Optimization of Oil Production Using Sucker Rod Pumps via Predictive Elimination of Paraffin Issues

This paper explores the application of predictive maintenance (PdM) to address paraffin deposition in sucker rod pump systems used for oil production. System maintenance has become critical for enhancing efficiency and reducing costs, while PdM, supported by advanced analytics and sensors, enables downtime prediction and maintenance optimization. Paraffin deposition is a significant problem in the oil industry, as it diminishes production capacity and increases expenses. This paper presents the use of the SCADA system, which enables the collection and analysis of data in real time. Furthermore, it proposes diagnostic methods for early detection of paraffin deposition using predictive maintenance, offering timely warnings to prevent production delays. While the proposed framework relies on interpretable statistical and physics-informed predictive models, the results indicate that further improvements could be achieved by integrating advanced artificial intelligence techniques to enhance adaptability, automation, and decision support in predictive maintenance systems.