Affiliations: [a] Institute for Automation, University of Loeben, Leoben, Austria | [b] Chair of Drilling, University of Leoben, Leoben, Austria
Correspondence:
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Corresponding author: Arghad Arnaout, Institute for Automation, University of Loeben, Leoben, Austria. E-mail: Arghad@stud.unileoben.ac.at
Abstract: Several sensor measurements collected from drilling rig during oil well drilling process. These measurements carry information not only about operational states of drilling rig but also about all high-level operations and activities performed by drilling crew. The work presented in this paper shed the light on analysis of hidden lost time in drilling process through automatic detection and classification of drilling operations. This paper develops a novel algorithm for detecting drilling events and operations in sensor data of drilling rig. Expectation Maximization EM and Piecewise Linear Approximation PLA algorithms applied for detecting drilling events. The Expectation Maximization algorithm performs high-level segmentation on hook-load sensor data. In addition, Piecewise Linear Approximation algorithm slices standpipe pressure; pump flow rate; rotational speed and torque of top drive motor into labeled segments (low-level segmentation). Merging results from both Expectation Maximization and Piecewise Linear Approximation gives the suggested algorithm ability to detect all drilling events and activities performed by drilling rig and crew. Moreover, this paper shows the usage of discrete orthonormal basis functions (Gram basis) as a tool to classify drilling operations from detected segments in drilling time series. The classification process performed in cooperation with the concept of Patterns Templates Base. The optimal polynomial degree to represent drilling operations has been concluded through analysis of polynomial spectrum of each drilling operation.
