Research On Energy Consumption Analysis And Optimization Of Joint Station Based On Big Data Mining

During the "Thirteenth Five-Year Plan" period,China's petrochemical industry still faces severe challenges in energy-saving,water-saving and low-carbon work.The total energy consumption continues to grow,the resource utilization rate is still low,the marginal effect of energy conservation will be gradually reduced,and the task of accomplishing the indicator is especially arduous.As an important link in oilfield production,gathering and transportation system should put energy conservation and consumption reduction at the top of the list.In the process of oilfield production,huge amounts of data have been accumulated.The era of big data has brought new dawn to the energy-saving and consumption reduction of the gathering and transportation system.Based on the production data from a certain coalfield in a certain oil field,this paper uses Python platform to preprocess the mass data,cluster analysis,correlation analysis and prediction,and then puts forward energy saving and consumption reduction measures and energy consumption optimization suggestions.Firstly,summarize all the algorithms of abnormal data point analysis,cluster analysis,correlation analysis and prediction of data mining technology used in big data analysis.Combining with the characteristics of gathering and transportation system,the requirements of production data and the applicability of algorithm,Of the algorithm for screening,resulting in this large data analysis algorithm,and a detailed introduction.Kmeans algorithm for cluster analysis,aprior algorithm for association analysis,and a combination of gray prediction and BP neural network for prediction techniques.Based on the mass production data of all kinds of equipment in a united station,the corresponding programs are compiled by using the Python language and the big data mining algorithm to realize the data preprocessing,cluster analysis,correlation analysis and prediction function.Data preprocessing includes data cleaning and standardization,clustering analysis is data discretization,data classification according to a specific order and a specific format,the classified data using the compiled aprior algorithm correlation analysis,the equipment energy consumption or the total system energy consumption factors in descending order,which come to various types of equipment operating parameters to set the scope and thus optimize the system energy consumption.Finally,according to the influence factors of energy consumption,the gray prediction and BP neural network prediction are carried out,and the trend of future energy consumption is obtained,so as to early warning the gathering and transportation system and put forward measures and suggestions for energy saving and consumption reduction.The energy consumption optimization analysis platform of C / S structure was designed and developed,which realized data preprocessing,cluster analysis,correlation analysis and prediction.It provides a convenient working environment for oil field gathering and transportation energy consumption analysis and optimization and makes attempts on the construction of oilfield digital and real-time information technology.