Predicting pump inspection cycles for oil wells based on stacking ensemble models
學年 112
學期 2
出版（發表）日期 2024-07-17
作品名稱 Predicting pump inspection cycles for oil wells based on stacking ensemble models
作品名稱（其他語言）
著者 Tsai, Tzong-ru
單位
出版者
著錄名稱、卷期、頁數 Mathematics 12(14),　2231
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.
關鍵字 pump inspection cycle;data mining;machine learning;ensemble model;reliability analysis
語言 en
ISSN 2227-7390
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Tzong-Ru Tsai
審稿制度 是
國別 CHE
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126458 )

SDGS 優質教育,產業創新與基礎設施,夥伴關係