A comprehensive data mining approach to estimate the rate of penetration: Application of neural network, rule based models and feature ranking

Rate of Penetration (ROP) estimation is one of the main factors in drilling optimization and minimizing the operation costs. However, ROP depends on many parameters which make its prediction a complex problem. In the presented study, a novel and reliable computational approach for prediction of ROP is proposed. Firstly, fscaret package in R environment was implemented to find out the importance and ranking of the inputs parameters. According to the feature ranking technique, weight on bit and mud weight had the highest impact on ROP based on their ranges within this dataset. Also, for developing further models Cubist method was applied to reduce the input vector from 13 to 6 and 4. Then, Random Forest (RF) and Monotone Multi-Layer Perceptron (MON-MLP) models were applied to predict ROP. The goodness of fit for all models were measured by RMSE and R2 in 10-fold cross validation scheme, and both models showed a reliable accuracy. In order to gain a deeper understanding of the relationships between input parameters and ROP, MON-MLP model with 6 inputs was used to check the effect of weight on bit, mud weight and viscosity. Finally, RF model with 4 variables was used to extract the most important rules from dataset as a transparent model.