摘要：To enhance the lifespan of electrical submersible pumps (ESPs) and identify key influencing factors, we develop a survival-informed deep learning framework that combines Cox proportional hazards (CPH) analysis with a long short-term memory (LSTM) network for remaining life prediction. A multiobjective optimization model based on the version 2 nondominated sorting genetic algorithm (NSGA-II) is further introduced to balance lifespan and production. A distinctive feature of the proposed framework is its integration of survival analysis, deep learning, and multiobjective optimization into a single methodology, enabling both accurate lifespan prediction and practical operational optimization. Field applications show that it outperforms contemporary deep learning baselines, achieving a magnitude of relative error (MRE) of 0.23, mean absolute error (MAE) of 0.05, and root mean squared error (RMSE) of 0.065 on the test set. In a field validation involving 30 ESP wells over a 1-year evaluation period, the proposed multiobjective optimization achieved up to 20.3% higher cumulative production compared with single-objective optimization, while maintaining a comparable average lifespan. These results demonstrate both predictive accuracy and significant operational value. ? 2026 Society of Petroleum Engineers.

ISSN号：1086-055X

卷、期、页：卷31期3：1802-1821

发表日期：2026-03-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：三区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：SPE Journal

参与作者：张贺,凌克刚,王卢婷

通讯作者：路鑫

第一作者：梁星原,韩国庆

论文类型：期刊论文

论文概要：梁星原,路鑫,韩国庆,张贺,凌克刚,王卢婷，Lifespan Prediction and Multiobjective Optimization of Electrical Submersible Pump Wells Using Machine Learning，SPE Journal，2026，卷31期3：1802-1821

论文题目：Lifespan Prediction and Multiobjective Optimization of Electrical Submersible Pump Wells Using Machine Learning