出版日期：2024-07-17 00:00:00

著者：Hua Xin; Shiqi Zhang; Yuhlong Lio; Tzong-Ru Tsai

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

審稿制度：是

國別：CHE

出版型式：電子版