[CCoE Notice] PhD Defense Schedule

[Knudsen, Rachel W]

Name: Mahmoud Hadi

Date: Monday December 9th 2019

Time: 10:00 am and 12:30 pm

Location: Engineering building 2, room E214.

Advisor: Dr. Gangbing Song



Intelligent Multi-Axis Control for Automatic Drilling System



Drilling optimization is a crucial field of research in the oil and gas upstream industry due to abrupt fluctuations in market condition. There are several factors that contribute to the improvement of the overall drilling operation, e.g. high rate of penetration, and the elimination of down hole vibrations such as stick-slip.

The technologies that are currently deployed on drilling rigs for automatic drilling and stick- slip mitigation are separate systems that work independently even though the drilling process is a multi-variable dynamic process. These systems sometimes compete against each other if they are not properly tuned. Anti stick-slip technologies that are available in the market cannot differentiate between top drive torque oscillations due to stick-slip versus torque oscillation due to automatic driller dysfunction. Current automatic drilling systems utilize simple PID control loops to hold drilling parameters and do not take in consideration the nonlinearities in drilling dynamics due to geological changes in formation and due to other factors, such as stiction between the drill string and the wellbore. Human driller must be trained to constantly tune the PID gains to ensure stability of the automatic drilling control system.

To achieve ideal drilling condition, a smarter automatic drilling system is required to adapt to changes in geological formation and to mitigate stick-slip vibration simultaneously in one control loop. The control loop needs to be designed based on rig equipment specification and utilizing modern control theory to compute optimal gains. This research presents a novel drilling control methodology that combines a Multi-Input Multi-Output (MIMO) Linear Quadratic Regulator (LQR) with Fuzzy logic to develop an Intelligent Multi-Axis Automatic Drilling System. Multi-axis automatic driller simultaneously sends commands to both drawworks and top drive to maintain the weight on the bit while regulating top drive torque which effectively helps in mitigating down hole stick-slip. Speed commands to drawworks and top drive are synchronized by the LQR controller which ensures that both equipment work together to attain optimal drilling condition. A feed-forward node utilizing ANFIS is also used to augment the SISO PI controller to make it adapt to change in drilling dynamics.

In this research, a small-scale drilling rig has been built in the Smart Materials and Structures Laboratory at University of Houston. Experimental results show that the proposed automatic drilling control algorithm can improve ROP by up to 45.37% and can improve stick-slip mitigation by up to 26.63% compared to the traditional control method that is currently used on drilling rigs.



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