ROP Prediction with Supervised Machine Learning; a Case Study

Abstract

Optimum drilling penetration rate, known as the rate of penetration (ROP) has played  a big role in drilling operations. Planning the well   ROP   always becomes a challenge for drilling engineers to calculate the drilling time needed for the section. Optimum ROP is achieved when the time to drill the section is as planned. Many empirical approaches were develop to model the ROP based on the drilling parameters, and might not always match  the actual ROP. In some cases, the actual ROP was slower than planned, which may increase the drilling cost, which needs to be avoided. Hence, some approaches using artificial intelligent (AI), and supervised machine learning  have been develop to overcome it. Supervised machine learning is used to develop a ROP model and ROP prediction for one of  the development fields,  based only on two wells drilling parameters data. The model was train using Gradient Boosting, Random Forest, and Support Vector Machine. Drilling parameter test data then is used to validate the model. The model of Random Forest  shows a good or promising result with R2 of 0.90,   Gradient Boosting shows R2 of 0.86, and Support Vector Machine with R2 0.72. Based on the models generated, the Random Forest has shown good trend which could be used for modeling ROP in the future development wells.