F. S. Young

TL;DR: In this article, a machine learning model is used to predict the rate of penetration (ROP) during drilling to a great accuracy as shown by Hegde, Wallace, and Gray (2015).

TL;DR: Real-time predictive capabilities of analytical and ML ROP models in a continuous learning setting is investigated, and cross-validation is investigated as a methodology to select the best performing ROP model in real-time.

TL;DR: An extensive review of the literature on ROP prediction, especially, with machine learning techniques, as well as how these models can be used to optimize the drilling activities is presented, enabling to see that machineLearning techniques can potentially outperform in terms of ROP-prediction accuracy on top of traditional or statistical models.

TL;DR: In this paper, the simultaneous effect of six variables on penetration rate using real field drilling data has been investigated, and the bat algorithm was used to identify optimal range of factors in order to maximize drilling rate of penetration.