CN114412447A - Fault detection method and device for screw pump well - Google Patents
Fault detection method and device for screw pump well Download PDFInfo
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Abstract
The invention relates to the technical field of oil extraction processes, in particular to a fault detection method and device for a screw pump well. The fault detection method and device of the screw pump well comprise the steps of obtaining the torque and the reference torque of a screw pump; determining the abnormal change condition and the change trend of the torque of the screw pump according to the torque of the screw pump and the reference torque; and determining the fault condition of the screw pump well according to the abnormal change condition and the change trend. The invention provides the fault detection method and the fault detection device for the screw pump well, which effectively improve the accuracy of working condition diagnosis of the screw pump well; the method reduces the resource consumption of the operation of the oil well edge end, improves the operation efficiency of the edge end program, and simultaneously makes up the defects of an artificial intelligence algorithm in the diagnosis of some unusual working conditions.
Description
Technical Field
The invention relates to the technical field of oil extraction processes, in particular to a fault detection method and device for a screw pump well.
Background
Screw pump oil extraction is a mature oil extraction process and is generally adopted by various large oil fields at home and abroad due to the characteristics of convenient control, strong adaptability to sand production and thick oil and the like. In recent years, with the advancement of oil field digitization and internet of things construction, a screw pump well working condition diagnosis technology based on real-time parameter acquisition becomes one of core technologies for deepened application of an oil and gas production internet of things. The technology is based on the characteristics of production data of the screw pump well, diagnoses main faults, serves as a main technical support for working condition monitoring and alarming early warning, provides important reference for measures and decisions related to production optimization of the screw pump well, and has very important significance for realizing intelligent management of the screw pump well.
At present, the technology has limited overall maturity and needs further research promotion. How to further combine the oil extraction engineering theory with the digital acquisition test and the artificial intelligence analysis is one of the keys for improving the technology.
Disclosure of Invention
Technical problem to be solved
The embodiment of the invention provides a fault detection method for a screw pump well, which aims to solve the problems of low accuracy of working condition diagnosis and low working efficiency of the screw pump well in the prior art.
(II) technical scheme
In order to solve the above problem, an embodiment of the present invention provides a method for detecting a fault of a screw pump well, including:
acquiring the torque and the reference torque of the screw pump;
determining the abnormal change condition and the change trend of the torque of the screw pump according to the torque of the screw pump and the reference torque;
and determining the fault condition of the screw pump well according to the abnormal change condition and the change trend.
Preferably, the obtaining of the torque of the screw pump includes:
acquiring input power of a motor;
and determining the torque of the screw pump according to the input power of the motor and the relation between the input power of the motor and the torque of the screw pump.
Preferably, the relationship between the motor input power and screw pump torque is determined according to the following equation:
wherein, PinThe method comprises the steps of inputting power to a motor, wherein M is screw pump torque, B is a reduction ratio of a reduction gear of a screw pump system, p is the number of pole pairs of the motor, eta is the efficiency of the reduction gear of the screw pump system, a, B and c are fitting coefficients, fi is the variable frequency of the motor, fw is power frequency, k is a no-load coefficient, and Sn is the rotating speed of the motor.
Preferably, the reference torque is acquired according to any one of the following manners:
the first method is as follows: calculating by adopting a theoretical torque value calculation method or an empirical formula;
the second method comprises the following steps: taking the torque of the screw pump well during normal production;
the third method comprises the following steps: and taking the statistical average value of the normal working conditions of the screw pumps of the same type under the same production parameters.
Preferably, the determining the abnormal condition of the change of the torque of the screw pump according to the torque of the screw pump and the reference torque comprises:
determining a first deviation according to the torque per second of the screw pump and the reference torque or according to the torque of the screw pump adjacent to two seconds, and determining that a change abnormal condition exists in the second-level data change Ts of the torque of the screw pump under the condition that the first deviation exceeds a first threshold value;
determining a second deviation according to the average torque per minute of the screw pump and the reference torque, and determining that a change abnormal condition exists in the variation Tm of the data of the screw pump in minutes under the condition that the second deviation exceeds a second threshold value;
determining a third deviation according to the average torque of the screw pump per hour and the reference torque, and determining that an abnormal change condition exists in the small-scale data change Th of the torque of the screw pump under the condition that the third deviation exceeds a third threshold;
and determining a fourth deviation according to the average daily torque of the screw pump and the reference torque, and determining that a change abnormal condition exists in the daily data change Td of the torque of the screw pump when the fourth deviation exceeds a fourth threshold value.
Preferably, the determining the trend of the change of the torque of the screw pump according to the torque of the screw pump and the reference torque comprises the following steps:
determining a minute-scale data change trend Delta Tm of the torque of the screw pump according to the torque of the screw pump in a first set time before each minute;
determining an hourly data change trend Delta Th of the torque of the screw pump according to the torque of the screw pump in a second set time before each hour;
and determining the daily data change trend delta Td of the torque of the screw pump according to the torque of the screw pump in the third set time before each day.
Preferably, the determining the fault condition of the screw pump well according to the abnormal change condition and the change trend comprises:
determining that a rod break fault exists in the screw pump well under the conditions that the Ts change abnormal condition is small abnormal, the Delta Tm is descending or stable, and the Tm is descended to a fifth threshold value within a fourth set time;
determining that a pipe leakage fault exists in the screw pump well under the conditions that the Tm change abnormal condition is a reduction abnormal condition, the Th is a reduction condition and the Th is reduced to a sixth threshold value;
determining that the screw pump well has a stator falling fault under the condition that the Ts change abnormal condition is a small abnormality, the Delta Tm is reduced or fluctuated, and the Tm is reduced to a seventh threshold value in a fifth set time;
determining that the screw pump well has a pump leakage fault under the condition that the Ts change abnormal condition is an abnormal increase condition and the Delta Tm is ascending, fluctuating or descending;
determining that stator swelling of the screw pump well exists if the Td change anomaly condition is an increasing anomaly and the Δ Td is a rising anomaly.
In another aspect, the present invention further provides a fault detection device for a screw pump well, comprising:
a first acquiring unit that acquires a torque of the screw pump and a reference torque;
the first determination unit is used for determining abnormal change conditions and change trends of the torque of the screw pump according to the torque of the screw pump and the reference torque;
and the second determining unit is used for determining the fault condition of the screw pump well according to the abnormal change condition and the change trend.
(III) advantageous effects
The fault detection method and the fault detection device for the screw pump well have the following advantages that:
(1) the accuracy of working condition diagnosis of the screw pump well is improved;
(2) the resource consumption of the operation of the oil well edge end is reduced, and the operation efficiency of the edge end program is improved;
(3) the method can make up the defects of the artificial intelligence algorithm in diagnosis of some unusual working conditions.
Drawings
FIG. 1 is a schematic flow chart of a method for detecting a fault in a screw pump well according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a fault diagnosis implementation process of a screw pump well according to an embodiment of the invention;
fig. 3 is a schematic structural diagram of a fault detection device for a screw pump well according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the invention provides a method for detecting a fault of a screw pump well, which specifically comprises the following steps:
step S1: acquiring the torque and the reference torque of the screw pump;
step S2: determining the abnormal change condition and the change trend of the torque of the screw pump according to the torque of the screw pump and the reference torque;
step S3: and determining the fault condition of the screw pump well according to the abnormal change condition and the change trend.
Wherein, the step S1 of obtaining the torque of the screw pump includes:
s11, acquiring input power of the motor;
and determining the torque of the screw pump according to the input power of the motor and the relation between the input power of the motor and the torque of the screw pump.
Wherein the relationship between the motor input power and the screw pump torque is determined according to the following formula:
according to the basic theory of electrical engineering, the relationship between the input power of the motor and the torque of the screw pump can be obtained under the condition of considering the frequency conversion, as shown in the formula (1)
Under the condition of frequency conversion, the no-load loss is a function of the frequency conversion frequency, and can be expressed as a polynomial fitting formula (2):
ΔP＝afi2+bfi+c (2)
substituting equation (2) into equation (1), resulting in equation (3)
Transforming equation (3) to obtain equation (4)
Wherein, PinThe method comprises the steps of inputting power to a motor, wherein M is screw pump torque, B is a reduction ratio of a reduction gear of a screw pump system, p is the number of pole pairs of the motor, eta is the efficiency of the reduction gear of the screw pump system, a, B and c are fitting coefficients, fi is the variable frequency of the motor, fw is power frequency, k is a no-load coefficient, and Sn is the rotating speed of the motor.
In the formula (3), b and p are constants under specific motor models, Pin, fi and Sn can be used for measuring the electrical parameters, fw is 50, and the coefficients to be determined are a, b, c, k and eta. And solving by using a least square method, and obtaining at least 5 groups of numerical values (Pin, fi, T and Sn) through continuous variable frequency speed regulation so as to solve the coefficient to be determined. It is recommended to take 10 points. And (5) after the undetermined coefficients are obtained through calculation, a, b, c, k and eta are obtained, and then the debugging of the equipment is completed. In normal application, the torque can be calculated by the formula (4).
When the device can be debugged (torque testing) on site, the torque does not need to be tested again as long as the motor and the fetching device are replaced. Namely, more accurate torque can be calculated according to the tested electrical parameters, and the extreme working condition can be diagnosed by combining the torque and the change of the electrical parameters.
The meaning of the symbols in the above formula:
Pininput power, kw;
Δ P, no load power, kw;
k, no-load coefficient, dimensionless;
fw, power frequency, generally 60 Hz;
fi, variable frequency
The reduction ratio is determined by a reduction gear of the screw pump system and is dimensionless;
p, pole pair number, determined by motor parameters, and is dimensionless;
eta, reduction unit efficiency, dimensionless;
a. b and c, fitting coefficients and dimensionless.
In step S1, the reference torque is obtained according to any one of the following manners:
the method comprises the following steps: the torque reference value is selected by firstly adopting a theoretical torque value calculation method provided by an oil extraction engineering manual or an empirical formula of a specific unit for calculation.
The second method comprises the following steps: taking a screw pump well, wherein the normal production is torque converted by power;
the third method comprises the following steps: and (3) extracting the statistical average value of the normal working conditions of the screw pumps of the same type under the same production parameters by using a big data analysis method.
Wherein determining a change abnormal condition of the screw pump torque according to the torque of the screw pump and the reference torque comprises:
determining a first deviation according to the torque per second of the screw pump and the reference torque or according to the torque of the screw pump adjacent to two seconds, and determining that a change abnormal condition exists in the second-level data change Ts of the torque of the screw pump under the condition that the first deviation exceeds a first threshold value;
determining a second deviation according to the average torque per minute of the screw pump and the reference torque, and determining that a change abnormal condition exists in the variation Tm of the data of the screw pump in minutes under the condition that the second deviation exceeds a second threshold value;
determining a third deviation according to the average torque of the screw pump per hour and the reference torque, and determining that an abnormal change condition exists in the small-scale data change Th of the torque of the screw pump under the condition that the third deviation exceeds a third threshold;
and determining a fourth deviation according to the average daily torque of the screw pump and the reference torque, and determining that a change abnormal condition exists in the daily data change Td of the torque of the screw pump when the fourth deviation exceeds a fourth threshold value.
Wherein, the determining the variation trend of the torque of the screw pump according to the torque of the screw pump and the reference torque comprises the following steps:
determining a minute-scale data change trend Delta Tm of the torque of the screw pump according to the torque of the screw pump in a first set time before each minute;
determining an hourly data change trend Delta Th of the torque of the screw pump according to the torque of the screw pump in a second set time before each hour;
and determining the daily data change trend delta Td of the torque of the screw pump according to the torque of the screw pump in the third set time before each day.
Wherein the determining the fault condition of the screw pump well according to the abnormal change condition and the change trend comprises:
determining that a rod break fault exists in the screw pump well under the conditions that the Ts change abnormal condition is small abnormal, the Delta Tm is descending or stable, and the Tm is descended to a fifth threshold value within a fourth set time;
determining that a pipe leakage fault exists in the screw pump well under the conditions that the Tm change abnormal condition is a reduction abnormal condition, the Th is a reduction condition and the Th is reduced to a sixth threshold value;
determining that the screw pump well has a stator falling fault under the condition that the Ts change abnormal condition is a small abnormality, the Delta Tm is reduced or fluctuated, and the Tm is reduced to a seventh threshold value in a fifth set time;
determining that the screw pump well has a pump leakage fault under the condition that the Ts change abnormal condition is an abnormal increase condition and the Delta Tm is ascending, fluctuating or descending;
determining that stator swelling of the screw pump well exists if the Td change anomaly condition is an increasing anomaly and the Δ Td is a rising anomaly.
The data processing aspect specifically includes:
the method is mainly applied to the change amplitude of the torque value converted by the electric parameter compared with the reference torque, and the parameters collected and calculated in real time are processed into the following data of four time dimensions for carrying out change rate and trend analysis:
1) ts: the second-level data change, the deviation exceeds 30 percent compared with the reference value or the front and back contrast values exceed 30 percent, and the early warning state is entered
2) Tm: and (3) changing the minute-level data, taking an average value of the data of 60 seconds per minute, comparing the average value with a reference value, warning when the change rate exceeds 20%, calculating the change trend (linear fitting of 60 points) delta Tm of the minute-level data in the past 60 minutes per minute, and dividing the change trend into four states of rising, falling, stability and fluctuation.
3) Th: and (3) the trend of the hourly data changes, the average value of 60 minutes of data per hour is taken, and compared with the reference value, the change exceeds 15% for early warning. Every hour, the change trend DeltaTh (72-point linear fitting) of the hour-level data in the past 72 hours is calculated and divided into four conditions of rising, falling, stability and fluctuation.
4) Td: the daily data trend changes, the average value of 24 hour-level data every day is taken as daily data, compared with a reference value, the change exceeds 10% for early warning, the change trend of the daily data in the past 30 days is calculated every day, and delta Td is divided into four conditions of rising, falling, stability and fluctuation.
(3) Fault diagnosis
1) And (3) rod break fault diagnosis: the following conditions are satisfied:
A. ts becomes smaller and early warning is carried out; B. the trend of Δ Tm is down + plateau; C. the Tm is reduced to below 30% of the reference value within half an hour.
2) And (3) diagnosing pipe leakage faults: the following conditions are satisfied:
A. warning when Tm is smaller; B. the trend of Δ Th is downward; C. th is reduced to 80% or less of the reference value.
3) And (3) stator falling fault diagnosis: the following conditions are satisfied:
A. ts becomes smaller and early warning is carried out; B. the trend of Δ Tm is down + fluctuating; C. the Tm is reduced to below 30% of the reference value within half an hour.
4) Pump leak (short term failure) fault diagnosis: the following conditions are satisfied:
A. ts becomes large and early warning is carried out; B. the trend of Δ Tm is up + wave + down;
5) the swelling of the stator is mainly characterized in that: the following conditions are satisfied:
A. td becomes large and early warning is performed; B. Δ Td is in a trend of rising;
6) non-failure working condition: under the condition of eliminating the faults and accurately acquiring data, the working condition can be determined as a non-fault working condition, and for the processing of the non-fault working condition, the related patent of the patent refers to a screw pump well working condition analysis and liquid quantity measurement method.
As shown in fig. 2, in practical application, the method for detecting the failure of the screw pump well can be divided into: firstly, carrying out electric parameter-to-torque conversion, then carrying out multi-dimensional processing on real-time torque data, and then carrying out fault diagnosis on the data change rate and the change trend.
As shown in fig. 3, the present invention also provides a failure detection device for a screw pump well, comprising:
a first acquiring unit 1 that acquires a torque of a screw pump and a reference torque;
a first determination unit 2 for determining abnormal change conditions and change trends of the screw pump torque according to the torque of the screw pump and the reference torque;
and the second determining unit 3 is used for determining the fault condition of the screw pump well according to the abnormal change condition and the change trend.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A method for detecting a failure of a screw pump well, comprising:
acquiring the torque and the reference torque of the screw pump;
determining the abnormal change condition and the change trend of the torque of the screw pump according to the torque of the screw pump and the reference torque;
and determining the fault condition of the screw pump well according to the abnormal change condition and the change trend.
2. The method of claim 1, wherein the deriving torque of a screw pump comprises:
acquiring input power of a motor;
and determining the torque of the screw pump according to the input power of the motor and the relation between the input power of the motor and the torque of the screw pump.
3. The method of claim 2, wherein the relationship between motor input power and screw pump torque is determined according to the following equation:
wherein, PinThe method comprises the steps of inputting power to a motor, wherein M is screw pump torque, B is a reduction ratio of a reduction gear of a screw pump system, p is the number of pole pairs of the motor, eta is the efficiency of the reduction gear of the screw pump system, a, B and c are fitting coefficients, fi is the variable frequency of the motor, fw is power frequency, k is a no-load coefficient, and Sn is the rotating speed of the motor.
4. A method according to any one of claims 1 to 3, wherein the reference torque is obtained according to any one of the following:
the first method is as follows: calculating by adopting a theoretical torque value calculation method or an empirical formula;
the second method comprises the following steps: taking the torque of the screw pump well during normal production;
the third method comprises the following steps: and taking the statistical average value of the normal working conditions of the screw pumps of the same type under the same production parameters.
5. The method of claim 4, wherein determining the changed abnormal condition of the screw pump torque from the torque of the screw pump and the reference torque comprises:
determining a first deviation according to the torque per second of the screw pump and the reference torque or according to the torque of the screw pump adjacent to two seconds, and determining that a change abnormal condition exists in the second-level data change Ts of the torque of the screw pump under the condition that the first deviation exceeds a first threshold value;
determining a second deviation according to the average torque per minute of the screw pump and the reference torque, and determining that a change abnormal condition exists in the variation Tm of the data of the screw pump in minutes under the condition that the second deviation exceeds a second threshold value;
determining a third deviation according to the average torque of the screw pump per hour and the reference torque, and determining that an abnormal change condition exists in the small-scale data change Th of the torque of the screw pump under the condition that the third deviation exceeds a third threshold;
and determining a fourth deviation according to the average daily torque of the screw pump and the reference torque, and determining that a change abnormal condition exists in the daily data change Td of the torque of the screw pump when the fourth deviation exceeds a fourth threshold value.
6. The method of claim 5, wherein determining the trend of change in the screw pump torque from the torque of the screw pump and the reference torque comprises:
determining a minute-scale data change trend Delta Tm of the torque of the screw pump according to the torque of the screw pump in a first set time before each minute;
determining an hourly data change trend Delta Th of the torque of the screw pump according to the torque of the screw pump in a second set time before each hour;
and determining the daily data change trend delta Td of the torque of the screw pump according to the torque of the screw pump in the third set time before each day.
7. The method of claim 6, wherein said determining a fault condition of said screw pump well from said anomalous change condition and said trend of change comprises:
determining that a rod break fault exists in the screw pump well under the conditions that the Ts change abnormal condition is small abnormal, the Delta Tm is descending or stable, and the Tm is descended to a fifth threshold value within a fourth set time;
determining that a pipe leakage fault exists in the screw pump well under the conditions that the Tm change abnormal condition is a reduction abnormal condition, the Th is a reduction condition and the Th is reduced to a sixth threshold value;
determining that the screw pump well has a stator falling fault under the condition that the Ts change abnormal condition is a small abnormality, the Delta Tm is reduced or fluctuated, and the Tm is reduced to a seventh threshold value in a fifth set time;
determining that the screw pump well has a pump leakage fault under the condition that the Ts change abnormal condition is an abnormal increase condition and the Delta Tm is ascending, fluctuating or descending;
determining that stator swelling of the screw pump well exists if the Td change anomaly condition is an increasing anomaly and the Δ Td is a rising anomaly.
8. A failure detection device for a screw pump well, comprising:
a first acquiring unit that acquires a torque of the screw pump and a reference torque;
the first determination unit is used for determining abnormal change conditions and change trends of the torque of the screw pump according to the torque of the screw pump and the reference torque;
and the second determining unit is used for determining the fault condition of the screw pump well according to the abnormal change condition and the change trend.
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| CN116256589A (en) * | 2023-05-15 | 2023-06-13 | 南京研控科技有限公司 | Intelligent diagnosis method and device for electric pump well, storage medium and server |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1851268A (en) * | 2006-06-13 | 2006-10-25 | 大庆油田有限责任公司 | Screw pump well pump condition electric parameter detecting method |
| JP2011112041A (en) * | 2009-11-30 | 2011-06-09 | Heishin Engineering & Equipment Co Ltd | Abnormality detection device for pumps, and method |
| CN107687332A (en) * | 2017-09-08 | 2018-02-13 | 张雅芝 | Screw pump and working conditions of oil extractor on-line analysis diagnosis and control method of adjustment and device are realized using electrical parameter |
| US20190063210A1 (en) * | 2014-06-03 | 2019-02-28 | Schlumberger Technology Corporation | Monitoring An Electric Submersible Pump For Failures |
| CN111379556A (en) * | 2018-12-27 | 2020-07-07 | 中国石油天然气股份有限公司 | Method and device for judging fault type of screw pump well |
| CN112649131A (en) * | 2020-11-28 | 2021-04-13 | 华能澜沧江水电股份有限公司 | Hydraulic ship lift synchronizing shaft torque test fault diagnosis method |
| CN113153722A (en) * | 2021-03-16 | 2021-07-23 | 四川宏华电气有限责任公司 | Fracturing pump torque abnormity automatic detection system applied to shale gas exploitation |
-
2021
- 2021-12-31 CN CN202111674260.3A patent/CN114412447B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1851268A (en) * | 2006-06-13 | 2006-10-25 | 大庆油田有限责任公司 | Screw pump well pump condition electric parameter detecting method |
| JP2011112041A (en) * | 2009-11-30 | 2011-06-09 | Heishin Engineering & Equipment Co Ltd | Abnormality detection device for pumps, and method |
| US20190063210A1 (en) * | 2014-06-03 | 2019-02-28 | Schlumberger Technology Corporation | Monitoring An Electric Submersible Pump For Failures |
| CN107687332A (en) * | 2017-09-08 | 2018-02-13 | 张雅芝 | Screw pump and working conditions of oil extractor on-line analysis diagnosis and control method of adjustment and device are realized using electrical parameter |
| CN111379556A (en) * | 2018-12-27 | 2020-07-07 | 中国石油天然气股份有限公司 | Method and device for judging fault type of screw pump well |
| CN112649131A (en) * | 2020-11-28 | 2021-04-13 | 华能澜沧江水电股份有限公司 | Hydraulic ship lift synchronizing shaft torque test fault diagnosis method |
| CN113153722A (en) * | 2021-03-16 | 2021-07-23 | 四川宏华电气有限责任公司 | Fracturing pump torque abnormity automatic detection system applied to shale gas exploitation |
Non-Patent Citations (1)
| Title |
|---|
| 田雷: "变频螺杆泵井光杆扭矩与电机输入有功功率的关系", 中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑, no. 2012, 15 December 2012 (2012-12-15), pages 019 - 77 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116256589A (en) * | 2023-05-15 | 2023-06-13 | 南京研控科技有限公司 | Intelligent diagnosis method and device for electric pump well, storage medium and server |
| CN116256589B (en) * | 2023-05-15 | 2023-08-04 | 南京研控科技有限公司 | Intelligent diagnosis method and device for electric pump well, storage medium and server |
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