Abstract
Several industries, including the oil and gas industry, are moving toward digitalization and automated systems in an effort to improve safety and efficiency. The Society of Petroleum Engineers (SPE) and Drilling System Automation Technical Section (DSATS) promote the growth of drilling automation in the industry by hosting the Drillbotics® competition at the university level. Students will be encouraged to think creatively in order to develop a completely autonomous tiny drilling rig capable of boring a directional hole through a rock mass and hitting various targets.
Since 2017, Norwegian University of Science and Technology (NTNU) has participated in this contest, and the teams have developed over each other's experience. The competition's aim for 2021/2022 was to design and build a completely autonomous drilling rig capable of directed drilling to intersect one or more target sites within a rock sample using up to 30-degree inclination and 15-degree azimuth modifications from the starting position.
This master's thesis concentrated on how to obtain the best Miniature Autonomous Directional Drilling Rig for the competition. I have accomplished this by resolving existing control system faults, introducing new functionality, and improving the existing system. The main problem from last year's competition was that when given target points in the Y-axis, the azimuth would steer the drill bit in the opposite direction of the target is solved. We have researched the opportunity to improve the accelerometer calibration and use a magnetometer for yaw estimation. Implemented the Dubins Curve for a better well path generation and the additional features in the GUI for more awareness for the operator since human factors have been a new concept introduced in this year's competition.
The plan was to implement Nonlinear Attitude Observer using Reference Vectors, but since the magnetometer is a dependency for generating reference vectors, it was concluded not to implement it. By running a lot of drilling runs, we have been able to solve existing errors and optimize the current system by tuning the extended Kalman Filter and the Nonlinear Model Predictive Control (NMPC). Shared challenges and lessons learned will contribute to the following year's team by saving time and helping them if they face similar challenges.