A New Drilling Rate of Penetration Prediction Model by Particle Swarm Optimization and Gradient Boosting Regression

The oil and gas industry continuously evolves to enhance operational efficiency and productivity while minimizing costs and environmental impact. Among the critical aspects of oil and gas operations, drilling efficiency is a key factor in accessing underground hydrocarbon reservoirs. Traditional machine learning models and current regression models have shown limitations in accurately modelling the rate of penetration due to the high nonlinearity of data. This project focuses on the rate of penetration prediction for drilling optimization. This study proposed a new drilling rate of penetration prediction model with the embedding of particle swarm optimization in a gradient boosting regression method. A solution representation of the particle is introduced as a hyperparameter strategy to explore the optimal parameter for predicting drilling datasets. Extensive experiments were carried out using two splitting strategies, One for All and All for One, across the drilling wells. The proposed GBR+PSO hybrid model achieved a mean absolute error of 1.205, representing a reduction of approximately 89.68% compared to the best-performing baseline model, K-Nearest Neighbor with the One for All splitting strategy, which achieved a mean absolute error of 11.68. The hybrid solution could enhance drilling ROP predictions, advancing the drilling rate of penetration strategy. It has the potential to support the development of autonomous drilling optimization, thus contributing to more efficient, reliable, and cost-effective drilling rate of penetration strategies in future drilling operations.