Electric submersible pump fault early warning method and device based on current change analysis

Abstract

The invention relates to the technical field of equipment fault diagnosis, in particular to an electric submersible pump fault early warning method and device based on current change analysis. According to the method, a current standardization formula, a time standardization formula and a current characteristic value calculation formula are constructed, a fault diagnosis model is constructed based on the current standardization formula, a plurality of actually measured current characteristic values are calculated according to current operation data after the fault diagnosis model is trained through a plurality of typical current characteristic values, and a fault diagnosis result is obtained. And comparing the plurality of actually measured current characteristic values with the plurality of typical current characteristic values to determine a plurality of different current characteristic types, and combining the plurality of different current characteristic types to determine the fault type of the electric submersible pump, so that the troubleshooting efficiency of the electric submersible pump can be improved, and the fault detection accuracy of the electric submersible pump can be improved. The fine and intelligent management level of the electric submersible pump is improved, and the production efficiency of an oil well platform is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment fault diagnosis, and in particular to an electric submersible pump fault early warning method and device based on current change analysis. Background Art

[0002] Electric submersible centrifugal pumps, hereinafter referred to as ESPs, are widely used in oilfield production, especially in thousand-meter drawdown wells. Due to their high head and large flow rate, ESPs are the preferred equipment. It is precisely because the working place of ESPs is generally located in thousand-meter drawdown wells that when ESPs fail, it will bring more complicated maintenance problems. One is that when ESPs fail, it often takes a long time for the staff to rush to the location of the ESPs, which will affect the normal working time of the ESPs. The other is that when the staff arrives at the scene, they can determine the type of fault according to the actual situation, which will delay the progress of maintenance. Both situations will delay the production progress; therefore, it is necessary to monitor the operating status of ESPs in real time and be able to warn of ESP failures, so as to solve the fault problems in advance and ensure the production efficiency of the oil well platform.

[0003] The Chinese patent publication number in the prior art: CN111461551B discloses a method for early warning of electric submersible pump faults based on deep learning and SPC criteria. The method of this technical solution is to construct a CNN-LSTM model and perform standardized training, and then estimate the monitoring parameter value of the electric submersible pump to be evaluated. Based on the gap between the monitoring parameter and the normal state, the health of the electric submersible pump is calculated, and the operation of the electric submersible pump is determined to be abnormal according to the health, thereby triggering an alarm mechanism, reducing the process of manual detection and saving labor costs. However, this technical solution does not determine the fault type when the electric submersible pump is abnormal based on the acquired monitoring parameter value, and thus cannot enable the staff to initiate the corresponding treatment measures in time, which will affect the production efficiency of the oil well platform. Summary of the invention

[0004] To this end, the present invention provides an electric submersible pump fault warning method and device based on current change analysis, which is used to solve the problem in the prior art that there is no current characteristic value calculation formula constructed according to the current change situation and the fault type of the electric submersible pump is analyzed based on the comparison of several measured current characteristic values ​​and several typical current characteristic values.

[0005] To achieve the above object, the present invention provides an electric submersible pump fault early warning method based on current change analysis, comprising: Constructing a current standardization formula, a time standardization formula, and a current characteristic value calculation formula, and constructing a fault diagnosis model based on the current standardization formula, the time standardization formula, and the current characteristic value calculation formula; Setting a training data set, obtaining a number of typical current characteristic values ​​corresponding to the fault point from the training data set, and training the fault diagnosis model based on the number of the typical current characteristic values; Periodically collecting current operation data in the electric submersible pump, and importing the current operation data into the trained fault diagnosis model to obtain a number of measured current characteristic values; Comparing the measured current characteristic values ​​with the typical current characteristic values ​​to determine a number of different current characteristic types; A plurality of the different current feature types are combined to determine a fault type of the electric submersible pump.

[0006] Furthermore, the process of constructing the current normalization formula, the time normalization formula and the current characteristic value calculation formula includes: The current normalization formula is as follows: , in, is the measured current value at the i-th point, in A; is the rated working current value, in A; It is the first sensitive factor; is the normalized current value at the i-th point; The time normalization formula is as follows: , in, is the measured time value corresponding to the measured current value at the i-th point, in units of h; is the longest time among several measured current curves, in h; It is the second sensitive factor; is the time value after normalization of the i-th point; The current characteristic value calculation formula is as follows: , in, is the measured current characteristic value, in A / h; is the eigenvalue correction factor.

[0007] Further, the process of determining the different current characteristic types includes: The different current characteristic types include a stable current characteristic type, a fluctuating current characteristic type, a shutdown characteristic type, a slowly decreasing characteristic type, a rapidly decreasing characteristic type, and an increasing characteristic type; If the absolute value of the measured current characteristic value is less than or equal to the stable current characteristic value, it is determined to be the stable current characteristic type; If the absolute value of the measured current characteristic value is greater than the first current characteristic value and less than or equal to the second current characteristic value, and the proportion of the measured characteristic value is less than the proportion of the second characteristic value and greater than the proportion of the first characteristic value, it is determined to be the fluctuating current characteristic type; If the measured current characteristic value is less than or equal to negative 1000, and the number of shutdowns is greater than or equal to 1, it is determined to be the shutdown characteristic type; If the measured current characteristic value is less than the negative first current characteristic value and greater than or equal to the negative third current characteristic value, and the proportion of the measured characteristic value is greater than or equal to the proportion of the third characteristic value, it is determined to be the slowly decreasing characteristic type; If the measured current characteristic value is less than the negative third current characteristic value and greater than or equal to negative 1000, and the proportion of the measured characteristic value is greater than or equal to the proportion of the third characteristic value, it is determined to be the rapid decrease characteristic type; If the measured current characteristic value is less than the fourth current characteristic value and greater than or equal to the first current characteristic value, and the proportion of the measured characteristic value is greater than or equal to the proportion of the fourth characteristic value, it is determined to be the added characteristic type.

[0008] Further, the process of determining the fault type of the electric submersible pump includes: combining a plurality of said different current feature types to obtain an analysis feature type group; If all of the analyzed feature type groups are of the fluctuating current feature type, it is determined that the fault type is that the well fluid in the environment where the electric submersible pump is located contains gas; If the analysis feature type group includes the stable current feature type and the shutdown feature type in sequence, determining that the fault type is a sudden shutdown of the electric submersible pump; If the analysis feature type group includes the rapid reduction feature type and the shutdown feature type more than twice in sequence, then it is determined that the fault type is the frequent restart of the electric submersible pump; If the analysis feature type group includes the stable current feature type, the slowly decreasing feature type, and the shutdown feature type in sequence, it is determined that the fault type is insufficient well fluid supply in the environment where the electric submersible pump is located; If the analysis feature type group includes the stable current feature type, the increase feature type and the shutdown feature type in sequence, then determining that the fault type is the overload shutdown of the electric submersible pump; If the analysis feature type group includes the fluctuating current feature type, the increasing feature type and the shutdown feature type in sequence, it is determined that the fault type is that the well fluid in the environment where the electric submersible pump is located contains impurities; If the analysis feature type group includes the stable current feature type, the slowly decreasing feature type, the stable current feature type and the shutdown feature type in sequence, then it is determined that the fault type is the failure of the underload protection of the electric submersible pump; If the analysis feature type group includes the stable current feature type, the slowly decreasing feature type, the fluctuating current feature type, and the shutdown feature type in sequence, it is determined that the fault type is the pump air lock of the electric submersible pump.

[0009] Furthermore, the process of obtaining the plurality of measured current characteristic values ​​includes: Obtaining a characteristic value variance according to a number of the measured current characteristic values ​​within a preset period; Determining whether the calculation process of the measured current characteristic value is qualified based on a comparison result of the characteristic value variance with a preset characteristic value variance, or based on a comparison result of an average value of several historical characteristic value variances obtained in several historical periods with the characteristic value variance; When it is determined that the calculation result of the measured current characteristic value is unqualified, the cause is determined based on a comparison result of a difference between a maximum value and a minimum value among a plurality of the measured current characteristic values ​​with a preset difference.

[0010] Further, the process of determining the cause according to the comparison result of the difference between the maximum value and the minimum value of the measured current characteristic values ​​with the preset difference includes: Calculate the difference between the maximum value and the minimum value of the plurality of measured current characteristic values, record it as the characteristic value amplitude difference, and record the preset difference as the preset characteristic amplitude difference; Determining the reason why the calculation of the measured current characteristic value is unqualified according to the comparison result of the characteristic value amplitude difference and the preset characteristic value amplitude difference; Determining corresponding processing based on the cause includes: increasing the first sensitivity factor and the second sensitivity factor or reducing the eigenvalue correction coefficient.

[0011] Further, the process of increasing the first sensitivity factor and the second sensitivity factor includes: Based on the comparison result of the pump hanging depth with the preset pump hanging depth, it is determined that the first sensitive factor and the second sensitive factor are increased, and the increase range of the first sensitive factor and the second sensitive factor is proportional to the pump hanging depth.

[0012] Furthermore, the process of reducing the characteristic value correction coefficient includes: Based on the comparison result of the amplitude difference and the preset amplitude difference, it is determined to reduce the characteristic value correction coefficient, and the reduction amplitude of the characteristic value correction coefficient is proportional to the amplitude difference; The amplitude difference is the difference between the characteristic value amplitude difference and the preset characteristic value amplitude difference.

[0013] Further, after completing the increase adjustment of the first sensitive factor and the second sensitive factor, the characteristic value correction coefficient is reduced based on the comparison result between the motor temperature and the preset motor temperature, and the reduction range of the characteristic value correction coefficient is proportional to the motor temperature.

[0014] The present invention also provides an electric submersible pump fault early warning device based on current change analysis, comprising: A model building module, used to build a current standardization formula, a time standardization formula and a current characteristic value calculation formula, and build a fault diagnosis model based on the current standardization formula, the time standardization formula and the current characteristic value calculation formula; A training module, connected to the model building module, for setting a training data set, obtaining a number of typical current characteristic values ​​corresponding to the fault point from the training data set, and training the fault diagnosis model based on the typical current characteristic values; A data acquisition module, connected to the model building module, for periodically acquiring current operation data in the electric submersible pump, and importing the current operation data into the trained fault diagnosis model to obtain a number of measured current characteristic values; a comparison module, which is connected to the model building module and the training module respectively, and is used to determine a number of different current characteristic types based on the comparison of the measured current characteristic values ​​with the typical current characteristic values, and to combine the different current characteristic types to determine the fault type of the electric submersible pump; an analysis module connected to the model building module, for determining whether the calculation process of the measured current characteristic value is qualified based on a comparison result of the variance of the measured current characteristic value and a preset variance, and generating a corresponding instruction when it is unqualified; An optimization module is connected to the analysis module and the model building module respectively, and is used to determine the first sensitive factor and the second sensitive factor or determine the characteristic value correction coefficient based on the instruction.

[0015] Compared with the prior art, the beneficial effect of the electric submersible pump fault early warning method based on current change analysis of the present invention is that the method constructs a current standardization formula, a time standardization formula and a current characteristic value calculation formula, and constructs a fault diagnosis model based on this, and after the fault diagnosis model is trained by several typical current characteristic values, several measured current characteristic values ​​are calculated according to the current operation data, and several measured current characteristic values ​​are compared with several typical current characteristic values ​​to determine several different current characteristic types, and the fault type of the electric submersible pump is determined by combining several different current characteristic types, thereby improving the efficiency of electric submersible pump fault troubleshooting, and improving the refined and intelligent management level of the electric submersible pump, thereby ensuring the production efficiency of the oil well platform.

[0016] Furthermore, the present invention can accurately determine the fault type of the corresponding submersible pump by combining several different current characteristic types obtained in chronological order, thereby achieving the corresponding fault determination without manual on-site detection, and playing an early warning function, so that equipment maintenance personnel can carry out targeted fault repairs, thereby improving the maintenance efficiency of the equipment and ensuring the production progress of the oil well platform.

[0017] Furthermore, the present invention further determines the calculation result of the measured current characteristic value based on the comparison result of the characteristic value variance with the preset characteristic value variance, determines whether the calculation of several measured current characteristic values ​​in the current cycle is qualified, and when it is determined that the calculation result of several measured current characteristic values ​​in the current cycle is unqualified, it can then be processed according to the specific reason to improve the calculation pass rate of the measured current characteristic value, thereby ensuring the accuracy of subsequent fault diagnosis results.

[0018] Furthermore, when comparing the eigenvalue variance with the preset eigenvalue variance, the present invention can further re-determine whether the calculation result of the measured current eigenvalue is qualified based on the average value of several historical eigenvalue variances in several historical periods before the current preset period and the comparison with the eigenvalue variance, thereby improving the accuracy of the determination result.

[0019] Furthermore, when determining that the calculation result of the measured current characteristic value is unqualified, the present invention can calculate the characteristic value amplitude difference by the maximum value and the minimum value among several measured current characteristic values, and determine the cause based on the comparison result of the characteristic value amplitude difference and the preset characteristic value amplitude difference, and generate a corresponding processing method based on the cause, thereby improving the calculation pass rate of the measured current characteristic value.

[0020] Furthermore, when the present invention determines that the reason for failure is that there are problems with the first sensitive factor in the constructed current standardization formula and the second sensitive factor in the time standardization formula, it can also determine to increase the first sensitive factor and the second sensitive factor based on the comparison result of the pump hanging depth with the preset pump hanging depth, thereby reducing the influence of the increase in current value caused by excessive oil pumping resistance of the submersible pump at a deeper depth on the standardization formula.

[0021] Furthermore, when the present invention determines that the reason for failure is that there is a problem with the characteristic value correction coefficient in the constructed current characteristic value calculation formula, it can also determine to reduce the characteristic value correction coefficient based on the comparison result of the amplitude difference and the preset amplitude difference, thereby reducing the impact caused by the large variation amplitude between several measured current characteristic values. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a module schematic diagram of an electric submersible pump fault warning device based on current change analysis in the present invention; Figure 2 It is a flow chart of a method for early warning of electric submersible pump failure based on current change analysis in the present invention; Figure 3 A logic determination diagram for determining whether the calculation result of the measured current characteristic value is qualified based on the characteristic value variance in the present invention; Figure 4 It is a logic decision diagram for determining the reason why the characteristic value of the measured current is unqualified based on the characteristic value amplitude difference and the corresponding processing method in the present invention. DETAILED DESCRIPTION

[0023] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0024] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0025] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0026] See also Figure 1As shown, it is a module schematic diagram of a device using an electric submersible pump fault warning method based on current change analysis in this embodiment, and the device is used to execute the electric submersible pump fault warning method based on current change analysis. The electric submersible pump in this embodiment is used for oil and gas field exploration and development. The device includes a model construction module, a training module, a data acquisition module, a comparison module, an analysis module and an optimization module. The model construction module is used to construct a current standardization formula, a time standardization formula and a current characteristic value calculation formula, and to construct a fault diagnosis model based on the current standardization formula, the time standardization formula and the current characteristic value calculation formula; the training module is connected to the model construction module to set a training data set, obtain a number of typical current characteristic values ​​corresponding to the fault point from the training data set, and train the fault diagnosis model based on the typical current characteristic values; the data acquisition module is connected to the model construction module to periodically collect the current operation data in the electric submersible pump, and import the current operation data into the trained fault diagnosis model to obtain a number of measured current characteristic values; the comparison module The analysis module is connected to the model building module and the training module respectively, and is used to determine a number of different current characteristic types based on the comparison between the measured current characteristic values ​​and the typical current characteristic values, and to determine the fault type of the submersible pump by combining the different current characteristic types; the analysis module is connected to the model building module, and is used to determine whether the calculation process of the measured current characteristic value is qualified based on the comparison result between the variance of the measured current characteristic values ​​and the preset variance, and to generate corresponding instructions when it is unqualified; the optimization module is connected to the analysis module and the model building module respectively, and is used to determine the first sensitive factor and the second sensitive factor or determine the characteristic value correction coefficient based on the instruction. By collecting the current value generated in the real-time operation of the submersible pump, and then calculating a number of measured current characteristic values ​​according to the collected current value through the fault diagnosis model, the current characteristic type in the submersible pump in the current cycle can be determined based on the comparison of the several measured current characteristic values ​​with the typical current characteristic values. According to the current characteristic type, the type of fault that has occurred or may occur in the submersible pump can be determined, so that maintenance personnel can adopt corresponding maintenance measures in advance, thereby improving the efficiency of troubleshooting the submersible pump, improving the refinement and intelligence level of the submersible pump, and ensuring the production efficiency of the oil well platform.

[0027] Specifically, in this embodiment, by selecting several groups of dynamic (current, voltage, motor temperature, pump inlet pressure, pump outlet pressure, pump suction inlet temperature, etc.) data sources corresponding to the real-time operation of the electric submersible pump and static (commissioning time, pump hanging depth, middle depth of oil layer, inner diameter of oil pipe, outer diameter of oil pipe, inner diameter of casing, outer diameter of casing, model, crude oil density, water density, natural gas density, saturation pressure, dissolved gas-oil ratio, geothermal gradient, motor model, motor power, number of stages, head, etc.) data sources corresponding to the oil well and the wellbore, they are used as training data sets, and the corresponding current characteristic values ​​when a fault occurs are selected as typical current characteristic values, and then the fault diagnosis model is trained using the typical current characteristic values.

[0028] See also Figure 2 As shown, it is a flow chart of a method for early warning of electric submersible pump failure based on current change analysis in this embodiment. The process includes the following steps: S1: constructing a current standardization formula, a time standardization formula and a current characteristic value calculation formula, and constructing a fault diagnosis model based on the current standardization formula, the time standardization formula and the current characteristic value calculation formula.

[0029] S2: Setting a training data set, obtaining a number of typical current characteristic values ​​corresponding to the fault point from the training data set, and training the fault diagnosis model based on the typical current characteristic values.

[0030] S3: Periodically collect current operation data in the electric submersible pump, and import the current operation data into the trained fault diagnosis model to obtain a number of measured current characteristic values.

[0031] S4: Compare the plurality of measured current characteristic values ​​with the plurality of typical current characteristic values ​​to determine a plurality of different current characteristic types.

[0032] S5: Combining a plurality of the different current feature types to determine the fault type of the electric submersible pump.

[0033] Among them, S3 includes the following steps: S31: determining whether the calculation process of the measured current characteristic value is qualified based on the comparison result of the variance of the measured current characteristic value and the preset variance, and generating a corresponding instruction if it is unqualified.

[0034] S32: Determine a first sensitive factor and a second sensitive factor or determine a characteristic value correction coefficient based on the instruction.

[0035] Furthermore, the process of constructing the current normalization formula, the time normalization formula and the current characteristic value calculation formula includes: The current normalization formula is as follows: , in, is the measured current value at the i-th point, in A; is the rated working current value, in A; It is the first sensitive factor; is the normalized current value at the i-th point.

[0036] The time normalization formula is as follows: , in, is the measured time value corresponding to the measured current value at the i-th point, in units of h; is the longest time among several measured current curves, in h; It is the second sensitive factor; is the normalized time value of the i-th point.

[0037] The current characteristic value calculation formula is as follows: , in, is the measured current characteristic value, in A / h; is the eigenvalue correction factor.

[0038] in, and It is also a correction factor, which is used to normalize the data within a reasonable range. The standardized calculation of time and current is performed to make the time value and current value meet the requirements of subsequent current characteristic value calculation.

[0039] Further, the process of determining the different current characteristic types includes: The different current characteristic types include a stable current characteristic type, a fluctuating current characteristic type, a shutdown characteristic type, a slowly decreasing characteristic type, a quickly decreasing characteristic type, and an increasing characteristic type.

[0040] If the absolute value of the measured current characteristic value is less than or equal to the stable current characteristic value, it is determined to be the stable current characteristic type; if the absolute value of the measured current characteristic value is greater than the first current characteristic value and less than or equal to the second current characteristic value, and the proportion of the measured characteristic value is less than the proportion of the second characteristic value and greater than the proportion of the first characteristic value, it is determined to be the fluctuating current characteristic type; if the measured current characteristic value is less than or equal to negative 1000, and the number of shutdowns is greater than or equal to 1, it is determined to be the shutdown characteristic type; if the measured current characteristic value is less than the negative first current characteristic value and greater than or equal to the negative third current characteristic value, and the proportion of the measured characteristic value is greater than or equal to the proportion of the third characteristic value, it is determined to be the slowly decreasing characteristic type; if the measured current characteristic value is less than the negative third current characteristic value and greater than or equal to negative 1000, and the proportion of the measured characteristic value is greater than or equal to the proportion of the third characteristic value, it is determined to be the rapidly decreasing characteristic type; if the measured current characteristic value is less than the fourth current characteristic value and greater than or equal to the first current characteristic value, and the proportion of the measured characteristic value is greater than or equal to the proportion of the fourth characteristic value, it is determined to be the increasing characteristic type.

[0041] The stable current characteristic type is as follows: , in, is the characteristic value of the stable current, in A / h; It is basically stable near 0, with fluctuations not exceeding ±0.05.

[0042] The fluctuating current characteristic type is in the following form: and , in, is the first current characteristic value, in A / h; is the second current characteristic value, in A / h; is the proportion of measured eigenvalues, is the proportion of the first eigenvalue, is the proportion of the second eigenvalue; A / h, A / h, and the numerical fluctuation error of the two does not exceed 10%; Indicates the proportion of the number of positive or negative current characteristic values ​​measured in the preset period to the total number of current characteristic values ​​measured in the preset period. , .

[0043] The shutdown feature type is as follows: , in, The number of downtimes.

[0044] The slowly decreasing characteristic type is in the following form: and , in, is the third current characteristic value, in A / h; is the proportion of the third eigenvalue; A / h, the fluctuation error of this value does not exceed 10%; Indicates the proportion of the number of negative current characteristic values ​​in the total number of current characteristic values ​​measured in the preset period. .

[0045] The rapid reduction characteristic type is as follows: and ; The form of adding feature types is as follows: and , in, is the fourth current characteristic value, in A / h; is the proportion of the fourth eigenvalue; A / h, the fluctuation error of this value does not exceed 10%; It indicates the proportion of the number of positive current characteristic values ​​in the total number of current characteristic values ​​measured in the preset period. It should be noted that, based on actual conditions and experience, as well as It can be set to other values ​​and is not specifically limited here.

[0046] Further, the process of determining the fault type of the electric submersible pump includes: Combine several different current feature types to obtain an analysis feature type group; if the analysis feature type group contains all the fluctuating current feature types, it is determined that the fault type is that the well fluid in the environment where the submersible pump is located contains gas, that is, the long-term presence of gas in the well fluid will cause pump gas lock; if the analysis feature type group contains the stable current feature type and the shutdown feature type in sequence, it is determined that the fault type is a sudden shutdown of the submersible pump; if the analysis feature type group contains the rapid decrease feature type and the shutdown feature type for more than two times in sequence, it is determined that the fault type is frequent restart of the submersible pump; if the analysis feature type group contains the stable current feature type, the slowly decrease feature type and the shutdown feature type in sequence, it is determined that the fault type is insufficient supply of well fluid in the environment where the submersible pump is located; if the analysis feature type group contains the stable current feature type, the increase feature type and the shutdown feature type in sequence, it is determined that the fault type is an overload shutdown of the submersible pump, that is, during the operation of the submersible pump, due to excessive load The current exceeds its rated value due to a large or certain abnormal conditions, thereby triggering a protection mechanism to stop the pump; if the analysis feature type group includes the fluctuating current feature type, the increasing feature type, and the shutdown feature type in sequence, it is determined that the fault type is that the well fluid in the environment where the submersible pump is located contains impurities, that is, the well fluid contains impurities and may cause solid blockage to the submersible pump; if the analysis feature type group includes the stable current feature type, the slowly decreasing feature type, the stable current feature type, and the shutdown feature type in sequence, it is determined that the fault type is the failure of the underload protection of the submersible pump, that is, the underload protection function of the submersible pump cannot work normally, and cannot cut off the power supply or sound an alarm in time when the submersible pump is in an underload state, which may cause damage to the submersible pump; if the analysis feature type group includes the stable current feature type, the slowly decreasing feature type, the fluctuating current feature type, and the shutdown feature type in sequence, it is determined that the fault type is the pump gas lock of the submersible pump, that is, a large amount of gas accumulates in the pump chamber of the submersible pump, making the pump unable to work normally.

[0047] Specifically, in this embodiment, the analysis feature type is determined by combining several different current feature types obtained by calculation in chronological order from front to back, and then the analysis feature type is matched with the typical fault type determined based on previous actual conditions, so as to quickly and accurately determine the fault possibility and fault type of the submersible pump in the current detection cycle, thereby playing a role in fault warning, providing a basis for improving the refined management and intelligent level of the submersible pump, and improving the efficiency of troubleshooting the submersible pump, thereby ensuring the production efficiency of the oil well platform where the submersible pump is located.

[0048] See also Figure 3As shown, it is a logic flow chart for determining whether the calculation result of the measured current characteristic value is qualified based on the characteristic value variance in this embodiment. The process of obtaining several measured current characteristic values ​​includes: Obtain a characteristic value variance based on several of the measured current characteristic values ​​within a preset period; determine whether the calculation process of the measured current characteristic value is qualified based on a comparison result of the characteristic value variance with a preset characteristic value variance, or based on a comparison result of an average value of several historical characteristic value variances obtained within several historical periods with the characteristic value variance; when it is determined that the calculation result of the measured current characteristic value is unqualified, determine the cause based on a comparison result of a difference between a maximum value and a minimum value among several of the measured current characteristic values ​​with a preset difference.

[0049] Specifically, in this embodiment, by importing the real-time current operation data in the submersible pump into the fault diagnosis model within the current preset period, a number of measured current characteristic values ​​corresponding to a number of detection nodes within the preset period can be obtained, and then the characteristic value variance is calculated based on the number of measured current characteristic values. In this embodiment, after obtaining the current operation data within the preset period for the submersible pump, a number of measured current characteristic values ​​corresponding to a number of detection nodes are calculated, and an average value is calculated based on the number of measured current characteristic values. The numerical fluctuation of the measured current characteristic value within this preset period should be maintained in the vicinity of the average value, and the fluctuation should be within the range of the average value. The dynamic range will not exceed the average value too much; in order to ensure that the measured current characteristic value used in the fault type determination is accurate and reasonable, the calculated measured current characteristic value can be further determined; in order to make a more accurate determination, the preset characteristic value variance E0 can be divided into a first preset characteristic value variance E1 and a second preset characteristic value variance E2, and the preset characteristic value variance standard E3=50, E1=E3-10, E2=E3+10 is set. It should be noted that E1, E2 and E3 can also be set to other values ​​according to actual needs; the comparison process based on the characteristic value variance E with E1 and E2 is as follows: If the characteristic value variance E is less than or equal to the first preset characteristic value variance E1, it means that the magnitudes of several measured current characteristic values ​​in the current preset period are relatively close and the values ​​are relatively concentrated. Therefore, it can be determined that the calculation result of the measured current characteristic value is qualified. At this time, the measured current characteristic value meets the requirements for subsequent comparison and calculation with several typical current characteristic values.

[0050] If the eigenvalue variance E is greater than the first preset eigenvalue variance E1 and less than or equal to the second preset eigenvalue variance E2, it is impossible to accurately determine whether the calculation result of the measured current eigenvalue is qualified. It is necessary to obtain several historical eigenvalue variances in several historical periods before the current preset period, and then calculate the average value based on the several historical eigenvalue variances. Based on the comparison between the variance average value and the eigenvalue variance, it is re-determined whether the calculation result of the measured current eigenvalue is qualified. The several historical measured current eigenvalues ​​in several historical periods here are confirmed data that can be used for subsequent calculations.

[0051] If the eigenvalue variance E is greater than the second preset eigenvalue variance E2, it means that the relative difference between the sizes of several measured current eigenvalues ​​in the current preset period is large, the values ​​are relatively dispersed, and the current values ​​fluctuate too violently in the preset period. Therefore, it can be determined that the calculation result of the measured current eigenvalue is unqualified. At this time, it is necessary to count the maximum and minimum values ​​of several measured current eigenvalues ​​in the current preset period, and then calculate the difference between the two, record it as the eigenvalue amplitude difference, and compare the eigenvalue amplitude difference with the preset eigenvalue amplitude difference to determine the cause of the unqualified. It is mainly determined by analyzing the fluctuation of several measured current eigenvalues ​​in the preset period to determine whether the current measured current eigenvalue can be used as the basis for subsequent calculations.

[0052] Furthermore, an average value is calculated based on several historical eigenvalue variances, which is recorded as the historical variance average value D. The historical variance average value D is compared with the eigenvalue variance E. The specific comparison results are as follows: If the historical variance average value D is smaller than the eigenvalue variance E, it means that the fluctuation amplitude of several measured current eigenvalues ​​within the current preset period is larger than the fluctuation amplitude of several historical measured current eigenvalues ​​within the historical period. Therefore, it can be determined that when the eigenvalue variance E is greater than the first preset eigenvalue variance E1 and less than or equal to the second preset eigenvalue variance E2, the calculation result of the measured current eigenvalue is unqualified.

[0053] If the historical variance average value D is greater than or equal to the eigenvalue variance E, it means that the fluctuation amplitude of several measured current eigenvalues ​​in the current preset period is smaller than the fluctuation amplitude of several historical measured current eigenvalues ​​in the historical period. Therefore, it can be determined that when the eigenvalue variance E is greater than the first preset eigenvalue variance E1 and less than or equal to the second preset eigenvalue variance E2, the calculation result for the measured current eigenvalue is qualified.

[0054] See also Figure 4As shown, it is a logic flow chart of determining the reason for the failure of the measured current characteristic value calculation based on the characteristic value amplitude difference and the corresponding processing method in this embodiment. The process of determining the reason based on the comparison result between the maximum value and the minimum value of several measured current characteristic values ​​and the preset difference value includes: Calculate the difference between the maximum value and the minimum value of the measured current characteristic values, record it as the characteristic value amplitude difference, and record the preset difference as the preset characteristic amplitude difference; determine the reason why the calculation of the measured current characteristic value is unqualified according to the comparison result of the characteristic value amplitude difference and the preset characteristic value amplitude difference; determine the corresponding processing based on the reason, including: increasing the first sensitivity factor and the second sensitive factor Or reduce the characteristic value correction factor .

[0055] Specifically, in this embodiment, a plurality of measured current characteristic values ​​calculated within a preset period are compared to determine a maximum value and a minimum value, and then a difference is calculated based on the maximum value and the minimum value. The preset characteristic value amplitude difference is the preset difference, and the preset characteristic value amplitude difference P0 is set to 50A / h. P0 can also be set to other values ​​as needed; the comparison process based on the characteristic value amplitude difference P and the preset characteristic value amplitude difference P0 is as follows: If the characteristic value amplitude difference P is less than or equal to the preset characteristic value amplitude difference P0, it means that the change amplitude of the measured current characteristic value within the current preset period is relatively small, and the fluctuation degree is relatively stable. At this time, it can be determined that the reason why the calculation of the measured current characteristic value is unqualified is that there are problems with the constructed current standardization formula and time standardization formula, specifically, there are problems with the first sensitive factor and the second sensitive factor. Therefore, the first sensitive factor and the second sensitive factor need to be re-corrected.

[0056] If the characteristic value amplitude difference P is greater than the preset characteristic value amplitude difference P0, it means that the change amplitude of the measured current characteristic value within the current preset period is relatively large and the fluctuation degree is relatively severe. At this time, it can be determined that the reason why the calculation of the measured current characteristic value is unqualified is that there is a problem with the constructed current characteristic value calculation formula, specifically, there is a problem with the characteristic value correction coefficient, and the characteristic value correction coefficient needs to be re-corrected.

[0057] Further, the process of increasing the first sensitivity factor and the second sensitivity factor includes: Based on the comparison result of the pump hanging depth with the preset pump hanging depth, it is determined that the first sensitive factor and the second sensitive factor are increased, and the increase range of the first sensitive factor and the second sensitive factor is proportional to the pump hanging depth.

[0058] Specifically, in this embodiment, the use environment of the electric submersible pump is a shallow oil well. When the pump hanging depth is greater, the pressure of the underground oil will also increase, and the resistance of the pump to pump oil will increase. Therefore, the motor needs a larger load to provide sufficient power, which may cause an increase in the current value. Therefore, in order to more accurately standardize the first sensitive factor and the second sensitive factor, the greater the amplitude that needs to be adjusted; in order to more accurately determine the increase amplitude of the first sensitive factor and the second sensitive factor, the preset pump hanging depth H0 can be divided into a first preset pump hanging depth H1 and a second preset pump hanging depth H2, and the preset pump hanging depth H3 is set to 1200 meters, H1=H3-200, H2=H3+200; the comparison process based on the pump hanging depth H with H1 and H2 is as follows: If the pump hanging depth H is less than or equal to the first preset pump hanging depth H1, the first factor adjustment coefficient can be used to increase the first sensitive factor to 1.15 times the initial value and the second sensitive factor to 1.17 times the initial value; if the pump hanging depth H is greater than the first preset pump hanging depth H1 and less than or equal to the second preset pump hanging depth H2, the second factor adjustment coefficient can be used to increase the first sensitive factor to 1.44 times the initial value and the second sensitive factor to 1.52 times the initial value; if the pump hanging depth H is greater than the second preset pump hanging depth H2, the third factor adjustment coefficient can be used to increase the first sensitive factor to 2.25 times the initial value and the second sensitive factor to 2.33 times the initial value. Since the larger the pump hanging depth H, the greater the influence of the current and the time corresponding to the current on the fault diagnosis process during acquisition, the greater the increase ratio of the first sensitive factor and the second sensitive factor; the value of the increase ratio can also be set to other values, with the goal of reducing the influence of the pump hanging depth H.

[0059] Furthermore, the process of reducing the characteristic value correction coefficient includes: Based on the comparison result of the amplitude difference and the preset amplitude difference, it is determined to reduce the eigenvalue correction coefficient, and the reduction amplitude of the eigenvalue correction coefficient is proportional to the amplitude difference; wherein the amplitude difference is the difference between the eigenvalue amplitude difference and the preset eigenvalue amplitude difference.

[0060] Specifically, in this embodiment, the preset amplitude difference B0 can be divided into a first preset amplitude difference B1 and a second preset amplitude difference B2, and the preset amplitude difference standard B3=80A / h, B1=B3-15, B2=B3+15 is set. It should be noted that B1, B2 and B3 can be adjusted according to actual conditions; the comparison process based on the amplitude difference B with B1 and B2 is as follows: If the amplitude difference B is less than or equal to the first preset amplitude difference B1, the characteristic value correction coefficient can be reduced to 0.95 times the initial value; if the amplitude difference B is greater than the first preset amplitude difference B1 and less than or equal to the second preset amplitude difference B2, the characteristic value correction coefficient can be reduced to 0.86 times the initial value; if the amplitude difference B is greater than the second preset amplitude difference B2, the characteristic value correction coefficient can be reduced to 0.75 times the initial value. When the amplitude difference B is large, the characteristic value correction coefficient needs to be reduced, and the reduction ratio of the characteristic value correction coefficient is set to reduce the impact of the amplitude difference.

[0061] Further, after completing the increase adjustment of the first sensitive factor and the second sensitive factor, the characteristic value correction coefficient is reduced based on the comparison result between the motor temperature and the preset motor temperature, and the reduction range of the characteristic value correction coefficient is proportional to the motor temperature.

[0062] Specifically, in this embodiment, the motor temperature here is the temperature generated by the motor when the electric submersible pump is working. After the adjustment of the first sensitive factor and the second sensitive factor is completed, it is also necessary to simultaneously consider that the characteristic value correction coefficient in the current characteristic value calculation formula is affected by the electric submersible pump; the general operating temperature range of the electric submersible pump in this embodiment is 40°C-180°C, and the preset motor temperature W0 can be divided into a first preset motor temperature W1 and a second preset motor temperature W2, and the preset motor temperature standard is set to W3=150°C, W1=W3-20, W2=W3+20. When the motor temperature is within a higher range, it will interfere with the collected current, resulting in a larger fluctuation range of the current value. Therefore, it is necessary to determine the reduction of the characteristic value correction coefficient based on the comparison result; the comparison process based on the motor temperature W with W1 and W2 is as follows: If the motor temperature W is greater than 100°C and less than or equal to the first preset motor temperature W1, the characteristic value correction coefficient can be reduced to 0.98 times the initial value. When the motor temperature is less than or equal to 100°C, it will not affect the current; if the motor temperature W is greater than the first preset motor temperature W1 and less than or equal to the second preset motor temperature W2, the characteristic value correction coefficient can be reduced to 0.95 times the initial value; if the motor temperature W is greater than the second preset motor temperature W2, the characteristic value correction coefficient can be reduced to 0.92 times the initial value. The reduction rate of the characteristic value correction coefficient at this time is different from the characteristic value correction coefficient reduced according to the amplitude difference; the reduction rate of the characteristic value correction coefficient is set to reduce the impact of the motor temperature.

[0063] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0064] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for early warning of electric submersible pump failure based on current change analysis, characterized in that: include: Constructing a current standardization formula, a time standardization formula, and a current characteristic value calculation formula, and constructing a fault diagnosis model based on the current standardization formula, the time standardization formula, and the current characteristic value calculation formula; Setting a training data set, obtaining a number of typical current characteristic values ​​corresponding to the fault point from the training data set, and training the fault diagnosis model based on the number of typical current characteristic values; Periodically collecting current operation data in the electric submersible pump, and importing the current operation data into the trained fault diagnosis model to obtain a number of measured current characteristic values; Comparing the measured current characteristic values ​​with the typical current characteristic values ​​to determine a number of different current characteristic types; A plurality of the different current feature types are combined to determine a fault type of the electric submersible pump.

2. The electric submersible pump fault early warning method based on current change analysis according to claim 1 is characterized in that: The process of constructing the current normalization formula, the time normalization formula and the current characteristic value calculation formula includes: The current normalization formula is as follows: ， in, is the measured current value at the i-th point, in A; is the rated working current value, in A; It is the first sensitive factor; is the normalized current value at the i-th point; The time normalization formula is as follows: ， in, is the measured time value corresponding to the measured current value at the i-th point, in units of h; is the longest time among several measured current curves, in h; It is the second sensitive factor; is the time value after normalization of the i-th point; The current characteristic value calculation formula is as follows: ， in, is the measured current characteristic value, in A / h; is the characteristic value correction coefficient.

3. The electric submersible pump fault early warning method based on current change analysis according to claim 2 is characterized in that: The process of determining the different types of current characteristics includes: The different current characteristic types include a stable current characteristic type, a fluctuating current characteristic type, a shutdown characteristic type, a slowly decreasing characteristic type, a rapidly decreasing characteristic type, and an increasing characteristic type; If the absolute value of the measured current characteristic value is less than or equal to the stable current characteristic value, it is determined to be the stable current characteristic type; If the absolute value of the measured current characteristic value is greater than the first current characteristic value and less than or equal to the second current characteristic value, and the proportion of the measured characteristic value is less than the proportion of the second characteristic value and greater than the proportion of the first characteristic value, it is determined to be the fluctuating current characteristic type; If the measured current characteristic value is less than or equal to negative 1000, and the number of shutdowns is greater than or equal to 1, it is determined to be the shutdown characteristic type; If the measured current characteristic value is less than the negative first current characteristic value and greater than or equal to the negative third current characteristic value, and the proportion of the measured characteristic value is greater than or equal to the proportion of the third characteristic value, it is determined to be the slowly decreasing characteristic type; If the measured current characteristic value is less than the negative third current characteristic value and greater than or equal to negative 1000, and the proportion of the measured characteristic value is greater than or equal to the proportion of the third characteristic value, it is determined to be the rapid decrease characteristic type; If the measured current characteristic value is less than the fourth current characteristic value and greater than or equal to the first current characteristic value, and the proportion of the measured characteristic value is greater than or equal to the proportion of the fourth characteristic value, it is determined to be the added characteristic type.

4. The electric submersible pump fault early warning method based on current change analysis according to claim 3 is characterized in that: The process of determining the fault type of the electric submersible pump includes: combining a plurality of said different current feature types to obtain an analysis feature type group; If all of the analyzed feature type groups are of the fluctuating current feature type, it is determined that the fault type is that the well fluid in the environment where the electric submersible pump is located contains gas; If the analysis feature type group includes the stable current feature type and the shutdown feature type in sequence, determining that the fault type is a sudden shutdown of the electric submersible pump; If the analysis feature type group includes the rapid reduction feature type and the shutdown feature type more than twice in sequence, then it is determined that the fault type is the frequent restart of the electric submersible pump; If the analysis feature type group includes the stable current feature type, the slowly decreasing feature type, and the shutdown feature type in sequence, it is determined that the fault type is insufficient well fluid supply in the environment where the electric submersible pump is located; If the analysis feature type group includes the stable current feature type, the increase feature type and the shutdown feature type in sequence, then determining that the fault type is the overload shutdown of the electric submersible pump; If the analysis feature type group includes the fluctuating current feature type, the increasing feature type and the shutdown feature type in sequence, it is determined that the fault type is that the well fluid in the environment where the electric submersible pump is located contains impurities; If the analysis feature type group includes the stable current feature type, the slowly decreasing feature type, the stable current feature type and the shutdown feature type in sequence, then it is determined that the fault type is the failure of the underload protection of the electric submersible pump; If the analysis feature type group includes the stable current feature type, the slowly decreasing feature type, the fluctuating current feature type, and the shutdown feature type in sequence, it is determined that the fault type is the pump air lock of the electric submersible pump.

5. The electric submersible pump fault early warning method based on current change analysis according to claim 4 is characterized in that: The process of obtaining the plurality of measured current characteristic values ​​comprises: Obtaining a characteristic value variance according to a number of the measured current characteristic values ​​within a preset period; Determining whether the calculation process of the measured current characteristic value is qualified based on a comparison result of the characteristic value variance with a preset characteristic value variance, or based on a comparison result of an average value of several historical characteristic value variances obtained in several historical periods with the characteristic value variance; When it is determined that the calculation result of the measured current characteristic value is unqualified, the cause is determined based on a comparison result of a difference between a maximum value and a minimum value among a plurality of the measured current characteristic values ​​with a preset difference.

6. The electric submersible pump fault early warning method based on current change analysis according to claim 5 is characterized in that: The process of determining the cause according to the comparison result of the difference between the maximum value and the minimum value of the measured current characteristic values ​​and the preset difference value comprises: Calculate the difference between the maximum value and the minimum value of the plurality of measured current characteristic values, record it as the characteristic value amplitude difference, and record the preset difference as the preset characteristic amplitude difference; Determining the reason why the calculation of the measured current characteristic value is unqualified according to the comparison result of the characteristic value amplitude difference and the preset characteristic value amplitude difference; Determining corresponding processing based on the cause includes: increasing the first sensitivity factor and the second sensitivity factor or reducing the eigenvalue correction coefficient.

7. The electric submersible pump fault early warning method based on current change analysis according to claim 6 is characterized in that: The process of increasing the first sensitivity factor and the second sensitivity factor includes: Based on the comparison result of the pump hanging depth with the preset pump hanging depth, it is determined that the first sensitive factor and the second sensitive factor are increased, and the increase range of the first sensitive factor and the second sensitive factor is proportional to the pump hanging depth.

8. The electric submersible pump fault early warning method based on current change analysis according to claim 6 is characterized in that: The process of reducing the characteristic value correction coefficient includes: Based on the comparison result of the amplitude difference and the preset amplitude difference, it is determined to reduce the characteristic value correction coefficient, and the reduction amplitude of the characteristic value correction coefficient is proportional to the amplitude difference; The amplitude difference is the difference between the characteristic value amplitude difference and the preset characteristic value amplitude difference.

9. The electric submersible pump fault early warning method based on current change analysis according to claim 7 is characterized in that: After the increase adjustment of the first sensitive factor and the second sensitive factor is completed, the characteristic value correction coefficient is reduced based on the comparison result of the motor temperature and the preset motor temperature, and the reduction range of the characteristic value correction coefficient is proportional to the motor temperature.

10. An electric submersible pump fault warning device based on current change analysis, characterized in that: The method for early warning of electric submersible pump faults based on current change analysis according to any one of claims 1 to 9 comprises: A model building module, used to build a current standardization formula, a time standardization formula and a current characteristic value calculation formula, and build a fault diagnosis model based on the current standardization formula, the time standardization formula and the current characteristic value calculation formula; A training module, connected to the model building module, for setting a training data set, obtaining a number of typical current characteristic values ​​corresponding to the fault point from the training data set, and training the fault diagnosis model based on the typical current characteristic values; A data acquisition module, connected to the model building module, for periodically acquiring current operation data in the electric submersible pump, and importing the current operation data into the trained fault diagnosis model to obtain a number of measured current characteristic values; a comparison module, which is connected to the model building module and the training module respectively, and is used to compare a number of the measured current characteristic values ​​with a number of the typical current characteristic values ​​to determine a number of different current characteristic types, and to combine a number of different current characteristic types to determine a fault type of the electric submersible pump; an analysis module connected to the model building module, for determining whether the calculation process of the measured current characteristic value is qualified based on a comparison result of the variance of the measured current characteristic value and a preset variance, and generating a corresponding instruction when it is unqualified; An optimization module is connected to the analysis module and the model building module respectively, and is used to determine the first sensitive factor and the second sensitive factor or determine the characteristic value correction coefficient based on the instruction.

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