Artificial neural network model for real-time prediction of the rate of penetration while horizontally drilling natural gas-bearing sandstone formations

Rate of penetration (ROP) is a critical parameter affecting the total cost of drilling an oil well. This study introduces an empirical equation developed based on the optimized artificial neural networks (ANNs) for estimation of the rate of penetration (ROP) in real-time while horizontally drilling natural gas-bearing sandstone reservoirs based on the surface measurable drilling parameters of the mud injection rate, drillstring rotation speed (DSR), standpipe pressure, torque, and weight on bit (WOB) in combination with ROP_c, which is a new parameter developed in this study based on regression analysis. The ANN model was learned and optimized using 1154 data points; the training parameters were collected while horizontally drilling natural gas-bearing sandstone formations in Well-A. An empirical equation for ROP estimation was developed based on the optimized ANN model. Moreover, 495 unseen data points from Well-A were used to test the developed ROP equation, which was finally validated on 2213 data points from Well-B. The predictability of the new ROP equation was compared with the available correlations. The results showed that, without considering ROP_c, the optimized ANN model estimated the ROP for the training dataset with an average absolute percentage error (AAPE) of 42.6% and correlation coefficient (R) of 0.424, while when ROP_c was considered as an input, the AAPE decreased to 5.11% and R increased to 0.991. The new empirical equation estimated the ROP for the testing data of Well-A with AAPE and R of 5.39% and 0.989 and for the validation data of Well-B with AAPE and R of 8.85% and 0.954, respectively. The new empirical equation overperformed all the available empirical correlations for ROP estimation.