Global model for failure prediction for artificial lift systems
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G06N-005/00
G06F-001/00
G06N-099/00
E21B-041/00
G06F-011/30
E21B-047/00
출원번호
US-0830875
(2013-03-14)
등록번호
US-9292799
(2016-03-22)
발명자
/ 주소
Liu, Yintao
Yao, Ke-Thia
Raghavendra, Cauligi S.
Wu, Anqi
Guo, Dong
Zheng, Jingwen
Olabinjo, Lanre
Balogun, Oluwafemi
Ershaghi, Iraj
출원인 / 주소
Chevron U.S.A. Inc.
대리인 / 주소
Lagatta, Andrew J.
인용정보
피인용 횟수 :
6인용 특허 :
2
초록▼
Methods and systems for predicting failures in an artificial lift system are disclosed. One method includes extracting one or more features from a dataset including time sampled performance of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset inc
Methods and systems for predicting failures in an artificial lift system are disclosed. One method includes extracting one or more features from a dataset including time sampled performance of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset including data from failed and normally operating artificial lift systems. The method also includes forming a learning model based on identified pre-failure signatures in the extracted features, the learning model configured to predict a failure of an artificial lift system based on observation of one of the identified pre-failure signatures in operational data received from the artificial lift system.
대표청구항▼
1. A method for predicting failures in an artificial lift system, the method comprising: extracting one or more features from a dataset including time sampled performance of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset including data from fa
1. A method for predicting failures in an artificial lift system, the method comprising: extracting one or more features from a dataset including time sampled performance of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset including data from failed and normally operating artificial lift systems;identifying pre-failure signatures based at least in part on a moving window of operational data in the extracted features preceding a known failure;forming a learning model based on identified pre-failure signatures in the extracted features, the learning model configured to predict a failure of an artificial lift system based on observation of one of the identified pre-failure signatures in operational data received from the artificial lift system; andpredicting one or more failures in an artificial lift system based on the learning model. 2. The method of claim 1, wherein forming the learning model includes labeling the extracted features to define interrelationships among the features included in the dataset. 3. The method of claim 2, wherein forming the learning model includes training a multi-class support vector machine using the labeled extracted features. 4. The method of claim 1, wherein extracting one or more features includes applying a moving median feature extraction process to the dataset. 5. The method of claim 4, wherein the moving median feature extraction process calculates a global median, a mid-term performance median, and a current performance median. 6. The method of claim 5, wherein extracting the one or more features includes calculating features by dividing at least one of the mid-term performance median or the current performance median by the global median. 7. The method of claim 1, wherein the dataset includes measurements of attributes from a plurality of pump off controllers. 8. The method of claim 7, wherein the attributes are selected from a group of attributes consisting of: card area;peak surface load;minimum surface load;strokes per minute;surface stroke length;flow line pressure;pump fillage;prior day cycles; anddaily run time. 9. The method of claim 8, further comprising calculating a card unchanged days attribute based on a number of days of one or more unchanged attributes received from a pump off controller. 10. The method of claim 8, further comprising calculating a daily runtime ratio based on the daily run time. 11. The method of claim 1, further comprising evaluating a precision of the failure prediction. 12. The method of claim 1, further comprising periodically updating the learning model with a refreshed dataset including time sampled performance of the plurality of artificial lift systems. 13. A computer-readable medium having computer-executable instructions stored thereon which, when executed by a computing system, cause the computing system to perform a method for predicting failures in an artificial lift system, the method comprising: extracting one or more features from a dataset including time sampled performance of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset including data from failed and normally operating artificial lift systems;identifying pre-failure signatures based at least in part on a moving window of operational data in the extracted features preceding a known failure;forming a learning model based on identified pre-failure signatures in the extracted features, the learning model configured to predict a failure of an artificial lift system based on observation of one of the identified pre-failure signatures in operational data received from the artificial lift system; andpredicting one or more failures in an artificial lift system based on the learning model. 14. A system for predicting failures in an artificial lift system, the system comprising: a processor;a memory communicatively connected to the processor and storing computer-executable instructions that, when executed by the processor, cause the system to:receive a dataset of time-sampled data from each of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset including data from failed and normally operating artificial lift systems;receive data labels from a user, the data labels defining one or more types of failures of artificial lift systems;identify pre-failure signatures based at least in part on a moving window of operational data in the extracted features preceding a known failure included in the one or more types of failures;generate a learning model by a multi-class support vector machine based on the labeled data, the learning model including one or more identified pre-failure signatures and predict a failure of an artificial lift system based on observation of one of the identified pre-failure signatures in operational data received from the artificial lift system. 15. The system of claim 14, wherein the plurality of artificial lift systems disposed across a plurality of different oil fields include one or more artificial lift systems selected from a group of systems consisting of: a gas lift;a hydraulic pumping unit;an electric submersible pump;a progressive cavity pump; anda rod pump. 16. The system of claim 14, wherein the system is further configured to extract one or more features from a dataset based on labels applied to the time-sampled data. 17. The system of claim 14, wherein updated data is provided to the multi-class support vector machine to generate an updated learning model. 18. The system of claim 14, wherein the system is further configured to apply a clustering algorithm comprising: collecting data representing artificial lift system failures having a common failure type into a first cluster; anddetermining one or more pre-failure signatures in the time-sampled data. 19. The system of claim 18, wherein the clustering algorithm further includes collecting data representing normal operation of an artificial lift system into a second cluster. 20. The system of claim 14, wherein the dataset includes measurements of attributes, and wherein the attributes are selected from a group of attributes consisting of: card area;peak surface load;minimum surface load;strokes per minute;surface stroke length;flow line pressure;pump fillage;prior day cycles; anddaily run time. 21. The method of claim 1, wherein the plurality of artificial lift systems disposed across a plurality of different oil fields include one or more artificial lift systems selected from a group of systems consisting of: a gas lift;a hydraulic pumping unit;an electric submersible pump;a progressive cavity pump; anda rod pump.
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이 특허에 인용된 특허 (2)
Liu, Yintao; Yao, Ke-Thia; Liu, Shuping; Raghavendra, Cauligi Srinivasa; Balogun, Oluwafemi Opeyemi; Olabinjo, Lanre, System and method for failure prediction for artificial lift systems.
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