Data and control tuning of a laboratory-scale autonomous drilling rig

Abstract

The main focus of this thesis is to tune a lab scale autonomous drilling rig. The rig requires a robust control architecture that can manage unforeseen risks and problems, and at the same time optimize the systems performance to maintain a high rate of penetration (ROP). Such the small scale drilling rig requires very short reaction time, for fault detection and problem management to achieve good performances. This thesis goes in to the dynamic process modelling, electromagnetic interference shielding and data filtering required to run the real-time lab scale autonomous drilling rig.

Results show that the simulations made in this thesis of the torsional vibration drill string is close to real torque measurements. Kalman filter has also been implemented, and show that it can give better WOB accuracy while drilling. Electromagnetic compatibility (EMC) has been improved and results in better data quality, giving more accurate controller parameters, which have increased the ROP on the small scale drilling rig.