Measures Recommendation Of Oil Well Maintenance Based On Pattern Matching And Nearest Neighbor Algorithm

With the development of oilfields in the middle and late stages,the number of oil well failures has gradually increased,and the workload of maintenance measures,investment in resources and costs have increased gradually.However,the oil fields solely rely on artificial experiences and a small amount of data to make the success rate and efficiency of most measures very low,resulting in diminishing return on measures.In order to change the current situation,this article mobilizes the existing massive data resources of the oilfield,combines the relevant professional knowledge of maintenance measures,establishes a special database of oil well maintenance measures,and builds a recommendation model for well maintenance measures based on the nearest neighbor pattern matching method to obtain some wells.Though analyzing the effect of similar oil well measures,we can optimize the effect of well-measured parameters of wells as the recommended results of the measures.Through case studying,the model can quickly find oil wells with similar well conditions,and the results recommended by the effect analysis are consistent with the actual conditions and can be used in the actual oil well maintenance measures recommendation.This method makes the use of oilfield data,the determination of measures and their parameters more scientific and rational.Increasing the success rate and efficiency of measures has important implications for saving the cost of oil fields and increasing the production of oil wells.