Learning characteristic parameters and dynamics of centrifugal pumps under multi-phase flow using physics-informed neural networks

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.