Failure Prediction for Rod Pump Artificial Lift Systems (2010)

Failure Prediction for Rod Pump Artificial Lift Systems Y.. Liu; Y.. Liu USC Information Sciences Institute Search for other works by this author on: This Site Google Scholar K.. Yao; K.. Yao USC Information Sciences Institute Search for other works by this author on: This Site Google Scholar S.. Liu; S.. Liu University of Southern California Search for other works by this author on: This Site Google Scholar C. S. Raghavendra; C. S. Raghavendra University of Southern California Search for other works by this author on: This Site Google Scholar T. L. Lenz; T. L. Lenz Chevron Search for other works by this author on: This Site Google Scholar L.. Olabinjo; L.. Olabinjo Chevron Search for other works by this author on: This Site Google Scholar B.. Seren; B.. Seren Chevron Search for other works by this author on: This Site Google Scholar S.. Seddighrad; S.. Seddighrad University of Southern California Search for other works by this author on: This Site Google Scholar C. G. Dinesh Babu C. G. Dinesh Babu University of Southern California Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Western Regional Meeting, Anaheim, California, USA, May 2010. Paper Number: SPE-133545-MS https://doi.org/10.2118/133545-MS Published: May 27 2010 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Liu, Y.. , Yao, K.. , Liu, S.. , Raghavendra, C. S., Lenz, T. L., Olabinjo, L.. , Seren, B.. , Seddighrad, S.. , and C. G. Dinesh Babu. "Failure Prediction for Rod Pump Artificial Lift Systems." Paper presented at the SPE Western Regional Meeting, Anaheim, California, USA, May 2010. doi: https://doi.org/10.2118/133545-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Western Regional Meeting Search Advanced Search Abstract Well failures in oil field assets lead to production loss and can greatly increase the operational expense. Predicting well failures before they occur can dramatically improve performance, such as by adjusting operating parameters to forestall failures or by scheduling maintenance to reduce unplanned repairs and to minimize downtime. This paper presents failure prediction framework and algorithms for rod pump artificial lift systems. It adapts state-of-the-art data mining approaches to learn patterns of dynamical pre-failure and normal well time series records, and to use these patterns to make failure predictions. Also, we develop a semi-supervised learning technique, called "random peek", in order to adjust the training process to cover more feature space and to overcome the bias caused by limited training samples. The data set from this paper is taken from a real-world asset using rod pump artificial lift systems. The results show that the failure prediction framework is capable of capturing future rod pump and tubing failures. Keywords: failure prediction, feature extraction, node, subsequence, time series, decision tree, multivariate time series, time series data, outlier, classification algorithm Subjects: Information Management and Systems, Artificial intelligence Copyright 2010, Society of Petroleum Engineers You can access this article if you purchase or spend a download.