Intelligent optimization method for tunnel rotary drilling based on drill string state estimation

The operating parameter optimization decision-making is the key to improve the efficiency of nearly horizontal rotary drilling of underground tunnels of coal mines. But it is difficult to obtain the downhole drilling parameters by Measurement While Drilling in current studies, and the accuracy is low for the rotary drilling optimization based on the hole opening data only. To address this problem, an intelligent optimization method for nearly horizontal rotary drilling in tunnels based on the drillstring state estimation was proposed by constructing a state observer to estimate the downhole drilling parameters. Firstly, an optimization objective evaluation method of Rate of Penetration (ROP) and bit wear was proposed by analyzing the rotary drilling characteristics of the underground tunnel in the coal mine, with consideration to the actual drilling constraints. Then, a lumped-parameter dynamic model of the drill string was established in axial and torsional dimensions, and a state observer was designed to estimate the motion state of the drill bit at the bottom of the hole. Finally, the NSGA-II multi-objective optimization algorithm was used to optimize the power head speed and feed pressure by integrating the data collected at the hole opening and the downhole state estimation, which was verified using actual drilling data from a coal mine in Huainan, Anhui Province, China. The results show that the optimized ROP based on the estimated downhole information of the drill string state is increased by 32.47%, which is more advantageous than the optimized ROP increased by 15.04% using only the measured data of the hole opening. It has important theoretical and practical significance for the realization of automatic and intelligent rotary drilling of tunnels in coal mine.