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

arXiv: https://arxiv.org/abs/1905.09746 · HTML: https://mlsanity.com/paper/1905.09746/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

## Built on by (most cited first, 4 of 4)

- [On gray-box modeling for virtual flow metering](https://mlsanity.com/paper/2103.12513/) (2021)
- [Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells](https://mlsanity.com/paper/2110.06059/) (2021)
- [Hybrid Machine Learning Modeling of Engineering Systems -- A Probabilistic Perspective Tested on a Multiphase Flow Modeling Case Study](https://mlsanity.com/paper/2205.09196/) (2022)
- [Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements](https://mlsanity.com/paper/2301.02585/) (2022)

## Abstract (arXiv)

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 R2 = 0.95 by using the Gradient Boosting algorithm as compared with R2 = 0.92 in case of Mukherjee and Brill correlation and R2 = 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: R2 = 0.806, 0.815 and 0.99 as compared with the highest values obtained by commonly used techniques: R2 = 0.82 (Beggs and Brill correlation), R2 = 0.823 (Mukherjee and Brill correlation) and R2 = 0.98 (Beggs and Brill correlation).