Design of collaborative filtering algorithm for pumping well lifting system

The traditional design method of pumping unit lifting system based on oil extraction engineering theory is difficult to effectively handle the actual situation of complex mines, and the reliability of the design scheme needs to be improved. Establish a database covering multiple reservoir types such as heavy oil, low permeability, and complex fault blocks, and apply collaborative filtering recommendation technology to explore patterns in the design scheme of pumping well lifting systems in the database, assist in optimizing design, and improve the efficiency of pumping well lifting systems. By standardizing the collected data of over 30000 sets of historical lifting schemes, a sample library for the design of pumping well lifting systems covering dimensions such as oil well geology, fluid, and production was obtained. On this basis, a typical architecture of a user based collaborative filtering recommendation system was analyzed, and a recommendation algorithm for the design of pumping well lifting systems was established. Based on the geological development characteristics of the wells to be designed, a lifting design scheme with high similarity in geological development conditions and good operational performance was matched from historical samples in the database for recommendation. Analyzing the examples of 15 wells, the average pump efficiency of the collaborative filtering lifting system design has increased by 7.84%, and the power consumption per 100 meters of liquid has decreased by 24%. The recommended plan has significantly improved compared to the current plan. The research provides new ideas and methods for the design of pumping well lifting schemes, and provides beneficial references and insights for the application of big data in oil fields.