# A Predictive Model for Steady-State Multiphase Pipe Flow: Machine Learning on Lab Data

## TL;DR

This paper introduces a machine learning-based method for predicting pressure drops in steady-state multiphase pipe flow, improving accuracy and applicability over traditional empirical and mechanistic models by training on lab data and validating on field cases.

## Contribution

The study develops a novel ML-driven approach for pressure drop prediction in multiphase flow, surpassing existing correlations and mechanistic models in accuracy and applicability.

## Key findings

- Gradient Boosting model achieves R^2 = 0.95 for pressure gradient prediction.
- Validation on field data shows R^2 up to 0.99, outperforming traditional methods.
- The method extends the applicability of pressure drop predictions in multiphase pipelines.

## Abstract

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).