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 (R
2). 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 R
2 of 0.99 in
the training set and with an RMSE of 0.42 and R
2 of 0.99 in the testing set. Furthermore, the RF predicted
the ECD in Well-2 with an RMSE of 0.35 and R
2 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.