Rate of penetration (ROP) has been a key indicator for drilling efficiency. Increasing ROP can lead to significant operational-time and cost savings in drilling. ROP can be estimated using the physics-based and data-driven models. The physics-based model, although it is derived from drilling physical principles, involves empirical coefficients and functional constrains for fitting that often lead to poor results. Recently, data-driven approach that utilize machine learning to analyze and learn patterns purely from the data could be use to overcome the drawback of the physics-based models. In this paper, we combine the insight from two physics-based models to determine the features that are used in the data-driven models. We rank the input features using adaptive boosting algorithm based on the input parameters in the Bingham's and Hareland and Rampersad's models. The results demonstrate that WOB, RPM, and rock strength have more influence on the ROP prediction compared to other drilling parameters in these two models. This outcome sets the base data-driven ROP model used in this study. Furthermore, ranking features using more drilling parameters are investigated, which inferring that flowrate, standpipe pressure, and torque also contribute on the ROP prediction. We include these new parameters, in addition to WOB, RPM, and rock strength, to develop an extended data-driven ROP model from the base model. We examine the base and extended models to estimate ROP using a dataset of a well drilled through a shale formation in North America. Statistical performance evaluation criteria including root-mean-square error and cross-correlation plots between the actual and estimated ROP are employed to evaluate the prediction accuracy and robustness, respectively. The results show good agreement between the actual and predicted ROP for both the base and extended models.

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