A systematic review of machine learning modeling processes and applications in ROP prediction in the past decade
Qian Li, Junping Li, Lan-Lan Xie
Abstract
Fossil fuels are undoubtedly important, and drilling technology plays an important role in realizing fossil fuel exploration; therefore, the prediction and evaluation of drilling efficiency is a key research goal in the industry. Limited by the unknown geological environment and complex operating procedures, the prediction and evaluation of drilling efficiency were very difficult before the introduction of machine learning algorithms. This review statistically analyses rate of penetration (ROP) prediction models established based on machine learning algorithms; establishes an overall framework including data collection, data preprocessing, model establishment, and accuracy evaluation; and compares the effectiveness of different algorithms in each link of the process. This review also compares the prediction accuracy of different machine learning models and traditional models commonly used in this field and demonstrates that machine learning models are the most effective technical means in current ROP prediction modeling.