Evaluation of the wellbore drillability while horizontally drilling sandstone formations using combined regression analysis and machine learning models

The rate of penetration (ROP) is an influential parameter in the optimization of oil well drilling because it has a huge impact on the total drilling cost. This study aims to optimize four machine learning models for real-time evaluation of the ROP based on drilling parameters during horizontal drilling of sandstone formations. Two well data sets were implemented for the model training–testing (Well-X) and validation (Well-Y). A total of 1224 and 524 datasets were implemented for training and testing the model, respectively. A correlation for ROP assessment was suggested based on the optimized artificial neural network (ANN) model. The precision of this equation and the optimized models were tested (524 datapoints) and validated (2213 datapoints), and their accuracy was compared to available ROP correlations. The developed ANN-based equation predicted the ROP with average absolute percentage errors (AAPE) of 0.3% and 1.0% for the testing and validation data, respectively. The new empirical equation and the optimized fuzzy logic and functional neural network models outperformed the available correlations in assessing the ROP. The support vector regression accuracy performance showed AAPE of 26.5%, and the correlation coefficient for the estimated ROP was 0.50 for the validation phase. The outcomes of this work could help in modeling the ROP prediction in real time during the drilling process.