Predicting rate of penetration (ROP) has always been of fundamental interest to the drilling industry. Early predictions can assist the engineer in changing parameters to reduce non-productive time (NPT) and achieve optimum ROP. This paper illustrates methods to predict the ROP in a computationally efficient manner using only data available at the surface. These methods can then be incorporated into real time drilling operations, first through a passive diagnostic tool, and then an integrated real-time control loop.

In this work, statistical learning techniques such as trees, bagged trees, and random forests (RF) are used to predict ROP. Trees provide easy interpretability and hence are favored over other non-linear techniques. However, accuracy is imperative in this procedure. Accuracy can be increased by using bootstrap aggregating (bagging) or Random Forests. These techniques are applied, using the statistical software computing package R and its numerous libraries. Statistical learning techniques have been applied to a data set which had nine predictors.

Applying trees to a data set yields great visualization of the data, but the lack of accuracy and can result in substantial overfitting. This shortcoming was rectified using bagging or RF methods to substantially increase accuracy. The results were promising in all cases and acceptable for real time predictions.

Scalability is another concern for real time operations. Computational efficiency of the methods were evaluated, and the best method was based on a combination of computational efficiency and accuracy. Potential time savings which would result from applying the model in real-time optimizations and demonstration of the power of machine learning techniques are included in this paper. Future improvements will be incorporated in real-time prediction during drilling.

State of the art statistical learning and machine learning techniques are applied to prediction of ROP, whereas previous prediction methods have not been based on real-time drilling data. The result is a computationally efficient model which can determine the right features for prediction at each step, while also incorporating engineering judgement and maintaining integrity of the statistical principles being employed. These methods can easily be extended to other drilling parameters such as MSE or Torque and Drag.

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