An online interval prediction method for rate of penetration （ROP） during the ocean drilling： A case study on the indoor simulation experiments by micro drilling ship

Abstract:

Ocean drilling is a major means for the exploration and development of marine energy resources and the study of crustal tectonic evolution， which is often faced with problems such as large disturbances in complex sea state and strong uncertainty in seafloor formation. This paper proposes an online interval prediction method for ROP during the ocean drilling process， and carry out the validation of the method with the indoor simulation experiment of micro drilling ship as an example， so as to lay an important foundation for the engineering application. Firstly， methods such as data resampling， data time-depth matching and data filtering are applied to pre-process the multi-source ocean drilling data. Secondly， methods such as Extreme Learning Machine （ELM） and Particle Swarm Optimization （PSO） are applied to establish a point prediction model of ROP. Furthermore， nonparametric estimation method is utilized to construct confidence intervals， establish ROP interval prediction model， and carry out ROP interval prediction. Finally， the interval prediction model parameters are updated online by moving window to realize online learning and optimization of the model. The comparative results of the indoor simulation experiments on the micro drilling ship verify that the proposed method has strong ROP prediction capability and robustness， and can provide a new engineering solution for the optimization and control of ROP during the ocean drilling process.