Predicting the rate of penetration (ROP) is a significant factor in drilling optimization and minimizing expensive drilling costs. However, due to the geological uncertainty and many uncontrolled operational parameters influencing the ROP, its prediction is still a complex problem for the oil and gas industries. In the present study, a reliable computational approach for the prediction of ROP is proposed. First, fscaret package in a R environment was implemented to find out the importance and ranking of the inputs’ parameters. According to the feature ranking process, out of the 25 variables studied, 19 variables had the highest impact on ROP based on their ranges within this dataset. Second, a new model that is able to predict the ROP using real field data, which is based on artificial neural networks (ANNs), was developed. In order to gain a deeper understanding of the relationships between input parameters and ROP, this model was used to check the effect of the weight on bit (WOB), rotation per minute (rpm), and flow rate (FR). Finally, the simulation results of three deviated wells showed an acceptable representation of the physical process, with reasonable predicted ROP values. The main contribution of this research as compared to previous studies is that it investigates the influence of well trajectory (azimuth and inclination) and mechanical earth modeling parameters on the ROP for high-angled wells. The major advantage of the present study is optimizing the drilling parameters, predicting the proper penetration rate, estimating the drilling time of the deviated wells, and eventually reducing the drilling cost for future wells.
Predicting Drilling Rate of Penetration Using Artificial Neural Networks
