Аңдатпа
This study explores the use of unsupervised machine learning to optimize operational efficiency and reduce downtime in oil well production through clustering analysis. Using production data from a major Kazakhstani oil operator, the study applies K-means clustering to categorize oil wells based on output patterns. The dataset includes more than three million entries over a two-year period, capturing daily values for oil volume, liquid volume, and water cut percentage. Data preprocessing involved normalization, outlier handling, and correlation analysis to ensure robust clustering performance. The optimal number of clusters (K = 10) was selected using the Elbow method. Cluster interpretations revealed distinct operational profiles, including stable, declining, and volatile well behavior. In particular, more than 70% of wells remained in their assigned clusters over time, suggesting temporal stability and operational consistency. By identifying risk-prone and underperforming wells, the results support proactive maintenance, informed resource allocation, and long-term planning. The study demonstrates how unsupervised learning techniques can support data-driven decision-making in the petroleum industry without reliance on expensive sensor infrastructure. This research provides a replicable framework for oilfield analysis and highlights the value of behavioral clustering as a predictive tool for operational optimization. Our methodology integrates unsupervised learning, dimensionality reduction techniques, and visual analytics to provide interpretable cluster groupings. The enhanced predictive model demonstrates the potential to support data-driven decision-making and resource allocation in oilfield operations. Keywords: machine learning; K-means clustering; predictive maintenance; oil well monitoring; statistical analysis.