Abstract
This study developed a data-driven surrogate model based on a deep neural network (DNN) to evaluate gas–liquid multiphase flow occurring in horizontal pipes. It estimated the liquid holdup and pressure gradient under a slip condition and different flow patterns, i.e., slug, annular, stratified flow, etc. The inputs of the surrogate modelling were related to the fluid properties and the dynamic data, e.g., superficial velocities at the inlet, while the outputs were the liquid holdup and pressure gradient observed at the outlet. The case study determined the optimal number of hidden neurons by considering the processing time and the validation error. A total of 350 experimental data were used: 279 for supervised training, 31 for validating the training performance, and 40 unknown data, not used in training and validation, were examined to forecast the liquid holdup and pressure gradient. The liquid holdups were estimated within less than 8.08% of the mean absolute percentage error, while the error of the pressure gradient was 23.76%. The R2 values confirmed the reliability of the developed model, showing 0.89 for liquid holdups and 0.98 for pressure gradients. The DNN-based surrogate model can be applicable to estimate liquid holdup and pressure gradients in a more realistic manner with a small amount of computating resources.
Highlights
The accurate evaluation of multiphase flow has been essential for optimum facility designs, and for the estimation of transport features in pre-installed pipes
The representative parameters used to explain gas–liquid flow characteristics are the liquid holdup and pressure gradient; the liquid holdup, i.e., the fraction of a part of pipe occupied by liquid, demonstrates the amount of liquid transported through pipes, while the pressure gradient, i.e., pressure drop divided by the given length of the horizontal pipe, is essential for designing the transport facilities
This study developed a noble machine learning approach based on a deep neural network to evaluate the liquid holdup and pressure gradient of a gas–liquid two-phase flow in a horizontal pipe
Summary
The accurate evaluation of multiphase flow has been essential for optimum facility designs, and for the estimation of transport features in pre-installed pipes. The oil and natural gas industries require time-consuming experimental analyses, but the flow characteristics, e.g., flow patterns, liquid holdup, superficial velocities, and pressure gradient, are uncertain. These uncertain parameters are important for demonstrating transport phenomena but they are linked nonlinearly. The experiments validated the numerical simulations with the closure relationships, as well as the mechanistic modules, e.g., OLGA (Schlumberger, Houston, USA) and Ledaflow (Kongsberg, Norway). The detailed designs of these numerical simulations become complicated, e.g., computational fluid dynamics, and require a large amount of computing resources and skillful user interactions [1,2]
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