Pattern discovery of liquid level time series data in oil tank
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Abstract:
The liquid level of oil tank time series data model is of great significance for oilfield production, management and disaster warning. Due to the miscellaneous data system in the field of oil and gas fields, these data are not classified and marked. There are some methods for manual screening and checking with graphical tools, which are not suitable for long time uninterrupted production of petroleum industry. In the face of the above problems and the shortcomings of the existing methods, a processing method which based on cascade piecewise linear representation and hierarchical clustering for the characteristics of reservoir tank level data is proposed. The observation sequence is transformed into discrete linear piecewise sequence, and each linear segment is clustered by unsupervised hierarchical clustering based on DTW distance, which can automatically discover the temporal pattern and assign identifiers to annotate the sequence. Based on the data of oil tank level sequence data, and the implied models and the changing rules were found. The method has a good ability to recognize and classify the time series change patterns of liquid level, without manual screening and inspection, and can view the changing patterns of different granularity according to the need, which can provide a reference and avenues for time series data pattern recognition and abnormality detection.
