraints, establish a parameter optimization model for the pumping unit and solve for the optimal production operation data;

[0013] Step S6: Adjust and optimize the pumping unit parameters based on the optimal production operation data to achieve energy saving and consumption reduction of the pumping unit.

[0014] Furthermore, in step S3, a multi-objective function is established to minimize the carbon emissions of the pumping unit system within the block and maximize the economic benefits within the block.

[0015] The method for optimizing pumping unit parameters according to claim 2 is characterized in that, assuming I represents the set of all pumping units in a certain oil production block, and i represents the pumping unit number, then... Within the time period T, there is ;

[0016] The objective function is established to minimize the carbon emissions of the pumping unit system within the block, as follows:

[0017]

[0018] In the formula: This represents the minimum carbon emissions of the pumping unit system within the block, expressed in tons. Carbon emissions from the pumping unit system within the block, in tons; The carbon emission factor of the power grid is ton / (MWh); The power consumption of the pumping unit system within the block is expressed in kWh.

[0019] Furthermore, the power consumption calculation formula for the pumping unit system within the block is as follows:

[0020]

[0021] In the formula: Let be the instantaneous power utilization rate of the motor of the i-th pumping unit at time t, % Let be the rated power of the motor of the i-th pumping unit at time t, in kW; Let be the no-load power of the motor of the i-th pumping unit at time t, in kW; ηi(t) is the rated efficiency of the i th pumping unit motor at time t.

[0022] Further, for the beam-type mechanical oil production system, when the pumping equipment, casing pressure, oil pressure and produced oil physical parameters are known, the power consumption of the pumping unit system in the block is a function of the stroke S of the pumping unit, the stroke frequency n of the pumping unit, the crank counterweight Wc, the balance radius Rc of the pumping unit and the rod string combination The power consumption of the pumping unit system in the block can be rewritten as follows:

[0023]

[0024] In the formula: ηi(t) is the power consumption of the i th pumping unit motor at time t, unit kW; S is the stroke of the i th pumping unit at time t, unit m; n is the stroke frequency of the i th pumping unit at time t, unit min -1 ; Wc is the crank counterweight of the i th pumping unit at time t, unit kg; Rc is the balance radius of the i th pumping unit at time t, unit m; is the rod string combination of the i th pumping unit at time t.

[0025] Further, under the condition of only considering the sale of crude oil in the oilfield and the purchase of electricity expenditure, taking the maximum economic benefit in the block as the optimization goal, the objective function is established as follows:

[0026]

[0027] In the formula: max F is the maximum economic benefit; P is the sale price of crude oil, unit yuan / ton; Qi(t) is the production of the i th pumping unit at time t, unit ton; Pb is the purchase price of electricity, unit yuan / kWh.

[0028] Further, in step S4, the model constraint conditions include well production constraints, suspension point load constraints, crank shaft net torque constraints, balance degree constraints, stroke frequency constraints, up and down stroke time constraints and rod string strength constraints.

[0029] Further, the well production constraints are as follows:

[0030]

[0031]

[0032]

[0033]

[0034] wherein: is the rated production of the i-th pumping unit equipment, in tons. D 0,i is the pumping unit system's pump diameter of the i-th pumping unit system, in mm; S 0,i is the pumping unit system's stroke length of the i-th pumping unit system, in m; n 0,i is the pumping unit system's stroke rate of the i-th pumping unit system, in min -1 ; a 0,i is the pumping unit system's pump displacement coefficient of the i-th pumping unit system.

[0035] Further, the maximum load of the i-th pumping unit's suspension point should not be greater than the maximum allowable load of the pumping unit, and the suspension point load constraint is as follows:

[0036]

[0037] wherein: is the maximum load of the i-th pumping unit's suspension point at time t; is the maximum allowable load of the i-th pumping unit's suspension point at time t.

[0038] Further, when the i-th pumping unit is working, the maximum net torque of the crankshaft should be less than the maximum allowable torque, and the crankshaft net torque constraint is as follows:

[0039]

[0040] wherein: is the maximum load of the i-th pumping unit's suspension point at time t; is the maximum allowable torque of the i-th pumping unit at time t.

[0041] Further, the power balance degree of the i-th pumping unit's power equipment motor is set to 85% to 100%, and the balance degree constraint is as follows:

[0042]

[0043] wherein: is the input power of the i-th pumping unit's suspension point's downstroke motor at time t; is the input power of the i-th pumping unit's suspension point's upstroke motor at time t.

[0044] Further, the stroke constraint is as follows:

[0045]

[0046] wherein: is the stroke of the i th pumping unit before frequency conversion control at time t; is the stroke of the i th pumping unit after frequency conversion control at time t; is the minimum stroke constraint of the i th pumping unit at time t; is the maximum stroke constraint of the i th pumping unit at time t.

[0047] Further, the upstroke and downstroke time constraints are as follows:

[0048]

[0049] In the formula: is the time required for the upstroke of the i th pumping unit at time t; is the time required for the downstroke of the i th pumping unit at time t.

[0050] Further, the rod string strength constraint of the i th pumping unit during production is as follows:

[0051]

[0052] In the formula: is the maximum stress at the rod top position of the i th pumping unit at time t, unit: MPa; is the maximum allowable stress, unit: MPa; is the use condition coefficient; is the tensile strength limit of the material, unit: MPa; is the minimum stress at the rod top position of the i th pumping unit at time t, unit: MPa.

[0053] Further, in the step S5, the pumping unit parameter optimization model is:

[0054] .

[0055] Further, in the step S5, the pumping unit parameter optimization model is solved by combining the enumeration method and the genetic algorithm.

[0056] Further, the optimal production operation data includes the stroke of the pumping unit, the stroke of the pumping unit, the crank counterweight, the pumping unit balance radius, and the motor input power.

[0057] The present application provides a pumping unit suspension point motion trajectory control method, which uses the above pumping unit parameter optimization method, adjusts the motor speed of the pumping unit according to the optimal production operation data, reduces the fluctuation degree of the pumping unit load, reduces the mechanical fatigue degree of the transmission and the rod string, and realizes the improvement of the operation efficiency of the pumping unit; adjusts the motor torque of the pumping unit according to the optimal production operation data, and realizes the optimization control of the pumping unit suspension point motion trajectory.

[0058] The application provides a pumping unit balancing module control method, using the pumping unit parameter optimization method, solving a pumping unit parameter optimization model to obtain an optimal balance degree setting value, and adjusting a balance block position by using the optimal balance degree setting value.

[0059] The application provides a pumping unit parameter optimization method, which automatically pre-warns a pumping unit well with low energy efficiency by comprehensively evaluating energy efficiency of production operation data, optimizes parameters of the production operation data, establishes a multi-objective function with minimum carbon emission of a pumping unit system in a block and maximum economic benefit in the block, establishes well production yield constraints, suspension point load constraints, crank shaft net torque constraints, balance degree constraints, stroke frequency constraints, up and down stroke time constraints and rod string strength constraints, establishes a pumping unit parameter optimization model according to the objective function and model constraint conditions, and solves the optimal production operation data, so as to provide a feasible energy-saving technical improvement scheme and an energy-saving operation scheme considering economic benefits of output for a single well and a well group (block); and the optimal production operation data is applied to hardware devices such as frequency regulation and balance block servo regulation, so as to realize intelligent control of energy-saving operation of a pumping unit single well and a well group (block).

[0060] The application provides a pumping unit suspension point motion trajectory control method, which optimizes control of a pumping unit suspension point motion trajectory in each working cycle of a pumping unit working system, adjusts a motor rotating speed of the pumping unit according to optimal production operation data, reduces fluctuation of a pumping unit load, reduces mechanical fatigue of transmission and a rod string, and improves operation efficiency of the pumping unit; the motor torque of the pumping unit is adjusted according to the optimal production operation data, so as to optimize control of the pumping unit suspension point motion trajectory, and solve the problems of difficulty in calculation of existing pumping unit parameters and control delay.

[0061] The application provides a pumping unit balancing module control method, which solves a pumping unit parameter optimization model to obtain an optimal balance degree setting value, and adjusts a balance block position by using the optimal balance degree setting value. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 The application provides a pumping unit parameter optimization method. DETAILED DESCRIPTION

[0063] To make the objectives, technical solutions and advantages of the embodiments of the application clearer, the technical solutions in the embodiments of the application will be described below in connection with the drawings in the embodiments of the application. Obviously, the described embodiments are some but not all of the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0064] Therefore, the following detailed description of the embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the application as claimed, but merely represents selected embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the application.

[0065] It should be noted that similar reference numerals and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings.

[0066] In the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting", "fixing" and the like should be understood broadly, for example, can be fixed connection, can also be detachable connection, or integral; can be directly connected, or indirectly connected through an intermediate medium, can be internal communication of two elements or interaction relationship between two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0067] In order to better understand the purpose, structure and function of the present application, the following further detailed description of the present application is made in combination with the drawings.

[0068] Embodiment 1

[0069] As shown in the drawings, the present application is a pumping unit parameter optimization method, comprising the following steps: Figure 1

[0070] Step S1: Energy efficiency evaluation is performed on the collected production operation data;

[0071] Step S2: According to the energy efficiency evaluation result, the production operation data is optimized;

[0072] Step S3: According to the environmental protection and energy saving demand, a target function is established;

[0073] Step S4: Model constraint conditions are established;

[0074] Step S5: According to the target function and the model constraint conditions, a pumping unit parameter optimization model is established, and the optimal production operation data is obtained by solving;

[0075] Step S6: The pumping unit parameters are adjusted and optimized according to the optimal production operation data, and the energy saving and consumption reduction of the pumping unit is realized.

[0076] Embodiment 2

[0077] As shown in the drawings, the present application is a pumping unit parameter optimization method, comprising the following steps: Figure 1 ​As shown, the oil pumping unit parameter optimization method of the present application comprises the following steps:

[0078] Step S1: energy efficiency evaluation is performed on the collected production operation data;

[0079] Step S2: according to the energy efficiency evaluation result, parameter optimization is performed on the production operation data;

[0080] Step S3: a target function is established according to the environmental protection and energy saving requirement;

[0081] Step S4: a model constraint condition is established;

[0082] Step S5: an oil pumping unit parameter optimization model is established according to the target function and the model constraint condition, and the optimal production operation data is obtained by solving;

[0083] Step S6: the oil pumping unit parameters are adjusted and optimized according to the optimal production operation data, so as to realize energy saving and consumption reduction of the oil pumping unit.

[0084] The difference between the present embodiment and the above embodiments is:

[0085] In the step S2, if the energy efficiency evaluation result is qualified, the oil pumping unit continues to operate according to the production operation data, and if the energy efficiency evaluation result is unqualified, parameter optimization is performed on the production operation data.

[0086] In the step S5, the optimal production operation data includes the stroke frequency of the oil pumping unit, the stroke of the oil pumping unit, the weight of the crank counterweight, the balance radius of the oil pumping unit and the input power of the motor.

[0087] The parameter optimization comprises the following steps:

[0088] In the step S3, a multi-objective function is established with the minimum carbon emission of the oil pumping unit system in the block and the highest economic benefit in the block;

[0089] Suppose I represents the set of all oil pumping units in a certain oil production block, and i represents the serial number of the oil pumping unit, then In a time period T, there is .

[0090] Target function 1: the electric energy required by the oil pumping unit system in the production process is supplied by the power grid, in order to reduce the environmental impact of power generation, the minimum carbon emission of the oil pumping unit system in the block is taken as the target function, as follows:

[0091]

[0092] In the formula: is the minimum carbon emission of the oil pumping unit system in the block; is the carbon emission of the oil pumping unit system in the block, unit ton; is the emission factor of carbon for the power grid, ton / (MWh); is the power consumption of the pumping unit system in the block, kW.

[0093] The power consumption of the pumping unit system in the block is calculated as follows:

[0094]

[0095] In the formula: is the instantaneous power utilization rate of the motor of the i-th pumping unit power equipment at time t, %; is the rated power of the motor of the i-th pumping unit power equipment at time t, kW; is the no-load power of the motor of the i-th pumping unit power equipment at time t, kW; is the rated efficiency of the motor of the i-th pumping unit power equipment at time t, %.

[0096] For the beam-type mechanical oil production system, when the pumping equipment, casing pressure, oil pressure and oil property parameters are known, the power consumption of the pumping unit in the block is a function of the stroke S of the pumping unit, the stroke frequency n of the pumping unit, the crank counterweight W c , the balance radius R c of the pumping unit and the rod string combination , so the power consumption of the pumping unit system in the block can be rewritten as follows:

[0097]

[0098] In the formula: is the power consumption of the motor of the i-th pumping unit power equipment at time t, kW; is the stroke of the i-th pumping unit at time t, m; is the stroke frequency of the i-th pumping unit at time t, min-1; is the crank counterweight of the i-th pumping unit at time t, kg; is the balance radius of the i-th pumping unit at time t, m; is the rod string combination of the i-th pumping unit at time t.

[0099] Objective function 2: In the case of only considering the sale of crude oil in the oilfield and the purchase of electricity expenditure, the maximum economic benefit in the block is taken as the optimization target, and the objective function is as follows:

[0100]

[0101] In the formula: is the sale price of crude oil, yuan / ton; is the production of the i-th pumping unit at time t, unit ton; is the purchase price of electricity, yuan / kWh.

[0102] In step S4, the model constraints include oil well production constraints, suspension point load constraints, crankshaft net torque constraints, balance degree constraints, stroke frequency constraints, up and down stroke time constraints, and rod string strength constraints.

[0103] Oil well production constraints;

[0104] The production of the oil well should meet normal production and be able to complete the production plan, so it should meet the production constraints, that is, not less than the minimum production and not greater than the maximum production. When taking the rated production as the benchmark, assuming that the production is not less than the rated production and not higher than 150% of the rated production, the following constraints are met:

[0105]

[0106]

[0107]

[0108]

[0109] In the formula: is the rated production of the i th pumping unit device, unit ton; D 0,i is the pumping unit diameter of the i th pumping unit system, mm; S 0,i is the suspension stroke length of the i th pumping unit system, m; n 0,i is the suspension stroke frequency of the i th pumping unit system, min -1 ; a 0,i is the pump displacement coefficient of the i th pumping unit system.

[0110] Suspension point load constraints;

[0111] The maximum load of the i th pumping unit suspension point should not be greater than the maximum allowable load of the pumping unit, and there is a constraint:

[0112]

[0113] In the formula: , are the maximum load and the maximum allowable load of the i th pumping unit suspension point at t, respectively.

[0114] Crankshaft net torque constraints;

[0115] When the i th pumping unit is working, the maximum net torque of the crankshaft should be less than the maximum allowable torque, and there is a constraint:

[0116]

[0117] In the formula: , respectively, the maximum net torque and the maximum allowable torque of the crankshaft of the i th pumping unit at time t.

[0118] Balance constraint;

[0119] The monitored electrical parameters can only represent the size of the current and cannot express its direction, so the balance degree is calculated by power, and the power balance degree of the i th pumping unit motor is set to 85% to 100%, with the constraint:

[0120]

[0121] In the formula: , is the input power of the motor at the bottom and top stroke of the i th pumping unit at time t.

[0122] Stroke constraint;

[0123] The frequency conversion of the motor affects the change of the stroke of the i th pumping unit, and the change of the stroke before and after the frequency conversion should be within the allowable range, with the constraint:

[0124]

[0125] In the formula: is the stroke of the i th pumping unit equipment before frequency conversion control at time t; is the stroke of the i th pumping unit equipment after frequency conversion control at time t; is the minimum stroke constraint of the i th pumping unit at time t; is the maximum stroke constraint of the i th pumping unit at time t.

[0126] Top and bottom stroke duration constraint; the size of the suspension point load of the pumping unit during the top stroke and the bottom stroke is not consistent. For the thin oil well, the slow top stroke speed and the fast bottom stroke speed are beneficial to improve the system efficiency, but the difference between the two should be within a reasonable range, with the constraint:

[0127]

[0128] In the formula: is the time required for the top stroke of the i th pumping unit at time t; is the time required for the bottom stroke of the i th pumping unit at time t.

[0129] Rod string strength constraint;

[0130] The i th pumping unit needs to check the strength during production, with the constraint as follows:

[0131]

[0132] In the formula: Let be the maximum stress at the rod tip position of the i-th pumping unit at time t, in MPa; The maximum allowable stress is expressed in MPa. To use condition coefficients; The tensile strength limit of the material, in MPa; Let be the minimum stress at the rod tip position of the i-th pumping unit at time t, in MPa.

[0133] In step S5, the oil pumping unit parameter optimization model is as follows:

[0134]

[0135] In step S5, a combination of enumeration and genetic algorithm is used to solve the oil pumping unit parameter optimization model;

[0136] Multi-objective optimization problems consist of two basic parts: ① two or more objective functions; ② several constraints. The mathematical model of a multi-objective programming problem can be described as follows:

[0137]

[0138]

[0139] In the formula, Let be a k-dimensional function vector; k is the number of objective functions; Let the decision variables be n-dimensional; x n For the first n One decision variable. Let G be an m-dimensional function vector; G be an m-dimensional constant vector; m is the number of constraints, g m For the first m One constraint condition.

[0140] One of the optimization objectives can be transformed into a constrained condition, which can be written as the following formula:

[0141]

[0142] In the formula, min f k ( x ) is the objective function f k ( x Minimize the value; f l ( x ) is the first l One objective function; For the first lthe boundary of the objective function; x for the decision variables.

[0143] For the above optimization model, the objective function 2 is converted into a constraint condition, at this time the model belongs to a nonlinear optimization problem of discrete variables, and an enumeration method and a genetic algorithm are combined to solve.

[0144] Embodiment 3

[0145] As Figure 1 shown, the oil pumping unit parameter optimization method of the application comprises the following steps:

[0146] Step S1: energy efficiency evaluation is performed on the collected production operation data;

[0147] Step S2: according to the energy efficiency evaluation result, the production operation data is optimized;

[0148] Step S3: a target function is established according to the environmental protection and energy saving demand;

[0149] Step S4: a model constraint condition is established;

[0150] Step S5: an oil pumping unit parameter optimization model is established according to the target function and the model constraint condition, and the optimal production operation data is solved;

[0151] Step S6: the oil pumping unit equipment parameters are adjusted and optimized according to the optimal production operation data, so that the energy saving and consumption reduction of the oil pumping unit are realized.

[0152] The embodiment is different from the above embodiments in that:

[0153] In the step S1, the production operation data comprises three-phase electric parameters, a work diagram, a balance degree and a liquid production, etc.

[0154] In the step S2, the energy efficiency evaluation comprises the following steps:

[0155] Step H1: an oil pumping unit energy efficiency evaluation index matrix is constructed;

[0156] Suppose that m oil pumping units are evaluated, and there are n energy efficiency evaluation indexes in total, denoted as , the energy efficiency evaluation index value is denoted as , the index matrix is denoted as , and the matrix form is as follows:

[0157]

[0158] j is the energy efficiency evaluation index serial number;

[0159] Step H2: the energy efficiency evaluation index matrix is standardized;

[0160] In order to avoid the error caused by different dimensions and different orders of magnitude of energy efficiency evaluation indexes, the energy efficiency evaluation index matrix is standardized to eliminate the influence, and the normalized energy efficiency evaluation index matrix is obtained , which is as follows:

[0161]

[0162] In the formula, y ij is the energy efficiency evaluation index value x ij is the value after standardization.

[0163] When has positive effect, it is:

[0164]

[0165] When has negative effect, it is:

[0166]

[0167] In the formula, is the minimum value when i belongs to the interval [1, m] x ij . is the maximum value when i belongs to the interval [1, m] x ij .

[0168] Step H3: the entropy weight method is used to calculate the weight of each energy efficiency evaluation index.

[0169] The entropy of each energy efficiency evaluation index of the oil pumping unit is calculated by the entropy weight method, and the entropy weight of each energy efficiency evaluation index of the oil pumping unit is further calculated, as follows:

[0170] The information entropy of the jth energy efficiency evaluation index of the oil pumping unit is calculated as follows:

[0171]

[0172] In the formula, is the entropy value of the jth energy efficiency evaluation index.

[0173] The weight of the jth energy efficiency evaluation index of the oil pumping unit is calculated as follows:

[0174]

[0175] In the formula, ω j is the weight of the jth energy efficiency evaluation index of the oil pumping unit. j

[0176] ​Step H4: Determine the normalized weighted index matrix by using the normalized energy efficiency evaluation index matrix and the weight of the energy efficiency evaluation index;

[0177] The normalized index matrix and the weight determine the normalized weighted index matrix Z, , which is as follows:

[0178]

[0179] In the formula, Z is the normalized weighted index matrix determined by the normalized energy efficiency evaluation index matrix and the weight of the energy efficiency evaluation index; Z ij is the normalized weighted index value.

[0180] Step H5: Determine the positive and negative ideal operating states of the energy efficiency evaluation system of the pumping unit by using the normalized weighted index matrix;

[0181] The positive ideal solution and the negative ideal solution of Z are as follows:

[0182] Positive ideal solution:

[0183]

[0184] Negative ideal solution:

[0185]

[0186] In the formula, Z + is the positive ideal value of the energy efficiency evaluation index; Z - is the negative ideal value of the energy efficiency evaluation index; is the minimum value when i belongs to the interval [1, n] z ij ; is the maximum value when i belongs to the interval [1, n] z ij ; , respectively, are the positive and negative ideal values of the nth energy efficiency evaluation index.

[0187] Step H6: Calculate the Euclidean distance between the energy efficiency evaluation index of the pumping unit and the positive and negative ideal solution states;

[0188] The Euclidean distance between each pumping unit scheme and the positive and negative ideal solution is calculated by the TOPSIS method, as follows:

[0189]

[0190]

[0191] In the formula, and The Euclidean distances between the i-th pumping unit and the j-th energy efficiency evaluation index and the positive and negative ideal solutions are respectively. Abbreviated as Euclidean distance; (Abbreviated as negative Euclidean distance); , These are the positive and negative ideal values ​​of the j-th energy efficiency evaluation index, respectively.

[0192] Step H7: Calculate the grey relational coefficient between the i-th pumping unit and the positive and negative ideal operating states;

[0193]

[0194]

[0195] In the formula, The resolution coefficient. When At this time, the resolution is best, usually taken as... ,and . and The grey relational coefficients of the i-th pumping unit and the j-th energy efficiency evaluation index with the positive and negative ideal solutions are respectively. Abbreviated as positive gray relational degree, (Abbreviated as negative grey relational degree).

[0196] Step H8: Calculate the weighted grey relational degree between the i-th pumping unit and the positive and negative ideal solutions, as follows:

[0197]

[0198]

[0199] In the formula, and , respectively, represent the weighted grey relational degree between the j-th energy efficiency evaluation index of the i-th pumping unit and the positive and negative ideal solutions.

[0200] Step H9: Dimensionless processing is performed on the Euclidean distance and weighted grey relational degree;

[0201] The Euclidean distance and weighted grey relational degree are standardized as follows:

[0202]

[0203] In the formula, and These are the standardized values ​​of the positive and negative Euclidean distances, respectively. and These are the standardized values ​​of the positive and negative weighted grey relational degrees, respectively. and respectively are the maximum and minimum values of the positive and negative Euclidean distances of the i th pumping unit; and respectively are the maximum and minimum weighted grey correlation degrees of the i th pumping unit with the positive and negative ideal solutions.

[0204] The comprehensive standardized weighted grey correlation degree and Euclidean distance are combined to calculate the comprehensive closeness value as follows:

[0205]

[0206]

[0207] In the formula, α and β are weight coefficients, respectively , and . and respectively are the positive and negative closeness values of the Euclidean distance and the weighted grey correlation degree, comprehensively reflect the closeness of the operating state of each pumping unit to the positive ideal state, and the greater the value, the better the closeness; comprehensively reflect the closeness of the operating state of each pumping unit to the negative ideal state of the pumping unit, and the greater the value, the worse the operation.

[0208] Step H10: calculating the relative closeness degree according to the comprehensive closeness value;

[0209] The relative closeness degree of each pumping unit scheme to the ideal operating state is calculated as follows:

[0210]

[0211] In the formula, is the relative closeness degree; , respectively are the positive and negative closeness values of the Euclidean distance and the weighted grey correlation degree of the i th pumping unit. The calculated relative closeness degree of the pumping unit is arranged. If the calculated relative closeness degree is greater, that is, the operating state of the pumping unit is better, on the contrary, it indicates that the operating condition of the pumping unit is poor. The evaluation index group of the pumping unit consists of economic index, reliability index and technical index, wherein the economic index includes manufacturing cost, operation cost, etc.; the reliability index includes whole machine life, average trouble-free working time, maintenance time, etc.; the technical index includes system efficiency, liquid production, balance degree and other indexes. Here, only the technical index is calculated. Taking 6 pumping units applied in a certain oilfield as an example, the comprehensive energy efficiency evaluation is carried out, and the evaluation indexes are 12 items such as system efficiency, liquid production, load utilization rate, etc. The basic parameters of the pumping units are shown in the following table 1.

[0212] Table 1 Pumping unit basic data table

[0213]

[0214] The base data of the energy efficiency evaluation index is standardized to obtain a standard index matrix. In the process, the ten evaluation index values of system efficiency, liquid production, load utilization rate, torque utilization rate, motor power factor, motor power utilization rate, transmission efficiency, pump efficiency, rod string efficiency, and balance degree are positively correlated with the efficiency of the pumping unit, while the sinking deviation degree of the pumping unit and the input power of the pumping unit are negatively correlated with the efficiency. The correct selection of the standardization processing formula is shown in Table 2:

[0215] Table 2 Standard index matrix data table of energy efficiency evaluation index

[0216]

[0217] The weight calculation result of the entropy weight method is shown in Table 3:

[0218] Table 3 Data table of entropy weight method calculation result

[0219]

[0220] The weight calculation result of the entropy weight method shows that the weight of system efficiency standardization is 6.602%, the weight of liquid production standardization is 14.363%, the weight of torque utilization rate standardization is 10.283%, the weight of load utilization rate standardization is 6.376%, the weight of motor power factor standardization is 6.981%, the weight of motor power utilization rate standardization is 7.174%, the weight of transmission efficiency standardization is 7.765%, the weight of pump efficiency standardization is 7.543%, the weight of rod string efficiency standardization is 6.91%, the weight of balance degree standardization is 6.807%, the weight of sinking deviation degree standardization is 11.249%, and the weight of input power standardization is 7.948%. Among them, the maximum index weight is the liquid production standardization (14.363%), and the minimum value is the load utilization rate standardization (6.376%). In summary, the result has a certain rationality through actual situation analysis, and therefore the use of entropy weight method to sort the pumping unit evaluation index conforms to the objective fact.

[0221] According to the calculation result of the above weight, the weighted processing of the matrix is carried out, and the result of the positive and negative ideal solutions is shown in Table 4.

[0222] Table 4 Data table of positive and negative ideal solution results

[0223]

[0224] Finally, the weighted grey correlation degree and Euclidean distance are calculated according to the positive and negative ideal solutions. The relative closeness of each heating furnace to the ideal operating state is calculated through the standardized result, and the sorting is carried out according to the numerical value size. The final evaluation result of the six pumping units is shown in Table 5:

[0225] Table 5 Final evaluation result data table

[0226]

[0227] According to the obtained relative closeness results, the operation conditions of each pumping unit can be evaluated. As shown in the chart, the relative closeness of the six test pumping units to the ideal operation state is between 0.4 and 0.7, and only one pumping unit has a relative closeness of more than 0.6. This indicates that the operation efficiency of most pumping units is low, which is a result of the low pass rate of one indicator or the influence of multiple indicators.

[0228] Through the above analysis, it can be seen that the pumping unit energy efficiency evaluation system model established based on the entropy weight-gray correlation-TOPSIS method has practical application value. The oilfield should regularly evaluate the energy efficiency of pumping units, screen out pumping units with low operation efficiency, and analyze the reasons through specific operation parameters. Effective measures should be taken to improve the economic benefits of the oilfield.

[0229] The present application provides a pumping unit parameter optimization method, which automatically warns the pumping unit well with low energy efficiency through energy efficiency evaluation of production operation data, and optimizes the production operation data parameters to minimize the carbon emissions of the pumping unit system in the block and maximize the economic benefits in the block, establishes a multi-objective function, establishes well production constraints, suspension point load constraints, crank shaft net torque constraints, balance degree constraints, stroke constraints, up and down stroke time constraints and rod strength constraints, establishes a pumping unit parameter optimization model according to the target function and model constraint conditions, and solves the optimal production operation data, thereby providing a feasible energy-saving technical transformation scheme and energy-saving operation scheme considering the economic benefits of the output of single well and well group (block); the optimal production operation data is applied, and hardware devices such as frequency regulation and balance block servo regulation are matched, to realize intelligent control of the energy-saving operation of the pumping unit single well and well group (block).

[0230] Example 4:

[0231] The present application provides a pumping unit parameter optimization method, which automatically warns the pumping unit well with low energy efficiency through energy efficiency evaluation of production operation data, and optimizes the production operation data parameters to minimize the carbon emissions of the pumping unit system in the block and maximize the economic benefits in the block, establishes a multi-objective function, establishes well production constraints, suspension point load constraints, crank shaft net torque constraints, balance degree constraints, stroke constraints, up and down stroke time constraints and rod strength constraints, establishes a pumping unit parameter optimization model according to the target function and model constraint conditions, and solves the optimal production operation data, thereby providing a feasible energy-saving technical transformation scheme and energy-saving operation scheme considering the economic benefits of the output of single well and well group (block); the optimal production operation data is applied, and hardware devices such as frequency regulation and balance block servo regulation are matched, to realize intelligent control of the energy-saving operation of the pumping unit single well and well group (block).

[0232] Example 5:

[0233] The application is a pumping unit balance module control method, using the pumping unit parameter optimization method, solving the pumping unit parameter optimization model to obtain the optimal balance degree setting value, and adjusting the balance block position by using the optimal balance degree setting value.

[0234] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. The present application can be variously changed and modified by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application is included in the protection scope of the present application.

Claims

1. A method of pumping unit parameter optimization, the method comprising: The method comprises the following steps: Step S1: energy efficiency evaluation is performed on the collected production operation data; Step S2: according to the energy efficiency evaluation result, parameter optimization is performed on the production operation data; Step S3: a multi-objective function is established in the manner of minimizing the carbon emission of the pumping unit system in the block and maximizing the economic benefit in the block; Step S4: a model constraint condition corresponding to the pumping unit operation parameter and the oil well production is established; Step S5: a pumping unit parameter optimization model is established according to the multi-objective function and the model constraint condition, and the optimal production operation data is obtained by solving the model; Step S6: the pumping unit parameter is adjusted and optimized according to the optimal production operation data, so as to realize energy saving and consumption reduction of the pumping unit. In step S3, let I represent the set of all pumping units in a certain oil production block, and let i represent the pumping unit serial number, then In a time period T, there are ; The target function is established in the manner of minimizing the carbon emission of the pumping unit system in the block, as follows: In the formula: is the minimum carbon emission of the pumping unit system in the block, unit ton; is the carbon emission of the pumping unit system in the block, unit ton; is the carbon emission factor of the power grid, ton / (MWh); is the power consumption of the pumping unit system in the block, unit kWh; In the case of only considering the oil field crude oil sales and electricity purchase expenditure, the target function is established in the manner of maximizing the economic benefit in the block, as follows: Wherein: max F is the maximum economic benefit; is the sale price of crude oil, unit yuan / ton; is the output of the i th pumping unit at time t, unit ton; is the purchase price of electricity, unit yuan / kWh; In step S4, the model constraint condition comprises an oil well production constraint, a suspension point load constraint, a crank shaft net torque constraint, a balance degree constraint, a stroke frequency constraint, an up-down stroke time length constraint and a rod string strength constraint; In step S6, the optimal production operation data comprises a stroke of the pumping unit, a stroke frequency of the pumping unit, a crank balance weight, a pumping unit balance radius and an input power of the motor.

2. The method of pumping unit parameter optimization of claim 1, wherein, The power consumption calculation formula of the pumping unit system in the block is as follows: In the formula: is the instantaneous power utilization rate of the i-th pumping unit power equipment motor at time t; is the rated power of the i-th pumping unit power equipment motor at time t, in kW; is the no-load power of the i-th pumping unit power equipment motor at time t, in kW; is the rated efficiency of the i-th pumping unit power equipment motor at time t.

3. The method of pumping unit parameter optimization of claim 1, wherein, For beam type mechanical oil production system, when the pumping equipment, casing pressure, oil pressure and produced oil physical property parameters are known, the power consumption of the pumping unit system in the block is the function of the stroke S of the pumping unit, the stroke n of the pumping unit, the crank balance weight W c , the pumping unit balance radius R c and the rod string combination , the power consumption of the pumping unit system in the block is as follows: In the formula: is the power consumption of the i th pumping unit power equipment motor at time t, unit kW; is the stroke of the i th pumping unit at time t, unit m; is the stroke of the i th pumping unit at time t, unit min -1 ; is the crank counterweight of the i th pumping unit at time t, unit kg; is the balance radius of the i th pumping unit at time t, unit m; is the rod column combination of the i th pumping unit at time t.

4. The method of claim 1, wherein, The oil well production constraint is as follows: In the formula: is the rated production of the i-th pumping unit device, in tons; D 0,i is the pumping unit diameter of the i-th pumping unit system, in mm; S 0,i is the length of the i-th pumping unit system's polished rod stroke, in m; n 0,i is the pumping rate of the i-th pumping unit system, min -1 ; a 0,i is the pump displacement coefficient of the i-th pumping unit system.

5. The method of pumping unit parameter optimization of claim 1, wherein, The maximum load of the suspension point of the i-th pumping unit should be less than the maximum allowable load of the pumping unit, and the suspension point load constraint is as follows: In the formula: is the maximum load of the i th pumping unit's suspension point at time t; is the maximum allowable load of the i th pumping unit's suspension point at time t.

6. The method of pumping unit parameter optimization of claim 1, wherein, When the pumping unit i is working, the maximum net torque of the crank shaft should be less than the maximum allowable torque, and the crank shaft net torque constraint is as follows: In the formula: is the maximum load of the i th pumping unit at time t; is the maximum allowable torque of the i th pumping unit at time t.

7. The method of pumping unit parameter optimization of claim 1, wherein, The power balance degree of the motor of the i-th pumping unit is set to 85% to 100%, and the balance degree constraint is as follows: In the formula: is the input power of the motor of the downstroke of the i-th pumping unit at time t; is the input power of the motor of the upstroke of the i-th pumping unit at time t.

8. The method of pumping unit parameter optimization of claim 1, wherein, The stroke frequency constraint is as follows: In the formula: is the stroke of the i-th pumping unit equipment before frequency conversion control at time t; is the stroke of the i-th pumping unit equipment after frequency conversion control at time t; is the minimum stroke constraint of the i-th pumping unit running at time t; is the maximum stroke constraint of the i-th pumping unit running at time t.

9. The method of pumping unit parameter optimization of claim 1, wherein, The up-down stroke time length constraint is as follows: In the formula: is the time required for the i-th pumping unit at time t for the upstroke; is the time required for the i-th pumping unit at time t for the downstroke.

10. The method of pumping unit parameter optimization of claim 1, wherein, The i-th pumping unit needs to check the strength during production, and the rod string strength constraint is as follows; In the formula: is the maximum stress at the rod top position of the i th pumping unit at time t, in units of MPa; is the maximum allowable stress, in units of MPa; is the use condition coefficient; is the tensile strength limit of the material, in units of MPa; is the minimum stress at the rod top position of the i th pumping unit at time t, in units of MPa.

11. The method of pumping unit parameter optimization of claim 1, wherein, In step S5, the pumping unit parameter optimization model is as follows: In the formula: is the minimum carbon emission of the pumping unit system in the block, unit ton; is the carbon emission of the pumping unit system in the block, unit ton; is the carbon emission factor of the power grid, ton / (MWh); is the power consumption of the pumping unit system in the block, unit kWh; max F is the maximum economic benefit; is the selling price of crude oil, unit yuan / ton; is the output of the i-th pumping unit at time t, unit ton; is the electricity purchase price, unit yuan / kWh; D0,i is the pumping unit diameter of the i-th pumping unit system, mm; S0,i is the length of the i-th pumping unit system's stroke, m; n0,i is the i-th pumping unit system's stroke rate, min-1; a0,i is the i-th pumping unit system's pump displacement coefficient; is the maximum load of the i-th pumping unit's suspension point at time t; is the maximum allowable load of the i-th pumping unit's suspension point at time t; the maximum load of the i-th pumping unit at time t; the maximum allowable torque of the i-th pumping unit at time t; Pdown(i, t) is the input power of the downstroke motor of the i-th pumping unit at time t; Pup(i, t) is the input power of the upstroke motor of the i-th pumping unit at time t; is the stroke of the i-th pumping unit equipment before frequency conversion control at time t; is the stroke of the i-th pumping unit equipment after frequency conversion control at time t; is the minimum stroke constraint of the i-th pumping unit running at time t; is the maximum stroke constraint of the i-th pumping unit running at time t; Tup(i, t) is the time needed for the upstroke of the i-th pumping unit at time t; Tdown(i, t) is the time needed for the downstroke of the i-th pumping unit at time t; is the maximum stress at the rod top position of the i th pumping unit at time t, unit MPa; is the maximum allowable stress, unit MPa; is the use condition coefficient; is the tensile strength limit of the material, unit MPa; is the minimum stress at the rod top position of the i th pumping unit at time t, unit MPa.

12. The method of pumping unit parameter optimization of claim 11, wherein, In step S5, the pumping unit parameter optimization model is solved by combining the enumeration method and the genetic algorithm.

13. A method of controlling the motion of a sucker rod pump, characterized by, The pumping unit parameter optimization method of any one of claims 1 to 12 is used to adjust the motor speed of the pumping unit according to the optimal production operation data, reduce the fluctuation degree of the pumping unit load, reduce the mechanical fatigue degree of the transmission and the rod string, and realize the improvement of the operation efficiency of the pumping unit; the motor torque of the pumping unit is adjusted according to the optimal production operation data, so as to realize the optimal control of the suspension point motion track of the pumping unit.

14. A method of controlling a balance module of a pumping unit, the method comprising: The pumping unit parameter optimization method of any one of claims 1 to 12 is used to obtain the optimal balance degree setting value by solving the pumping unit parameter optimization model, and the balance block is adjusted by using the optimal balance degree setting value.

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