  Engineering simulators used for steady-state multiphase pipe flows are
commonly utilized to predict pressure drop. Such simulators are typically based
on either empirical correlations or first-principles mechanistic models. The
simulators allow evaluating the pressure drop in multiphase pipe flow with
acceptable accuracy. However, the only shortcoming of these correlations and
mechanistic models is their applicability. In order to extend the applicability
and the accuracy of the existing accessible methods, a method of pressure drop
calculation in the pipeline is proposed. The method is based on well
segmentation and calculation of the pressure gradient in each segment using
three surrogate models based on Machine Learning algorithms trained on a
representative lab data set from the open literature. The first model predicts
the value of a liquid holdup in the segment, the second one determines the flow
pattern, and the third one is used to estimate the pressure gradient. To build
these models, several ML algorithms are trained such as Random Forest, Gradient
Boosting Decision Trees, Support Vector Machine, and Artificial Neural Network,
and their predictive abilities are cross-compared. The proposed method for
pressure gradient calculation yields $R^2 = 0.95$ by using the Gradient
Boosting algorithm as compared with $R^2 = 0.92$ in case of Mukherjee and Brill
correlation and $R^2 = 0.91$ when a combination of Ansari and Xiao mechanistic
models is utilized. The method for pressure drop prediction is also validated
on three real field cases. Validation indicates that the proposed model yields
the following coefficients of determination: $R^2 = 0.806, 0.815$ and 0.99 as
compared with the highest values obtained by commonly used techniques: $R^2 =
0.82$ (Beggs and Brill correlation), $R^2 = 0.823$ (Mukherjee and Brill
correlation) and $R^2 = 0.98$ (Beggs and Brill correlation).
