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Several sensor measurements are collected from drilling rig during oil well drilling process. These measurements carry information not only about the operational states of the drilling rig but also about all higher level operations and activities performed by drilling crew. Automatic detection and classification of such drilling operations and states is considered as a big challenge in drilling industry. Furthermore, the possibility of detecting such events opens the door to detect and analyze hidden lost time of the drilling process. This paper presents a novel algorithm for drilling time series segmentation using Expectation Maximization and Piecewise Linear Approximation algorithms. The suggested algorithm shows that the incorporation of prior-knowledge about the drilling process is a key step to segment drilling time series successfully. The Expectation Maximization algorithm is used to segment drilling time series based on hook-load sensor measurements. In addition, Piecewise Linear Approximation is hired in our approach to slice standpipe pressure, pump flow rate and rotational speed (RPM) and torque of the top drive motor. Merging the results from both, Expectation Maximization and Piecewise Linear Approximation, gives the suggested algorithm the dynamic ability to detect all drilling events and activities. |
