Real-time prediction of multivariate ROP (rate of penetration) based on machine learning regression algorithms: Algorithm comparison, model evaluation and parameter analysis

Abstract

ROP (Rate of Penetration) is a comprehensive indicator of the rock drilling process and how efficiently predicting drilling rates is important to optimize resource allocation, reduce drilling costs and manage drilling hazards. However, the traditional model is difficult to consider the multiple factors, which makes the prediction accuracy difficult to meet the real drilling requirements. In order to provide efficient, accurate and comprehensive information for drilling operation decision-making, this study evaluated the applicability of four typical regression algorithms based on machine learning for predicting pore pressure in Troll West field, namely SVR (Support Vector Regression), Linear regression, Regression Tree and Gradient Boosting regression. These methods allow more parameters input. By comparing the prediction results of these typical regression algorithms based on R2(R-Square), explained variance, mean absolute error, mean squared error, median absolute error and other performance indicators, it was found that each method predicted different results, among which Gradient Boosting regression has the best results, their prediction accuracy is high and the error is very low. The prediction accuracy of these methods is positively correlated with the proportion of the training data set. With the increase of logging features, the prediction accuracy is gradually improved. In the prediction of adjacent wells, the ROP prediction methods can achieve a certain prediction effect, which shows that this method is suitable for ROP prediction in Troll West field.

Interpretable Feature Construction and Incremental Update Fine-Tuning Strategy for Prediction of Rate of Penetration

“…In recent years, driven by advances in artificial intelligence (AI), which has been widely used in many scenarios [9][10][11][12] and has gained significant attention for ROP prediction [13][14][15], many scholars have tried to use AI technology to solve complex non-linear problems in petroleum engineering. Intelligent models aim to approximate the complex relationship between the ROP and influencing factors by leveraging their powerful non-linear fitting capabilities.…”

“…Artificial neural networks (ANNs) have been widely used, demonstrating promising results in ROP prediction [16,17]. Other machine-learning models, such as random forest [16,18,19], extreme gradient boosting [14,20], long and short-term memory (LSTM) networks [21,22] and hybrid networks [23,24], have been applied to predict ROP based on historical drilling data. Bizhani et al [25] addressed the issue of uncertainty in data-driven models by developing a Bayesian neural network model for predicting ROP.…”