Study on Stick-Slip Early Warning Based on LSTM
Authors: Wang B. , Li J. , Huang H. L. , Yang H. W. , Gao R. Y. , Zhang G. , Luo M. , Li W. T. 
Published: September 2022

Stick-slip is a solid constraint for shortening the drilling cycle and reducing the drilling cost, so it is crucial to establish a reliable stick-slip early warning model for drilling engineering. The existing early warning methods have low robustness and weak real-time performance, for which an LSTM-based stick-slip early warning model is proposed in this paper. Firstly, the triaxial acceleration is analyzed in the time and frequency domains, and its stationarity is tested. Then, the original data is denoised based on wavelet transform. Finally, the LSTM-based triaxial acceleration prediction model is proposed. The results show that when stick-slip occurs, the amplitude of triaxial acceleration vibration is larger, the power spectrum density is smaller than normal drilling, and there are apparent peaks. The triaxial acceleration is a stationary non-white noise sequence. The stick-slip early warning model developed in this paper can determine whether stick-slip will occur at least 3 minutes in advance, thus guiding operators to adjust drilling process parameters in time to mitigate the occurrence of stick-slip.

1. INTRODUCTION

The friction between the drill string and borehole wall, drill bit, and rock is easy to cause stick-slip (Ozaki and Hashiguchi, 2010), which frequently occurs during drilling and seriously affects drilling aging. At present, stick-slip is mainly studied from mechanism model, signal analysis, and machine learning. The principle of the mechanism model is to use the drill string dynamics knowledge to learn the stability of the drill string system and the characteristics of stick-slip (Hong et al., 2016; Jia et al., 2018; Tian and Detournay, 2021). However, this method usually requires the assumption of harsh physical conditions, and its accuracy and reliability are low when the downhole environment becomes complicated. The signal analysis method focuses on identifying stick-slip by comparing the difference in drilling parameters between stick-slip and normal drilling (Teng et al., 2017; Huang et al., 2019). The machine learning algorithm has a strong nonlinear fitting ability and has been applied to stick-slip identification by some scholars. (Millan et al., 2019) established a stick-slip identification model based on SVM using surface drilling parameters. (Tang et al., 2021) established a stick-slip grade evaluation model based on XGBoost using near-bit triaxial acceleration data and Bayesian optimization. Although the signal analysis and machine learning methods can effectively identify stick-slip, the surface drilling parameters cannot truly reflect the working state of the bit (Zhu et al., 2014). The near-bit parameters that can genuinely reflect the working condition of the bit cannot be transmitted to the surface in real-time, which makes the existing identification methods have severe lag. If the near-bit parameters related to stick-slip can be predicted in advance, the working condition of the bit can be early identified, and operators can adjust the drilling parameters in time to avoid the occurrence of stick-slip. The Recurrent neural network (RNN) has unique advantages in time series prediction. (Meor et al., 2021) established hook load prediction model based on RNN, which effectively prevented the occurrence of the sticking. In this paper, we take advantage of the robust time series prediction of RNN to establish an LSTM-based stick-slip early warning model, which can effectively mitigate the occurrence of stick-slip during the drilling.