The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Untuk mengetahui keadaan kerja telaga yang dipam rod di bawah tanah, kita sentiasa perlu menganalisis gambar rajah dinamometer, yang dijana oleh sensor beban dan sensor anjakan. Telaga yang dipam batang biasanya terletak di tempat yang mempunyai cuaca yang melampau, dan penderia ini dipasang pada beberapa peralatan minyak khas di udara terbuka. Apabila masa berlalu, penderia terdedah kepada menjana data yang tidak stabil dan tidak betul. Malangnya, penderia beban terlalu mahal untuk kerap dipasang semula. Oleh itu, gambar rajah dinamometer yang terhasil kadangkala tidak dapat membuat diagnosis yang tepat. Sebaliknya, sebagai peralatan yang sangat diperlukan untuk telaga yang dipam rod, motor elektrik mempunyai hayat yang lebih lama dan tidak mudah dipengaruhi oleh cuaca. Keluk kuasa elektrik semasa tempoh sapuan juga boleh mencerminkan keadaan kerja di bawah tanah, tetapi jauh lebih sukar untuk dijelaskan daripada gambar rajah dinamometer. Surat ini mempersembahkan seni bina pembelajaran mendalam yang baru, yang boleh mengubah lengkung kuasa elektrik kepada imej rajah dinamometer tanpa dimensi. Kami menjalankan percubaan kami pada set data dunia sebenar dan keputusan menunjukkan bahawa kaedah kami boleh mendapatkan ketepatan transformasi yang mengagumkan.
Junfeng SHI
Petro China
Wenming MA
Yantai University
Peng SONG
Yantai University
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Salinan
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
Salinan
@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},}
Salinan
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 -