A novel drilling rate of penetration (ROP) prediction method using data pre-processing techniques and T-S fuzzy inference

Rate of penetration (ROP) prediction is crucial for the drilling optimization and cost-savings. In this paper, a novel drilling ROP prediction method is proposed and the prediction model can be divided into two stages (data pre-processing and T-S fuzzy inference modeling). In the first stage, four data pre-processing techniques (Reduction, re-sampling, wavelet filtering, and normalization) are used step by step to improve the quality of drilling data. In the second stage, T-S fuzzy inference method is introduced to establish the ROP prediction model. The experiment is executed by using the data from actual drilling process and the results demonstrate the effectiveness of proposed method in prediction accuracy compared with two conventional methods (response surface method and support vector regression).