Analysis and Recommendation of Frequent Patterns of Long-Life Pumping Wells Based on Data Mining

The theoretical regulation of production parameters in oil production engineering plays a significant role in the management of beam pumps. However, it falls short in identifying the inherent relationships among historical production data, thus failing to address the problem at its core. Valuable information can be extracted from historical well experiences through data mining techniques, offering new insights for adjusting production measures. To achieve this objective, an analysis is conducted to explore the factors and patterns influencing the exemption period of oil wells. Various methods, including expert experience and correlation analysis, are employed to process and selectively identify relevant features. Drawing upon the principles of oil production engineering and leveraging advanced big data processing techniques, these features are encoded to construct a comprehensive sample set that represents long-life wells. Subsequently, association rule mining is applied to uncover frequent patterns exhibited by these long-life wells. By setting a minimum support threshold of 0.01, the mining process encompasses a substantial dataset comprising over 1700 wells, leading to the discovery of more than 100 meaningful association rules. These rules are further prioritized and visualized based on their lift values, providing valuable insights into the experiential knowledge base related to effective measures for long-life well patterns. Consequently, this knowledge base becomes an invaluable asset, offering support for informed decision-making in terms of production parameter control and aiding in the development of scientifically guided production strategies.