Abstract: The use of big data mining technology can transform the mass data of oil production engineering into ideas of guiding oilfield production. There are numerous factors which affect the power consumption indicators for lifting one ton of liquid to 100 m, so it is not quite clear which is the main factor that affect the power consumption indicators for lifting. This will need the big data mining technology to analyze the effect of various factors on power consumption indicators for lifting. With the goal of power consumption for lifting, a relevant mathematic analytic model was built, and data mining software was developed based on the mass data of oilfield production database, and tens of related factors were mined which affected the power consumption for lifting in artificially lifted wells of Al Oilfield. The target scope of pump efficiency and submergence was quantified. The future trend of power consumption indicators for lifting was predicted, and suggestions for related measures and adjustment were come up with. The developed big data software of oil production engineering was a carrier which realized big data management, data mining and result presentation, including functions like system management, data preprocessing, functional modules, function application, display of graphic report, analytic model, and progress visualization, providing a practical data mining tool platform for our customers.