Abstract
Abstract
Equivalent circulation density (ECD) is an important part of drilling fluid calculations. Analytical equations based on the conservation of mass and momentum are used to determine the ECD at various depths in the wellbore. However, these equations do not incorporate important factors that have a direct impact on the ECD, such as bottom-hole temperature, pipe rotation and eccentricity, and wellbore roughness. This work introduced different intelligent machines that could provide a real-time accurate estimation of the ECD for horizontal wells, namely, the support vector machine (SVM), random forests (RF), and a functional network (FN). Also, this study sheds light on how principal component analysis (PCA) can be used to reduce the dimensionality of a data set without loss of any important information. Actual field data of Well-1, including drilling surface parameters and ECD measurements, were collected from a 5–7/8 in. horizontal section to develop the models. The performance of the models was assessed in terms of root-mean-square error (RMSE) and coefficient of determination (R2). Then, the best model was validated using unseen data points of 1152 collected from Well-2. The results showed that the RF model outperformed the FN and SVM in predicting the ECD with an RMSE of 0.23 and R2 of 0.99 in the training set and with an RMSE of 0.42 and R2 of 0.99 in the testing set. Furthermore, the RF predicted the ECD in Well-2 with an RMSE of 0.35 and R2 of 0.95. The developed models will help the drilling crew to have a comprehensive view of the ECD while drilling high-pressure high-temperature wells and detect downhole operational issues such as poor hole cleaning, kicks, and formation losses in a timely manner. Furthermore, it will promote safer operation and improve the crew response time limit to prevent undesired events.