Development of a Hybrid AI Model for Fault Prediction in Rod Pumping System for Petroleum Well Production

Aoxue Zhang, Yanlong Zhao (), Xuanxuan Li, Xu Fan, Xiaoqing Ren, Qingxia Li and Leishu Yue
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Aoxue Zhang: School of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China
Yanlong Zhao: School of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China
Xuanxuan Li: School of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China
Xu Fan: School of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China
Xiaoqing Ren: School of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China
Qingxia Li: Region of Luliang Oilfield, PetroChina Xinjiang Oilfield Company, Karamay 834000, China
Leishu Yue: School of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China

Energies, 2024, vol. 17, issue 21, 1-15

Abstract: Rod pumping systems are widely used in oil wells. Accurate fault prediction could reduce equipment fault rate and has practical significance in improving oilfield production efficiency. This paper analyzed the production journal of rod pumping wells in block X of Xinjiang Oilfield. According to the production journal, oil well maintenance operations are primarily caused by five types of faults: scale, wax, corrosion, fatigue, and wear. These faults make up approximately 90% of all faults. 1354 oil wells in the block that experienced workover operations as a result of the aforementioned factors were chosen as the research objects for this paper. To lower the percentage of data noise, wavelet threshold denoising and variational mode decomposition were used. Based on the bidirectional long short-term memory network, an intelligent model for fault prediction was built. It was trained and verified with the help of the sparrow search algorithm. Its efficacy was demonstrated by testing various deep learning models in the same setting and with identical parameters. The results show that the prediction accuracy of the model is the highest compared with other 11 models, reaching 98.61%. It is suggested that the model using artificial intelligence can provide an accurate fault warning for the oilfield and offer guidance for the maintenance of the rod pumping system, which is meant to reduce the occurrence of production stagnation and resource waste.

Keywords: rod pumping system; oilfield; fault prediction; artificial intelligence; fault causes (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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