Half a century experience in rate of penetration management: Application of machine learning methods and optimization algorithms - A review

Abstract

Rate of penetration (ROP) management is a matter of importance in drilling operations and it has been considered in different studies. Different machine learning methods such as simple and optimized artificial neural networks (ANNs), support vector machines (SVMs), fuzzy logic (FL) or adaptive neuro-fuzzy inference system (ANFIS), ensemble methods of machine learning and meta-heuristic algorithms have been used for this purpose so far. In this article, some of the studies by using these methods as the main approach for ROP management are reviewed to achieve a better understanding of this concept, its economic benefits and also its research capacities. Results indicate that ANNs are the most popular machine learning method in ROP management, while simple ANNs excel the modified types in this regard. Still, modified ANNs outperform simple ones in terms of prediction accuracy, but as ANNs fall short in superior prediction performance, other machine learning approaches of ROP management such as linear regression (LR), random forest (RF) and gradient boosting method (GBM) have compensated this shortcoming and proved their efficiency and applicability. • Applications of different machine learning methods and optimization algorithms for ROP management are reviewed. • Application of ANN, FL, ANFIS and SVM along with other data-driven methods are compared based on ROP prediction precision. • Ensemble methods of machine learning have found a wide verity of applications in ROP management projects and studies. • Application of meta-heuristic optimization methods are investigated based on the ROP increase from the base case. • In the literature, MLP-ANN and PSO algorithms have been used for ROP prediction and optimization more repeatedly.