Core Data Analysis Based On Data Mining

The analysis of oilfield core data can help people develop oilfields better and increase oilfield recovery.With the continuous improvement of technology in the field of oil and gas exploration,the data generated during the development of oil fields has grown exponentially.Traditional core data analysis methods cannot efficiently analyze massive oil field production data and cannot discover the underlying information.With the popularization of data mining technology in the petroleum field,the analysis of core data through big data analysis technology has become a valuable research.This paper introduces the research status of data mining technology in the petroleum field at home and abroad,analyzes the current problems in applying data mining technology to the petroleum industry,and analyzes the core data of Xingshugang Oilfield in Daqing Oilfield.First,the basic situation of the target block is summarized,then the core data is pre-processed,the noise data is processed using KNN padding method,and all core data is normalized.Then use the principal component analysis method and Kmeans algorithm in data mining to process and analyze the core data of the target block,and find out the relationship between each parameter and the displacement efficiency.Finally,based on the K-nearest neighbor algorithm in data mining,according to software design principles,a flood displacement efficiency prediction software was developed by using Python language,and some historical data from the core of Daqing Oilfield were used to verify the software’s accuracy.The engineering requirements provide a favorable reference for the further development of the oilfield.