A novel dynamic model for the online prediction of rate of penetration and its industrial application to a drilling process. (January 2022) - Gan, Chao; Cao, Wei-Hua; Liu, Kang-Zhi; Wu, Min
Abstract: Accurate prediction of the rate of penetration (ROP) is a difficult issue in the drilling process, especially under complex formation conditions. Many methods, such as mechanism and machine learning, were introduced to investigate it. However, most of them are offline prediction methods which may not be capable of capturing the online trend of ROP. In this paper, a novel dynamic model for ROP prediction is proposed considering the process characteristics, which consists of three stages. In the first stage, the correlations between ROP and eight drilling parameters are analyzed, and the rotational speed, weight on bit, depth are selected as the model inputs. In the second stage, the drilling data are pre-processed by using the filtering and re-sampling techniques. In the last stage, the moving window strategy, extreme learning machine, and 10-fold cross validation are used to establish the ROP model. Our main idea of online prediction of ROP lies in this last stage. Specifically, two steps (modeling and prediction) are executed alternately in the moving drilling depth windows so as to predict the ROP more accurately. Finally, the proposed ROP prediction model is applied to the drilling well ZK3 in Xiangyang area, Central China. The prediction accuracy is improved by at least 7% compared with seven well-known ROP prediction methods, two online and five offline, which validates the effectiveness of the proposed method. It is believed that the proposed model provides a basis for intelligent optimization control in drilling process. Highlights: A novel intelligent dynamic model is proposed for the online prediction of drilling ROP. The influence of the length of moving window and distance between two windows are investigated thoroughly. The proposed model can learn autonomously in the drilling process and has a fast computation speed. The proposed method has a higher prediction accuracy than two online and five offline well-known conventional methods.