Optimizing Rate of Penetration in Drilling Operations with Metaheuristic Algorithm

The rate of penetration (ROP) in drilling operations is a critical factor that can significantly affect the overall cost of drilling activities. Achieving an optimum ROP is crucial in reducing non-productive time and increasing drilling efficiency. In this study, we proposed a novel approach to predict ROP using a hybrid method Extreme Learning Machine and Grey Wolf Optimization algorithm (ELM-GWO). We use the Grey Wolf Optimization (GWO) algorithm for optimizing the weights and biases between input and hidden layers of ELM and updating the predictive model at each formation to reduce the dimension of input data and mitigate the impact of non-real-time data, such as formation properties, on the bit speed prediction. The model has been trained and tested using data collected from an Algerian field. The results of the statistical and graphical evaluation criteria showed that the ELM-GWO model exhibited higher accuracy and generalization performance compared to the ELM-PSO (Particle Swarm Optimization) and ELM-WOA (Whale Optimization Algorithm) models.