Time Series Data Processing Algorithm in Deep Water Drilling

Abstract

The machine learning technology can be applied to the drilling data of deep water. The drilling data is an internally associated multi -dimensional time series data set, which contains a certain amount of abnormal data that greatly affect the data mining process. Because of the high dimension and large scale in drilling data set, existing detection algorithms perform poorly in drilling data sets. To achieve more effective outlier detection, we propose a data processing algorithm based on fusion outlier detection method. Firstly, Isolation Forest, Elliptic Envelope and Local Outlier Factor are used to detect outliers, judge the abnormal data in different conditions, which are weighted to judge and remove the outliers. Secondly, Savitzky-Golay(SG) filter is used to smooth the data, which eliminates the burrs in the data and get clean time series data. Finally, the proposed algorithm is tested in the real drilling data sets. Compared with existing methods, the experiments show that the proposed algorithm can achieve better performance, the RMSE and MAE values are 0.454 and 0.361, respectively.