Predicting pump inspection cycles for oil wells based on stacking ensemble models

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 期刊論文 > Item 987654321/126458

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題名:	Predicting pump inspection cycles for oil wells based on stacking ensemble models
作者:	Tsai, Tzong-ru
關鍵詞:	pump inspection cycle;data mining;machine learning;ensemble model;reliability analysis
日期:	2024-07-17
上傳時間:	2024-11-11 12:05:29 (UTC+8)
摘要:	Beam pumping is currently the broadly used method for oil extraction worldwide. A pumpjack shutdown can be incurred by failures from the load, corrosion, work intensity, and downhole working environment. In this study, the duration of uninterrupted pumpjack operation is defined as the pump inspection cycle. Accurate prediction of the pump inspection cycle can extend the lifespan, reduce unexpected pump accidents, and significantly enhance the production efficiency of the pumpjack. To enhance the prediction performance, this study proposes an improved two-layer stacking ensemble model, which combines the power of the random forests, light gradient boosting machine, support vector regression, and Adaptive Boosting approaches, for predicting the pump inspection cycle. A big pump-related oilfield data set is used to demonstrate the proposed two-layer stacking ensemble model can significantly enhance the prediction quality of the pump inspection cycle.
關聯:	Mathematics 12(14),　2231
DOI:	10.3390/math12142231
顯示於類別:	[統計學系暨研究所] 期刊論文

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