Abstract
Fluid losses into formations are a common operational issue that is frequently encountered when drilling across naturally or induced fractured formations. This could pose significant operational risks, such as well control, stuck pipe, and wellbore instability, which, in turn, lead to an increase in well time and cost. This research aims to use and evaluate different machine learning techniques, namely, support vector machine (SVM), random forest (RF), and K nearest neighbor (K-NN) in predicting the loss of circulation rate (LCR) while drilling using solely mechanical surface parameters and interpretation of the active pit volume readings. Actual field data of seven wells, which had suffered partial or severe loss of circulation, were used to build predictive models with an 80:20 training-to-test data ratio, while Well No. 8 was used to compare the performance of the developed models. Different performance metrics were used to evaluate the performance of the developed models. The root-mean-square error (RMSE) and correlation coefficient (R) were used to evaluate the performance of the models in predicting the LCR while drilling. The results showed that K-NN outperformed other models in predicting the LCR in Well No. 8 with an R of 0.90 and an RMSE of 0.17.