Leveraging Weibull Analysis and Python Automation for Enhanced Failure Prediction of Electrical Submersible Pumps

Abstract The primary objective of this study is to predict the failure of electrical submersible pumps (ESPs) by utilizing Weibull distribution analysis and Python coding, thereby enhancing the reliability and operational efficiency of ESPs in the petroleum industry. Moreover, it assists in intervention planning and rig count optimization. This study employs a systematic approach involving the collection of ESP failure data, which is then analyzed using Weibull distribution to determine the shape and scale parameters. Python scripts are utilized to automate data processing, fitting the Weibull model, and conducting goodness-of-fit tests including the Anderson-Darling and Kolmogorov-Smirnov tests. This automation facilitates efficient data handling and analysis, ensuring accurate and reproducible results. The analysis reveals that the Weibull distribution provides a good fit for the ESP failure data, as indicated by the p-values from the goodness-of-fit tests. The shape and scale parameters derived from the Weibull model enable the estimation of Mean Time to Failure (MTTF) and the probability of failure over specified time intervals. These insights are critical for maintenance planning and reliability improvement of ESPs. Furthermore, the automation process significantly reduces the time and effort required for data analysis, making it a practical tool for ongoing reliability assessments. This paper introduces a novel integration of Weibull analysis with Python automation for ESP failure prediction, offering a significant improvement over traditional manual method. The approach enhances the reliability analysis process, providing valuable insights that contribute to the optimization of ESP maintenance strategies in the petroleum industry.