Machine Learning Based Prediction of Pressure Drop, Liquid-Holdup and Flow Pattern in Multiphase Flows
Authors: Tyagi Ankit , Gupta Abhineet 
Published: June 2023

Abstract
The design and analysis of multiphase flows in wells, pipelines, and risers, as used in the oil and gas industry, are typically based on either empirical correlations (e.g., Beggs and Brill, Mukherjee and Brill, Duns and Ros) or on first-principles mechanistic models (e.g., the Shell Flow Correlations, the TUFFP Unified model, the Leda Flow Point model, or the OLGA flow correlations).
Over the last three decades, Shell has developed its own set of two-phase and three-phase flow models that calculate the flow characteristics in pipelines, such as the flow pattern, the pressure drop, and the liquid hold-up. These multiphase flow (sub)models are collectively known as the Shell Flow Correlations (SFC).
The objective of the present work is to explore the possibility of improving the prediction of multiphase pipeline transport through developing a data-driven method for the prediction of the flow pattern, pressure drop and liquid hold-up in a pipeline section. We expect to achieve higher accuracy than mechanistic tools by leveraging the knowledge from the experimental dataset and faster prediction of large amount of data. The method is based on using three surrogate models based on Machine Learning (ML) algorithms trained on a representative lab data set from the SFC experimental database. The first model predicts the flow pattern, the second one the liquid, and the third one the pressure gradient. This may extend the accuracy and the applicability range of the "Shell Flow Explorer tool" in predicting the multiphase flow characteristics in pipe sections.
The approach taken to achieve the goal of the present work is that first synthetic datasets are generated from Shell Flow Explorer and OLGA tool. Different machine learning algorithms are trained on this synthetic dataset. The same trained Machine Learning (ML) algorithm can be used for transfer learning with experimental data sets and the models are evaluated with the predictions from Shell Flow explorer and OLGA tool. In the future, the same machine learning algorithm can be used to incrementally train and test on the new sets of experimental data. After iterations on several new experimental datasets, the machine learning algorithm performance can be assessed on different test dataset distribution independent of the train and validation instances.
This document lists the details of the methodology of the data driven models, and the predictions from these data driven models are compared against the results from SFC and OLGA tools. The models developed could easily be integrated with real time data for the better prediction and anomaly detection. Such models can also be used to explore the design space and perform analyses. Additionally, they can be used for system simulation while an asset is operating and connected to IoT (Internet of Things) platform for enhanced monitoring, asset optimisation, diagnostics, and predictive maintenance. This opens a whole new era in value creation for industries to optimize operations and maintenance, as well as further accelerate the new- development process adding value along the value chain of the Shell business.