# Predictive Analytics for Sucker Rod Pump Failures in Kazakhstan Oil Wells Using Machine Learning
###### Abstract  
In the process of developing mature deposits, a number of geological and technological complications arise. In order to increase the smooth operation of downhole pumping equipment in oil and gas wells, companies use various methods and techniques. This article presents a novel methodology for predicting downhole pumping equipment failures. A detailed analysis was conducted on historical data regarding downhole pumping equipment failures, which were then incorporated into algorithms to calculate the operation of downhole equipment. As a result, it was discovered that in order to predict failures of downhole equipment, it is crucial to consider the historical data of the field and perform an assessment of the well's potential. In the process of building a failure prediction model, the authors encountered the quality and completeness of historical data from the pilot field. They concluded that the data classes needed to be more balanced. The authors applied machine learning approaches to an imbalanced dataset. The significance of our approach lies in its ability to forecast equipment failures, thereby ensuring the smooth operation of wells operated by sucker rod pumps. downhole pumping equipment; oil and gas wells; sucker rod pump; failure; predictive analytics; machine learning +
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using machine learning algorithms based on field sensor data, real-time physics-based model data, and well failure history to predict ESP failures and identify faulty components in advance. The paper details the steps of database standardization, data pre-processing, machine-learning algorithm selection, supervised training, and validation dataset creation, essential for building a model to predict downhole pumping equipment failures. It also discusses ESP domain knowledge used for feature engineering to improve model metrics iteratively. In [5], the authors present a predictive analytics approach to early failure detection of downhole pumping equipment. It details the development and testing of an algorithm predicting ESP failures based on field data using multi-level data preprocessing, additional physical and analytical features, and an ensemble machine learning model. The approach, tested on numerous ESP-equipped wells, shows a forecasting accuracy ranging from 60% to 88%, depending on the forecasting period. The study provides insights into using machine learning to prevent ESP failures in oilfields with numerous wells. The forecasting models were developed based on influential criteria. The authors of [6] presented a predictive model for detecting pump failures in ESPs, highlighting ESPs' importance in oil reservoirs and the associated challenges of low industry run life and high workover costs. The proposed model uses principal component analysis (PCA) to analyze real-time ESP data and predict failures. The paper covers the data collection process, model training, and validation. In [7], the authors discuss advanced analytics driven by physics-based models for failure prediction in oil and gas artificial lift systems. Phase I results of training early failure prediction models with historical data and evaluating them through a blind test are presented. Phase II involves operationalizing these models in real time, reassessing their accuracy, precision, and early prediction capabilities. The paper concludes that ESP Predictive Analytics can significantly improve ESP surveillance and monitoring, enabling proactive actions to enhance recovery time, reduce deferment, and increase asset efficiency and production optimization. In [8], the authors studied fluid leakage and non-leakage cases during kill operations and developed a numerical method to calculate essential parameters for kill operations using the displacement kill method. The primary indicator of a reliable downhole equipment operation is the time between equipment failures. Increasing the time between failures reduces the number of well failures.