Research And Application Of Massive Data Mining Technology In Oilfield

In recent years,data mining technology has been widely used in various fields.It has incomparable advantages in dealing with massive data and knowledge discovery.There is a huge amount of data in the oilfield production,where exists some hidden rules.It is difficult to find these rules because of the limited ability of artificial analysis,however,the data mining technology can make up for this problem.In this paper,data mining technology is used to analyze and predict oilfield production.In this paper,the technical route of application of data mining technology in oilfield production forecast is determined at first.The algorithms which are associated with data preprocessing,data classification and data prediction in the data mining technology are researched,the main contents are:1.The production data attribute reduction algorithm of rough set theory is optimized.The attribute weight is described by the dependence degree and importance degree of attributes,which is taken as the selection criterion of initial population of particle swarm optimization algorithm,narrowing the search range of solution space.Finally,the migration and directional operation of bacterial foraging algorithm are introduced to realized the local search function of the algorithm,improving the ability of finding the optimal reduction results in the process of attribute reduction,thus,the optimal reduction result of production attribute is obtained;2.By using database management system and embedded SQL based on C#,the production data is queried directly in the production database,making up for the insufficient that mass data can't be classified by C4.5 algorithm.At the same time,the Fayyad boundary point theorem is used to solve the problem of time consuming when the optimal threshold value is selected by,which improves the execution efficiency of C4.5 algorithm.With the growth of samples in production database,the efficiency and accuracy of the algorithm will not be affected,which ensures a better adaptability of it;3.Combined forecasting method is used to predict the complex variable of oilfield production,which is affected by many factors.First,the multivariate linear regression method is used to test the significance of variables,aiming to retain significant variables,then analysis method based on ARMA time series is used to predict these reserved variables.Finally,the comprehensive forecasting model is established by neural network,so as to improve the accuracy of prediction;4.Based on the improved data mining algorithm above,Microsoft Visual Studio 2010 programming software,Oracle10 g database and its management system and embedded SQL language based on C# are applied under the Windows7 operating environment to design a C/S structure decision support system for oilfield production analysis.Finally,the system is tested by actual production data to verify that it can meet the demand of oilfield production decision.