Investigating and Ranking the Rate of Penetration (ROP) Features for Petroleum Drilling Monitoring and Optimization

Authors : Ijegwa David Acheme; Osemengbe Oyaimare Uddin; Ayodeji Samuel Makindes

Volume/Issue : Volume 8 - 2023, Issue 10 - October

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The drilling phase has been reported to be the most expensive phase of oil exploration and production, hence several research efforts have been targeted at improving its efficiency. The rate of penetration (ROP) has also been identified as the most important metric for improving drilling performance, hence, several research efforts have reported different methods of predicting ROP optimal values. Recently, artificial intelligence (AI) and machine learning (ML) models have been reported for the prediction of ROP. However, the ROP is influenced by several factors, and the interactions among these factors introduces a kind of complexity that affects its accurate prediction. This research work sets out to achieve two important objectives, firstly, to investigate and rank the most important factors for the prediction of the ROP, and secondly, to carry out a comparative study and ranking of selected machine learning algorithms for the prediction of ROP. In order to achieve this, the open source volve dataset which is a complete set of data from the North Sea oil field was utilized. Eighteen (18) machine learning models were built using this dataset and their performances compared. The result showed the random forest regressor with an RMSE value of 0.0010 and R2 score of 0.891 as the most efficient algorithm among the eighteen chosen for this work. Further experimentation also revealed the most influential factors for predicting the rate of penetration, these features in order of importance are; measured depth, bit rotation per minute, formation porosity, shale volume, water saturation, log permeability. The output of this study work offers a blueprint for choosing algorithms and features when implementing ML solutions for optimizing oil drilling, and this is helful in the development of real-time ROP prediction models and hybridization.

Keywords : Rate of Penetration Prediction, oil drilling, machine learning, feature selections


Paper Submission Last Date
31 - December - 2023

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