Prediction rotary drilling penetration rate in lateritic soils using machine learning models

The city’s population growth in developing countries over the past two decades led to a sharp increase in water supply needs. This generates proliferation of drilling companies and a highly competitive environment, in Cameroon in particular. A regular optimization of drilling operations in geological formations appears to be crucial and urgent, so as to reduce drilling costs since the drilling equipment used is too expensive and sometimes scarce. The present paper investigated an accurate machine learning model for the penetration rates (ROP) prediction in lateritic soil covers layers. The present study investigates various machine learning techniques including the linear regression, K-Nearest Neighbors, ridge regression and Random Forest, to predict the penetration rate. Data from four (04) defined parameters: the percussion pressure, Pp (MPa); the blowing pressure, Ps (MPa); the pressure of compressor, Pc (MPa); the rotation speed, Vr (tr/min) have been used to build the dataset, for training and validation tests, with an 70/30 ratio. The drilling time and drilling depth ranges from 0.3 to 2.8 h, and from 0.85 to 4.6 m respectively, for a constant rotation speed of 1350 tr/min while values of pressures range between 2.5 and 294.3 MPa. The key performance metrics including correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE) were calculated for each method to evaluate the accuracy of the predictions. Results show that the Random Forest model exhibited the best accuracy, with a R2 value of 0.999, with RMSE and MAE values of 0.0225 and 0.0121 respectively. A relatively high accuracy has been obtained for the K-nearest neighbors method with R2, RMSE and MAE values of 0.933, 0.1547 and 0.0802 respectively. Relatively low to values of metrics have also been obtained for linear regression and ridge regression methods. Related R2, RMSE, MAE values obtained are respectively 0.8612, 0.2232 and 0.1724 for the linear regression, and 0.8447, 0.2361 and 0.1894 for the ridge regression method. The discussion of the obtained results shows that, the Rain Forest model predicts the ROP value with a highly good accuracy, and can thus greatly contribute in reducing the costs and time related to drilling operations in lateritic soil covers context.