Typically, rod pump system failures are determined using the dynamometer card which may miss some early warnings. This paper presents a novel approach for early failure detection in rod pump wells using more than 14 parameters that indicate the daily functions of rod pump wells and employs advanced machine learning techniques. Our system recognizes failing, failed as well as normal situations by learning their patterns/signature from historical pump data, that include card area, peak-surface load, minimum-surface load, daily run-time, and production data. These data are automatically pre-processed using expert domain knowledge to reduce noise and to fill-in missing data. Our approach is novel in two ways. First, our machine learning algorithm AdaBNet uses boosting to learn several Bayesian Network models and then combines these models with different weights to form a stronger boosted model. Second, our approach generates this single boosted model that is applicable across all the wells in a field, as opposed to well-specific approaches that generate one model per well. This model detects anomalies, pre- failure and failure signals and generates corresponding alerts. Early fault detection in rod pump wells is useful for automatic monitoring of large number of assets remotely, and could be extended to other artificial lift systems. We used a training data set of 12 wells to construct the learning model for the AdaBNet algorithm and tested the algorithm on 426 wells from the same field. The results show that our algorithm detects failures with accuracy higher than 90%. This framework can help field operators not only to remotely recognize and predict failures in advance, but also to help prioritize the available manpower, save significant time, reduce operating expense (OPEX), downtime and lost production. Early fault detection in rod pump systems can allow for proactive maintenance that can delay and even prevent future well failures. The proposed algorithm can enable production engineers remotely detect failures and anomalies before they occur, and assess the situation at control centers before taking any remedial or corrective actions. This approach to using a single model for an entire field is superior to other approaches with individual model for each well.
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