Rod pump is by far the most widely used artificial lift method within the oil industry to improve the oil production for reservoirs with energy levels too low to lift fluids to the surface. It is roughly responsible for about two-thirds of the artificial lift producing oil wells. Well failures, including surface, reservoir and down-hole failures, in rod pump artificial lift systems commonly occur, leading to substantial downtime, production loss and operation expense. Correct identification of well failures and scheduling appropriate maintenance will reduce inappropriate repairs, minimize downtime and subsequently improve operation efficiency.This paper presents a systematic Pattern Recognition approach to detect the well failures. In this method, we start with the pattern recognition workflow, explain how we choose the right data and define good features based on the domain knowledge and how we extract features utilizing signal processing techniques. Finally, we will describe how we establish the classifiers via heuristics instead of training. Through this practical application of pattern recognition, we describe some fundamental concepts and steps to build a successful application using pattern recognition technologies.This method was firstly tested on one year history data of 100 rod pump production wells from an active oilfield and the results were compared with the state-of-the-art method from Liu et al (2010); and then on six months of history data of 100 rod pump production wells. The pilot test results showed that our method could correctly identify the well failure with success rate of 82-86% and only 11-15% of false alarm. Comparing to Liu's the state-of-the-art method (2010), our method has much lower false alarm rate while with similar success rate of detecting failure, thus confirming that our systematic method is more capable of detecting well failures successfully.

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