IEICE TRANSACTIONS on Information
Transform Electric Power Curve into Dynamometer Diagram Image Using Deep Recurrent Neural Network
Summary :
To learn the working situation of rod-pumped wells under ground, we always need to analyze dynamometer diagrams, which are generated by the load sensor and displacement sensor. Rod-pumped wells are usually located in the places with extreme weather, and these sensors are installed on some special oil equipments in the open air. As time goes by, sensors are prone to generating unstable and incorrect data. Unfortunately, load sensors are too expensive to frequently reinstall. Therefore, the resulting dynamometer diagrams sometimes cannot make an accurate diagnosis. Instead, as an absolutely necessary equipment of the rod-pumped well, the electric motor has much longer life and cannot be easily impacted by the weather. The electric power curve during a swabbing period can also reflect the working situation under ground, but is much harder to explain than the dynamometer diagram. This letter presented a novel deep learning architecture, which can transform the electric power curve into the dimensionless dynamometer diagram image. We conduct our experiments on a real-world dataset, and the results show that our method can get an impressive transformation accuracy.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.8 pp.2154-2158
- Publication Date
- 2018/08/01
- Publicized
- 2018/05/09
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDL8027
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Junfeng SHI
Petro China
Wenming MA
Yantai University
Peng SONG
Yantai University
Keyword
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Junfeng SHI, Wenming MA, Peng SONG, "Transform Electric Power Curve into Dynamometer Diagram Image Using Deep Recurrent Neural Network" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 8, pp. 2154-2158, August 2018, doi: 10.1587/transinf.2018EDL8027.
Abstract: To learn the working situation of rod-pumped wells under ground, we always need to analyze dynamometer diagrams, which are generated by the load sensor and displacement sensor. Rod-pumped wells are usually located in the places with extreme weather, and these sensors are installed on some special oil equipments in the open air. As time goes by, sensors are prone to generating unstable and incorrect data. Unfortunately, load sensors are too expensive to frequently reinstall. Therefore, the resulting dynamometer diagrams sometimes cannot make an accurate diagnosis. Instead, as an absolutely necessary equipment of the rod-pumped well, the electric motor has much longer life and cannot be easily impacted by the weather. The electric power curve during a swabbing period can also reflect the working situation under ground, but is much harder to explain than the dynamometer diagram. This letter presented a novel deep learning architecture, which can transform the electric power curve into the dimensionless dynamometer diagram image. We conduct our experiments on a real-world dataset, and the results show that our method can get an impressive transformation accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8027/_p
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@ARTICLE{e101-d_8_2154,
author={Junfeng SHI, Wenming MA, Peng SONG, },
journal={IEICE TRANSACTIONS on Information},
title={Transform Electric Power Curve into Dynamometer Diagram Image Using Deep Recurrent Neural Network},
year={2018},
volume={E101-D},
number={8},
pages={2154-2158},
abstract={To learn the working situation of rod-pumped wells under ground, we always need to analyze dynamometer diagrams, which are generated by the load sensor and displacement sensor. Rod-pumped wells are usually located in the places with extreme weather, and these sensors are installed on some special oil equipments in the open air. As time goes by, sensors are prone to generating unstable and incorrect data. Unfortunately, load sensors are too expensive to frequently reinstall. Therefore, the resulting dynamometer diagrams sometimes cannot make an accurate diagnosis. Instead, as an absolutely necessary equipment of the rod-pumped well, the electric motor has much longer life and cannot be easily impacted by the weather. The electric power curve during a swabbing period can also reflect the working situation under ground, but is much harder to explain than the dynamometer diagram. This letter presented a novel deep learning architecture, which can transform the electric power curve into the dimensionless dynamometer diagram image. We conduct our experiments on a real-world dataset, and the results show that our method can get an impressive transformation accuracy.},
keywords={},
doi={10.1587/transinf.2018EDL8027},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Transform Electric Power Curve into Dynamometer Diagram Image Using Deep Recurrent Neural Network
T2 - IEICE TRANSACTIONS on Information
SP - 2154
EP - 2158
AU - Junfeng SHI
AU - Wenming MA
AU - Peng SONG
PY - 2018
DO - 10.1587/transinf.2018EDL8027
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2018
AB - To learn the working situation of rod-pumped wells under ground, we always need to analyze dynamometer diagrams, which are generated by the load sensor and displacement sensor. Rod-pumped wells are usually located in the places with extreme weather, and these sensors are installed on some special oil equipments in the open air. As time goes by, sensors are prone to generating unstable and incorrect data. Unfortunately, load sensors are too expensive to frequently reinstall. Therefore, the resulting dynamometer diagrams sometimes cannot make an accurate diagnosis. Instead, as an absolutely necessary equipment of the rod-pumped well, the electric motor has much longer life and cannot be easily impacted by the weather. The electric power curve during a swabbing period can also reflect the working situation under ground, but is much harder to explain than the dynamometer diagram. This letter presented a novel deep learning architecture, which can transform the electric power curve into the dimensionless dynamometer diagram image. We conduct our experiments on a real-world dataset, and the results show that our method can get an impressive transformation accuracy.
ER -
English
