Streaming big data analysis and dynamic pre-processing in deep geological drilling process： A case study on the 3000m scientific drilling project in Dandong， Liaoning province

Abstract:

The data quality in deep geological drilling process is poor， and traditional methods are hard to effectively remove all kinds of data noise such as spikes and burrs. A streaming big data analysis and dynamic pre-processing method for deep geological drilling process was proposed and successfully applied to the 3000m scientific drilling project in Dandong， Liaoning province. Firstly， the process mechanism and requirements of data processing are deeply analyzed， and the framework of streaming big data analysis and dynamic pre-processing in deep geological drilling process is established. After that， the outliers in the process data are removed by limiting filtering combined with the distribution characteristics of the process data and the driller’s manual operation experience. Then， the moving window strategy is introduced to dynamically process the big data of convective drilling， and savitzky Golay filter is used in each window to further improve the data quality. Finally， results of simulations and engineering application show that the proposed method has good engineering applicability and effectiveness.