Device life matching optimization method for controllable shock wave generator

Abstract

The invention discloses a device life matching optimization method related to a controllable shock wave generator. The service lives of a capacitor, a battery, a power converter and a gas switch in the controllable shock wave generator are key factors influencing the work of the controllable shock wave generator, a mathematical model of the service life matching coefficient and profit of each device of the controllable shock wave generator is built according to a cost-benefit analysis method, and the model takes the service life of the capacitor as a reference; setting the service life of other devices and the ratio thereof as a device service life matching coefficient, taking the service life matching coefficient as the input, taking the net profit as the output, taking the reliability of each device into consideration, and correcting the model; and searching an optimal solution of life matching and net profit of each device of the controllable shock wave by using a simulated annealing algorithm. The obtained optimal net profit and life matching coefficient relation curve can provide reference for a controllable shock wave generator application decision maker.

Description

Technical Field

[0001] The invention belongs to the technical field of pulse power devices, and in particular relates to a device life matching optimization method for a controllable shock wave generator. Background Art

[0002] As conventional oil and gas resources around the world are gradually depleted, the development of unconventional oil and gas resources has become the key to future energy development. Shale oil, as an unconventional oil and gas resource, has abundant reserves. In shale oil reservoirs, oil and gas resources are trapped in dense shale layers with extremely low permeability. Traditional shale oil development technologies, such as hydraulic fracturing, usually require large amounts of water and chemicals, and may cause potential pollution to the environment. Controllable shock wave technology can directly act on the shale layer by precisely controlling the intensity and range of the shock wave, inducing the generation and expansion of cracks, improving the permeability of the reservoir, and significantly increasing the production of shale oil. Compared with traditional hydraulic fracturing technology, shock wave technology can more efficiently form cracks in the target area while reducing the impact on non-target areas, thereby reducing resource waste and increasing output.

[0003] The controllable shock wave generator is the core equipment of the controllable shock wave technology. Its core components include capacitors, gas switches, batteries and power converters. Whether each component can work normally will directly affect the normal operation of the controllable shock wave generator. In the past, when making decisions through the design scheme of the economically efficient controllable shock wave generator equipment, it was not judged from the perspective of the life matching of each component. The life matching of each component is based on the life of a certain component, and the ratio of the life of the remaining components to it is generally between 0-1. The matching of the life of each component will lead to changes in the design and manufacturing costs of each component, thereby affecting the final profit; and the relationship between whether the life of each component matches the final economic benefit is related to the number of times the controllable shock wave generator goes down the well and the number of times it works down the well in a single time. Therefore, if the life matching of two or more components is considered, each life matching coefficient has a corresponding optimal net profit solution. Therefore, in order to meet this demand, the most important indicator net profit is taken as the optimization target, and other factors are taken as constraints to construct a mathematical model. On this basis, a suitable optimization algorithm is selected to solve the optimization problem and find the optimal solution. Summary of the invention

[0004] Aiming at the problem of how to analyze and judge the economic benefits of a controllable shock wave generator from the perspective of the life matching of each component and taking into account the reliability factors of each component, the present invention provides a device life matching optimization method for a controllable shock wave generator, which combines a cost-benefit analysis method with a simulated annealing algorithm to calculate the optimal solution of the net profit corresponding to each life matching coefficient.

[0005] To achieve the above object, the present invention adopts the following technical solution: for calculating the optimal solution of net profit corresponding to each life matching coefficient, a device life matching optimization method for a controllable shock wave generator includes the following specific steps:

[0006] 1) First, define the life matching coefficient;

[0007] 2) Determine the relationship between the matching coefficient and cost of each device, and then establish a mathematical model of device life matching coefficient and net profit based on cost-benefit analysis;

[0008] 3) Consider the reliability of each device and modify the mathematical model;

[0009] 4) Under a certain life matching coefficient, combined with the total number of downhole operations of the controllable shock wave generator and the number of single downhole operations under normal circumstances, the simulated annealing optimization algorithm is used to calculate the maximum net profit corresponding to each life matching coefficient, and finally the relationship curve between the optimal net profit and the life matching coefficient is obtained.

[0010] The setting of the life matching coefficient is based on the life of one of the components. Generally, the component with the longest life is selected, and the ratio of the life of other components to it is used as the life matching coefficient. In the controllable shock wave generator, the life of the capacitor, battery, power converter, and gas switch are key factors affecting its operation. The initial life of the capacitor, gas switch, battery, and power converter are determined to be M1, M2, and M3, respectively, and the corresponding initial costs are W1, W2, and W3, respectively. Based on the life of the capacitor, the life matching coefficient X of the gas switch and the life matching coefficient Y of the battery and power converter are determined respectively;

[0011] The mathematical model of device life matching coefficient and profit based on cost-benefit analysis method is first determined by respectively determining the function of cost and life matching coefficient of gas switch, battery and power converter: W2′=W2+K1*e (X*M1-M2) / 100 , W3′=W3+K2*(Y*M1-M3), where K1 and K2 are constant coefficients; then determine the fixed cost W0 for each trip down the well, set the initial number of trips down the well N1, the number of normal trips down the well N2, determine the single trip down the well profit and the controllable shock wave single trip down the well working function P1=K*N2, K is a constant coefficient. The total cost includes the fixed cost of the trip down the well N1*W0, the cost of the capacitor, gas switch, battery and power converter, and the other cost of the controllable shock wave generator W7. Finally, within the life cycle of a capacitor, according to the net profit = total trip down the well profit - total cost, determine the mathematical model of the device life matching coefficient and the net profit.

[0012] The reliability of each component is considered and the mathematical model is modified. First, the reliability standard of the core components of the controllable shock wave generator must be defined. Then the failure rates of the capacitor, gas switch, battery and power converter must be determined to be R1, R2 and R3 respectively. Finally, the loss caused by the failure of the component is calculated and added to the cost to modify the mathematical model.

[0013] The standard for defining the reliability of the core components of the controllable shock wave generator is the probability of being able to work normally, and the failure rate of the component is the probability of not being able to work normally due to a failure. The failure rate can be calculated through historical statistical data or fuzzy set theory. For each component, after a failure occurs, it can continue to work normally after being repaired, and the component will only be replaced when it reaches the end of its service life.

[0014] The loss caused by the failure of the calculated device is calculated based on the failure rate and the average number of failures of each device within the life cycle of a capacitor and added to the cost to correct the mathematical model. The average number of failures of the capacitor, gas switch, battery and power converter within the life cycle of a capacitor are: M1*R1, M1*R2, M1*R3 respectively.

[0015] The total number of times the controllable shock wave generator is put into the well and the number of single downhole operations under normal circumstances are combined, because they will affect the cost and profit, it is necessary to establish a simulated annealing optimization algorithm to calculate the maximum net profit corresponding to each life matching coefficient.

[0016] The specific steps of establishing a simulated annealing optimization algorithm to calculate the maximum net profit corresponding to each life matching coefficient are as follows: first, determine the objective function, with net profit as the objective function; second, determine the initial solution N=[N1, N2] of the optimization algorithm, set constraints, and then use the simulated annealing algorithm to calculate the optimal solution for the maximum net profit, the number of downhole trips, and the number of downhole working times under normal circumstances of the controllable shock wave generator under each life matching coefficient, and obtain the relationship curve between the optimal net profit and the life matching coefficient.

[0017] The constraints are set as follows: first, the number of magazines containing energy rod push mechanisms in the controllable shock wave device is 50 rounds, and this is used as a condition to constrain the number of underground working times N2, that is, N2≤50; secondly, the life of a capacitor is used as a cycle as the second constraint, that is, the total number of working times must be less than or equal to the capacitor life.

[0018] The device life matching optimization method for a controllable shock wave generator provided by the present invention is aimed at the core device of the controllable shock wave generator, and the functional relationship between the net profit and the life matching coefficient is calculated by using the cost-benefit analysis method. At the same time, the simulated annealing algorithm is combined with the net profit as the objective function to obtain the optimal solution of the number of downhole times and the number of downhole work times under normal conditions under a determined life matching coefficient, and the optimal net profit is output at the same time. The relationship curve between the net profit and the life matching coefficient can provide a reference for decision makers of the controllable shock wave generator application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A basic flow chart of the method for optimizing the device life and profit of a controllable shock wave generator according to the present invention is presented.

[0020] Figure 2 A flowchart of the specific steps of the algorithm used in the method for optimizing the device life and profit of a controllable shock wave generator of the present invention is presented.

[0021] Figure 3 The relationship curve between the net profit and the life matching coefficient of each device in the implementation example is shown.

[0022] Figure 4 The relationship curve between the net profit alone and the gas switch life matching coefficient in the implementation example is presented.

[0023] Figure 5 The relationship curve between the profit alone and the battery and power converter life matching coefficient in the implementation example is presented. DETAILED DESCRIPTION

[0024] The present invention is described in detail below by way of examples. It is necessary to point out that the examples are only used to further illustrate the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the above invention. In the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0025] A device life matching optimization method for a controllable shock wave generator, the basic process is shown in the attached figure Figure 1As shown, the following steps are included: first, define the life matching coefficients X and Y of the gas switch, battery and power converter, determine the initial number of times N1 to go down the well, the number of times N2 to work normally in the well at a time, consider the reliability factors of each device, and then use the cost-benefit analysis method to establish the final mathematical model of the device life matching coefficient and profit, and then increase the matching coefficient from the initial value X0, Y0 at a uniform speed of 0.05 until it is equal to 1, use the simulated annealing algorithm to add disturbances to the parameter N0, and within a capacitor life cycle, combine the mathematical model of the device life matching coefficient and profit to calculate the maximum net profit corresponding to each device life matching coefficient. Further expansion is shown as follows:

[0026] 1. Construct a mathematical model of device life and profit. The specific model includes: first determine that the initial life of the capacitor, gas switch, battery and power converter is M1, M2, M3 respectively, and the corresponding initial costs are W1, W2, W3 respectively. Select the maximum value of each device life M=max[M1, M2, M3] as the reference value. Assuming that M1 is used as the reference value, X0=M2 / M1 and Y0=M3 / M1 are the initial life matching coefficients of the gas switch, battery and power converter respectively; as the matching coefficient gradually increases to 1, the life of the gas switch, battery and power converter also gradually increases, and their costs also gradually increase. Set the function of their switch life and cost as:

[0027] W2′=W2+K1*e (X*M1-M2) / 100

[0028] Where W2′ is the cost of the gas switch after the life change, X is the life matching coefficient of the gas switch after the change; K1 is the constant coefficient.

[0029] The function of setting battery and power converter life and cost is:

[0030] W3′=W3+K2*(Y*M1-M3)

[0031] Wherein W3′ is the cost of the battery and power converter after the life span changes, Y is the matching coefficient of the battery and power converter after the change, and K2 is a constant coefficient.

[0032] In a specific embodiment, the initial life, initial cost, and function of life and cost may be different for different controllable shock wave generator gas switches, batteries, and power converters.

[0033] Define the number of times you go down the well N1, the number of times you work underground in normal situations N2, set the fixed cost of each time you go down the well W0, and the function of the profit of each time you go down the well and the number of times you work is:

[0034] P1=K*N2

[0035] Where K is a constant coefficient. The total cost includes the fixed cost of going down the well N1*W0, the cost of the capacitor, gas switch, battery and power converter itself, and other costs of the controllable shock wave generator W7. Finally, within the life cycle of a capacitor:

[0036] Net profit = total downhole profit - total cost

[0037] Considering the reliability of each device, the net profit model is modified. First, the failure rates of capacitors, gas switches, batteries and power converters are determined to be R1, R2 and R3 respectively. The failure rate is the probability that each device cannot work normally. The failure rate can be determined directly based on the statistical data of the working history of each device. If the data is incomplete, the failure rate of each device can be estimated based on fuzzy theory. Then the average number of failures of capacitors, gas switches, batteries and power converters in a capacitor life cycle is: M1*R1, M1*R2, M1*R3, respectively. The single maintenance cost is set to W4, W5 and W6 respectively. The loss caused by the failure of the device is added to the cost to modify the mathematical model. Because the time of the failure cannot be determined, the number of times the controllable shock wave generator works underground may not reach N2 times when the failure occurs, resulting in an increase in the number of times N1 goes down the well in a capacitor life cycle, which increases the total fixed cost N1*W0 of each downhole. Therefore, the cost considering the increase in the reliability of each device should be an interval range. Here, only the maximum value is considered, corresponding to each failure. The number of times the controllable shock wave generator goes down the well does not reach N2 times.

[0038] At the same time, because the number of magazines containing the energy rod push mechanism in the controllable shock wave device is 50, this is used as a condition to constrain the number of underground working times N2, that is, N2≤50, and the second constraint condition is the life cycle of the capacitor, that is, the total number of working times N1*N2 must be less than or equal to the capacitor life. The life matching coefficient X gradually increases from the initial value X0 to 1, and the life matching coefficient Y gradually increases from Y0 to 1, and the net profit corresponding to each life coefficient is calculated.

[0039] 2. Use the simulated annealing algorithm to calculate the optimal net profit solution, the number of times of going down the well, and the optimal number of times of working underground under normal circumstances corresponding to each determined life matching coefficient.

[0040] The steps of the simulated annealing algorithm are shown in the attached figure. Figure 2 As shown, the details are as follows:

[0041] 1) First, set the control parameters - initial temperature T0, cooling rate q, Metropolis chain length L, and termination temperature Tend, set the initial parameters of the device life and profit optimization model N0 = [N1, N2], and use the cost-benefit analysis method to calculate the net profit P(N0). At this time, the corresponding number of iterations count = 0, and the temperature attenuation function is T = q*T0.

[0042] 2) Randomly generate a perturbation for the current solution N, and after transformation, obtain a new parameter N = N0 + r * v, where -2 < r < 2 and v is the step size. Calculate the net profit increment dp = P(N) - P(N1) corresponding to the new solution N, and determine whether to accept the new solution N according to the Metropolis criterion. The judgment criterion is dp = P(N) - P(N1) > 0 or exp(dp / T) > rand, where rand is a uniformly distributed random number randomly generated in the interval (0, 1).

[0043] 3) Repeat step 2 for L times to obtain an optimal solution of the Metropolis process with a chain length of L.

[0044] 4) Determine whether the termination criterion is satisfied. If it is satisfied, output the current optimal solution and the optimal net profit, and the algorithm stops; otherwise, execute step 5.

[0045] 5) The iteration count = count + 1, update the current solution to the optimal solution obtained in step 3, decrease the temperature according to the attenuation function T, and repeat the Metropolis process.

[0046] Finally, the optimal net profit value, the number of times of going down the well, and the optimal solution of the number of times of working in the well under normal conditions corresponding to each life matching coefficient can be calculated, and the relationship curve between the optimal net profit and the life matching coefficient can be obtained.

[0047] Implementation example

[0048] 1. Construct a mathematical model of device life and profit. The specific model includes: First, determine that the initial lives of the capacitor, gas switch, battery, and power converter are M1 = 2000, M2 = 1500, M3 = 1200 respectively. Select the maximum value M = max[M1, M2, M3] of the lives of each device as the reference value. Taking M1 = 2000 as the reference value, the initial values X = M2 / M1 = 0.75 and Y = M3 / M1 = 0.6 are the life matching coefficients of the gas switch, battery, and power converter respectively. As the matching coefficient gradually increases to 1, the lives of the gas switch, battery, and power converter also gradually increase, and their costs also gradually increase. Set the functional formula of the switch life and cost as:

[0049] W2′ = 1e4 + 1e4 * e (X*2000-1500) / 100

[0050] where W2′ is the cost of the gas switch and X is the life matching coefficient of the gas switch;

[0051] Set the functional formula of the battery and power converter life and cost as:

[0052] W3′ = 1e4 + 3e3 * (Y * 2000 - 1200)

[0053] Where W3′ is the cost of the battery and power converter, and Y is the life matching coefficient of the battery and power converter;

[0054] Determine the number of times you go down the well N1, the number of times you work underground in a normal situation N2, the fixed cost of a single trip down the well W0 = 5e3 yuan, and the function of the profit of each trip down the well and the number of times you work is:

[0055] P1=2000*N2

[0056] According to the working history statistics of each device, the failure rates of capacitors, gas switches, batteries and power converters are determined to be R1 = 0.0012, R2 = 0.0018, and R3 = 0.0009 respectively. The average number of failures of capacitors, gas switches, batteries and power converters in a capacitor life cycle is M1*R1, M1*R2, and M1*R3 respectively, and the single maintenance costs are W4 = 1.2e3 yuan, W5 = 1e3 yuan, and W6 = 1.6e3 yuan respectively. The total cost of other devices of the controllable impulse generator except capacitors, gas switches, batteries and power converters is W7 = 1e5 yuan.

[0057] The lifetime matching coefficient X gradually increases from the initial value of 0.75 to 1, and the lifetime matching coefficient Y gradually increases from 0.6 to 1. The simulated annealing algorithm is used to calculate the optimal net profit solution corresponding to each lifetime coefficient. The simulated annealing algorithm parameters are set as follows: control parameters--initial temperature T0=600, cooling rate q=0.98, Metropolis chain length L=200, termination temperature Tend=0.1, and the initial parameters of the device lifetime and profit optimization model are set as N0=[N1, N2]=[50, 40]. The net profit P(N0) is calculated using the cost-benefit analysis method. The corresponding number of iterations count=0 at the beginning, and the temperature decay function is T=0.98*T0.

[0058] The final calculation result is as follows Figure 3 , Figure 4 , Figure 5 As shown in the figure, the relationship curve between the optimal net profit and the life matching coefficient is obtained. From the results in the figure, whether the life matching coefficients of the gas switch, battery and power converter are considered separately, or both are considered at the same time, it can be seen that the net profit decreases with the increase of the life matching coefficient. The obtained relationship curve between the optimal net profit and the life matching coefficient can provide a reference for decision makers in the application of controllable shock wave generators.

Claims

1. A device life matching optimization method for a controllable shock wave generator, characterized in that: The specific steps include: 1) First, define the life matching coefficient; 2) Determine the relationship between the matching coefficient and cost of each device, and then establish a mathematical model of the device life matching coefficient and net profit based on the cost-benefit analysis method; 3) Consider the reliability of each device and modify the mathematical model; 4) Under a certain life matching coefficient, combined with the total number of downhole operations of the controllable shock wave generator and the number of single downhole operations under normal circumstances, the simulated annealing optimization algorithm is used to calculate the maximum net profit corresponding to each life matching coefficient, and finally the relationship curve between the optimal net profit and the life matching coefficient is obtained.

2. The device life matching optimization method for a controllable shock wave generator according to claim 1, characterized in that: The setting of the life matching coefficient is based on the life of one of the components. Generally, the component with the longest life is taken, and the ratio of the life of other components to it is used as the life matching coefficient. In the controllable shock wave generator, the life of the capacitor, battery, power converter and gas switch is the key factor affecting its operation. The initial life of the capacitor, gas switch, battery and power converter is determined to be M1, M2 and M3 respectively, and the corresponding initial costs are W1, W2 and W3 respectively. Based on the life of the capacitor, the life matching coefficient X of the gas switch and the life matching coefficient Y of the battery and power converter are determined respectively.

3. The device life matching optimization method for a controllable shock wave generator according to claim 1, characterized in that: The mathematical model of device life matching coefficient and profit based on cost-benefit analysis method is first determined by respectively determining the function of cost and life matching coefficient of gas switch, battery and power converter: W2′=W2+K1*e (X*M1-M2) / 100 , W3′=W3+K2*(Y*M1-M3), where K1 and K2 are constant coefficients; then determine the fixed cost W0 for each downhole trip, set the initial number of downhole trips N1 and the number of normal downhole work times N2, determine the function of the profit of a single downhole trip and the number of controllable shock wave working times in the well P1=K*N2, K is a constant coefficient, the total cost includes the fixed cost of downhole trip N1*W0, the cost of capacitors, gas switches, batteries and power converters, and other costs of the controllable shock wave generator W7, finally, within the life cycle of a capacitor, determine the mathematical model of the device life matching coefficient and the net profit based on the net profit = total downhole profit - total cost.

4. The device life matching optimization method for a controllable shock wave generator according to claim 1 is characterized in that: The reliability of each component is considered and the mathematical model is modified. First, the reliability standard of the core components of the controllable shock wave generator must be defined. Then the failure rates of the capacitor, gas switch, battery and power converter must be determined to be R1, R2 and R3 respectively. Finally, the loss caused by the failure of the component is calculated and added to the cost to modify the mathematical model.

5. The device life matching optimization method for a controllable shock wave generator according to claim 4, characterized in that: The standard for defining the reliability of the core components of the controllable shock wave generator is the probability of being able to work normally, and the failure rate of the component is the probability of failure to work normally due to a failure. The failure rate can be calculated through historical statistical data or fuzzy set theory. After a failure occurs, each component can continue to work normally after being repaired, and the component will only be replaced when its service life is reached.

6. The device life matching optimization method for a controllable shock wave generator according to claim 4 or 5, characterized in that: The loss caused by the failure of the calculated device is calculated based on the failure rate and the average number of failures of each device within the life cycle of a capacitor and added to the cost to correct the mathematical model. The average number of failures of the capacitor, gas switch, battery and power converter within the life cycle of a capacitor are: M1*R1, M1*R2, M1*R3 respectively.

7. The device life matching optimization method for a controllable shock wave generator according to claim 1, characterized in that: The total number of times the controllable shock wave generator is put into the well and the number of single downhole operations under normal circumstances are combined, because they will affect the cost and profit, it is necessary to establish a simulated annealing optimization algorithm to calculate the maximum net profit corresponding to each life matching coefficient.

8. The device life matching optimization method for a controllable shock wave generator according to claim 7, characterized in that: The specific steps of establishing a simulated annealing optimization algorithm to calculate the maximum net profit corresponding to each life matching coefficient are as follows: first, determine the objective function, with net profit as the objective function; second, determine the initial solution N=[N1, N2] of the optimization algorithm, set constraints, and then use the simulated annealing algorithm to calculate the optimal solution for the maximum net profit, the number of downhole trips, and the number of downhole working times under normal circumstances of the controllable shock wave generator under each life matching coefficient, and obtain the relationship curve between the optimal net profit and the life matching coefficient.

9. The device life matching optimization method for a controllable shock wave generator according to claim 8, characterized in that: The constraints are set as follows: first, the number of magazines containing energy rod push mechanisms in the controllable shock wave device is 50 rounds, and this is used as a condition to constrain the number of underground working times N2, that is, N2≤50; secondly, the life of a capacitor is used as a cycle as the second constraint, that is, the total number of working times must be less than or equal to the capacitor life.

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