  Electrical submersible pumps (ESP) are the second most used artificial
lifting equipment in the oil and gas industry due to their high flow rates and
boost pressures. They often have to handle multiphase flows, which usually
contain a mixture of hydrocarbons, water, and/or sediments. Given these
circumstances, emulsions are commonly formed. It is a liquid-liquid flow
composed of two immiscible fluids whose effective viscosity and density differ
from the single phase separately. In this context, accurate modeling of ESP
systems is crucial for optimizing oil production and implementing control
strategies. However, real-time and direct measurement of fluid and system
characteristics is often impractical due to time constraints and economy.
Hence, indirect methods are generally considered to estimate the system
parameters. In this paper, we formulate a machine learning model based on
Physics-Informed Neural Networks (PINNs) to estimate crucial system parameters.
In order to study the efficacy of the proposed PINN model, we conduct
computational studies using not only simulated but also experimental data for
different water-oil ratios. We evaluate the state variable's dynamics and
unknown parameters for various combinations when only intake and discharge
pressure measurements are available. We also study structural and practical
identifiability analyses based on commonly available pressure measurements. The
PINN model could reduce the requirement of expensive field laboratory tests
used to estimate fluid properties.
