The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning
Aiming at the problems of the current production and operation status of the progressive cavity pump (PCP) in coalbed methane (CBM) wells which cannot be timely monitored, quantitatively evaluated, and accurately predicted, a five-step method for evaluating and predicting the health status of PCP we...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2021/6641395 |
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| _version_ | 1832550540863602688 |
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| author | Chaodong Tan Song Wang Hanwen Deng Guoqing Han Guanghao Du Wenrong Song Xiongying Zhang |
| author_facet | Chaodong Tan Song Wang Hanwen Deng Guoqing Han Guanghao Du Wenrong Song Xiongying Zhang |
| author_sort | Chaodong Tan |
| collection | DOAJ |
| description | Aiming at the problems of the current production and operation status of the progressive cavity pump (PCP) in coalbed methane (CBM) wells which cannot be timely monitored, quantitatively evaluated, and accurately predicted, a five-step method for evaluating and predicting the health status of PCP wells is proposed: data preprocessing, principal parameter optimization, health index construction, health degree division, and health index prediction. Therein, a health index (HI) formulation was made based on deep learning, and a statistical method was used to define the health status of PCP wells as being healthy, subhealthy, or faulty. This allowed further research on the HI prediction model of PCP wells based on the long short-term memory (LSTM) network. As demonstrated in the study, they can reflect both the change trend and the contextual relevance of the health status of PCP wells with high accuracy to achieve real-time, quantitative, and accurate assessment and prediction. At the same time, the conclusion gives good guidance on the production performance analysis and failure warning of the PCP wells and suggests a new direction for the health status assessment and warning of other artificial lift equipment. |
| format | Article |
| id | doaj-art-29e467653d52425582aaeced46558b67 |
| institution | Kabale University |
| issn | 1468-8115 1468-8123 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-29e467653d52425582aaeced46558b672025-02-03T06:06:29ZengWileyGeofluids1468-81151468-81232021-01-01202110.1155/2021/66413956641395The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep LearningChaodong Tan0Song Wang1Hanwen Deng2Guoqing Han3Guanghao Du4Wenrong Song5Xiongying Zhang6State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum, Changping, Beijing 102249, ChinaState Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum, Changping, Beijing 102249, ChinaState Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum, Changping, Beijing 102249, ChinaState Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum, Changping, Beijing 102249, ChinaCollege of Artificial Intelligence, China University of Petroleum, Changping, Beijing 102249, ChinaBeijing Yadan Petroleum Technology Development Co., Ltd., Changping, 102200, ChinaBeijing Yadan Petroleum Technology Development Co., Ltd., Changping, 102200, ChinaAiming at the problems of the current production and operation status of the progressive cavity pump (PCP) in coalbed methane (CBM) wells which cannot be timely monitored, quantitatively evaluated, and accurately predicted, a five-step method for evaluating and predicting the health status of PCP wells is proposed: data preprocessing, principal parameter optimization, health index construction, health degree division, and health index prediction. Therein, a health index (HI) formulation was made based on deep learning, and a statistical method was used to define the health status of PCP wells as being healthy, subhealthy, or faulty. This allowed further research on the HI prediction model of PCP wells based on the long short-term memory (LSTM) network. As demonstrated in the study, they can reflect both the change trend and the contextual relevance of the health status of PCP wells with high accuracy to achieve real-time, quantitative, and accurate assessment and prediction. At the same time, the conclusion gives good guidance on the production performance analysis and failure warning of the PCP wells and suggests a new direction for the health status assessment and warning of other artificial lift equipment.http://dx.doi.org/10.1155/2021/6641395 |
| spellingShingle | Chaodong Tan Song Wang Hanwen Deng Guoqing Han Guanghao Du Wenrong Song Xiongying Zhang The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning Geofluids |
| title | The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning |
| title_full | The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning |
| title_fullStr | The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning |
| title_full_unstemmed | The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning |
| title_short | The Health Index Prediction Model and Application of PCP in CBM Wells Based on Deep Learning |
| title_sort | health index prediction model and application of pcp in cbm wells based on deep learning |
| url | http://dx.doi.org/10.1155/2021/6641395 |
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