Artificial neural networks model for prediction of the rate of penetration while horizontally drilling carbonate formations

This study introduces an empirical equation for estimation of the rate of penetration (ROP) while horizontally drilling carbonate formations based on the surface measurable drilling parameters, well log data, and the extracted weights and biases of an optimized artificial neural networks (ANN) model. The ANN model was trained using 3000 datasets of different surface measurable drilling parameters including the torque, rotation speed, and weight-on-bit, with the conventional well log data of the deep resistivity, gamma-ray, and formation bulk density, and their corresponding ROP, the self-adaptive differential evolution algorithm was applied to optimize the ANN model's design parameters. For the training dataset, the ROP was predicted with the optimized ANN model with an average absolute percentage error (AAPE) and a correlation coefficient (R) of 5.12% and 0.960, respectively. The developed empirical equation was tested on another unseen dataset (531 data points) collected from the same training well; where it predicted the ROP with AAPE of 5.80% and R of 0.951.